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## **Meet the editor**

Xinghua Guo obtained his Ph.D. in Mass Spectrometry/ Chemistry at the Chinese Academy of Sciences, China, before he moved to Germany (Alexander von-Humboldt Foundation fellow), the Netherlands and Austria. He has been working in the field of instrumental analysis, using a broad range of MS, LC-MS and GC-MS, with interests in various method developments, ranging from

instrumental fundamentals, applications and organic sample preparation. Since 2006 he has worked as Assistant Professor at the Graz University of Technology (Austria), where he obtained the habilitation status (Uni. Doz.) in Analytical Chemistry, gave lectures in 'LC-MS Bioanalysis' and 'Organic Instrumental Analysis', and supervised several dissertations. Currently he is specializing in characterization of small molecules in biosimilar development in the Analytical Characterization Group at Sandoz Biopharmaceuticals, Kundl, Austria. Xinghua has (co-)authored over 70 scientific publications and 2 patents.

Contents

**Preface VII**

Chapter 1 **Gas Chromatography-Mass Spectrometry 1** Elena Stashenko and Jairo René Martínez

**Acids and Their Esters 39**

**Gas Chromatography 57**

Chapter 4 **Gas Chromatography in Metabolomics Study 83** Yunping Qiu and Deborah Reed

Ernesto Bernal

Serban Moldoveanu

**Elastomers 155**

María Teresa Núñez-Cardona

James P. Lewicki and Robert S. Maxwell

Chapter 2 **Analytical Aspects of the Flame Ionization Detection in**

**Comparison with Mass Spectrometry with Emphasis on Fatty**

Jesuí Vergilio Visentainer, Thiago Claus, Oscar Oliveira Santos Jr, Lucas Ulisses Rovigatti Chiavelli and Swami Arêa Maruyama

Chapter 3 **Limit of Detection and Limit of Quantification Determination in**

Chapter 5 **The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals 103**

Chapter 6 **Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria 135**

Chapter 7 **Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex Silicone**

### Contents

### **Preface XI**



### Chapter 8 **Measurement of Off-Gases in Wood Pellet Storage 181** Fahimeh Yazdanpanah, Shahab Sokhansanj, Hamid Rezaei, C. Jim

Lim, Anthony K. Lau, X. Tony Bi, S. Melin, Jaya Shankar Tumuluru and Chang Soo Kim

Preface

in daily applications.

The steady development and improvement of gas chromatographic instrumentations and applications in combination with advanced detection techniques has been leading GC tech‐ niques to one of the ideal methods of choices since decades. It will remain an irreplaceable analytical technique in many research areas for both quantitative analysis and qualitative characterization/identification, although it has been facing a dramatic impact as a result of the rapid development of liquid chromatography-mass spectrometry. With this book it is not the intention to cover the field comprehensively, but to highlight a few areas where sig‐ nificant advances have been reported recently and/or a revisit of basic concepts is deserved. It provides an overview of selected instrumentation developments, frontline and modern research as well as recent industrial applications, aiming at providing a practical reference

First a review on recent developments of GC-MS is given, followed by a practical guidance of quantification of fatty acids using GC-FID and determination of detection limits in general. Chapter 4 critically reviews one of the ever expanding research areas: GC-based metabolo‐ mics applications in biomedical, plant and microbial research. It covers aspects from sample preparation, derivatization, data processing and interpretation to application areas. Further‐ more, Chapter 5 illustrates the power of GC-MS profiling of antioxidants in botanicals with practical examples. On the other hand, Chapter 7 focuses on the applications of pyrolysis GC-MS for the study of degradation and aging of complex synthetic silicone elastomers, which

This book is a collection of contributions of several experts in their representing fields, for which I am very grateful to all of them. It contains useful hand-on practices of fundamen‐ tals, sample preparation (derivatization) and data processing in daily research. This book is

**Dr. Xinghua Guo**

Biopharmaceuticals Sandoz GmbH Kundl, Austria

Analytical Characterization

Uni.-Doz.

demonstrates the advantages and limitations of the technique in material sciences.

recommended to both basic and experienced researchers in gas chromatography.

### Preface

Chapter 8 **Measurement of Off-Gases in Wood Pellet Storage 181**

and Chang Soo Kim

**VI** Contents

Fahimeh Yazdanpanah, Shahab Sokhansanj, Hamid Rezaei, C. Jim Lim, Anthony K. Lau, X. Tony Bi, S. Melin, Jaya Shankar Tumuluru

> The steady development and improvement of gas chromatographic instrumentations and applications in combination with advanced detection techniques has been leading GC tech‐ niques to one of the ideal methods of choices since decades. It will remain an irreplaceable analytical technique in many research areas for both quantitative analysis and qualitative characterization/identification, although it has been facing a dramatic impact as a result of the rapid development of liquid chromatography-mass spectrometry. With this book it is not the intention to cover the field comprehensively, but to highlight a few areas where sig‐ nificant advances have been reported recently and/or a revisit of basic concepts is deserved. It provides an overview of selected instrumentation developments, frontline and modern research as well as recent industrial applications, aiming at providing a practical reference in daily applications.

> First a review on recent developments of GC-MS is given, followed by a practical guidance of quantification of fatty acids using GC-FID and determination of detection limits in general. Chapter 4 critically reviews one of the ever expanding research areas: GC-based metabolo‐ mics applications in biomedical, plant and microbial research. It covers aspects from sample preparation, derivatization, data processing and interpretation to application areas. Further‐ more, Chapter 5 illustrates the power of GC-MS profiling of antioxidants in botanicals with practical examples. On the other hand, Chapter 7 focuses on the applications of pyrolysis GC-MS for the study of degradation and aging of complex synthetic silicone elastomers, which demonstrates the advantages and limitations of the technique in material sciences.

> This book is a collection of contributions of several experts in their representing fields, for which I am very grateful to all of them. It contains useful hand-on practices of fundamen‐ tals, sample preparation (derivatization) and data processing in daily research. This book is recommended to both basic and experienced researchers in gas chromatography.

> > **Dr. Xinghua Guo** Uni.-Doz. Analytical Characterization Biopharmaceuticals Sandoz GmbH Kundl, Austria

**Chapter 1**

**Gas Chromatography-Mass Spectrometry**

in some cases, the geometry and spatial isomerism of the molecule.

Gas chromatography (GC) is a widely applied technique in many branches of science and technology. For over half a century, GC has played a fundamental role in determining how many components and in what proportion they exist in a mixture. However, the ability to establish the nature and chemical structure of these separated and quantified compounds is ambiguous and reduced, and requires a spectroscopic detection system. The most used, is the mass spectrometric detector (MSD), which allows obtaining the "fingerprint" of the molecule, i.e., its mass spectrum. Mass spectra provide information on the molecular weight, elemental composition, if a high resolution mass spectrometer is used, functional groups present, and,

In a gas chromatographic system, the sample to be analyzed may be a liquid solution or a collection of molecules adsorbed on a surface, e.g., the solid-phase microextraction (SPME) system. During the transfer into the GC, the sample is volatilized by rapid exposure to a zone kept at relatively high temperature (200-300°C) and mixed with a stream of carrier gas (Ar, He, N2, or H2). The resulting gaseous mixture enters the separation section, a chromatographic column, which in its current version is a fused-silica tubular capillary coated internally with a thin polymer film. Upon their displacement through the column, analyte molecules are partitioned between the gas carrier stream (mobile phase) and the polymer coating (stationary phase), to an extent which depends mainly on their chemical structure. At the end of the separation section, the molecules reach a detection system in which a specific physical property (thermal conductivity) or a physico-chemical process (ionization in a flame, electron capture) gives rise to an electric signal which is proportional to the amount of molecules of the same

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Elena Stashenko and Jairo René Martínez

http://dx.doi.org/10.5772/57492

**2. Gas chromatography**

**1. Introduction**

Additional information is available at the end of the chapter

### **Gas Chromatography-Mass Spectrometry**

Elena Stashenko and Jairo René Martínez

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57492

### **1. Introduction**

Gas chromatography (GC) is a widely applied technique in many branches of science and technology. For over half a century, GC has played a fundamental role in determining how many components and in what proportion they exist in a mixture. However, the ability to establish the nature and chemical structure of these separated and quantified compounds is ambiguous and reduced, and requires a spectroscopic detection system. The most used, is the mass spectrometric detector (MSD), which allows obtaining the "fingerprint" of the molecule, i.e., its mass spectrum. Mass spectra provide information on the molecular weight, elemental composition, if a high resolution mass spectrometer is used, functional groups present, and, in some cases, the geometry and spatial isomerism of the molecule.

### **2. Gas chromatography**

In a gas chromatographic system, the sample to be analyzed may be a liquid solution or a collection of molecules adsorbed on a surface, e.g., the solid-phase microextraction (SPME) system. During the transfer into the GC, the sample is volatilized by rapid exposure to a zone kept at relatively high temperature (200-300°C) and mixed with a stream of carrier gas (Ar, He, N2, or H2). The resulting gaseous mixture enters the separation section, a chromatographic column, which in its current version is a fused-silica tubular capillary coated internally with a thin polymer film. Upon their displacement through the column, analyte molecules are partitioned between the gas carrier stream (mobile phase) and the polymer coating (stationary phase), to an extent which depends mainly on their chemical structure. At the end of the separation section, the molecules reach a detection system in which a specific physical property (thermal conductivity) or a physico-chemical process (ionization in a flame, electron capture) gives rise to an electric signal which is proportional to the amount of molecules of the same

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

identity. A data system permits to process these data to produce a graph of the variation of this detector signal with time (chromatogram). Thus, four principal sections are distinguish‐ able in the chromatograph: introduction (injector), separation (chromatographic column), detection, and data handling units. Each section has its own function and its responsibility for the quality of the analysis and the results obtained. The injection system, for example, should ideally transfer the sample to the column quantitatively, without discrimination on molecular weights or volatility, and without chemical alteration (decomposition or isomerization). It is a critical step, especially for quantitative analysis. For correct GC operation, among other conditions, this gateway to the column should remain unpolluted, clean, inert, and leak-free. The main requirement for an analyte in GC is that it should be volatile enough to be present in detectable amounts in the mobile phase. Substances with low vapor pressure will not enter the chromatographic column, will accumulate at the injection system, and may eventually clog its conduits. Very polar, thermolabile, ionic and high-molecular weight compounds are not compatible with regular GC analysis. Depending on the molecular structure of the analyte and the functional groups available, it is possible in some cases to obtain a chemical derivative which has a higher vapor pressure and is therefore more amenable to GC analysis.

isolate particular substances or fractions at reduced pressure (vacuum) in special columns (molecular distillation) and many operate at industrial scale. However, in general, these

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 3

In order to improve extraction efficiency and substantially reduce distillation times, micro‐ wave radiation has been actively used in the past 30 years as heat source [2,3]. Microwaveassisted hydrodistillation (MWHD) is a common example of a lab-scale technique for essential oil and other volatile mixture isolation which requires about ¼ of the time employed when conventional heating is used in HD. Microwave-assisted extraction is used mostly for the extraction of solid samples (plant material, soil, tissue, etc.), with water or an organic solvent

The second family of techniques includes various methods of extraction where the analyte isolation is based on differences in their solubilities in solvent(s) or in the adsorption or absorption on a material such as a microporous solid (activated carbon, silica gel, alumina, molecular sieves, etc.) or a porous polymer (PDMS, Tenax, Porapak, Chromosorb, synthetic

Liquid-liquid extraction (LLE) in continuous or batch mode is the most used solubility-based extraction method for liquid samples. Extraction selectivity is achieved by a proper choice of solvent (polarity, boiling point, dielectric constant, hydrogen bonding capacity, availability, accessibility, cost, etc.). The extract obtained must often undergo clean-up and concentration processes. However, LLE poses a number of technical problems, among others, emulsion formation, high cost, long extraction times, automation difficulties, toxic solvent disposal, possibility of cross contamination, etc. Despite these difficulties, the number of published works that involve the use of LLE grew during the last decade [5]. Solid-phase extraction (SPE) has emerged during the last 4 decades as an alternative to LLE [6]. It substantially reduces the use of solvents and combines extraction, clean-up and target analyte concentration into a single step. In addition, the method includes the possibility of automation. Another solubility-based extraction technique employs a solvent at temperature and pressure above its critical point. Supercritical fluid extraction (SFE) requires an initially high investment in equipment, but has been used increasingly in areas in which traces of the extraction agent are not desired (natural products, environmental, food, forensic, and in many industrial applications). In this regard, carbon dioxide is the most common fluid employed because when the extract is returned to standard temperature and pressure conditions, CO2 separates completely in a spontaneous manner because it is a permanent gas under this conditions. SFE selectivity is achieved by varying the operating conditions (temperature and pressure), which modify the supercritical fluid density and in turn, the solubility of the analyte [7]. The use of co-solvents (ethanol, acetone) is another option, particularly, to enhance the extraction of polar substances. For GC-MS analysis, the SFE extract often requires additional cleaning steps to remove fats or pig‐

ments, waxes or other high-molecular weight compounds (Figure 1).

Soxhlet extraction is a solubility-based extraction method for solid samples. It´s one of the most traditional extraction methods, used in the analysis of soil, polymers, natural products, etc., in areas such as biochemistry, agricultural sciences, biology, or environmental research. This exhaustive extraction affords mixtures which require clean-up to remove heavy components

techniques are not suitable for studying and isolating compounds at trace levels.

as the extracting agent [4].

resins, etc.).

One of the most important characteristics of the chromatographic column is its resolution, or the ability to separate components with very similar distribution constants between the mobile and stationary phases (KD). Chromatographic resolution is a function of many operational parameters. Among them, the nature of the stationary phase, mobile phase, temperature, the size of the column, that is, its length (L), inner diameter (ID) and the thickness of the stationary phase (df ). As the number of components in the mixture increases, and the structural similarity between its components grows (isomerism), longer columns are required for complete compound separation. Alternatively, for the same purpose, one can employ smaller internal diameter columns. Obviously, increasing the length of the column markedly increases the analysis time. So, as the analysis of polyaromatic hydrocarbons (PAHs) or controlled drugs is regularly accomplished using 30 m long columns, the separation of hydrocarbons in gasoline requires longer, 100 m columns.

#### **2.1. Sample preparation for GC analysis**

Sample preparation for GC analysis involves techniques which preferentially isolate volatile and semi-volatile substances and prevent the presence of ionic or high molecular weight species in the mixture to be injected into the GC. These techniques can be divided roughly into three major groups: Distillation, Extraction, and Headspace Methods. Additionally, there are some *sui generis* methods which combine techniques from two different groups, for example, SDE, Simultaneous Distillation – Solvent Extraction [1]. Distillation techniques exploit differences (must be large) in physicochemical properties (volatility or vapor pressure). When applied to vegetal material or to other solid matrix which contains volatile compounds, the distillation can be performed in different ways: (1) Steam distillation (SD), (2) hydrodistillation (HD), or (3) Water-Steam distillation. The resulting extracts or distillates are volatile mixtures suitable to GC or GC-MS analysis, which may only require a drying step (addition of sodium sulfate) prior to injection into the chromatograph. Some distillation processes are designed to isolate particular substances or fractions at reduced pressure (vacuum) in special columns (molecular distillation) and many operate at industrial scale. However, in general, these techniques are not suitable for studying and isolating compounds at trace levels.

identity. A data system permits to process these data to produce a graph of the variation of this detector signal with time (chromatogram). Thus, four principal sections are distinguish‐ able in the chromatograph: introduction (injector), separation (chromatographic column), detection, and data handling units. Each section has its own function and its responsibility for the quality of the analysis and the results obtained. The injection system, for example, should ideally transfer the sample to the column quantitatively, without discrimination on molecular weights or volatility, and without chemical alteration (decomposition or isomerization). It is a critical step, especially for quantitative analysis. For correct GC operation, among other conditions, this gateway to the column should remain unpolluted, clean, inert, and leak-free. The main requirement for an analyte in GC is that it should be volatile enough to be present in detectable amounts in the mobile phase. Substances with low vapor pressure will not enter the chromatographic column, will accumulate at the injection system, and may eventually clog its conduits. Very polar, thermolabile, ionic and high-molecular weight compounds are not compatible with regular GC analysis. Depending on the molecular structure of the analyte and the functional groups available, it is possible in some cases to obtain a chemical derivative

which has a higher vapor pressure and is therefore more amenable to GC analysis.

phase (df

2 Advances in Gas Chromatography

requires longer, 100 m columns.

**2.1. Sample preparation for GC analysis**

One of the most important characteristics of the chromatographic column is its resolution, or the ability to separate components with very similar distribution constants between the mobile and stationary phases (KD). Chromatographic resolution is a function of many operational parameters. Among them, the nature of the stationary phase, mobile phase, temperature, the size of the column, that is, its length (L), inner diameter (ID) and the thickness of the stationary

between its components grows (isomerism), longer columns are required for complete compound separation. Alternatively, for the same purpose, one can employ smaller internal diameter columns. Obviously, increasing the length of the column markedly increases the analysis time. So, as the analysis of polyaromatic hydrocarbons (PAHs) or controlled drugs is regularly accomplished using 30 m long columns, the separation of hydrocarbons in gasoline

Sample preparation for GC analysis involves techniques which preferentially isolate volatile and semi-volatile substances and prevent the presence of ionic or high molecular weight species in the mixture to be injected into the GC. These techniques can be divided roughly into three major groups: Distillation, Extraction, and Headspace Methods. Additionally, there are some *sui generis* methods which combine techniques from two different groups, for example, SDE, Simultaneous Distillation – Solvent Extraction [1]. Distillation techniques exploit differences (must be large) in physicochemical properties (volatility or vapor pressure). When applied to vegetal material or to other solid matrix which contains volatile compounds, the distillation can be performed in different ways: (1) Steam distillation (SD), (2) hydrodistillation (HD), or (3) Water-Steam distillation. The resulting extracts or distillates are volatile mixtures suitable to GC or GC-MS analysis, which may only require a drying step (addition of sodium sulfate) prior to injection into the chromatograph. Some distillation processes are designed to

). As the number of components in the mixture increases, and the structural similarity

In order to improve extraction efficiency and substantially reduce distillation times, micro‐ wave radiation has been actively used in the past 30 years as heat source [2,3]. Microwaveassisted hydrodistillation (MWHD) is a common example of a lab-scale technique for essential oil and other volatile mixture isolation which requires about ¼ of the time employed when conventional heating is used in HD. Microwave-assisted extraction is used mostly for the extraction of solid samples (plant material, soil, tissue, etc.), with water or an organic solvent as the extracting agent [4].

The second family of techniques includes various methods of extraction where the analyte isolation is based on differences in their solubilities in solvent(s) or in the adsorption or absorption on a material such as a microporous solid (activated carbon, silica gel, alumina, molecular sieves, etc.) or a porous polymer (PDMS, Tenax, Porapak, Chromosorb, synthetic resins, etc.).

Liquid-liquid extraction (LLE) in continuous or batch mode is the most used solubility-based extraction method for liquid samples. Extraction selectivity is achieved by a proper choice of solvent (polarity, boiling point, dielectric constant, hydrogen bonding capacity, availability, accessibility, cost, etc.). The extract obtained must often undergo clean-up and concentration processes. However, LLE poses a number of technical problems, among others, emulsion formation, high cost, long extraction times, automation difficulties, toxic solvent disposal, possibility of cross contamination, etc. Despite these difficulties, the number of published works that involve the use of LLE grew during the last decade [5]. Solid-phase extraction (SPE) has emerged during the last 4 decades as an alternative to LLE [6]. It substantially reduces the use of solvents and combines extraction, clean-up and target analyte concentration into a single step. In addition, the method includes the possibility of automation. Another solubility-based extraction technique employs a solvent at temperature and pressure above its critical point. Supercritical fluid extraction (SFE) requires an initially high investment in equipment, but has been used increasingly in areas in which traces of the extraction agent are not desired (natural products, environmental, food, forensic, and in many industrial applications). In this regard, carbon dioxide is the most common fluid employed because when the extract is returned to standard temperature and pressure conditions, CO2 separates completely in a spontaneous manner because it is a permanent gas under this conditions. SFE selectivity is achieved by varying the operating conditions (temperature and pressure), which modify the supercritical fluid density and in turn, the solubility of the analyte [7]. The use of co-solvents (ethanol, acetone) is another option, particularly, to enhance the extraction of polar substances. For GC-MS analysis, the SFE extract often requires additional cleaning steps to remove fats or pig‐ ments, waxes or other high-molecular weight compounds (Figure 1).

Soxhlet extraction is a solubility-based extraction method for solid samples. It´s one of the most traditional extraction methods, used in the analysis of soil, polymers, natural products, etc., in areas such as biochemistry, agricultural sciences, biology, or environmental research. This exhaustive extraction affords mixtures which require clean-up to remove heavy components and other interferences. Long experimental times and high solvent consumption in Soxhlet extraction have led to its replacement in many applications by most efficient methods, such as Accelerated Solvent Extraction (ASE) [8]. In ASE, the use of higher temperature and pressure permits to attain higher selectivity while significantly reducing the time and the amount of solvent required. Extraction, concentration and clean-up are performed in a single step, and the equipment may be coupled in line with GC-MS.

Solid-phase microextraction (SPME) constitutes a fundamental development in sample preparation techniques. In about 20 years of growth, this technique has become the dominant approach to sample preparation for the analysis of volatile substances in biological, environ‐ mental, forensic (arson, explosives residue, analysis of drugs in body fluids and tissues) studies, food quality control, or the monitoring of different processes *in vitro* and *in vivo* (breath analysis, fermentation processes, microbiological, pharmacokinetics, etc.) [9,10]. In SPME, the extraction occurs by the mass transfer of analytes from the sample to a polymer coating over a fused-silica fiber. This transfer continues until the chemical potential of each substance in all phases is the same. The fiber is subsequently inserted at the injection port of the chromatograph for direct admission of the analytes into the chromatographic system. SPME sampling with the polymer-coated fiber may take place by exposing it to the headspace above the sample (liquid or solid), or by immersion of the fiber in the sample (liquid), directly, or within a protective membrane. The SPME sampling process is amenable to automation and operation coupled to a GC. One of the most interesting applications of SPME is the target-analyte extraction accompanied by its derivatization on the fiber, which has been previously loaded

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 5

The use of the vapor phase above the sample (headspace) as a means for performing extraction processes has great advantages from the analytical point of view, because it eliminates many interferences, mostly the solvent peak, which can impede the detection of volatile compounds. In the study of natural products, direct headspace sampling has allowed the study of the volatile fraction around whole plants or parts thereof (Figure 3) [12]. Classical approaches to monitor the headspace above a sample may be divided into static and dynamic versions. The volatile fractions thus collected are suitable for GC- MS analysis, as they are free of interference from nonvolatile very polar (ionic) or high - molecular weight matrix components. Static headspace consists of the direct transfer of a headspace volume to the GC injection port. This is the sampling method regularly used in the analysis of BTEX (benzene, toluene, ethylbenzene, and xylenes) in water, quantification of trihalomethanes in drinking water, ethanol, volatile compounds in paints, determination of residual solvents in polymers and pharmaceuticals, hydrocarbons in soil, analysis of beverages and perfumes, among many other applications [13]. When the target-analyte concentration in the sample is very low (ppt, ppb), enrichment (preconcentration) is required, and in these cases, dynamic headspace is the preferred sampling mode. Basically, by purging the vapor phase with an inert gas, the analytes are collected in a solvent or are "frozen " (cryo-trapping) or are ad(b)sorpted in a sorbent (purge and trap, P & T), and later on are recovered by the action of temperature (thermal desorption) [14] or are

Sorption-based sampling techniques, which involve aspects of both static and dynamic headspace, have been developed in the past 2-3 decades. The great development experienced by the SPME technique has served to identify, through its many applications, some areas for improvement. This has generated a collection of new methods of micro-scale sampling, that achieve better concentration of the analytes of interest and in several cases, the use of a larger volume of sorbent phase (polymer, normally) permits the collection of larger amounts of analyte than SPME. In the inside needle capillary adsorption trap (INCAT), a polymer layer

with a reagent (Figure 2) [11].

eluted with solvent.

**Figure 1.** Total ion currents (TICs, chromatograms) of the coffee fresh flower extracts obtained by (A) extraction with hexane (without clean-up) and (B) extraction with supercritical CO2 followed by clean-up and fat elimination. GC-MS (Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). Split 1:30.

Solid-phase microextraction (SPME) constitutes a fundamental development in sample preparation techniques. In about 20 years of growth, this technique has become the dominant approach to sample preparation for the analysis of volatile substances in biological, environ‐ mental, forensic (arson, explosives residue, analysis of drugs in body fluids and tissues) studies, food quality control, or the monitoring of different processes *in vitro* and *in vivo* (breath analysis, fermentation processes, microbiological, pharmacokinetics, etc.) [9,10]. In SPME, the extraction occurs by the mass transfer of analytes from the sample to a polymer coating over a fused-silica fiber. This transfer continues until the chemical potential of each substance in all phases is the same. The fiber is subsequently inserted at the injection port of the chromatograph for direct admission of the analytes into the chromatographic system. SPME sampling with the polymer-coated fiber may take place by exposing it to the headspace above the sample (liquid or solid), or by immersion of the fiber in the sample (liquid), directly, or within a protective membrane. The SPME sampling process is amenable to automation and operation coupled to a GC. One of the most interesting applications of SPME is the target-analyte extraction accompanied by its derivatization on the fiber, which has been previously loaded with a reagent (Figure 2) [11].

and other interferences. Long experimental times and high solvent consumption in Soxhlet extraction have led to its replacement in many applications by most efficient methods, such as Accelerated Solvent Extraction (ASE) [8]. In ASE, the use of higher temperature and pressure permits to attain higher selectivity while significantly reducing the time and the amount of solvent required. Extraction, concentration and clean-up are performed in a single step, and

C23

C25

C27

20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 Time-->

Benzyl salicylate

Heneicosane, C21

Benzyl benzoate

8-Heptadecene

Cinnamyl acetate

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 Time-->

**Figure 1.** Total ion currents (TICs, chromatograms) of the coffee fresh flower extracts obtained by (A) extraction with hexane (without clean-up) and (B) extraction with supercritical CO2 followed by clean-up and fat elimination. GC-MS

(Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). Split 1:30.

Palmitic acid **A**

Stearic acid

**B**

Palmitoleic acid

Linoleic acid

Linolenic acid

C29

Tricosane, C23

Pentacosane, C25

Heptacosane, C27

the equipment may be coupled in line with GC-MS.

Nerol

Neral

8-Heptadecene

Hexadecanal

C15

4 Advances in Gas Chromatography

The use of the vapor phase above the sample (headspace) as a means for performing extraction processes has great advantages from the analytical point of view, because it eliminates many interferences, mostly the solvent peak, which can impede the detection of volatile compounds. In the study of natural products, direct headspace sampling has allowed the study of the volatile fraction around whole plants or parts thereof (Figure 3) [12]. Classical approaches to monitor the headspace above a sample may be divided into static and dynamic versions. The volatile fractions thus collected are suitable for GC- MS analysis, as they are free of interference from nonvolatile very polar (ionic) or high - molecular weight matrix components. Static headspace consists of the direct transfer of a headspace volume to the GC injection port. This is the sampling method regularly used in the analysis of BTEX (benzene, toluene, ethylbenzene, and xylenes) in water, quantification of trihalomethanes in drinking water, ethanol, volatile compounds in paints, determination of residual solvents in polymers and pharmaceuticals, hydrocarbons in soil, analysis of beverages and perfumes, among many other applications [13]. When the target-analyte concentration in the sample is very low (ppt, ppb), enrichment (preconcentration) is required, and in these cases, dynamic headspace is the preferred sampling mode. Basically, by purging the vapor phase with an inert gas, the analytes are collected in a solvent or are "frozen " (cryo-trapping) or are ad(b)sorpted in a sorbent (purge and trap, P & T), and later on are recovered by the action of temperature (thermal desorption) [14] or are eluted with solvent.

Sorption-based sampling techniques, which involve aspects of both static and dynamic headspace, have been developed in the past 2-3 decades. The great development experienced by the SPME technique has served to identify, through its many applications, some areas for improvement. This has generated a collection of new methods of micro-scale sampling, that achieve better concentration of the analytes of interest and in several cases, the use of a larger volume of sorbent phase (polymer, normally) permits the collection of larger amounts of analyte than SPME. In the inside needle capillary adsorption trap (INCAT), a polymer layer

Figure 2. Total ion current (TIC, chromatogram) of the coffee roasted beans volatile fraction isolated by (**A**) P&T (purging with nitrogen and trapping with dichloromethane). GC-MS (electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm), split 1:30; and (**B**) Headspace-SPME with simultaneous aldehyde on-fiber derivatization with pentafluorophenylhydrazine (PFPH). Temperature: 50°C and exposition time: 30 min. GC-ECD, DB-5MS nonpolar capillary column (30 m x **Figure 2.** Total ion current (TIC, chromatogram) of the coffee roasted beans volatile fraction isolated by (A) P&T (purg‐ ing with nitrogen and trapping with dichloromethane). GC-MS (Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm), split 1:30; and (B) Headspace-SPME with simultaneous aldehyde on-fiber deriva‐ tization with pentafluorophenylhydrazine (PFPH). Temperature: 50°C and exposition time: 30 min. GC-ECD, DB-5MS nonpolar capillary column (30 m x 0.25 mm x 0.25 μm). Split 1:30.

0.25 mm x 0.25 μm). Split 1:30.

coats the inside of the needle of a syringe, or the inside is filled with activated carbon [15]. Headspace solid-phase dynamic extraction (HS-SPDE) is a sampling technique in which the needle of a hermetic gas sampling syringe is coated with absorbent polymer (PDMS) on which the analytes are deposited when the syringe is actuated repeatedly over the sample [16]. The number of suction and discharge cycles determines the amount of accumulated analyte. Stir bar solid-phase extraction (SBSE) and tape sorption extraction (STE) are additional examples of sampling techniques using higher absorbent phase volume. In the first one, the magnetic stir bar is coated with absorbent polymer. Upon exposure to the solution containing the

analytes of interest, the stir bar is subjected to thermal desorption for transfer of the analytes to the chromatograph [17]. In STE, a thin strip of PDMS is placed for a fixed time in direct contact with the plant surface, the skin, or the object to be sampled. Analytes are subsequently

**Figure 3.** Total ion currents (TICs, chromatograms) of the passion fruit flowers (*Passiflora edulis*) volatile fractions ob‐ tained by headspace- SPME from different flower parts: (A) male stamens and (B) female carpels. Notice the composi‐ tion differences which are very important in the pollination processes. CAR/PDMS-fibre coating. Headspace-SPME exposition time: 30 min. GC-MS (Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 Time-->

methyl salicylate Benzyl alcohol

5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 Time-->

benzaldehyde Benzyl alcohol

**A**

7

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492

**B**

1,4-dimethoxybenzene

benzaldehyde

*trans-*-ocimene

transferred to the chromatograph by thermal desorption [18].

μm). Splitless injection mode.

*trans-*-ocimenenonanal

**Figure 3.** Total ion currents (TICs, chromatograms) of the passion fruit flowers (*Passiflora edulis*) volatile fractions ob‐ tained by headspace- SPME from different flower parts: (A) male stamens and (B) female carpels. Notice the composi‐ tion differences which are very important in the pollination processes. CAR/PDMS-fibre coating. Headspace-SPME exposition time: 30 min. GC-MS (Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). Splitless injection mode.

coats the inside of the needle of a syringe, or the inside is filled with activated carbon [15]. Headspace solid-phase dynamic extraction (HS-SPDE) is a sampling technique in which the needle of a hermetic gas sampling syringe is coated with absorbent polymer (PDMS) on which the analytes are deposited when the syringe is actuated repeatedly over the sample [16]. The number of suction and discharge cycles determines the amount of accumulated analyte. Stir bar solid-phase extraction (SBSE) and tape sorption extraction (STE) are additional examples of sampling techniques using higher absorbent phase volume. In the first one, the magnetic stir bar is coated with absorbent polymer. Upon exposure to the solution containing the

0.25 mm x 0.25 μm). Split 1:30.

nonpolar capillary column (30 m x 0.25 mm x 0.25 μm). Split 1:30.

a - Octanal b - Nonanal c - Decanal d - Undecanal e - Dodecanal

6 Advances in Gas Chromatography

Figure 2. Total ion current (TIC, chromatogram) of the coffee roasted beans volatile fraction isolated by (**A**) P&T (purging with nitrogen and trapping with dichloromethane). GC-MS (electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm), split 1:30; and (**B**) Headspace-SPME with simultaneous aldehyde on-fiber derivatization with pentafluorophenylhydrazine (PFPH). Temperature: 50°C and exposition time: 30 min. GC-ECD, DB-5MS nonpolar capillary column (30 m x

**Figure 2.** Total ion current (TIC, chromatogram) of the coffee roasted beans volatile fraction isolated by (A) P&T (purg‐ ing with nitrogen and trapping with dichloromethane). GC-MS (Electron impact, 70 eV), DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm), split 1:30; and (B) Headspace-SPME with simultaneous aldehyde on-fiber deriva‐ tization with pentafluorophenylhydrazine (PFPH). Temperature: 50°C and exposition time: 30 min. GC-ECD, DB-5MS

2.5 5 7.5 10 12.5 15 17.5 20 22.5 min

a

d

e

b c

10.00 20.00 30.00 40.00 50.00 60.00 min

**A**

**B**

analytes of interest, the stir bar is subjected to thermal desorption for transfer of the analytes to the chromatograph [17]. In STE, a thin strip of PDMS is placed for a fixed time in direct contact with the plant surface, the skin, or the object to be sampled. Analytes are subsequently transferred to the chromatograph by thermal desorption [18].

#### **2.2. Sample introduction systems**

Another absolutely essential parameter to determine how many components in a mixture are detected is the injection mode of the mixture into the GC. The main objective of an injection system, or simply the injector or the injection port, is to transfer the sample to the chromato‐ graphic column in a rapid and quantitatively reproducible manner. Ideally, this critical step in GC, has to meet a number of conditions. These include: (1) The sample must enter the column in a chromatographic band as thin as possible, to reduce peak broadening; (2) the percentage composition or relationship among the components of the mixture analyzed should be the same before and after injection; that is, there should be no discrimination by molecular weight, or volatility at the injection port; (3) during their transfer to the column, components should not react with each other or undergo chemical change, e.g., isomerization, hydrolysis, poly‐ merization, etc. or become adsorbed onto the injector body. The sample must be transferred quantitatively without alterations. The sample injection must be precise, accurate, reproduci‐ ble and predictable, quantitative and without discrimination of any kind. The injection mode is selected according to the following criteria: (1) type of chromatographic column (packed column, mega–bore, capillary), (2) the chemical nature of the analytes (molecular weight, volatility, polarity, temperature stability, reactivity, etc.) and (3) the purpose of the analysis (qualitative or quantitative, to detect traces or, conversely, to measure high concentrations of some components, etc.).

injection port is dramatically reduced and so is the probability of the thermal decomposition of some components (Figure 5). A mode of complete sample transfer to the column, which avoids component discrimination by their volatility or molecular weight, is the on-column injection, i.e., applying the sample directly to the column head. Very detailed work on the design and application of various methods of sample injection to GC equipment has been

Abundance **A**

Piperitone

Carvone

Limonene

Limonene

Figure 4. Total ion currents (TICs, chromatograms) of the *Lippia alba* (Verbenaceae family) essential oil obtained by hydrodistillation. **A**. 1:30 split injection (ratio, 1:30, Inj. vol.1 μL) and **B**. Splitless injection mode (Inj. vol.1 μL). Almost twice the number of compounds were registered in the chromatogram obtained by splitless injection (ca. 90) in comparison to those registered in split-mode injection (>0,05%). GC-MS (electron impact, 70 eV), DB-1MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm).

**Figure 4.** Total ion currents (TICs, chromatograms) of the *Lippia alba* (Verbenaceae family) essential oil obtained by hydrodistillation. A. 1:30 Split injection (ratio, 1:30, Inj. vol.1 μL) and B. Splitless injection mode (Inj. vol.1 μL). Almost twice the number of compounds were registered in the chromatogram obtained by splitless injection (ca. 90) in com‐ parison to those registered in split-mode injection (>0,05%). GC-MS (Electron impact, 70 eV), DB-1MS nonpolar capil‐

Different configurations of the liner offer efficiency and uniformity of the mixing process. A specific liner configuration is recommended for each type of injection, information which can be found in the catalogs of different manufacturers of these accessories. The inlet temperature should be high enough to ensure complete evaporation of all analytes, but not so high that

15.00 20.00 25.00 30.00 35.00 40.00 45.00

15.00 20.00 25.00 30.00 35.00 40.00 45.00

Abundance **B**

Piperitone

Carvone Bicyclosesquiphellandrene

Bicyclosesquiphellandrene

Time-->

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 9

Time-->

developed by Dr. Konrad Grob [19,20].

lary column (60 m x 0.25 mm x 0.25 μm).

Here are some considerations on the most common injection ports for capillary columns. The "universal" injector does not exist. For capillary columns two main types of injectors are available: vaporization and on-column (direct injection into the column without prior sample volatilization) injectors. There are also devices that can be connected in line with the GC injection port, for example, headspace sampler, pyrolysis, a purge and trap system, a thermal desorption system, a switching valve to transfer gas samples, etc. The injection of the sample can occur either manually or via an autosampler. In the vaporization injector, the sample (1 – 2 µL) crosses a septum to enter a glass cylinder which acts as the liner of a chamber which is heated independently. Upon exposure to the high liner temperature (> 200 °C), the sample vaporizes quickly and the resulting gaseous mixture is swept towards the column by the carrier gas. Conventional vaporization injectors can operate in two primary modes, i.e., split (sample is divided at the column inlet) and splitless (without division of the sample) [19]. In a standard split/splitless injection port, the gas flow may transport all of the injected sample to the column (splitless mode) or only a fraction of it (split mode). Concentrated samples are injected in the split mode so that only a small aliquot enters the column (Figure 4). On the other hand, when analyzing trace level compounds, for example, with environmental or forensic samples, complete transfer of the sample to the column, without division, is required (splitless mode). This mode involves slowly transferring (ca. 1 mL/min) the sample to the column through the hot injection port, which may lead to thermal decomposition and/or isomerization of some components of the mixture. A special pulsed splitless mode has been devised to enable the analysis of thermally labile compounds at trace levels, which is performed by increasing pressure on the top of the column during the injection, while the sample is transferred to the column. In this case, the residence time of the sample in the hot region (250-300 °C) of the injection port is dramatically reduced and so is the probability of the thermal decomposition of some components (Figure 5). A mode of complete sample transfer to the column, which avoids component discrimination by their volatility or molecular weight, is the on-column injection, i.e., applying the sample directly to the column head. Very detailed work on the design and application of various methods of sample injection to GC equipment has been developed by Dr. Konrad Grob [19,20].

**2.2. Sample introduction systems**

8 Advances in Gas Chromatography

some components, etc.).

Another absolutely essential parameter to determine how many components in a mixture are detected is the injection mode of the mixture into the GC. The main objective of an injection system, or simply the injector or the injection port, is to transfer the sample to the chromato‐ graphic column in a rapid and quantitatively reproducible manner. Ideally, this critical step in GC, has to meet a number of conditions. These include: (1) The sample must enter the column in a chromatographic band as thin as possible, to reduce peak broadening; (2) the percentage composition or relationship among the components of the mixture analyzed should be the same before and after injection; that is, there should be no discrimination by molecular weight, or volatility at the injection port; (3) during their transfer to the column, components should not react with each other or undergo chemical change, e.g., isomerization, hydrolysis, poly‐ merization, etc. or become adsorbed onto the injector body. The sample must be transferred quantitatively without alterations. The sample injection must be precise, accurate, reproduci‐ ble and predictable, quantitative and without discrimination of any kind. The injection mode is selected according to the following criteria: (1) type of chromatographic column (packed column, mega–bore, capillary), (2) the chemical nature of the analytes (molecular weight, volatility, polarity, temperature stability, reactivity, etc.) and (3) the purpose of the analysis (qualitative or quantitative, to detect traces or, conversely, to measure high concentrations of

Here are some considerations on the most common injection ports for capillary columns. The "universal" injector does not exist. For capillary columns two main types of injectors are available: vaporization and on-column (direct injection into the column without prior sample volatilization) injectors. There are also devices that can be connected in line with the GC injection port, for example, headspace sampler, pyrolysis, a purge and trap system, a thermal desorption system, a switching valve to transfer gas samples, etc. The injection of the sample can occur either manually or via an autosampler. In the vaporization injector, the sample (1 – 2 µL) crosses a septum to enter a glass cylinder which acts as the liner of a chamber which is heated independently. Upon exposure to the high liner temperature (> 200 °C), the sample vaporizes quickly and the resulting gaseous mixture is swept towards the column by the carrier gas. Conventional vaporization injectors can operate in two primary modes, i.e., split (sample is divided at the column inlet) and splitless (without division of the sample) [19]. In a standard split/splitless injection port, the gas flow may transport all of the injected sample to the column (splitless mode) or only a fraction of it (split mode). Concentrated samples are injected in the split mode so that only a small aliquot enters the column (Figure 4). On the other hand, when analyzing trace level compounds, for example, with environmental or forensic samples, complete transfer of the sample to the column, without division, is required (splitless mode). This mode involves slowly transferring (ca. 1 mL/min) the sample to the column through the hot injection port, which may lead to thermal decomposition and/or isomerization of some components of the mixture. A special pulsed splitless mode has been devised to enable the analysis of thermally labile compounds at trace levels, which is performed by increasing pressure on the top of the column during the injection, while the sample is transferred to the column. In this case, the residence time of the sample in the hot region (250-300 °C) of the

Figure 4. Total ion currents (TICs, chromatograms) of the *Lippia alba* (Verbenaceae family) essential oil obtained by hydrodistillation. **A**. 1:30 split injection (ratio, 1:30, Inj. vol.1 μL) and **B**. Splitless injection mode (Inj. vol.1 μL). Almost twice the number of compounds were registered in the chromatogram obtained by splitless injection (ca. 90) in comparison to those registered in split-mode injection (>0,05%). GC-MS (electron impact, 70 eV), DB-1MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). **Figure 4.** Total ion currents (TICs, chromatograms) of the *Lippia alba* (Verbenaceae family) essential oil obtained by hydrodistillation. A. 1:30 Split injection (ratio, 1:30, Inj. vol.1 μL) and B. Splitless injection mode (Inj. vol.1 μL). Almost twice the number of compounds were registered in the chromatogram obtained by splitless injection (ca. 90) in com‐ parison to those registered in split-mode injection (>0,05%). GC-MS (Electron impact, 70 eV), DB-1MS nonpolar capil‐ lary column (60 m x 0.25 mm x 0.25 μm).

Different configurations of the liner offer efficiency and uniformity of the mixing process. A specific liner configuration is recommended for each type of injection, information which can be found in the catalogs of different manufacturers of these accessories. The inlet temperature should be high enough to ensure complete evaporation of all analytes, but not so high that

and the development of a "memory effect". More commonly, the temperatures used in conventional vaporizing injectors range from 200 to 320 °C, the temperature of 250 °C being quite common in the practice of GC [21-24]. It is important to note that when the liquid sample is vaporized, it expands many times with respect to its initial volume. Expansion volumes depend on: (1) the type of solvent, (2) inlet temperature and (3) inlet pressure of the carrier gas at the top of the column [23]. Before injection, all these factors must be taken into account, i.e., what amount of sample to be injected, in which solvent and under what conditions (injection temperature and inlet pressure in the column). The flashback, i.e., the "return" of the sample vapor by the purge gas line, may occur when the expanded volume of the injected sample volume exceeds that of the liner (ca. 0.5- 1 mL). Excess vapors can escape and pressure is created not only by the carrier gas line (flashback), but also by the septum purge line; the more volatile components of the sample can be "lost" and this will affect the accuracy and precision of the

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 11

The injection port is a critical site in any chromatographic system, since it may be the cause of contamination (high noise) and the source of so-called ghost peaks, whose origin may be related to recondensation of the less volatile substances in the bottom of the septum or the carrier gas transfer line from flashback problems [24]. Injection efficiency, optimum speed and consistency of the mixture entering the column will determine the width of the chromato‐ graphic peak, its symmetry, and, ultimately, efficient and reproducible separation and quantification. When choosing the injection technique, the following variables are chosen and optimized: (1) the injection mode (split, splitless, on-column or programmed temperature vaporizer, PTV), (2) the volume of the sample to be injected, (3) injector temperature, (4) the type of liner (shape, volume, packaging), (5) the initial temperature of the column (more critical for injection in splitless mode), (6) the form of injection, manually or automatically, (7) the rate at which the septum is pierced or the needle is removed from the injection port (important parameters for decreasing analyte discrimination based on volatility), (8) the solvent and the number of washes of the syringe with different solvents, (9) the type of septum (material, density, thermal stability, bleeding), for instance, the conventional septum or Merlin Micro‐

The chromatographic column is the heart of the GC. There are two types of columns, packed and open tubular, often called capillaries (when the ID is equal to or less than 0.32 mm). In packed columns, solid particles (silica, alumina, molecular sieves, porous synthetic polymers, etc.) serve as support or are embedded in a liquid (squalene, polyethylene glycol, etc.), which acts as a stationary phase as means of separating components within the GC. Because the gas solubility in liquids is very low, solid phases are particularly suitable for the separation of gaseous components, such as permanent gases, natural and refinery gas, volatile aldehydes and alcohols, car exhaust, etc. The use of packed columns is fairly limited today, because open tubular columns have much higher separation efficiencies. Current gas analysis uses porous layer open tubular columns (PLOT), a type of capillary columns, where analyte adsorption is

combined with the high separation efficiency provided by capillary columns [21].

sealTM, which is highly recommended for GC-MS operations.

GC analysis.

**2.3. Separation procedure**

**Figure 5.** Chromatographic peak of ethyl benzoate obtained by GC-MS (Electron impact, 70 eV), on the DB-5MS non‐ polar capillary column (60 m x 0.25 mm x 0.25 μm). The sample was injected in different modes: split, splitless and pulsed splitless. Notice the retention times shift and the change of chromatographic areas. B. The influence of injec‐ tion port temperature on the chromatographic peak area of limonene (monoterpene, C10H16). DB-5MS column (60 m x 0.25 mm x 0.25 μm). 1:30 Split ratio.

causes thermal decomposition. Too low temperatures will produce incomplete volatilization, a loss of analytes and a slow entry of volatilized substances to the column, causing chroma‐ tographic peak broadening. A potential problem is the systematic contamination of the injector and the development of a "memory effect". More commonly, the temperatures used in conventional vaporizing injectors range from 200 to 320 °C, the temperature of 250 °C being quite common in the practice of GC [21-24]. It is important to note that when the liquid sample is vaporized, it expands many times with respect to its initial volume. Expansion volumes depend on: (1) the type of solvent, (2) inlet temperature and (3) inlet pressure of the carrier gas at the top of the column [23]. Before injection, all these factors must be taken into account, i.e., what amount of sample to be injected, in which solvent and under what conditions (injection temperature and inlet pressure in the column). The flashback, i.e., the "return" of the sample vapor by the purge gas line, may occur when the expanded volume of the injected sample volume exceeds that of the liner (ca. 0.5- 1 mL). Excess vapors can escape and pressure is created not only by the carrier gas line (flashback), but also by the septum purge line; the more volatile components of the sample can be "lost" and this will affect the accuracy and precision of the GC analysis.

The injection port is a critical site in any chromatographic system, since it may be the cause of contamination (high noise) and the source of so-called ghost peaks, whose origin may be related to recondensation of the less volatile substances in the bottom of the septum or the carrier gas transfer line from flashback problems [24]. Injection efficiency, optimum speed and consistency of the mixture entering the column will determine the width of the chromato‐ graphic peak, its symmetry, and, ultimately, efficient and reproducible separation and quantification. When choosing the injection technique, the following variables are chosen and optimized: (1) the injection mode (split, splitless, on-column or programmed temperature vaporizer, PTV), (2) the volume of the sample to be injected, (3) injector temperature, (4) the type of liner (shape, volume, packaging), (5) the initial temperature of the column (more critical for injection in splitless mode), (6) the form of injection, manually or automatically, (7) the rate at which the septum is pierced or the needle is removed from the injection port (important parameters for decreasing analyte discrimination based on volatility), (8) the solvent and the number of washes of the syringe with different solvents, (9) the type of septum (material, density, thermal stability, bleeding), for instance, the conventional septum or Merlin Micro‐ sealTM, which is highly recommended for GC-MS operations.

#### **2.3. Separation procedure**

causes thermal decomposition. Too low temperatures will produce incomplete volatilization, a loss of analytes and a slow entry of volatilized substances to the column, causing chroma‐ tographic peak broadening. A potential problem is the systematic contamination of the injector

0.25 mm x 0.25 μm). 1:30 Split ratio.

10 Advances in Gas Chromatography

**Figure 5.** Chromatographic peak of ethyl benzoate obtained by GC-MS (Electron impact, 70 eV), on the DB-5MS non‐ polar capillary column (60 m x 0.25 mm x 0.25 μm). The sample was injected in different modes: split, splitless and pulsed splitless. Notice the retention times shift and the change of chromatographic areas. B. The influence of injec‐ tion port temperature on the chromatographic peak area of limonene (monoterpene, C10H16). DB-5MS column (60 m x

11.05 11.10 11.15 11.20 11.25 11.30 11.35 11.40 11.45 11.50

38.74 38.76 38.78 38.80 38.82 38.84 38.86 38.88 38.90 38.92 38.94 Time-->

150 °C

200 °C

250 °C

Split 1:30

38.76 Split 1:100

38.84

38.83

Pulsed splitless

Splitless

<sup>300</sup> °<sup>C</sup> **<sup>B</sup>**

**A**

Time-->

The chromatographic column is the heart of the GC. There are two types of columns, packed and open tubular, often called capillaries (when the ID is equal to or less than 0.32 mm). In packed columns, solid particles (silica, alumina, molecular sieves, porous synthetic polymers, etc.) serve as support or are embedded in a liquid (squalene, polyethylene glycol, etc.), which acts as a stationary phase as means of separating components within the GC. Because the gas solubility in liquids is very low, solid phases are particularly suitable for the separation of gaseous components, such as permanent gases, natural and refinery gas, volatile aldehydes and alcohols, car exhaust, etc. The use of packed columns is fairly limited today, because open tubular columns have much higher separation efficiencies. Current gas analysis uses porous layer open tubular columns (PLOT), a type of capillary columns, where analyte adsorption is combined with the high separation efficiency provided by capillary columns [21].

Column efficiency is expressed by the so-called number of theoretical plates, N, or by means of the height equivalent to a theoretical plate, HETP [22]. The higher is the number of N and lower the value of HETP, the higher the resolution (efficiency) of the column. As indicated by its name, open tubular columns have an opening, an internal space, which allows the carrier gas to move freely without having to penetrate or pass through, with strength, the porous support. This latter process, the tortuous pass, accompanied by the phenomenon of multiple paths, is typical for packed columns and is the main cause of their much lower efficiency compared with open tubular columns [22]. The GC column selection process should take into account the following parameters and properties: (1) phase type (nature, polarity), (2) dimen‐ sions (length, L, and inner diameter, ID) and (3) phase thickness. Stationary phase polarity is chosen according to the polarity of the analytes of the mixture, following the general principle of "like dissolves like". But often, it is difficult to find a "universal" stationary phase, since in many samples both polar and nonpolar compounds can be found, with diverse volatility (Figure 6). In nonpolar columns (polydimethylsiloxanes), analyte elution happens according to their volatility, i.e., in increasing order of their boiling points. An example is the technique of simulated distillation for the analysis of petroleum fractions [25]. In polar columns (e.g., polyethyleneglycols), the predominant factors for analyte separation are its polarity (the dipole moment, µ) and the strength of its interaction with the stationary phase (dipole, hydrogen bonding). Wall-coated open tubular columns (WCOT) are the most commonly used in capillary GC and their stationary phases can be divided, roughly, into two great classes: (1) substituted siloxane-type polymers with a wide range of polarities and (2) poly(ethylenegly‐ col) (PEG), which are highly polar. The polysiloxanes may possess different substituents which provide to them some polarizability. The polysiloxane phases range from nonpolar 100% poly(dimethylsiloxane) slightly polar 5-95% -phenyl poly(methylsiloxane), to polar and strongly polar phases containing trifluoropropyl and cyanopropyl substituents, respectively. Modern GC columns have cross-linked and immobilized (chemically bonded to the column wall) polymer stationary phases. This provides uniformity, chemical and thermal stability, and reproducibility of results during extended use. Chiral stationary phases for separating optical isomers give rise to a separate family of columns. Among these, good performance has been achieved by the presence of cyclodextrins within the stationary phase. Enantiomers appear in the chromatogram with different retention times due to the different strength of their associ‐ ation with the cyclodextrins when these adducts involve the non-polar cyclodextrin´s inner cavity [26].

Capillary columns are divided according to their length, L, into: (1) short (5-15 m), (2) medium (20-30 m) and (3) large (50-100 m). Perhaps the length L = 30 m is the most common column used for many analyses (drugs, pesticides, polyaromatic hydrocarbons, etc.) [21,22]. With increasing column length the number of theoretical plates (N) and resolution also increase, but the duration of the chromatographic run is greatly increased. Long columns are required for the analysis of complex, multicomponent (> 50 substances) samples, for example, gasoline, perfumes, essential oils, aromas, VOCs, etc. [22,23], or when there are isomers in the mixture which have similar boiling points or dipole moments and, consequently, their distribution constants, KD, are very close. The longer the columns are, the greater the pressure drop across them (the pressure difference between inlet and outlet of the column). Also, due to their greater thermal inertia, the heating rate of these columns must be slower (2-6 °C min-1), in order to achieve reproducible change in temperature. The analysis of polyaromatic hydrocarbons (PAHs) can be successfully accomplished in 30 m-long columns, whereas the separation of hydrocarbons in gasoline requires longer 100 m columns. The main drawbacks of long columns are extended analysis time and higher cost, but very high sensitivity and resolution can be achieved. A compromise has to be found among resolution, sensitivity and time (cost) of analysis. A significant alternative to increase the resolution of the column and shorten analysis time is to reduce the column internal diameter, which in capillary columns most commonly is 0.25 or 0.32 mm. When the column length is doubled, the resolution is also doubled, but when ID is reduced by half, the resolution increases almost fourfold. Decreasing I.D. leads to a marked reduction in analysis time. This led to the development of a new branch in GC, named

oven temperature program was used with both columns: 50 °C (5 min), 4 °C min-1 to 250 °C (15 min).

Figure 6. Comparison of the total ion currents (TICs, chromatograms) of the patchouli (*Pogostemon cablin*) essential oil obtained by hydrodistillation. The complex mixture of sesquiterpene hydrocarbons

**Figure 6.** Comparison of the total ion currents (TICs, chromatograms) of the patchouli (*Pogostemon cablin*) essential oil obtained by hydrodistillation. The complex mixture of sesquiterpene hydrocarbons and their oxygenated deriva‐ tives (alcohols and aldehydes) was analyzed in two capillary columns of the same dimensions (60 m x 0.25 mm x 0.25 m), but different stationary phases: (A) DB-WAX and (B) DB-5MS. GC-MS (Electron impact, 70 eV), Split 1:30. The same

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

20.00 30.00 40.00 50.00 60.00 70.00 80.00

Patchoulenol

wall) polymer stationary phases. This provides uniformity, chemical and thermal stability, and reproducibility of results during extended use. Chiral stationary phases for separating optical isomers give rise to a separate family of columns. Among these, good performance has been achieved by the presence of cyclodextrins within the stationary phase. Enantiomers appear in the chromatogram with different retention times due to the different strength of their association with the cyclodextrins when these adducts involve the non-polar

**A**

13

Patchoulenol

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492

Time-->

**B**

Time-->

cyclodextrin´s inner cavity [26].

Abundance

Abundance

wall) polymer stationary phases. This provides uniformity, chemical and thermal stability, and reproducibility of results during extended use. Chiral stationary phases for separating optical isomers give rise to a separate family of columns. Among these, good performance has been achieved by the presence of cyclodextrins within the stationary phase. Enantiomers

cyclodextrin´s inner cavity [26].

Column efficiency is expressed by the so-called number of theoretical plates, N, or by means of the height equivalent to a theoretical plate, HETP [22]. The higher is the number of N and lower the value of HETP, the higher the resolution (efficiency) of the column. As indicated by its name, open tubular columns have an opening, an internal space, which allows the carrier gas to move freely without having to penetrate or pass through, with strength, the porous support. This latter process, the tortuous pass, accompanied by the phenomenon of multiple paths, is typical for packed columns and is the main cause of their much lower efficiency compared with open tubular columns [22]. The GC column selection process should take into account the following parameters and properties: (1) phase type (nature, polarity), (2) dimen‐ sions (length, L, and inner diameter, ID) and (3) phase thickness. Stationary phase polarity is chosen according to the polarity of the analytes of the mixture, following the general principle of "like dissolves like". But often, it is difficult to find a "universal" stationary phase, since in many samples both polar and nonpolar compounds can be found, with diverse volatility (Figure 6). In nonpolar columns (polydimethylsiloxanes), analyte elution happens according to their volatility, i.e., in increasing order of their boiling points. An example is the technique of simulated distillation for the analysis of petroleum fractions [25]. In polar columns (e.g., polyethyleneglycols), the predominant factors for analyte separation are its polarity (the dipole moment, µ) and the strength of its interaction with the stationary phase (dipole, hydrogen bonding). Wall-coated open tubular columns (WCOT) are the most commonly used in capillary GC and their stationary phases can be divided, roughly, into two great classes: (1) substituted siloxane-type polymers with a wide range of polarities and (2) poly(ethylenegly‐ col) (PEG), which are highly polar. The polysiloxanes may possess different substituents which provide to them some polarizability. The polysiloxane phases range from nonpolar 100% poly(dimethylsiloxane) slightly polar 5-95% -phenyl poly(methylsiloxane), to polar and strongly polar phases containing trifluoropropyl and cyanopropyl substituents, respectively. Modern GC columns have cross-linked and immobilized (chemically bonded to the column wall) polymer stationary phases. This provides uniformity, chemical and thermal stability, and reproducibility of results during extended use. Chiral stationary phases for separating optical isomers give rise to a separate family of columns. Among these, good performance has been achieved by the presence of cyclodextrins within the stationary phase. Enantiomers appear in the chromatogram with different retention times due to the different strength of their associ‐ ation with the cyclodextrins when these adducts involve the non-polar cyclodextrin´s inner

Capillary columns are divided according to their length, L, into: (1) short (5-15 m), (2) medium (20-30 m) and (3) large (50-100 m). Perhaps the length L = 30 m is the most common column used for many analyses (drugs, pesticides, polyaromatic hydrocarbons, etc.) [21,22]. With increasing column length the number of theoretical plates (N) and resolution also increase, but the duration of the chromatographic run is greatly increased. Long columns are required for the analysis of complex, multicomponent (> 50 substances) samples, for example, gasoline, perfumes, essential oils, aromas, VOCs, etc. [22,23], or when there are isomers in the mixture which have similar boiling points or dipole moments and, consequently, their distribution constants, KD, are very close. The longer the columns are, the greater the pressure drop across them (the pressure difference between inlet and outlet of the column). Also, due to their greater

cavity [26].

12 Advances in Gas Chromatography

Figure 6. Comparison of the total ion currents (TICs, chromatograms) of the patchouli (*Pogostemon cablin*) essential oil obtained by hydrodistillation. The complex mixture of sesquiterpene hydrocarbons **Figure 6.** Comparison of the total ion currents (TICs, chromatograms) of the patchouli (*Pogostemon cablin*) essential oil obtained by hydrodistillation. The complex mixture of sesquiterpene hydrocarbons and their oxygenated deriva‐ tives (alcohols and aldehydes) was analyzed in two capillary columns of the same dimensions (60 m x 0.25 mm x 0.25 m), but different stationary phases: (A) DB-WAX and (B) DB-5MS. GC-MS (Electron impact, 70 eV), Split 1:30. The same oven temperature program was used with both columns: 50 °C (5 min), 4 °C min-1 to 250 °C (15 min).

thermal inertia, the heating rate of these columns must be slower (2-6 °C min-1), in order to achieve reproducible change in temperature. The analysis of polyaromatic hydrocarbons (PAHs) can be successfully accomplished in 30 m-long columns, whereas the separation of hydrocarbons in gasoline requires longer 100 m columns. The main drawbacks of long columns are extended analysis time and higher cost, but very high sensitivity and resolution can be achieved. A compromise has to be found among resolution, sensitivity and time (cost) of analysis. A significant alternative to increase the resolution of the column and shorten analysis time is to reduce the column internal diameter, which in capillary columns most commonly is 0.25 or 0.32 mm. When the column length is doubled, the resolution is also doubled, but when ID is reduced by half, the resolution increases almost fourfold. Decreasing I.D. leads to a marked reduction in analysis time. This led to the development of a new branch in GC, named fast chromatography, which has found very interesting applications [27-29]. The columns employed are thin or ultra-thin (0.1 or 0.05 mm), but shorter (1-5 m) than in conventional GC [29]. The "fast" columns also require a detector with very fast response, particularly in the case of comprehensive GCxGC; in the fast -GC-MS configuration, the most effective mass analyzer is the time-of-flight (TOF) [30]. The main shortcoming of fast GC is its low sample capacity, which for many analyses, for example, environmental or forensic, can become a serious limitation. Inlet pressures in the overhead GC column increase greatly as the ID is reduced. For example, 30 m columns (100 °C) with IDs of 0.32 and 0.10 mm require carrier gas (helium) pressures of 9.5 and 114 psi, respectively. Roughly, the resolution of the column increases with its length and with the decrease of ID and phase thickness, while the analysis time increases with the rise of the column ID and of the thickness of the stationary phase and, above all, with the length of the column. The main tasks after correctly selecting the column should focus on the installation of the column and its conditioning, maintenance and precautions to be taken during operation. The latter is related to the care of the GC column, which includes not exceeding the permitted temperature limits, preventing the entry of oxygen, water, mineral salts, acids or bases or high molecular weight compounds (interference), maintaining a hermetically sealed GC system, devoid of leakage. With time, gaskets, seals, traps (for moisture, hydrocarbons or oxygen) wear out or saturate and should be replaced.

mass spectra. Complete separation of the components of essential oils can be achieved with complete gas chromatography (comprehensive GCxGC), which has been applied very successfully to such mixtures [32]. Multidimensional chromatography allows separating in a second column the peaks of partially or completely co-eluted substances, through the opera‐ tion of "heart -cutting" using switching pneumatic valves (nowadays micro-fluidics technol‐ ogy) between the two columns and diverting part of the effluent from the first to the second column. This technique has played an important role in the development of methods for complex mixtures separation. Multidimensional chromatography requires at least two detectors and some configurations can have three columns in the same or in separate chro‐

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 15

Along with this and other conventional approaches, nowadays there is a modern solution, although relatively inaccessible to many laboratories, due to its cost, for the separation of multicomponent mixtures. It is called complete, total, or more commonly, comprehensive chromatography, GCxGC. It uses two columns connected by a modulator. In contrast to conventional multi-dimensional gas chromatography, GC x GC requires a single detector and both columns can be in the same or in two separate ovens. There are different types of modulators, e.g., thermal rotary modulator (sweeper), "jet" cryogenic system, valve modulator, and longitudinal cryogenic modulator, among others [32]. The eluent from the first column is split into very small slices by means of the modulator, which transfers them one after another, in a row, from the first into the second column. The first column (1D) is a conventional column, with length of 25 or 30 m, and the second column (2D) is a fast chromatography, a very short micro-bore column. Stationary phases in both columns are "orthogonal", i.e., if the first one is non-polar, the second column is polar, and vice versa. Modulation time, i.e., the transfer of a tiny portion of the first column eluent to the second, must be very short and commensurate, but not longer, with the elution time of a component in the second column. Thus, the latter is very short and very thin and allows separating the components in a few seconds. Since the second column is connected to the detector (MSD, FID or µ-ECD), it should have high reading and signal processing speeds. In most cases, the time-of-flight mass detector (TOF) is the best,

The detection system differentiates the analyte molecules from the mobile phase, which is transparent to the detector. Detector response (signal) is based on measuring a physical property of the flow of analyte molecules (ionic current, thermal conductivity, fluorescence, refractive index, photon emission, electron capture, etc.). The signal should be proportional to the amount of analyte that emerges from the column, thus establishing an interdependent relationship and carrying out a quantitative analysis, which is an essential part of a chroma‐ tographic determination and leads to the answer on how many components and in what proportion they are present in a mixture. Common detectors used in gas chromatography are classified into (1) universal detectors, e.g., thermal conductivity detector (TCD), mass selective detector (MSD) operated in full scan mode, or infrared detector; (2) selective detectors, e.g., nitrogen-phosphorus detector (NPD), electron capture detector (ECD) or flame photometric

matographic ovens.

although still very expensive option [32].

**2.5. Overview of GC detectors**

For capillary columns, the most recommended carrier gases are helium or hydrogen; an average linear velocity of 25-35 cm/s allows obtaining the required separation efficiency. Since the gas viscosity varies with temperature, the average linear velocity of the carrier gas will decrease during the chromatographic run as oven temperature increases. For substances with high distribution constants (KD), this reduction in the flow rate of the carrier gas impinge upon the shape of their chromatographic peaks (asymmetry, tailing) and increases the retention times and the overall analysis time. Hence, special devices have been introduced, the electronic pressure controls, which, among other functions, allow maintaining constant flow in the column during the chromatographic run so that carrier gas pressure input to the system is changed with increasing oven temperature, in a synchronized, electronically governed manner.

#### **2.4. Multidimensional and comprehensive separation systems**

Essential oils, fragrant liquids consisting of volatile secondary metabolites isolated by distil‐ lation of aromatic plants are multicomponent mixtures, which may contain up to 300 substan‐ ces [31]. The essential oil is an excellent model for gas chromatography, as its constituents possess different functional groups; among them are hydrocarbons, alcohols, aldehydes, ethers and esters, acids, and others. Most of these substances belong to the family of terpenoids (C10 and C15), but also include phenolic derivatives. The concentration of the constituents in the essential oils can vary over a wide range, from µg/kg or mg/kg to g/kg. Many components of the oils have isomers. This leads to two problems in reaching the analytical goals, namely: (1) total separation of all components, and (2) the correct assignment of their structures. For some substances present in the essential oils very close retention times are observed, at least in one of the columns (non-polar or polar); in addition, some constituents possess very similar mass spectra. Complete separation of the components of essential oils can be achieved with complete gas chromatography (comprehensive GCxGC), which has been applied very successfully to such mixtures [32]. Multidimensional chromatography allows separating in a second column the peaks of partially or completely co-eluted substances, through the opera‐ tion of "heart -cutting" using switching pneumatic valves (nowadays micro-fluidics technol‐ ogy) between the two columns and diverting part of the effluent from the first to the second column. This technique has played an important role in the development of methods for complex mixtures separation. Multidimensional chromatography requires at least two detectors and some configurations can have three columns in the same or in separate chro‐ matographic ovens.

Along with this and other conventional approaches, nowadays there is a modern solution, although relatively inaccessible to many laboratories, due to its cost, for the separation of multicomponent mixtures. It is called complete, total, or more commonly, comprehensive chromatography, GCxGC. It uses two columns connected by a modulator. In contrast to conventional multi-dimensional gas chromatography, GC x GC requires a single detector and both columns can be in the same or in two separate ovens. There are different types of modulators, e.g., thermal rotary modulator (sweeper), "jet" cryogenic system, valve modulator, and longitudinal cryogenic modulator, among others [32]. The eluent from the first column is split into very small slices by means of the modulator, which transfers them one after another, in a row, from the first into the second column. The first column (1D) is a conventional column, with length of 25 or 30 m, and the second column (2D) is a fast chromatography, a very short micro-bore column. Stationary phases in both columns are "orthogonal", i.e., if the first one is non-polar, the second column is polar, and vice versa. Modulation time, i.e., the transfer of a tiny portion of the first column eluent to the second, must be very short and commensurate, but not longer, with the elution time of a component in the second column. Thus, the latter is very short and very thin and allows separating the components in a few seconds. Since the second column is connected to the detector (MSD, FID or µ-ECD), it should have high reading and signal processing speeds. In most cases, the time-of-flight mass detector (TOF) is the best, although still very expensive option [32].

#### **2.5. Overview of GC detectors**

fast chromatography, which has found very interesting applications [27-29]. The columns employed are thin or ultra-thin (0.1 or 0.05 mm), but shorter (1-5 m) than in conventional GC [29]. The "fast" columns also require a detector with very fast response, particularly in the case of comprehensive GCxGC; in the fast -GC-MS configuration, the most effective mass analyzer is the time-of-flight (TOF) [30]. The main shortcoming of fast GC is its low sample capacity, which for many analyses, for example, environmental or forensic, can become a serious limitation. Inlet pressures in the overhead GC column increase greatly as the ID is reduced. For example, 30 m columns (100 °C) with IDs of 0.32 and 0.10 mm require carrier gas (helium) pressures of 9.5 and 114 psi, respectively. Roughly, the resolution of the column increases with its length and with the decrease of ID and phase thickness, while the analysis time increases with the rise of the column ID and of the thickness of the stationary phase and, above all, with the length of the column. The main tasks after correctly selecting the column should focus on the installation of the column and its conditioning, maintenance and precautions to be taken during operation. The latter is related to the care of the GC column, which includes not exceeding the permitted temperature limits, preventing the entry of oxygen, water, mineral salts, acids or bases or high molecular weight compounds (interference), maintaining a hermetically sealed GC system, devoid of leakage. With time, gaskets, seals, traps (for

moisture, hydrocarbons or oxygen) wear out or saturate and should be replaced.

**2.4. Multidimensional and comprehensive separation systems**

manner.

14 Advances in Gas Chromatography

For capillary columns, the most recommended carrier gases are helium or hydrogen; an average linear velocity of 25-35 cm/s allows obtaining the required separation efficiency. Since the gas viscosity varies with temperature, the average linear velocity of the carrier gas will decrease during the chromatographic run as oven temperature increases. For substances with high distribution constants (KD), this reduction in the flow rate of the carrier gas impinge upon the shape of their chromatographic peaks (asymmetry, tailing) and increases the retention times and the overall analysis time. Hence, special devices have been introduced, the electronic pressure controls, which, among other functions, allow maintaining constant flow in the column during the chromatographic run so that carrier gas pressure input to the system is changed with increasing oven temperature, in a synchronized, electronically governed

Essential oils, fragrant liquids consisting of volatile secondary metabolites isolated by distil‐ lation of aromatic plants are multicomponent mixtures, which may contain up to 300 substan‐ ces [31]. The essential oil is an excellent model for gas chromatography, as its constituents possess different functional groups; among them are hydrocarbons, alcohols, aldehydes, ethers and esters, acids, and others. Most of these substances belong to the family of terpenoids (C10 and C15), but also include phenolic derivatives. The concentration of the constituents in the essential oils can vary over a wide range, from µg/kg or mg/kg to g/kg. Many components of the oils have isomers. This leads to two problems in reaching the analytical goals, namely: (1) total separation of all components, and (2) the correct assignment of their structures. For some substances present in the essential oils very close retention times are observed, at least in one of the columns (non-polar or polar); in addition, some constituents possess very similar

The detection system differentiates the analyte molecules from the mobile phase, which is transparent to the detector. Detector response (signal) is based on measuring a physical property of the flow of analyte molecules (ionic current, thermal conductivity, fluorescence, refractive index, photon emission, electron capture, etc.). The signal should be proportional to the amount of analyte that emerges from the column, thus establishing an interdependent relationship and carrying out a quantitative analysis, which is an essential part of a chroma‐ tographic determination and leads to the answer on how many components and in what proportion they are present in a mixture. Common detectors used in gas chromatography are classified into (1) universal detectors, e.g., thermal conductivity detector (TCD), mass selective detector (MSD) operated in full scan mode, or infrared detector; (2) selective detectors, e.g., nitrogen-phosphorus detector (NPD), electron capture detector (ECD) or flame photometric detector (FPD), among others, and (3) specific detectors, e.g., MSD operated in selected ion monitoring (SIM) mode, thermal energy analyzer (TEA), or atomic emission detector (AED). The flame ionization detector (FID) could be considered "near- universal" since only water and permanent gases are transparent. There is no well-defined boundary between the specific and selective detectors [33]. Specific detectors are actually highly selective detection systems, which can obtain a response to one particular compound, an analyte or target of interest present in a complex mixture. For example, in a urine sample, detect and quantify testosterone, which is one of the analytes to be determined quantitatively for doping control in sports. The selectivity of a detector response is related to a substance that has a specific atom, for example, nitrogen, phosphorus (NPD) or sulfur (FPD) or a functional group, e.g., electronegative group(s) (ECD) or unsaturated groups and aromatic rings (PID, photoionization detector) or the substances, which have a common structural fragment, for example, a phenyl, benzyl or an acyl radical, etc. (MSD operated in SIM mode; Figure 7).

capillary column attached to them, the dead volume formed could generate chromatograph‐ ic peak broadening and affect resolution. This difference in volumes is offset by an auxiliary gas (make-up), usually nitrogen, whose flow is 20-40 mL/min, depending on the detector type. High make-up flows lead to more symmetrical and thin chromatographic peaks, but compromise sensitivity. The trend in modern GC instruments is to reduce the volume of the detector. Today, there are micro versions of several detectors, for example, µ-ECD or µ-TCD. Most of the GC detectors are destructive detectors. These include, for example, ionization detectors (FID, NPD, FPD, MSD), whose response is sensitive to the change in mass of the analyte, while the response of non-destructive detectors (TCD, infrared detector, ECD, PID) is sensitive to the change of the analyte concentration. That is, when operating the TCD or ECD it is important to maintain the flow of gases (carrier gas and make-up or auxiliary) constant. Nondestructive detectors can precede in series a destructive detector;

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 17

The major limitation posed by conventional detectors (FID, TCD, ECD, NPD) is the ambiguity to uniquely identify an unknown substance. Absolute retention times (tR) or relative to an internal standard (tRR) and certified standards, are used for the presumptive identification of a compound. This is a screening analysis. Confirmatory identification of a compound in a complex mixture, analyzed by GC, necessarily requires obtaining the "fingerprint", i.e., spectrum, of the compound. The mass spectrum (MS) has information about a unique combination of charged fragments (ions) generated during the dissocia‐ tion or fragmentation of the previously ionized molecule. The complementarity of chroma‐ tographic analysis (screening) with confirmatory spectral data is achieved using the combination of GC-MSD. A GC-MSD analysis provides information on (1) retention times, (2) chromatographic areas and (3) mass spectra of each component in a mixture, obtained by its ionization with electron impact (EI) at 70 eV. The value of 70 eV was established as a standard to obtain the MS, which are also part of the databases and commercial spec‐ tral libraries, because for most organic compounds, the highest ionization efficiency and reproducibility are achieved with bombarding electrons of this energy. The ionization energy (potential) of organic molecules varies from 7 to 14 eV. During the collision with electrons of 70 eV, energy is transferred to the molecules to ionize them and form the

, but also to produce fragmentation of those excited M+\* ions, which

possess sufficient internal energy to break a chemical bond. The spectra obtained with lower energy electrons (15-20 eV) are called low voltage spectra, but they are rarely used in the GC- MSD method, because they often cannot achieve the sensitivity required for the

The coupled technique GC-MSD is the most used tandem combination in instrumental analytical chemistry and is applied to investigations in forensic, environmental, natural products, foods, flavors and many other areas [34]. Among the chromatographic spectroscopic detectors (AED, IR or MSD), useful for the determination of the chemical structure or elemental composition of analytes, the mass spectrometer is the most widely used today thanks to its sensitivity, operability and, above all, to a large amount of *sui generis* structural information

e.g., in tandem TCD followed by FID or NPD.

molecular ion, M+

that it can provide [35].

analysis.

**Figure 7.** Selective mass-spectrometry detection of alkyl benzenes present in the volatile fraction isolated by P&T from roasted coffee beans. GC-MS-SIM (Electron impact, 70 eV, selected monitoring of *m/z* 91 ions, characteristic for alkyl benzenes). DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). Split ratio 1:30.

All detectors are distinguished by their sensitivity, minimum detection and quantitation levels (in the case of MSD, also by the minimum identification level), linearity, sensitivity to changes in gas flow, temperature or pressure. The sensors have different noise levels, volumes, are distinguished by a higher or lower sensitivity and sophistication, for simplic‐ ity in operation, cost, and other typical features to be taken into account when choosing and operating a GC detector. Because the volumes of GC detectors are larger than the capillary column attached to them, the dead volume formed could generate chromatograph‐ ic peak broadening and affect resolution. This difference in volumes is offset by an auxiliary gas (make-up), usually nitrogen, whose flow is 20-40 mL/min, depending on the detector type. High make-up flows lead to more symmetrical and thin chromatographic peaks, but compromise sensitivity. The trend in modern GC instruments is to reduce the volume of the detector. Today, there are micro versions of several detectors, for example, µ-ECD or µ-TCD. Most of the GC detectors are destructive detectors. These include, for example, ionization detectors (FID, NPD, FPD, MSD), whose response is sensitive to the change in mass of the analyte, while the response of non-destructive detectors (TCD, infrared detector, ECD, PID) is sensitive to the change of the analyte concentration. That is, when operating the TCD or ECD it is important to maintain the flow of gases (carrier gas and make-up or auxiliary) constant. Nondestructive detectors can precede in series a destructive detector; e.g., in tandem TCD followed by FID or NPD.

detector (FPD), among others, and (3) specific detectors, e.g., MSD operated in selected ion monitoring (SIM) mode, thermal energy analyzer (TEA), or atomic emission detector (AED). The flame ionization detector (FID) could be considered "near- universal" since only water and permanent gases are transparent. There is no well-defined boundary between the specific and selective detectors [33]. Specific detectors are actually highly selective detection systems, which can obtain a response to one particular compound, an analyte or target of interest present in a complex mixture. For example, in a urine sample, detect and quantify testosterone, which is one of the analytes to be determined quantitatively for doping control in sports. The selectivity of a detector response is related to a substance that has a specific atom, for example, nitrogen, phosphorus (NPD) or sulfur (FPD) or a functional group, e.g., electronegative group(s) (ECD) or unsaturated groups and aromatic rings (PID, photoionization detector) or the substances, which have a common structural fragment, for example, a phenyl, benzyl or an acyl radical,

4.00 8.00 12.00 16.00 20.00 Time-->

**Figure 7.** Selective mass-spectrometry detection of alkyl benzenes present in the volatile fraction isolated by P&T from roasted coffee beans. GC-MS-SIM (Electron impact, 70 eV, selected monitoring of *m/z* 91 ions, characteristic for alkyl

All detectors are distinguished by their sensitivity, minimum detection and quantitation levels (in the case of MSD, also by the minimum identification level), linearity, sensitivity to changes in gas flow, temperature or pressure. The sensors have different noise levels, volumes, are distinguished by a higher or lower sensitivity and sophistication, for simplic‐ ity in operation, cost, and other typical features to be taken into account when choosing and operating a GC detector. Because the volumes of GC detectors are larger than the

benzenes). DB-5MS nonpolar capillary column (60 m x 0.25 mm x 0.25 μm). Split ratio 1:30.

7 8

GC-MS Full scan GC-MS-SIM (*m/z*91)

<sup>5</sup> <sup>6</sup>

1 - Toluene

3 – *o*-Xylene

2 – *m*-, *p-*Xylenes

4 – Ethyl benzene 5 - C3-Benzene 6 - C4-Benzene 7 - *iso*-C4-Benzene 8 - C5-Benzene

etc. (MSD operated in SIM mode; Figure 7).

2

3 4

1

16 Advances in Gas Chromatography

The major limitation posed by conventional detectors (FID, TCD, ECD, NPD) is the ambiguity to uniquely identify an unknown substance. Absolute retention times (tR) or relative to an internal standard (tRR) and certified standards, are used for the presumptive identification of a compound. This is a screening analysis. Confirmatory identification of a compound in a complex mixture, analyzed by GC, necessarily requires obtaining the "fingerprint", i.e., spectrum, of the compound. The mass spectrum (MS) has information about a unique combination of charged fragments (ions) generated during the dissocia‐ tion or fragmentation of the previously ionized molecule. The complementarity of chroma‐ tographic analysis (screening) with confirmatory spectral data is achieved using the combination of GC-MSD. A GC-MSD analysis provides information on (1) retention times, (2) chromatographic areas and (3) mass spectra of each component in a mixture, obtained by its ionization with electron impact (EI) at 70 eV. The value of 70 eV was established as a standard to obtain the MS, which are also part of the databases and commercial spec‐ tral libraries, because for most organic compounds, the highest ionization efficiency and reproducibility are achieved with bombarding electrons of this energy. The ionization energy (potential) of organic molecules varies from 7 to 14 eV. During the collision with electrons of 70 eV, energy is transferred to the molecules to ionize them and form the molecular ion, M+ , but also to produce fragmentation of those excited M+\* ions, which possess sufficient internal energy to break a chemical bond. The spectra obtained with lower energy electrons (15-20 eV) are called low voltage spectra, but they are rarely used in the GC- MSD method, because they often cannot achieve the sensitivity required for the analysis.

The coupled technique GC-MSD is the most used tandem combination in instrumental analytical chemistry and is applied to investigations in forensic, environmental, natural products, foods, flavors and many other areas [34]. Among the chromatographic spectroscopic detectors (AED, IR or MSD), useful for the determination of the chemical structure or elemental composition of analytes, the mass spectrometer is the most widely used today thanks to its sensitivity, operability and, above all, to a large amount of *sui generis* structural information that it can provide [35].

#### **2.6. Mass selective detector**

A mass spectrometer attached to the gas chromatograph is often referred to as "mass detector" or "mass selective detector" (MSD). The MSD consists of an ionization chamber which in the large majority of situations uses electron ionization or electron impact (El) to provide the energy to the analytes which could fragment and generate the ions to be detected. Chemical ionization (Cl) of positive ions or negative ions, is a complementary, mild ionization method. CI is used as an alternative when no molecular ions are registered in the mass spectra obtained by El, since fragmentation is excessively intense, due to the high lability of molecular ions, M +• (extremely short lifetime, < 10-6 s) [36]. Ions formed in the ionization chamber are removed therefrom by a series of electrodes that collimate (focus) and accelerate and direct them to a mass analyzer. The potential energy created by the accelerating field (E) is converted into kinetic energy. The analyzer separates ions according to their *m/z* ratio. Several types of mass analyzers may be used in the GC-MS equipment. Quadrupole (Q) and ion trap (lT) are the most common; in recent years, there has been an increase in the use of time-of-flight analyzers (TOF); lately, resonance ion-cyclotron analyzers with Fourier transform (FT-ICR) and orbitrap have become commercially available. The use of magnetic deflection mass analyzers is much less frequent [34,37,38].

Besides its use in performing quantitative analysis with higher selectivity and sensitivity, the SIM mode helps to reduce the possibilities of false positives in substance identification. Presumptive identifications are based solely on matching retention times of target analytes and standard compounds, but this result should be confirmed by GC-MSD-SIM, when

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 19

To setup a GC-MSD analysis in SIM mode, generally three characteristic "diagnostic" ions are chosen (one for quantification and other ones as "qualifiers"). The ion selection criteria attend the following recommendations: 1) the ion´s abundance should be higher than 30%; 2) the mass of the selected ion should be high, preferably, because low-mass ions are common to many substances; 3) the selected ion should be structurally representative of the molecule. For example, it could be a molecular ion, or a genetically-related fragment; 4) the selected ion should not coincide with those from background (*m/z* 17, 18, 28, 32, 40, 43, 44), stationary phase bleeding (*e.g.,*, *m/z* 73, 147, 207, 281, 355), thermal degradation of the septum, or plasticizers (*m/z* 149). The partial ion currents of the selected ions are measured in a retention time window in which the standard substance elutes (e.g., tR ± 0.5 min). Then the sample is analyzed under the same GC-MS-SIM operational conditions used with the standard substance. When there are several substances of interest, the same procedure is applied to each one and the selected ions are scanned only within the retention time window of its corresponding standard substance. The identity of the target analyte in the sample can be confirmed only when the following 3 criteria are satisfied: 1) the retention times of the analyte and the standard substance match; 2) the S/N ratio is higher than 1:3 – 1:10; 3) the intensity ratios of the selected ions are the same in the spectra of the analyte and of the standard substance. If the retention times are the same (a necessary, but not sufficient condition), but the intensity ratios of the selected ions in the spectra of the target compound and of the standard do not match, that is, are not identical, differ by more than 15%, the situation of a " false positive" is observed, i.e., the alleged presence

The need to unequivocally identify the components of complex mixtures was the motivation for the development of different instrumental coupling techniques (tandem), including the widely and successfully used (with volatilizable substances), gas chromatography coupled with mass spectrometry (MS). GC-MS is an extremely favorable, synergistic union, as the compounds susceptible to be analyzed by GC (low-molecular weight, medium or low polarity, in ppb-ppm concentration) are also compatible with the MS requirements. Besides both analyses proceed in the same aggregation state (vapor phase). The only "conflict" (short-term and already resolved) between GC and MS, were the different working pressures, i.e., atmospheric at the GC column exit and low (10-5 - 10-6 Torr) in the ionization chamber, respectively. This drawback was overcome by technically introducing an efficient vacuum pump (turbomolecular and gas-jet pumps) and, above all due to the introduction of gas chromatography capillary columns (internal diameter 0.18 to 0.32 mm id, traditionally used in GC- MS), which are inserted directly into the ionization chamber of a mass detector [34,40].

compounds of interest are in the mixture at trace levels [39].

of the target analyte in the sample is not confirmed [40].

**3. Gas chromatography-mass spectrometry**

The charm –from an analytical standpoint-, of a mass selective detector, is its ability to operate in three modes of data acquisition, namely: universal, selective and specific. When a scan is complete (the MSD detector functions as a universal detector), the mass spectra obtained for all mixture components are the basis for recognizing or identifying them. The mass scanning rate depends on the type of analyzer used (quadrupole, ion trap, time-of-flight or magnetic sector) and the mass range to cover. The mass range for the entire sweep is established in accordance with the nature of the sample, that is, the range of molecular weights of its components. The low end of the scanned mass range for aliphatic compounds, alcohols, amines, etc., can be set to *m/z* 30-40 (lower masses are not recommended, as they correspond to background signals); the minimum mass for aromatics can be set to *m/z* 50. The upper end of the scan range should correspond to the molecular weight of the heaviest substance present in the mixture, plus 40 - 50 units. If the mass range is chosen poorly, for example, if for the GC-MSD analysis of low molecular weight, highly volatile compounds (molecular weight less than 150 Da), a very wide mass range is set, e.g., *m/z* 30-550, the number of spectra obtained per unit of time will be small, which affects the quality, reproducibility and reliability of the analytical data [34-36]. On the other hand, high scan speeds demand fast response from the accompanying hardware, to avoid loss of information.

Selected ion monitoring, SIM, is the selective mode of operation of the MSD. Instead of scanning a mass range, only a discrete set of *m/z* values is monitored. This permits to obtain a much higher signal accumulation per unit time, which means that the sensitivity is greater (30-100 times) than when full scan is used. GC-MS-SIM analysis of an extract or a complex mixture permits the selective detection of homologous molecules, isomers, or structural derivatives, when the selected ion is a common feature in their mass spectra. However, the detection becomes specific, highly selective, when the monitored ions constitute a unique combination which is found only in the mass spectrum of the target analyte.

Besides its use in performing quantitative analysis with higher selectivity and sensitivity, the SIM mode helps to reduce the possibilities of false positives in substance identification. Presumptive identifications are based solely on matching retention times of target analytes and standard compounds, but this result should be confirmed by GC-MSD-SIM, when compounds of interest are in the mixture at trace levels [39].

To setup a GC-MSD analysis in SIM mode, generally three characteristic "diagnostic" ions are chosen (one for quantification and other ones as "qualifiers"). The ion selection criteria attend the following recommendations: 1) the ion´s abundance should be higher than 30%; 2) the mass of the selected ion should be high, preferably, because low-mass ions are common to many substances; 3) the selected ion should be structurally representative of the molecule. For example, it could be a molecular ion, or a genetically-related fragment; 4) the selected ion should not coincide with those from background (*m/z* 17, 18, 28, 32, 40, 43, 44), stationary phase bleeding (*e.g.,*, *m/z* 73, 147, 207, 281, 355), thermal degradation of the septum, or plasticizers (*m/z* 149). The partial ion currents of the selected ions are measured in a retention time window in which the standard substance elutes (e.g., tR ± 0.5 min). Then the sample is analyzed under the same GC-MS-SIM operational conditions used with the standard substance. When there are several substances of interest, the same procedure is applied to each one and the selected ions are scanned only within the retention time window of its corresponding standard substance. The identity of the target analyte in the sample can be confirmed only when the following 3 criteria are satisfied: 1) the retention times of the analyte and the standard substance match; 2) the S/N ratio is higher than 1:3 – 1:10; 3) the intensity ratios of the selected ions are the same in the spectra of the analyte and of the standard substance. If the retention times are the same (a necessary, but not sufficient condition), but the intensity ratios of the selected ions in the spectra of the target compound and of the standard do not match, that is, are not identical, differ by more than 15%, the situation of a " false positive" is observed, i.e., the alleged presence of the target analyte in the sample is not confirmed [40].

### **3. Gas chromatography-mass spectrometry**

**2.6. Mass selective detector**

18 Advances in Gas Chromatography

less frequent [34,37,38].

accompanying hardware, to avoid loss of information.

A mass spectrometer attached to the gas chromatograph is often referred to as "mass detector" or "mass selective detector" (MSD). The MSD consists of an ionization chamber which in the large majority of situations uses electron ionization or electron impact (El) to provide the energy to the analytes which could fragment and generate the ions to be detected. Chemical ionization (Cl) of positive ions or negative ions, is a complementary, mild ionization method. CI is used as an alternative when no molecular ions are registered in the mass spectra obtained by El, since fragmentation is excessively intense, due to the high lability of molecular ions, M +• (extremely short lifetime, < 10-6 s) [36]. Ions formed in the ionization chamber are removed therefrom by a series of electrodes that collimate (focus) and accelerate and direct them to a mass analyzer. The potential energy created by the accelerating field (E) is converted into kinetic energy. The analyzer separates ions according to their *m/z* ratio. Several types of mass analyzers may be used in the GC-MS equipment. Quadrupole (Q) and ion trap (lT) are the most common; in recent years, there has been an increase in the use of time-of-flight analyzers (TOF); lately, resonance ion-cyclotron analyzers with Fourier transform (FT-ICR) and orbitrap have become commercially available. The use of magnetic deflection mass analyzers is much

The charm –from an analytical standpoint-, of a mass selective detector, is its ability to operate in three modes of data acquisition, namely: universal, selective and specific. When a scan is complete (the MSD detector functions as a universal detector), the mass spectra obtained for all mixture components are the basis for recognizing or identifying them. The mass scanning rate depends on the type of analyzer used (quadrupole, ion trap, time-of-flight or magnetic sector) and the mass range to cover. The mass range for the entire sweep is established in accordance with the nature of the sample, that is, the range of molecular weights of its components. The low end of the scanned mass range for aliphatic compounds, alcohols, amines, etc., can be set to *m/z* 30-40 (lower masses are not recommended, as they correspond to background signals); the minimum mass for aromatics can be set to *m/z* 50. The upper end of the scan range should correspond to the molecular weight of the heaviest substance present in the mixture, plus 40 - 50 units. If the mass range is chosen poorly, for example, if for the GC-MSD analysis of low molecular weight, highly volatile compounds (molecular weight less than 150 Da), a very wide mass range is set, e.g., *m/z* 30-550, the number of spectra obtained per unit of time will be small, which affects the quality, reproducibility and reliability of the analytical data [34-36]. On the other hand, high scan speeds demand fast response from the

Selected ion monitoring, SIM, is the selective mode of operation of the MSD. Instead of scanning a mass range, only a discrete set of *m/z* values is monitored. This permits to obtain a much higher signal accumulation per unit time, which means that the sensitivity is greater (30-100 times) than when full scan is used. GC-MS-SIM analysis of an extract or a complex mixture permits the selective detection of homologous molecules, isomers, or structural derivatives, when the selected ion is a common feature in their mass spectra. However, the detection becomes specific, highly selective, when the monitored ions constitute a unique

combination which is found only in the mass spectrum of the target analyte.

The need to unequivocally identify the components of complex mixtures was the motivation for the development of different instrumental coupling techniques (tandem), including the widely and successfully used (with volatilizable substances), gas chromatography coupled with mass spectrometry (MS). GC-MS is an extremely favorable, synergistic union, as the compounds susceptible to be analyzed by GC (low-molecular weight, medium or low polarity, in ppb-ppm concentration) are also compatible with the MS requirements. Besides both analyses proceed in the same aggregation state (vapor phase). The only "conflict" (short-term and already resolved) between GC and MS, were the different working pressures, i.e., atmospheric at the GC column exit and low (10-5 - 10-6 Torr) in the ionization chamber, respectively. This drawback was overcome by technically introducing an efficient vacuum pump (turbomolecular and gas-jet pumps) and, above all due to the introduction of gas chromatography capillary columns (internal diameter 0.18 to 0.32 mm id, traditionally used in GC- MS), which are inserted directly into the ionization chamber of a mass detector [34,40].

#### **3.1. Ionization modes**

The essence of a mass spectrometric method revolves around the process of ionization of the molecule, with or without subsequent cleavage or fragmentation. In most cases the ionization of the molecule is dissociative. Its mechanisms can be various, e.g., subtraction or addition of an electron, protonation or deprotonation, nucleophilic, electrophilic addition or subtraction and cluster formation, among some other processes leading to ion formation. The ionization of a molecule is an energy consuming process, which can be supplied by accelerated or thermal electrons (electron impact or electron capture), by photons (photoionization, corona discharge, laser beam), by atoms or ions accelerated by a high electrostatic field gradient or thermal impact, among other mechanisms. A rather large number of methods have been developed to transfer energy for the ionization process, to thermolabile, high- or low-molecular weight, polar or non-polar molecules, in the gas phase (electron impact, EI, chemical ionization, CI, photoionization, PI, field ionization, FI) or in the condensed phase (field desorption, FD, laser desorption, LD, fast atom bombardment, FAB, plasma desorption, PD, secondary ion mass spectrometry, SIMS, matrix-assisted laser desorption ionization, MALDI) [36].

ionization energy of 70 eV. All this facilitates the comparison of the spectra taken on different spectrometers with those of databases and other instruments. The bombarding electron energy of 70 eV far exceeds that required to ionize organic molecules. The ionization energies (or ionization potential, IP) for organic molecules lie in the range of 6 to 13 eV, and depend on their molecular structure. For example, to ionize the cyclohexane molecule, 9.9.eV of energy are required; for benzene 9.2 eV, toluene 8.8 eV, pyridine 8.2 eV, and naphthalene 8.1 eV [41,42].

The ionization energy of an organic molecule decreases with the presence therein of π electrons (unsaturated bonds, aromatic ring) or heteroatoms (N, O, S) and with this the ionization cross section of the molecule increases. Small molecules such as H2O, HCN, CO or CO2 have quite high ionization energies, namely, 12.6, 13.9, 14.0, and 13.3 eV, respectively (and also their heats of formation are large). When organic molecules are ionized with the loss of 18, 27, 28 or 44 mass units, these small molecular species are practically never recorded as ions in their mass spectra, precisely by having ionization potentials higher than those of any complementary

of the Stevenson-Audier rule [43,44], which states that for a fragmentation process (usually referred to simple bond breaking, but also applied to some simple rearrangement processes) the positive charge is localized on the fragment ion that possesses lower ionization potential. If the molecular ion is not recorded in the mass spectra of volatile analytes and thermally stable substances (alcohols, esters, aliphatic, branched hydrocarbons, etc.), this can be explained by its high lability and very short lifetime (<10-6 s), and by the absence in the molecule of structural elements (double bonds, aromatic rings, heteroatoms) to enable stabilization by different mechanisms, generally, through effective charge delocalization. The molecular ion will not become distinguishable in the spectrum by reducing the bombarding electrons energy. If they are not recorded at 70 eV electron energy, they are not detected either on the so-called low voltage spectra, at lower electron energies (10 - 30 eV). This is because the sensitivity of the method falls dramatically, that is, the number of molecular ions or fragment ions decreases with the electron energy. For volatile, thermostable but labile molecules in whose mass spectra no molecular ions are recorded, "soft" ionization methods should be used, e.g., chemical ionization (CI) or in some cases, chemical derivatization turns out to be a useful solution [41].

The electron ionization occurs under reduced pressure (vacuum) of 10-5-10-6 Torr (the mean free path is of the order of meters). It is an endothermic process accompanied by the formation of M+• ions and their monomolecular dissociation. However, fragmentation is not a random process. The occurrence of certain fragment ions (fragmentation pattern) is a reflection of: (1) molecular structure, (2) the energy of bombarding electrons, and (3) the excess of internal energy acquired by the ionized molecule. The number of ions (ionic current) detected may be affected by pressure in the system, contamination of the ion source or by aging or deterioration of the dynode surfaces of the electron multiplier. The range of ionization methods for high molecular weight substances (e.g., proteins, nucleotides, polysaccharides, etc.), highly polar molecules (e.g., amino acids, sugars, glycosides, vitamins, pesticides etc.) or thermo-labile substances [40-42] is much larger and technically more sophisticated. Ionization of heavy or polar molecules, usually happens in parallel or immediately after desorption from the surface, where they are bidimensionally distributed as a "solution" in a very low volatile solvent.

+•. This is also a reflection

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 21

fragment ions, e.g., (M - H2O)+•, (M - HCN)+•, (M - CO)+• or (M - CO2)

The ionization of neutral molecules is followed by fragmentation, or dissociative ionization, whose product ions can be separated with varying degrees of accuracy, depending on the "spectroscopic balance", i.e, analyzer used. Obtaining a mass spectrum is a *sui generis* energy balance which involves energies ranging from electronic excitation of a molecule (e.g., ultraviolet-visible spectroscopy) up to its atomization (absorption or atomic emission). The ineludible first step is to ionize the molecule (removing or adding an electron, or a proton), which informs about its molecular mass or exact elemental composition when the resulting molecular ion is detected. On the other hand, it is necessary that some of the ionized molecules dissociate or fragment, which reveals what constituent groups comprise the molecule and how they are combined. Electron ionization (EI) is the oldest technique for organic molecule ionization; it is the most widespread and the one with the largest number of applications, for example, GC-MS. It is not so accurate to call it "electron impact", since the size and energy of an electron, compared to an organic molecule of 150-500 Da, does not really allow "impacting" it, such as a ping-pong ball little impact would cause to a rhino. The interaction takes place between the bombarding electron and an electron belonging to the molecule [34-37, 40].

The interaction of an accelerated electron with a molecule leads to the excitation of molecular electrons and a molecular ion, M+•, will be formed if the imparted energy permits it, that is, if it is enough to remove an electron from the neutral molecule in the vapor phase. The formed ion's molecular mass is "numerically" equal to that of the molecule because in comparison, an electron mass is negligible. Electrons are excellent agents for ionizing organic molecules. First, because it is easy to obtain them: just pass an electric current through a tungsten or rhenium wire (filament or cathode). Second, their energy is adjustable with the voltage applied between cathode (thermo-electron emitter) and anode (ground connection), where the average standard energy -worldwide accepted convention- is 70 eV. For most organic molecules, the maximum ionization efficiency is already reached with bombarding electrons with energy of 50-60 eV. Standard mass spectra are taken at 70 eV because they so achieve the highest repeatability and reproducibility. Mass spectra libraries are also formed with the spectra obtained by electron ionization energy of 70 eV. All this facilitates the comparison of the spectra taken on different spectrometers with those of databases and other instruments. The bombarding electron energy of 70 eV far exceeds that required to ionize organic molecules. The ionization energies (or ionization potential, IP) for organic molecules lie in the range of 6 to 13 eV, and depend on their molecular structure. For example, to ionize the cyclohexane molecule, 9.9.eV of energy are required; for benzene 9.2 eV, toluene 8.8 eV, pyridine 8.2 eV, and naphthalene 8.1 eV [41,42].

**3.1. Ionization modes**

20 Advances in Gas Chromatography

The essence of a mass spectrometric method revolves around the process of ionization of the molecule, with or without subsequent cleavage or fragmentation. In most cases the ionization of the molecule is dissociative. Its mechanisms can be various, e.g., subtraction or addition of an electron, protonation or deprotonation, nucleophilic, electrophilic addition or subtraction and cluster formation, among some other processes leading to ion formation. The ionization of a molecule is an energy consuming process, which can be supplied by accelerated or thermal electrons (electron impact or electron capture), by photons (photoionization, corona discharge, laser beam), by atoms or ions accelerated by a high electrostatic field gradient or thermal impact, among other mechanisms. A rather large number of methods have been developed to transfer energy for the ionization process, to thermolabile, high- or low-molecular weight, polar or non-polar molecules, in the gas phase (electron impact, EI, chemical ionization, CI, photoionization, PI, field ionization, FI) or in the condensed phase (field desorption, FD, laser desorption, LD, fast atom bombardment, FAB, plasma desorption, PD, secondary ion mass

spectrometry, SIMS, matrix-assisted laser desorption ionization, MALDI) [36].

The ionization of neutral molecules is followed by fragmentation, or dissociative ionization, whose product ions can be separated with varying degrees of accuracy, depending on the "spectroscopic balance", i.e, analyzer used. Obtaining a mass spectrum is a *sui generis* energy balance which involves energies ranging from electronic excitation of a molecule (e.g., ultraviolet-visible spectroscopy) up to its atomization (absorption or atomic emission). The ineludible first step is to ionize the molecule (removing or adding an electron, or a proton), which informs about its molecular mass or exact elemental composition when the resulting molecular ion is detected. On the other hand, it is necessary that some of the ionized molecules dissociate or fragment, which reveals what constituent groups comprise the molecule and how they are combined. Electron ionization (EI) is the oldest technique for organic molecule ionization; it is the most widespread and the one with the largest number of applications, for example, GC-MS. It is not so accurate to call it "electron impact", since the size and energy of an electron, compared to an organic molecule of 150-500 Da, does not really allow "impacting" it, such as a ping-pong ball little impact would cause to a rhino. The interaction takes place between the bombarding electron and an electron belonging to the molecule [34-37, 40].

The interaction of an accelerated electron with a molecule leads to the excitation of molecular electrons and a molecular ion, M+•, will be formed if the imparted energy permits it, that is, if it is enough to remove an electron from the neutral molecule in the vapor phase. The formed ion's molecular mass is "numerically" equal to that of the molecule because in comparison, an electron mass is negligible. Electrons are excellent agents for ionizing organic molecules. First, because it is easy to obtain them: just pass an electric current through a tungsten or rhenium wire (filament or cathode). Second, their energy is adjustable with the voltage applied between cathode (thermo-electron emitter) and anode (ground connection), where the average standard energy -worldwide accepted convention- is 70 eV. For most organic molecules, the maximum ionization efficiency is already reached with bombarding electrons with energy of 50-60 eV. Standard mass spectra are taken at 70 eV because they so achieve the highest repeatability and reproducibility. Mass spectra libraries are also formed with the spectra obtained by electron

The ionization energy of an organic molecule decreases with the presence therein of π electrons (unsaturated bonds, aromatic ring) or heteroatoms (N, O, S) and with this the ionization cross section of the molecule increases. Small molecules such as H2O, HCN, CO or CO2 have quite high ionization energies, namely, 12.6, 13.9, 14.0, and 13.3 eV, respectively (and also their heats of formation are large). When organic molecules are ionized with the loss of 18, 27, 28 or 44 mass units, these small molecular species are practically never recorded as ions in their mass spectra, precisely by having ionization potentials higher than those of any complementary fragment ions, e.g., (M - H2O)+•, (M - HCN)+•, (M - CO)+• or (M - CO2) +•. This is also a reflection of the Stevenson-Audier rule [43,44], which states that for a fragmentation process (usually referred to simple bond breaking, but also applied to some simple rearrangement processes) the positive charge is localized on the fragment ion that possesses lower ionization potential. If the molecular ion is not recorded in the mass spectra of volatile analytes and thermally stable substances (alcohols, esters, aliphatic, branched hydrocarbons, etc.), this can be explained by its high lability and very short lifetime (<10-6 s), and by the absence in the molecule of structural elements (double bonds, aromatic rings, heteroatoms) to enable stabilization by different mechanisms, generally, through effective charge delocalization. The molecular ion will not become distinguishable in the spectrum by reducing the bombarding electrons energy. If they are not recorded at 70 eV electron energy, they are not detected either on the so-called low voltage spectra, at lower electron energies (10 - 30 eV). This is because the sensitivity of the method falls dramatically, that is, the number of molecular ions or fragment ions decreases with the electron energy. For volatile, thermostable but labile molecules in whose mass spectra no molecular ions are recorded, "soft" ionization methods should be used, e.g., chemical ionization (CI) or in some cases, chemical derivatization turns out to be a useful solution [41].

The electron ionization occurs under reduced pressure (vacuum) of 10-5-10-6 Torr (the mean free path is of the order of meters). It is an endothermic process accompanied by the formation of M+• ions and their monomolecular dissociation. However, fragmentation is not a random process. The occurrence of certain fragment ions (fragmentation pattern) is a reflection of: (1) molecular structure, (2) the energy of bombarding electrons, and (3) the excess of internal energy acquired by the ionized molecule. The number of ions (ionic current) detected may be affected by pressure in the system, contamination of the ion source or by aging or deterioration of the dynode surfaces of the electron multiplier. The range of ionization methods for high molecular weight substances (e.g., proteins, nucleotides, polysaccharides, etc.), highly polar molecules (e.g., amino acids, sugars, glycosides, vitamins, pesticides etc.) or thermo-labile substances [40-42] is much larger and technically more sophisticated. Ionization of heavy or polar molecules, usually happens in parallel or immediately after desorption from the surface, where they are bidimensionally distributed as a "solution" in a very low volatile solvent. Modifiers, polarization promoters, are added or are present as incrustations in a solid [45]. The desorption from the surface and molecular ionization occur almost simultaneously: various primary and secondary ions are generated by means of accelerated atoms (FAB), accelerated ions (SIMS), or high-energy photons (one of the versions is MALDI), which impinge on the sample holder which houses the substance in a properly prepared matrix [46].

field of coupled techniques with both gas and liquid chromatography, because of its high resolution and sensitivity. The mass selective detectors are distinguished by their properties or specifications and analytical scope. The most important parameters include: (1) resolution, (2) the maximum mass that can be measured, and (3) the ion transmission. Low resolution mass analyzers (single focus magnetic deflection, quadrupoles, ion traps, linear TOF) measure nominal masses (integers) and cannot distinguish isobaric species, whereas high resolution analyzers are capable of determining exact masses (4-6 digits after the comma), a measurement that leads to the unambiguous determination of the elemental and isotopic composition. One of the most simple, classic examples, is the measurement of isobaric ions of the species CO+

with high resolution analyzers (magnetic and electric sectors, high-resolution TOF or ICR-MS) working in the exact mass scale [55]. Of course, the gases CO, N2 and C2H2 in a mixture can also be separated and quantified by GC using PLOT type columns. The mass range in which each analyzer operates is important and above all, the maximum mass that it can measure. For example, TOF analyzers have no limits to measure high mass (while the quadrupole and ion trap analyzers do); the mass range of the TOF with reflecton is virtually infinite. The mass range, resolution and accuracy that each analyzer can reach depend on its configuration, the ion separation mechanism and the degree of "monoenergizing" of ions with the same mass, i.e., the ability to decrease the dispersion in time or space, which causes signal (partial ion current) broadening. Ion transmission in mass analyzers is measured as the ratio between the ions formed in the ionization chamber and those which, after passing through the mass analyzer, reach the detector. Scanning analyzers (sector, quadrupole) have lower ion trans‐ mission values than the simultaneous transmission analyzers, i.e., TOF, orbitrap, FT-ICR-MS.

Frequently, excessive chemical noise is observed in the ion current of extracts obtained from biological samples, food, soil, etc. This leads to a failure to achieve the required specificity and to detect and identify reliably analytes of interest, when analyzing a complex mixture with many interferences or impurities. Usually, the signal/noise (S/N) increases with the number of steps in an analytical procedure. In instrumental analysis, this is the typical case of a tandem system, for example, GC-MS or LC-MS, including multidimensional or tandem mass spec‐ trometry (MS-MS). Equal to the cleaning of an extract, which increases the S/N in the process of final instrumental analysis, a tandem mass spectrometer includes filtering steps in its operation. For instance, in a triple quadrupole (QqQ), during the first step of ion separation, the first analyzer (MS1) executes a specific clean-up, to distinguish, in a complex mixture, the characteristic ions of the analyte; the second mass analyzer (MS2), records only signals that are characteristic of the target analyte, free from interfering signals. A device located between the two analyzers is where the selected ion can be activated, i.e. its internal energy can be increased, leading to its dissociation and the formation of fragment-ions (ion-products), which are recorded in the analyzer MS2. This device operates as a collisions cell, which cause the

, whose "separation" is impossible with a low resolution analyzer. It is achieved

N2 +

and C2H4

+

The latter generally have a higher sensitivity [56].

disassociation of stable ions selected by the first analyzer.

**3.3. Tandem mass spectrometry**

,

23

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492

Special methods of ionization techniques are used in tandem setups, i.e., in the coupling of liquid chromatography (LC) with mass spectrometry, LC- MS, where the liquid sample could be nebulized in one of the different interfaces available, e.g., assisted by the effect of temper‐ ature and high electrical potential; for example, thermospray (TSI) [47], electrospray (ESI) [48], or atmospheric pressure chemical ionization (APCI) [49] methods. Different mechanisms lead to the formation of ionized species, which include molecules with multiple protonation, or deprotonation, clusters, among other ions generated.

The most common technique for the ionization of "small" molecules (more than 90% of applications) is the electron impact, while the positive ion (PICI) or negative ion (NICI) chemical ionization is an important complement used when molecular ions are not recorded in the spectra obtained by EI [50]. When the molecular weight of the substance should be determined, it is clearly deduced from the CI spectra based on the mass of the protonated molecular ion, MH+ [or deprotonated (M-H)- , for NICI], along with the cluster ions, usually formed by electrophilic addition of secondary ions from reactant gases (methane, ammonia, *iso*-butane, etc.).

Both electron ionization and chemical ionization are used in the technique of gas chromatog‐ raphy-mass spectrometry. Unfortunately, the use of CI requires, generally, the change of the ionic volume (ionization chamber), because the residual pressures that are used in CI are much higher (up to 1 mm Hg) than those used in EI (10-5-10-7 Torr). In fact, less than 10% of all substances in the planet can be ionized in the vapor phase by EI or CI, as the ionization process by these techniques has as major limitation the low volatility or thermal instability of many organic substances. Volatilization is a stage prior to the ionization by EI or CI; the two processes are separate both in time and space. The separation of ions in the GC-MS technique can occur with virtually all types of mass analyzers, e.g., ion traps (IT) [51], quadrupole (Q) [52], magnetic deflection analyzer [53], as well as the configurations of several tandem analyzers (e.g., QqQ, triple quadrupole [54]), and today, for the GC x GC configuration, the time-of-flight (TOF) analyzer [55].

### **3.2. Mass analyzers**

The mass selective detectors are divided into two groups. The first group corresponds to scanning analyzers. These include sector analyzers, e.g., magnetic deflection of single or double focus (when an electrostatic analyzer is added). The magnetic sector analyzer was historically the first mass analyzer employed. However, its use in GC- MS is not common. Quadrupole analyzers are the most frequently used in tandem GC-MS systems. The second group consists of simultaneous ion transmission analyzers. These include the time-of-flight (TOF), different types of ion trap (lT) and Fourier transform mass analyzers, specifically, ioncyclotron resonance mass spectrometers (ICR-MS), which have gained recent popularity in the field of coupled techniques with both gas and liquid chromatography, because of its high resolution and sensitivity. The mass selective detectors are distinguished by their properties or specifications and analytical scope. The most important parameters include: (1) resolution, (2) the maximum mass that can be measured, and (3) the ion transmission. Low resolution mass analyzers (single focus magnetic deflection, quadrupoles, ion traps, linear TOF) measure nominal masses (integers) and cannot distinguish isobaric species, whereas high resolution analyzers are capable of determining exact masses (4-6 digits after the comma), a measurement that leads to the unambiguous determination of the elemental and isotopic composition. One of the most simple, classic examples, is the measurement of isobaric ions of the species CO+ , N2 + and C2H4 + , whose "separation" is impossible with a low resolution analyzer. It is achieved with high resolution analyzers (magnetic and electric sectors, high-resolution TOF or ICR-MS) working in the exact mass scale [55]. Of course, the gases CO, N2 and C2H2 in a mixture can also be separated and quantified by GC using PLOT type columns. The mass range in which each analyzer operates is important and above all, the maximum mass that it can measure. For example, TOF analyzers have no limits to measure high mass (while the quadrupole and ion trap analyzers do); the mass range of the TOF with reflecton is virtually infinite. The mass range, resolution and accuracy that each analyzer can reach depend on its configuration, the ion separation mechanism and the degree of "monoenergizing" of ions with the same mass, i.e., the ability to decrease the dispersion in time or space, which causes signal (partial ion current) broadening. Ion transmission in mass analyzers is measured as the ratio between the ions formed in the ionization chamber and those which, after passing through the mass analyzer, reach the detector. Scanning analyzers (sector, quadrupole) have lower ion trans‐ mission values than the simultaneous transmission analyzers, i.e., TOF, orbitrap, FT-ICR-MS. The latter generally have a higher sensitivity [56].

#### **3.3. Tandem mass spectrometry**

Modifiers, polarization promoters, are added or are present as incrustations in a solid [45]. The desorption from the surface and molecular ionization occur almost simultaneously: various primary and secondary ions are generated by means of accelerated atoms (FAB), accelerated ions (SIMS), or high-energy photons (one of the versions is MALDI), which impinge on the

Special methods of ionization techniques are used in tandem setups, i.e., in the coupling of liquid chromatography (LC) with mass spectrometry, LC- MS, where the liquid sample could be nebulized in one of the different interfaces available, e.g., assisted by the effect of temper‐ ature and high electrical potential; for example, thermospray (TSI) [47], electrospray (ESI) [48], or atmospheric pressure chemical ionization (APCI) [49] methods. Different mechanisms lead to the formation of ionized species, which include molecules with multiple protonation, or

The most common technique for the ionization of "small" molecules (more than 90% of applications) is the electron impact, while the positive ion (PICI) or negative ion (NICI) chemical ionization is an important complement used when molecular ions are not recorded in the spectra obtained by EI [50]. When the molecular weight of the substance should be determined, it is clearly deduced from the CI spectra based on the mass of the protonated

formed by electrophilic addition of secondary ions from reactant gases (methane, ammonia,

Both electron ionization and chemical ionization are used in the technique of gas chromatog‐ raphy-mass spectrometry. Unfortunately, the use of CI requires, generally, the change of the ionic volume (ionization chamber), because the residual pressures that are used in CI are much higher (up to 1 mm Hg) than those used in EI (10-5-10-7 Torr). In fact, less than 10% of all substances in the planet can be ionized in the vapor phase by EI or CI, as the ionization process by these techniques has as major limitation the low volatility or thermal instability of many organic substances. Volatilization is a stage prior to the ionization by EI or CI; the two processes are separate both in time and space. The separation of ions in the GC-MS technique can occur with virtually all types of mass analyzers, e.g., ion traps (IT) [51], quadrupole (Q) [52], magnetic deflection analyzer [53], as well as the configurations of several tandem analyzers (e.g., QqQ, triple quadrupole [54]), and today, for the GC x GC configuration, the time-of-flight (TOF)

The mass selective detectors are divided into two groups. The first group corresponds to scanning analyzers. These include sector analyzers, e.g., magnetic deflection of single or double focus (when an electrostatic analyzer is added). The magnetic sector analyzer was historically the first mass analyzer employed. However, its use in GC- MS is not common. Quadrupole analyzers are the most frequently used in tandem GC-MS systems. The second group consists of simultaneous ion transmission analyzers. These include the time-of-flight (TOF), different types of ion trap (lT) and Fourier transform mass analyzers, specifically, ioncyclotron resonance mass spectrometers (ICR-MS), which have gained recent popularity in the

, for NICI], along with the cluster ions, usually

sample holder which houses the substance in a properly prepared matrix [46].

deprotonation, clusters, among other ions generated.

[or deprotonated (M-H)-

molecular ion, MH+

22 Advances in Gas Chromatography

*iso*-butane, etc.).

analyzer [55].

**3.2. Mass analyzers**

Frequently, excessive chemical noise is observed in the ion current of extracts obtained from biological samples, food, soil, etc. This leads to a failure to achieve the required specificity and to detect and identify reliably analytes of interest, when analyzing a complex mixture with many interferences or impurities. Usually, the signal/noise (S/N) increases with the number of steps in an analytical procedure. In instrumental analysis, this is the typical case of a tandem system, for example, GC-MS or LC-MS, including multidimensional or tandem mass spec‐ trometry (MS-MS). Equal to the cleaning of an extract, which increases the S/N in the process of final instrumental analysis, a tandem mass spectrometer includes filtering steps in its operation. For instance, in a triple quadrupole (QqQ), during the first step of ion separation, the first analyzer (MS1) executes a specific clean-up, to distinguish, in a complex mixture, the characteristic ions of the analyte; the second mass analyzer (MS2), records only signals that are characteristic of the target analyte, free from interfering signals. A device located between the two analyzers is where the selected ion can be activated, i.e. its internal energy can be increased, leading to its dissociation and the formation of fragment-ions (ion-products), which are recorded in the analyzer MS2. This device operates as a collisions cell, which cause the disassociation of stable ions selected by the first analyzer.

The classic configuration of a tandem mass spectrometer is the connection in series of MS1, collision- activated chamber and MS2, followed by a system for the detection and measurement of ionic currents. Tandem mass systems are divided into two large groups [38] depending on the types of mass analyzers involved. The first group is made up of tandem-in-time mass spectrometers. These include linear and quadrupolar ion traps, orbital traps (orbitrap) and FT-ICR-MS. In tandem-in-time instruments the ions produced in the ionization region are trapped, are isolated, fragmented and then separated according to their *m/z* ratio in the same physical space. The cascade of dissociation reactions of ions pre-selected and then activated and subsequently monitored, take place in the same analyzer, but occur consecutively as a function of time, thus allowing the successive record of ions which are son, grandson, grand-grandson, etc., (MS)n, of the original selected ion. The second group of tandem mass (MS/MS) instruments is made up of the so-called tandem-in-space mass spectrometers. In these, at least 2 analyzers are separated in space. With these spectrometers it is possible to study not only product-ions, but also precursor ions, the reactions (transitions) between 2 related ions, or monitoring the loss of a neutral fragment. A triple quadrupole, designated as QQQ, or QqQ, belongs to this type of tandem mass spectrometers. Hybrid MS/MS configurations involve combining several analyzers of different nature or different operating principles, for example, quadrupole (Q) or ion trap (IT) with a magnetic sector analyzer (B) alone or in conjunction with electrostatic analyzer (E) or a time-of-flight ( TOF) analyzer. This gives rise to different hybrid tandem equipment, e.g., EBE, EBEB, B-QI-Q2, QEB, Q-TOF, IT-TOF-TOFB, EB-TOF-TOF EBE, QBE, and other possible combinations of analyzers. Of course, the union of several analyzers greatly increases the cost of the instrument and the complexity of their operation, but, simultaneously, increases the amount and quality of analytical information obtained, the degree of reliability and specificity [55,56].

technique is very helpful and is a valuable analytical reinforcement, required in situations where (1) there is a high chemical noise in spectra acquired in SlM mode, (2) characteristic ions coelute with isobaric impurities (same nominal mass), (3) the structure of the compound is unknown and requires additional structural information (often, it is necessary to activate molecular, quasi-molecular or protonated ions, or some stable fragment ions, to extract additional structural information through the products in which fragment-ions are dissociat‐ ed), (4) the fragmentogram obtained in SIM mode requires an additional confirmatory information and finally, (5) higher sensitivity and specificity are required in the analysis (pesticide residues, petroleum biomarkers, anabolic steroids and other doping agents, etc.).

The triple quadrupole configuration provides a range of analytical experiments and modes of ionic current acquisition each of which provides certain specific information. The following

This is the traditional type of ionic current acquisition, similar to that employed in spectrom‐ eters with a single quadrupole analyzer (mass filter). The first analyzer (MS1) performs a full sweep and records mass spectra of every analyte emerging from the GC or LC column, which has been ionized and fragmented into molecular ion and different ion-products. In the GC-MS technique, *ca*. 0.05-1 ng of a compound are sufficient to obtain a mass spectrum that meets quality criteria [34,41]. The intermediate quadrupole (Q2) and the mass analyzer (MS2) operate

This acquisition mode is also well known and is widely practiced in mass spectrometers with a single quadrupole. In this case, the first analyzer (MS1) allows free passage only to a small number of selected ions (usually 3), typical or characteristic of the target analyte, which is selectively sought in a complex mixture. The other two quadrupoles just transmit the ions filtered by MS1. The mass fragmentogram is built based on the partial ion currents recorded. Since each selected ion is measured for a longer period, e.g., approximately 50 ms instead of 50 µs, chemical noise is reduced (S/N is increased), lower detection levels (by a factor of 10-100) are reached, and this permits the detection of a target analyte in quantities of pg or less. SIM mode is widely used for recording both a compound of interest in a complex mixture, as well as for sensitive quantitation, but also to register groups of homologous compounds, by monitoring their characteristic ions, namely for *n*-paraffins: *m/z* 57, 71, 85, for fatty acid methyl esters: ion at *m/z* 74; for alkylbenzenes: *m/z* 91, 105, and for phthalates: *m/z* 149, 167, among

In this acquisition mode a precursor or parent ion is selected in the first mass analyzer or filter

collisions cell. The Q2 quadrupole (q) operates only with applied radiofrequency (RF-mode),

) passes MS1 and is directed to the

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 25

(MS1), operating in SIM mode. The chosen parent ion (m+

describes each of the possible modes of acquisition of a QQQ instrument.

*3.3.1. Full scan*

only in ion transmission mode [52,54].

*3.3.2. Selected Ion Monitoring, SIM*

other analytes of interest.

*3.3.3. Product ion scan*

Tandem MS/MS instruments involve 2 stages of mass analysis separated by a reaction of activated or induced dissociation of ions. This happens between the mass measurement before and after the fragmentation of the ions selected in the first stage. The fragmentations are caused by collisions of the selected ions with inert gas molecules (He, Ar, Xe or N2, at a pressure of 0.1-0.3 Pa), and by the accelerating potential of an electrostatic field applied in a collisions cell, also called cell of activated or induced collisions. When employing soft ionization methods (e.g., chemical ionization, Cl) or in the coupling of liquid chromatography with mass spec‐ trometry with electrospray (ESI) or atmospheric pressure chemical ionization (APCI) molec‐ ular (or poliprotonated, multicharged) ions practically do not fragment, and this leads to a lack of information required for the structural elucidation of the molecule. The "energization" of the stable (non-dissociated) ions (cations), for example, molecular or quasi- molecular ion, multiprotonated species, cluster, etc., allows to forcibly induce their dissociation to subse‐ quently extract structural information complementary to the molecular mass or elemental composition (when using high resolution mass analyzers).

Using a detection system with more than one mass analyzer such as a triple quadrupole is appropriate for the analysis of target compounds at trace level (ppt - ppb range) in complex matrices, with the presence of interferences, as in the cases of food samples, biological fluids, animal and plant tissues, soil, wastewater and other environmental samples. The MS/MS technique is very helpful and is a valuable analytical reinforcement, required in situations where (1) there is a high chemical noise in spectra acquired in SlM mode, (2) characteristic ions coelute with isobaric impurities (same nominal mass), (3) the structure of the compound is unknown and requires additional structural information (often, it is necessary to activate molecular, quasi-molecular or protonated ions, or some stable fragment ions, to extract additional structural information through the products in which fragment-ions are dissociat‐ ed), (4) the fragmentogram obtained in SIM mode requires an additional confirmatory information and finally, (5) higher sensitivity and specificity are required in the analysis (pesticide residues, petroleum biomarkers, anabolic steroids and other doping agents, etc.).

The triple quadrupole configuration provides a range of analytical experiments and modes of ionic current acquisition each of which provides certain specific information. The following describes each of the possible modes of acquisition of a QQQ instrument.

### *3.3.1. Full scan*

The classic configuration of a tandem mass spectrometer is the connection in series of MS1, collision- activated chamber and MS2, followed by a system for the detection and measurement of ionic currents. Tandem mass systems are divided into two large groups [38] depending on the types of mass analyzers involved. The first group is made up of tandem-in-time mass spectrometers. These include linear and quadrupolar ion traps, orbital traps (orbitrap) and FT-ICR-MS. In tandem-in-time instruments the ions produced in the ionization region are trapped, are isolated, fragmented and then separated according to their *m/z* ratio in the same physical space. The cascade of dissociation reactions of ions pre-selected and then activated and subsequently monitored, take place in the same analyzer, but occur consecutively as a function of time, thus allowing the successive record of ions which are son, grandson, grand-grandson, etc., (MS)n, of the original selected ion. The second group of tandem mass (MS/MS) instruments is made up of the so-called tandem-in-space mass spectrometers. In these, at least 2 analyzers are separated in space. With these spectrometers it is possible to study not only product-ions, but also precursor ions, the reactions (transitions) between 2 related ions, or monitoring the loss of a neutral fragment. A triple quadrupole, designated as QQQ, or QqQ, belongs to this type of tandem mass spectrometers. Hybrid MS/MS configurations involve combining several analyzers of different nature or different operating principles, for example, quadrupole (Q) or ion trap (IT) with a magnetic sector analyzer (B) alone or in conjunction with electrostatic analyzer (E) or a time-of-flight ( TOF) analyzer. This gives rise to different hybrid tandem equipment, e.g., EBE, EBEB, B-QI-Q2, QEB, Q-TOF, IT-TOF-TOFB, EB-TOF-TOF EBE, QBE, and other possible combinations of analyzers. Of course, the union of several analyzers greatly increases the cost of the instrument and the complexity of their operation, but, simultaneously, increases the amount and quality of analytical information obtained, the degree of reliability

Tandem MS/MS instruments involve 2 stages of mass analysis separated by a reaction of activated or induced dissociation of ions. This happens between the mass measurement before and after the fragmentation of the ions selected in the first stage. The fragmentations are caused by collisions of the selected ions with inert gas molecules (He, Ar, Xe or N2, at a pressure of 0.1-0.3 Pa), and by the accelerating potential of an electrostatic field applied in a collisions cell, also called cell of activated or induced collisions. When employing soft ionization methods (e.g., chemical ionization, Cl) or in the coupling of liquid chromatography with mass spec‐ trometry with electrospray (ESI) or atmospheric pressure chemical ionization (APCI) molec‐ ular (or poliprotonated, multicharged) ions practically do not fragment, and this leads to a lack of information required for the structural elucidation of the molecule. The "energization" of the stable (non-dissociated) ions (cations), for example, molecular or quasi- molecular ion, multiprotonated species, cluster, etc., allows to forcibly induce their dissociation to subse‐ quently extract structural information complementary to the molecular mass or elemental

Using a detection system with more than one mass analyzer such as a triple quadrupole is appropriate for the analysis of target compounds at trace level (ppt - ppb range) in complex matrices, with the presence of interferences, as in the cases of food samples, biological fluids, animal and plant tissues, soil, wastewater and other environmental samples. The MS/MS

composition (when using high resolution mass analyzers).

and specificity [55,56].

24 Advances in Gas Chromatography

This is the traditional type of ionic current acquisition, similar to that employed in spectrom‐ eters with a single quadrupole analyzer (mass filter). The first analyzer (MS1) performs a full sweep and records mass spectra of every analyte emerging from the GC or LC column, which has been ionized and fragmented into molecular ion and different ion-products. In the GC-MS technique, *ca*. 0.05-1 ng of a compound are sufficient to obtain a mass spectrum that meets quality criteria [34,41]. The intermediate quadrupole (Q2) and the mass analyzer (MS2) operate only in ion transmission mode [52,54].

### *3.3.2. Selected Ion Monitoring, SIM*

This acquisition mode is also well known and is widely practiced in mass spectrometers with a single quadrupole. In this case, the first analyzer (MS1) allows free passage only to a small number of selected ions (usually 3), typical or characteristic of the target analyte, which is selectively sought in a complex mixture. The other two quadrupoles just transmit the ions filtered by MS1. The mass fragmentogram is built based on the partial ion currents recorded. Since each selected ion is measured for a longer period, e.g., approximately 50 ms instead of 50 µs, chemical noise is reduced (S/N is increased), lower detection levels (by a factor of 10-100) are reached, and this permits the detection of a target analyte in quantities of pg or less. SIM mode is widely used for recording both a compound of interest in a complex mixture, as well as for sensitive quantitation, but also to register groups of homologous compounds, by monitoring their characteristic ions, namely for *n*-paraffins: *m/z* 57, 71, 85, for fatty acid methyl esters: ion at *m/z* 74; for alkylbenzenes: *m/z* 91, 105, and for phthalates: *m/z* 149, 167, among other analytes of interest.

#### *3.3.3. Product ion scan*

In this acquisition mode a precursor or parent ion is selected in the first mass analyzer or filter (MS1), operating in SIM mode. The chosen parent ion (m+ ) passes MS1 and is directed to the collisions cell. The Q2 quadrupole (q) operates only with applied radiofrequency (RF-mode), which allows transmitting ions from the first analyzer to MS2. The collision gas supplied to the cell (usually, He, Ar or N2), by means of collisions with the selected ions, provides the additional energy (ion excitation process); an applied potential in Q2 ( q ) permits to accelerate the ions and convert part of their kinetic energy into additional internal energy (rotational, vibrational and electronic). This potential should be optimized in order to obtain higher sensitivity (Figure 8). The increase of internal energy of the ions, that is, their "energizing" or "activation", leads to their dissociation and the formation of different fragment-ions (product ions) which are then directed to the second mass analyzer or filter (MS2). This analyzer scans the mass range smaller than the mass of the selected m+ parent ion, since the product ions obviously weigh less than their predecessors. It should be noted, that a product-ion mass spectrum will lack the companion signals from isotopes. This acquisition mode is perhaps the most common and well known in the triple quadrupole system, and is used for various purposes and applications. It can be used in the analysis of complex mixtures or substances with impurities, without prior separation in a chromatographic column. If a soft ionization method is used, for example, positive ion chemical ionization, the mixture of ionized substan‐ ces in the ion source, will produce protonated molecular ions (MH+ ). The first mass analyzer MS1 selects one by one the protonated molecular ions, which are directed to the activated collision cell (q), where they dissociate; fragment ions formed from each MH+ are separated by the MS2 analyzer (Q3) and the spectrum is recorded. In this case, the first analyzer (Q1) acts as a "chromatographic column", the second quadrupole (q) operates as an "ionization cham‐ ber", and the MS2 analyzer (Q3) fulfills the role of the mass analyzer, which makes a complete ion scan. Thus, time savings will be achieved by not using a chromatographic column and focusing the QqQ system work in the search of selected compounds of interest present in a complex mixture without prior chromatographic separation.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5

*3.3.5. Constant neutral loss scan*

+EI MRM (303.0000 -> 182.0000) ExtPlantBMRM.D Smooth

38.174

as collision gas, and at different collision energies (potentials, 0, 5, 10, and 20 eV).

only allow free passage to ions with *m/z* 52, 53 and 54.

*3.3.6. Multiple reaction monitoring, MRM*

Counts vs. Acquisition Time (min) 38.09 38.1 38.11 38.12 38.13 38.14 38.15 38.16 38.17 38.18 38.19 38.2 38.21 38.22 38.23 38.24 38.25 38.26 38.27 38.28 38.29 38.3 38.31

**Figure 8.** Single reaction monitoring (SRM) (metastable ion transition) between the molecular ion of cocaine (*m/z* 303) and one of the most abundant and characteristic ion-product (*m/z* 182), 303->182, obtained by QqQ-MS with N2

In this QqQ operational mode, both mass analyzers, MS1 and MS2, operate in scan mode simultaneously. The scanned masses in both analyzers correspond to ions with a fixed difference, which equals the mass of the selected neutral fragment. If ions f1, f2, f3, f4, etc., pass the first analyzer (MS1), and then go through the activated collision chamber (Q2), they may experience fragmentation. The fragmentation products that will be able to pass the next analyzer (MS2) will be only those ions with the pre-established, fixed, mass difference, that is, f1-Δm, f2-Δm, f3-Δm, f4-Δm, etc. For example, if the linked scan of both analyzers corresponds to a mass difference of 28 units, if ions with *m/z* 80, 81, and 82 pass the first analyzer, MS2 will

This is one of the most *sui generis*, interesting, methods of ionic current acquisition of the triple quadrupole, because it allows exploring and properly using its advantages, to turn it into a specific, highly selective and sensitive GC (or LC) mass detector. Unfortunately, when the acquisition of the ionic current is done in the SIM mode, the probability that the selected ion and a signal from the background (chemical noise) actually match is not null. This non negligible possibility of false positive or false negative results, lowers the reliability of the SIM acquisition method. In order to avoid these problems, instead of monitoring characteristic ions, the MRM experiment rather focuses on recording transitions (or transition reactions) between

20 eV

SRM: 303 182

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 27

10 eV

5 eV

0 eV

#### *3.3.4. Parent ion scan*

This mode of operation is used to find in a mass spectrum those (precursor) ions that can generate a given fragment (product ion). In this case, the first quadrupole (MS1) operates in the full scan mode, while the third quadrupole (MS2) operates in selected ion monitoring mode. This selected ion is the product ion (daughter ion) of interest, whose precursors are sought. Technically, this takes place as follows: in the MS2 analyzer (Q3) - only product ions with specific mass are filtered, while the first analyzer, MS1, allows passage to all ions with a mass above that of the selected product ion. These ions cross the activated collision chamber (q) where they are fragmented, resulting in, among others, the product ion of interest. MS2 operating in SIM mode is the next filter, which only daughter ions are allowed to cross. For example, in a complex mixture of 30-40 components it is of interest to detect only phthalates (plasticizer). The common ion, diagnostic for this group of compounds, is recorded at *m/z* 149. Then, all ions with the mass/charge ratio higher than *m/z* 149 pass by the first analyzer, operated in scan mode; the analyzer MS2 operates in SIM mode, filtering only ions with *m/z* 149, which are formed in the activated collision chamber from the precursor ions - phthalates. The mass fragmentogram finally recorded contains only chromatographic peaks corresponding to phthalates; thus the mass analyzer, QqQ operated in a precursor ion scanning becomes a selective chromatographic analyzer.

**Figure 8.** Single reaction monitoring (SRM) (metastable ion transition) between the molecular ion of cocaine (*m/z* 303) and one of the most abundant and characteristic ion-product (*m/z* 182), 303->182, obtained by QqQ-MS with N2 as collision gas, and at different collision energies (potentials, 0, 5, 10, and 20 eV).

#### *3.3.5. Constant neutral loss scan*

which allows transmitting ions from the first analyzer to MS2. The collision gas supplied to the cell (usually, He, Ar or N2), by means of collisions with the selected ions, provides the additional energy (ion excitation process); an applied potential in Q2 ( q ) permits to accelerate the ions and convert part of their kinetic energy into additional internal energy (rotational, vibrational and electronic). This potential should be optimized in order to obtain higher sensitivity (Figure 8). The increase of internal energy of the ions, that is, their "energizing" or "activation", leads to their dissociation and the formation of different fragment-ions (product ions) which are then directed to the second mass analyzer or filter (MS2). This analyzer scans

obviously weigh less than their predecessors. It should be noted, that a product-ion mass spectrum will lack the companion signals from isotopes. This acquisition mode is perhaps the most common and well known in the triple quadrupole system, and is used for various purposes and applications. It can be used in the analysis of complex mixtures or substances with impurities, without prior separation in a chromatographic column. If a soft ionization method is used, for example, positive ion chemical ionization, the mixture of ionized substan‐

MS1 selects one by one the protonated molecular ions, which are directed to the activated

by the MS2 analyzer (Q3) and the spectrum is recorded. In this case, the first analyzer (Q1) acts as a "chromatographic column", the second quadrupole (q) operates as an "ionization cham‐ ber", and the MS2 analyzer (Q3) fulfills the role of the mass analyzer, which makes a complete ion scan. Thus, time savings will be achieved by not using a chromatographic column and focusing the QqQ system work in the search of selected compounds of interest present in a

This mode of operation is used to find in a mass spectrum those (precursor) ions that can generate a given fragment (product ion). In this case, the first quadrupole (MS1) operates in the full scan mode, while the third quadrupole (MS2) operates in selected ion monitoring mode. This selected ion is the product ion (daughter ion) of interest, whose precursors are sought. Technically, this takes place as follows: in the MS2 analyzer (Q3) - only product ions with specific mass are filtered, while the first analyzer, MS1, allows passage to all ions with a mass above that of the selected product ion. These ions cross the activated collision chamber (q) where they are fragmented, resulting in, among others, the product ion of interest. MS2 operating in SIM mode is the next filter, which only daughter ions are allowed to cross. For example, in a complex mixture of 30-40 components it is of interest to detect only phthalates (plasticizer). The common ion, diagnostic for this group of compounds, is recorded at *m/z* 149. Then, all ions with the mass/charge ratio higher than *m/z* 149 pass by the first analyzer, operated in scan mode; the analyzer MS2 operates in SIM mode, filtering only ions with *m/z* 149, which are formed in the activated collision chamber from the precursor ions - phthalates. The mass fragmentogram finally recorded contains only chromatographic peaks corresponding to phthalates; thus the mass analyzer, QqQ operated in a precursor ion scanning becomes a

parent ion, since the product ions

). The first mass analyzer

are separated

the mass range smaller than the mass of the selected m+

ces in the ion source, will produce protonated molecular ions (MH+

complex mixture without prior chromatographic separation.

*3.3.4. Parent ion scan*

26 Advances in Gas Chromatography

selective chromatographic analyzer.

collision cell (q), where they dissociate; fragment ions formed from each MH+

In this QqQ operational mode, both mass analyzers, MS1 and MS2, operate in scan mode simultaneously. The scanned masses in both analyzers correspond to ions with a fixed difference, which equals the mass of the selected neutral fragment. If ions f1, f2, f3, f4, etc., pass the first analyzer (MS1), and then go through the activated collision chamber (Q2), they may experience fragmentation. The fragmentation products that will be able to pass the next analyzer (MS2) will be only those ions with the pre-established, fixed, mass difference, that is, f1-Δm, f2-Δm, f3-Δm, f4-Δm, etc. For example, if the linked scan of both analyzers corresponds to a mass difference of 28 units, if ions with *m/z* 80, 81, and 82 pass the first analyzer, MS2 will only allow free passage to ions with *m/z* 52, 53 and 54.

### *3.3.6. Multiple reaction monitoring, MRM*

This is one of the most *sui generis*, interesting, methods of ionic current acquisition of the triple quadrupole, because it allows exploring and properly using its advantages, to turn it into a specific, highly selective and sensitive GC (or LC) mass detector. Unfortunately, when the acquisition of the ionic current is done in the SIM mode, the probability that the selected ion and a signal from the background (chemical noise) actually match is not null. This non negligible possibility of false positive or false negative results, lowers the reliability of the SIM acquisition method. In order to avoid these problems, instead of monitoring characteristic ions, the MRM experiment rather focuses on recording transitions (or transition reactions) between ion pairs ( precursor and product). Selected precursor ions (F1) are filtered in the first analyzer (MS1, SIM mode), while only ions F2, product of the transition or dissociation reaction F1 → F2 are allowed to pass the second analyzer MS2 (operating in SIM mode). Both ions must be stable and, in general, abundant in the mass spectrum of the analyte. Reaction monitoring of precursor and daughter (ion product) ions almost completely cancels the probability of coincidence of the analyte signal with one from the background and raises the value of S/N. Typically, the registration of two independent transitions along with chromatographic retention can confirm unequivocally the occurrence of an target analyte in a complex mixture (Figure 9). The use of triple quadrupole operated in MRM mode, is of particular importance for the analysis of compounds (target analytes) present at trace level in highly polluted, complex, multi interference matrices; for example, in pesticide residue analysis in foods, plants or biological or environmental samples [57]. Another important application is the analysis of biomarkers in petroleum [58]. transition or dissociation reaction F1 → F2 are allowed to pass the second analyzer MS2 (operating in SIM mode). Both ions must be stable and, in general, abundant in the mass spectrum of the analyte. Reaction monitoring of precursor and daughter (ion product) ions almost completely cancels the probability of coincidence of the analyte signal with one from the background and raises the value of S/N. Typically, the registration of two independent transitions along with chromatographic retention can confirm unequivocally the occurrence of an target analyte in a complex mixture (Figure 9). The use of triple quadrupole operated in MRM mode, is of particular importance for the analysis of compounds (target analytes) present at trace level in highly polluted, complex, multi interference matrices; for example, in pesticide residue analysis in foods, plants or biological or environmental samples [57].

are available, many of them are not easily accessible for a large number of analytes. The second strategy is the combination of several approaches, among which are the following: (a) retention indices (RI), in conjunction with (b) experimental mass spectra (EI, 70 eV ) and (c) their comparison with those of databases of retention indices obtained in columns of orthogonal polarity (polar and nonpolar) and of standard mass spectra (EI, 70 eV). The combination of several experimental parameters and data, i.e., retention times measured in both columns and mass spectra is mandatory for the structural identification of components in a mixture. The identification can be tentative (preliminary, presumptive) or confirmatory. Confirmation (positive or unambiguous) requires, in many cases, the use of a certified standard compound. Multiple analytes from complex mixtures, however, can have similar retention times or their mass spectra seem alike or have only very small quantitative differences (ion intensities). Limonene epoxides, xylenes, and many structurally similar terpenes, are examples of this situation. However, the possibility of simultaneous coincidence of both the retention indices calculated in both columns (polar and nonpolar) and of mass spectra for two different substances, in fact, is very remote, almost unlikely. For some cases, such as those that may have legal implications, i.e., environmental, forensic cases or disputes, control of doping agents in sports competitions, it is absolutely mandatory to use certified standard substances for identification and confirmation. The analysis of an essential oil, perfume, aroma, and fragrance fractions (or any other complex mixture ), in order to quantify and identify its components, done by one-dimensional chromatography, should comply with the following conditions: (1) using preferably long capillary columns (50, 60 m), (2) performing the analysis in two capillary columns with orthogonal phase (e.g., DB-1 or DB-5 and DB-WAX), (3) obtaining experimental mass spectra EI (70 eV) and doing a comparative search, preferably, on various mass spectra databases (e.g., NIST, Wiley, Adams), (4) calculating linear retention indices in two columns, polar and nonpolar, and (5) using standard compounds for further structural confirmation (Figure 10, Table 1). The combination of all these parameters allows confirmatory identification of the mixture components. In GC- MS analysis it is very important to ensure that the chro‐ matographic peaks are "homogeneous", as the co-elution of various substances can lead to

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 29

structural misassignments.

**4.1. Mass spectra interpretation**

**Compound Capillary column**

α-Pinene 939-942 932 1036-1038 β-Phellandrene 1025-1032 1025 1213-1216 Sabinene 972-976 969 1130

**Table 1.** Linear retention indices (LRI) of some monoterpenes, measured on different stationary phase columns.

The large number of fragment ions (cations and cation-radicals) in the mass spectrum is due, firstly, to their formation from molecular ions which have very different excesses of internal

**DB-1 DB-5 DB-WAX**

Another important application is the analysis of biomarkers in petroleum [58].

odorata, Annonaceae family). Determination of diazinon present in the extract, using different acquisition modes: full scan, SIM [characteristic ions, m/z 304 (M+.), 179 and 136] and single reaction monitoring, SRM, of the ion transition m/z 304 179. Notice the higly selective and sensitive detection of diazinon (2 ppb) in the ylang-ylang extract, using QqQ-MS in the metastable ion transition mode (SRM) . **Figure 9.** GC-QqQ-MS analysis of the extract obtained from ylang-ylang flowers (*Cananga odorata,* Annonaceae fami‐ ly). Determination of diazinon present in the extract, using different acquisition modes: full scan, SIM [characteristic ions, *m/z* 304 (M+.), 179 and 136] and single reaction monitoring, SRM, of the ion transition *m/z* 304 →179. Notice the highly selective and sensitive detection of diazinon (2 ppb) in the ylang-ylang extract, using QqQ-MS in the metastable ion transition mode (SRM).

#### There are two basic strategies in GC- MS, for the identification of compounds. The first is the use of standard substances (certified reference material). However, not always all **4. Data analysis and interpretation**

4. Data analysis and interpretation

analytes. The second strategy is the combination of several approaches, among which are the following: (a) retention indices (RI), in conjunction with (b) experimental mass spectra (EI, 70 eV ) and (c) their comparison with those of databases of retention indices obtained in There are two basic strategies in GC- MS, for the identification of compounds. The first is the use of standard substances (certified reference material). However, not always all standards

columns of orthogonal polarity (polar and nonpolar) and of standard mass spectra (EI, 70 eV). The combination of several experimental parameters and data, i.e., retention times

standards are available, many of them are not easily accessible for a large number of

are available, many of them are not easily accessible for a large number of analytes. The second strategy is the combination of several approaches, among which are the following: (a) retention indices (RI), in conjunction with (b) experimental mass spectra (EI, 70 eV ) and (c) their comparison with those of databases of retention indices obtained in columns of orthogonal polarity (polar and nonpolar) and of standard mass spectra (EI, 70 eV). The combination of several experimental parameters and data, i.e., retention times measured in both columns and mass spectra is mandatory for the structural identification of components in a mixture. The identification can be tentative (preliminary, presumptive) or confirmatory. Confirmation (positive or unambiguous) requires, in many cases, the use of a certified standard compound. Multiple analytes from complex mixtures, however, can have similar retention times or their mass spectra seem alike or have only very small quantitative differences (ion intensities). Limonene epoxides, xylenes, and many structurally similar terpenes, are examples of this situation. However, the possibility of simultaneous coincidence of both the retention indices calculated in both columns (polar and nonpolar) and of mass spectra for two different substances, in fact, is very remote, almost unlikely. For some cases, such as those that may have legal implications, i.e., environmental, forensic cases or disputes, control of doping agents in sports competitions, it is absolutely mandatory to use certified standard substances for identification and confirmation. The analysis of an essential oil, perfume, aroma, and fragrance fractions (or any other complex mixture ), in order to quantify and identify its components, done by one-dimensional chromatography, should comply with the following conditions: (1) using preferably long capillary columns (50, 60 m), (2) performing the analysis in two capillary columns with orthogonal phase (e.g., DB-1 or DB-5 and DB-WAX), (3) obtaining experimental mass spectra EI (70 eV) and doing a comparative search, preferably, on various mass spectra databases (e.g., NIST, Wiley, Adams), (4) calculating linear retention indices in two columns, polar and nonpolar, and (5) using standard compounds for further structural confirmation (Figure 10, Table 1). The combination of all these parameters allows confirmatory identification of the mixture components. In GC- MS analysis it is very important to ensure that the chro‐ matographic peaks are "homogeneous", as the co-elution of various substances can lead to structural misassignments.


**Table 1.** Linear retention indices (LRI) of some monoterpenes, measured on different stationary phase columns.

#### **4.1. Mass spectra interpretation**

ion pairs ( precursor and product). Selected precursor ions (F1) are filtered in the first analyzer (MS1, SIM mode), while only ions F2, product of the transition or dissociation reaction F1 → F2 are allowed to pass the second analyzer MS2 (operating in SIM mode). Both ions must be stable and, in general, abundant in the mass spectrum of the analyte. Reaction monitoring of precursor and daughter (ion product) ions almost completely cancels the probability of coincidence of the analyte signal with one from the background and raises the value of S/N. Typically, the registration of two independent transitions along with chromatographic retention can confirm unequivocally the occurrence of an target analyte in a complex mixture (Figure 9). The use of triple quadrupole operated in MRM mode, is of particular importance for the analysis of compounds (target analytes) present at trace level in highly polluted, complex, multi interference matrices; for example, in pesticide residue analysis in foods, plants or biological or environmental samples [57]. Another important application is the analysis of

transition or dissociation reaction F1 → F2 are allowed to pass the second analyzer MS2 (operating in SIM mode). Both ions must be stable and, in general, abundant in the mass spectrum of the analyte. Reaction monitoring of precursor and daughter (ion product) ions almost completely cancels the probability of coincidence of the analyte signal with one from the background and raises the value of S/N. Typically, the registration of two independent transitions along with chromatographic retention can confirm unequivocally the occurrence of an target analyte in a complex mixture (Figure 9). The use of triple quadrupole operated in MRM mode, is of particular importance for the analysis of compounds (target analytes) present at trace level in highly polluted, complex, multi interference matrices; for example, in pesticide residue analysis in foods, plants or biological or environmental samples [57].

Benzyl benzoate

Diazinon

Full scan

SIM:

SRM: 304179

137, 179, 304

 Figure 9. GC-QqQ-MS analysis of the extract obtained from ylang-ylang flowers (Cananga odorata, Annonaceae family). Determination of diazinon present in the extract, using different acquisition modes: full scan, SIM [characteristic ions, m/z 304 (M+.), 179 and 136] and single reaction monitoring, SRM, of the ion transition m/z 304 179. Notice the higly selective and sensitive detection of diazinon (2 ppb) in the ylang-ylang extract, using QqQ-

**Figure 9.** GC-QqQ-MS analysis of the extract obtained from ylang-ylang flowers (*Cananga odorata,* Annonaceae fami‐ ly). Determination of diazinon present in the extract, using different acquisition modes: full scan, SIM [characteristic ions, *m/z* 304 (M+.), 179 and 136] and single reaction monitoring, SRM, of the ion transition *m/z* 304 →179. Notice the highly selective and sensitive detection of diazinon (2 ppb) in the ylang-ylang extract, using QqQ-MS in the metastable

There are two basic strategies in GC- MS, for the identification of compounds. The first is the use of standard substances (certified reference material). However, not always all standards are available, many of them are not easily accessible for a large number of analytes. The second strategy is the combination of several approaches, among which are the following: (a) retention indices (RI), in conjunction with (b) experimental mass spectra (EI, 70 eV ) and (c) their comparison with those of databases of retention indices obtained in columns of orthogonal polarity (polar and nonpolar) and of standard mass spectra (EI, 70 eV). The combination of several experimental parameters and data, i.e., retention times

There are two basic strategies in GC- MS, for the identification of compounds. The first is the use of standard substances (certified reference material). However, not always all standards

MS in the metastable ion transition mode (SRM) .

4. Data analysis and interpretation

**4. Data analysis and interpretation**

ion transition mode (SRM).

Another important application is the analysis of biomarkers in petroleum [58].

biomarkers in petroleum [58].

28 Advances in Gas Chromatography

The large number of fragment ions (cations and cation-radicals) in the mass spectrum is due, firstly, to their formation from molecular ions which have very different excesses of internal

without undergoing thermal decomposition, prior to their ionization. Even so, many of the volatilizable and thermostable molecules do not exhibit molecular ions in their mass spectra, only fragment ions, something that limits obtaining information on molecular weight. In fact, less than 10 % of all existing molecules are suitable for analysis by mass spectrometry by electron ionization. The excluded cases are highly polar species (e.g., salts, amino acids), those of high-molecular weight (e.g., proteins, nucleic acids, polymers), and thermolabile com‐ pounds (e.g., sugars). In some cases, chemical derivatization of the molecule permits to

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 31

The user of mass spectrometry wonders at the beginning, interpreting mass spectra, why molecular ions with the same structure can generate several very different products and can be fragmented by parallel, competitive routes. The type and ratio (abundance) in a mass spectrum of daughter, parent and metastable ions is based on the following factors: (1) distribution of internal energies of molecular ions formed (also valid for fragment ions with excess energy that also dissociate afterwards); (2) activation energy of the process of fragmen‐ tation, Ea, and (3 ) slope of the curve of log K, where K is the dissociation constant of the parent ion through one of the possible reaction coordinates. Rearrangement (or transposition) processes are often accompanied by the formation of intense metastable ions; rearrangement products often "survive" in low voltage (10-15 eV ) mass spectra, as the Ea energies for their formation, generally, are lower than those required for a simple bond rupture. As the log K curve becomes steeper, fewer metastable ions are observed in the mass spectrum correspond‐ ing to this fragmentation reaction coordinate, and particularly, most likely, this process will be of simple rupture. Accordingly, the structure of the molecule, the excess energy that it acquired during its ionization, the activation energies of possible dissociation processes and their rate, and the frequency factors involved in bond cleavage, are variables that determine the fragmentation pattern of a whole molecule and its product-ions that will be recorded in its mass spectrum. In a mass spectrum, obtained by EI, different signals are recorded, including the molecular ion (if its lifetime is greater than 10-6 s), fragment-ions, isotope ions, sometimes, also multicharged ion signals (such as in polyaromatic compounds). Mass spectrometers with specific configurations also detect metastable ions (products of ion fragmentation out of the ionization chamber). Each one of these ions provides structural information more or less important to the structural elucidation of the molecule. Molecular ions indicate the value of molecular mass and the elemental composition when high resolution MS is employed. Intense molecular ion signals indicate that the ionic structure can stabilize its positive charge and this normally suggests aromaticity or highly conjugated unsaturations. It is common that mass spectra of branched hydrocarbons, alcohols, and secondary or tertiary amines are devoid of the molecular ion signal. A soft ionization method, such as chemical ionization, should be used in these cases. Fragment ions contain the primary information for molecular structure elucidation. These may be cations (even electron number) or radical cations (odd electron number). They may result from simple rupture or rearrangement. Certain common fragment losses are associated with the presence of specific groups in the molecule. Alcohol molecular ions easily decay with the loss of either an OH group or an H2O molecule, giving rise to the

increase its volatility and thermal stability and decrease its polarity.

**4.2. Ion types**

Standard compound Sample) **Figure 10.** A. The use of *n*-hydrocarbon mixture co-injected with the sample for linear retention indices (LRI) calcula‐ tion. B. Standard compounds used in the process of sample components identification.

energy. All molecular ions with internal energies lower than the potential energy of appearance of an ion fragment with the lowest formation activation energy will be recorded in the mass spectrum as not dissociated molecular ions. Their intensity in the spectrum depends on the molecular structure and, particularly, their ability to delocalize (stabilize) the positive charge, which allows the ion M+• to exist for longer time than is required for its detection (ca. > 10-5 s) in a mass spectrometer. One of the major limitations of the electron ionization technique lies in the fact that molecules that are ionized, should be in the vapor phase, i.e. being volatilizable without undergoing thermal decomposition, prior to their ionization. Even so, many of the volatilizable and thermostable molecules do not exhibit molecular ions in their mass spectra, only fragment ions, something that limits obtaining information on molecular weight. In fact, less than 10 % of all existing molecules are suitable for analysis by mass spectrometry by electron ionization. The excluded cases are highly polar species (e.g., salts, amino acids), those of high-molecular weight (e.g., proteins, nucleic acids, polymers), and thermolabile com‐ pounds (e.g., sugars). In some cases, chemical derivatization of the molecule permits to increase its volatility and thermal stability and decrease its polarity.

#### **4.2. Ion types**

energy. All molecular ions with internal energies lower than the potential energy of appearance of an ion fragment with the lowest formation activation energy will be recorded in the mass spectrum as not dissociated molecular ions. Their intensity in the spectrum depends on the molecular structure and, particularly, their ability to delocalize (stabilize) the positive charge, which allows the ion M+• to exist for longer time than is required for its detection (ca. > 10-5 s) in a mass spectrometer. One of the major limitations of the electron ionization technique lies in the fact that molecules that are ionized, should be in the vapor phase, i.e. being volatilizable

**Figure 10.** A. The use of *n*-hydrocarbon mixture co-injected with the sample for linear retention indices (LRI) calcula‐

Standard compound Sample)

8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00

10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

*P*-Cymene

Limonene

Terpinolene

Linalool

c17 c18

carvone

c19 c20 c22

piperitenone

c21

c23 c24 c25

Time-->

**B**

Time-->

**A**

200000

Abundance

400000

c11

α-Pinene

β-Myrcene

tion. B. Standard compounds used in the process of sample components identification.

c12

limonene

c13 c14

c15 c16

c10

600000

800000

1000000

1200000

1400000

1600000

1800000

Abundance

30 Advances in Gas Chromatography

The user of mass spectrometry wonders at the beginning, interpreting mass spectra, why molecular ions with the same structure can generate several very different products and can be fragmented by parallel, competitive routes. The type and ratio (abundance) in a mass spectrum of daughter, parent and metastable ions is based on the following factors: (1) distribution of internal energies of molecular ions formed (also valid for fragment ions with excess energy that also dissociate afterwards); (2) activation energy of the process of fragmen‐ tation, Ea, and (3 ) slope of the curve of log K, where K is the dissociation constant of the parent ion through one of the possible reaction coordinates. Rearrangement (or transposition) processes are often accompanied by the formation of intense metastable ions; rearrangement products often "survive" in low voltage (10-15 eV ) mass spectra, as the Ea energies for their formation, generally, are lower than those required for a simple bond rupture. As the log K curve becomes steeper, fewer metastable ions are observed in the mass spectrum correspond‐ ing to this fragmentation reaction coordinate, and particularly, most likely, this process will be of simple rupture. Accordingly, the structure of the molecule, the excess energy that it acquired during its ionization, the activation energies of possible dissociation processes and their rate, and the frequency factors involved in bond cleavage, are variables that determine the fragmentation pattern of a whole molecule and its product-ions that will be recorded in its mass spectrum. In a mass spectrum, obtained by EI, different signals are recorded, including the molecular ion (if its lifetime is greater than 10-6 s), fragment-ions, isotope ions, sometimes, also multicharged ion signals (such as in polyaromatic compounds). Mass spectrometers with specific configurations also detect metastable ions (products of ion fragmentation out of the ionization chamber). Each one of these ions provides structural information more or less important to the structural elucidation of the molecule. Molecular ions indicate the value of molecular mass and the elemental composition when high resolution MS is employed. Intense molecular ion signals indicate that the ionic structure can stabilize its positive charge and this normally suggests aromaticity or highly conjugated unsaturations. It is common that mass spectra of branched hydrocarbons, alcohols, and secondary or tertiary amines are devoid of the molecular ion signal. A soft ionization method, such as chemical ionization, should be used in these cases. Fragment ions contain the primary information for molecular structure elucidation. These may be cations (even electron number) or radical cations (odd electron number). They may result from simple rupture or rearrangement. Certain common fragment losses are associated with the presence of specific groups in the molecule. Alcohol molecular ions easily decay with the loss of either an OH group or an H2O molecule, giving rise to the (M-17)+ and (M-18)+ ions. Fragment ions C6H5 + and C7H7 + dissociate with the loss of an acetylene molecule (C2H2) and produce ions at *m/z* 51 and 65, respectively. The substance´s elemental composition is derived from the relative abundance of isotopic ions. The presence of Cl, Br, S or Si can be easily determined from the isotopic peak pattern. A quality requirement of a mass spectrum is the clear distinction of isotopic ions. Multiply charged ions are found in the mass spectra of a limited class of substances, for example, those containing heteroatoms (N, S, O), aromatic or heteroaromatic rings, high unsaturation level, or when several of these elements are combined in the molecule. Their intensity is relatively low and may have fractional values. The signal at *m/z* 64 in the naphthalene mass spectrum (M+ , *m/z* 128) corresponds to a doubly charged molecular ion, M2+. Metastable ions are formed out of the ionization chamber from ions with life time longer than a microsecond, but less than the time required to reach the detector. Their apparent mass m\* (a fractional number) relates the masses of parent (m1) and daughter (m2) ions according to m\* = m2 2 /m1. Metastable ions are frequently registered in mass spectra obtained with magnetic sector analyzers. A mass spectrum of benzoic acid contains m\* signals corresponding to processes of successive loss of radical OH and the CO molecule: M+ → (M - OH)<sup>+</sup> and (M - OH)+ → [(M - OH) - CO]<sup>+</sup> . These signals confirm the genetic link between these ions. The absence of an m\* ion corresponding to the direct process M+ → (M – COOH)+ indicates that this process does not take place.

of formation) can be observed more clearly, based on differences in intensities of characteristic fragment ions, or in ionization potentials. Generally, dissociative ionization processes can be divided into the following two major groups: (1) single rupture reactions (homolytic or heterolytic) and (2) rearrangement reactions, which can be rearrangement of the molecular

transpositions, for example, the classic McLafferty rearrangement [34]. The most common single rupture reactions are cation formation (for example, in hydrocarbon chains), allylic and benzylic ruptures, retro-Diels-Alder (RDA) reaction in monounsaturated cyclic systems, and acyl ion formation, among others. Generally, the energy required (activation energy, Ea, of the process) for rearrangement is lower than that required for single rupture. Single rupture processes are generally less selective than the molecular skeleton or hydrogen transpositions, and are also less susceptible to steric effects in the molecule compared to rearrangement processes. The mass spectra obtained with 70 eV or lower voltage (10-30 eV) of the same substance differ markedly. In the latter, molecular ions prevail (relative increment) and some products survive, basically, hydrogen rearrangements or transpositions, which require lower activation energy. The binding energy of atoms in the ionized molecule is in the order of 2-4 eV, whereas the ionization energy of the organic molecule is of the order of 6-13 eV. The energy of the bond involved in the dissociation is an important factor, especially when there are competitive processes to form predominantly one product versus another. The interpretation of mass spectra of organic molecules, obtained by electron ionization, is based on the study of the fragmentation pattern, which depends on molecular ion and ion-fragment dissociation mechanisms. Fragmentation schemes are not necessarily speculative, because numerous experimental verifications of fragmentation routes can be found. Different methods are included within the experimental tools to establish fragmentation schemes and mechanisms: (1) using soft (CI, FI) ionization techniques, (2) chemical derivatization, (3) isotopic labeling (e.g., with D, 15N, 18O ) and chemical marking (introduction to the molecule of substituents that are not released during the fragmentation, e.g., F, OCH3, etc. ); (4) high resolution mass spectrometry, which allows to establish the elemental composition of each ion; (5) metastable ion study to determine the relationship between parent ions and subsidiary ions, (6) use of tandem configurations and study of collision-activated dissociation (CID, Collision-Induced Dissociation); (7) photoionization (PI) and determination of energies (potential) of ionization and ion appearance; (8) quantum mechanical calculations of formation energies and ion structures. Obviously, not all experimental techniques can be performed on a single mass spectrometer, but the whole set will provide sufficient information to correctly set the scheme or even the mechanism for the fragmentation of an organic molecule ionized by electrons. Many references [34,35,36,41,42,48,52], contain information about fragmentation of organic molecules, their mechanisms and the classification of simple rupture or transposition proc‐ esses. Generally, it is considered that the energies involved during the electron ionization are very high (70 eV) and can "level" or mask thermodynamic differences generated by the spatial characteristics in isomeric molecules. However, steric factors exert pronounced effects on activated complex structure and are more relevant to the rearrangement processes. Increasing the internal energy of ion M+• causes a decrease of rearrangement products. Decreasing the energy of bombarding electrons to 10-20 eV, can reduce the internal energy of the ions M+• and

+

) or reactions accompanied by hydrogen

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 33

skeleton (for example, tropylium ion formation, C7H7

#### **4.3. Fragmentation patterns**

The fragmentation pattern (*m/z*, the amount, I%, of ions that are recorded in a mass spectrum) shows how a molecule dissociates once ionized; this does not occur randomly, because it is derived from the molecular structure (nature of bonded atoms, chemical bonding strength), their spatial arrangement, ionization potential and internal energy that acquires during its collision with an electron. The fragmentation pattern is unique to each molecular structure and contains clearly detectable differences (often only quantitative), including those for the isomers (positional, geometric or stereoisomers). The mass spectrum provides information on: (1) molecular mass: if the molecular ion does not appear in the EI spectrum, it is necessary to obtain the spectrum via soft ionization, e.g., CI, but sometimes, derivatization of the molecule [35,37] can be used, in order to find its molecular weight and also to increase its volatility (that of the derivative) and the sensitivity with which it can be detected; (2) elemental composition: it is possible to know it, based on the analysis of a mass spectrum obtained by a high resolution mass spectrometer. Isotope ions or typical fragment losses (-HCN,-CO,-OH, -HS,-NH2, etc.) also allow inferences about the elemental composition of the substance; the simplest case is the presence of Cl or Br atoms in the molecule; (3) functional groups in the molecule can be elucidated by noticing in the spectrum signals corresponding ions with characteristic mass, which indicate their presence, e.g., phenyl (*m/z* 77), benzyl (*m/z* 91), benzoyl, C6H5CO, (*m/z* 105), or by detecting fragments, the products of the loss of small neutral molecules, e.g., water, CO2, CO, C2H2, HCN, alkyl radicals (CH3, C2H5, C3H7, etc.); these fragment ions are related to the presence of these structural groups in the molecule; (4) spatial structure: sometimes it is possible to establish the spatial configuration based on the mass spectrum, especially when the spectra of both stereoisomers can be compared or when using low voltage mass spectra (10-20 eV) or chemical ionization, on which the differences in conformational energy (enthalpy of formation) can be observed more clearly, based on differences in intensities of characteristic fragment ions, or in ionization potentials. Generally, dissociative ionization processes can be divided into the following two major groups: (1) single rupture reactions (homolytic or heterolytic) and (2) rearrangement reactions, which can be rearrangement of the molecular skeleton (for example, tropylium ion formation, C7H7 + ) or reactions accompanied by hydrogen transpositions, for example, the classic McLafferty rearrangement [34]. The most common single rupture reactions are cation formation (for example, in hydrocarbon chains), allylic and benzylic ruptures, retro-Diels-Alder (RDA) reaction in monounsaturated cyclic systems, and acyl ion formation, among others. Generally, the energy required (activation energy, Ea, of the process) for rearrangement is lower than that required for single rupture. Single rupture processes are generally less selective than the molecular skeleton or hydrogen transpositions, and are also less susceptible to steric effects in the molecule compared to rearrangement processes. The mass spectra obtained with 70 eV or lower voltage (10-30 eV) of the same substance differ markedly. In the latter, molecular ions prevail (relative increment) and some products survive, basically, hydrogen rearrangements or transpositions, which require lower activation energy. The binding energy of atoms in the ionized molecule is in the order of 2-4 eV, whereas the ionization energy of the organic molecule is of the order of 6-13 eV. The energy of the bond involved in the dissociation is an important factor, especially when there are competitive processes to form predominantly one product versus another. The interpretation of mass spectra of organic molecules, obtained by electron ionization, is based on the study of the fragmentation pattern, which depends on molecular ion and ion-fragment dissociation mechanisms. Fragmentation schemes are not necessarily speculative, because numerous experimental verifications of fragmentation routes can be found. Different methods are included within the experimental tools to establish fragmentation schemes and mechanisms: (1) using soft (CI, FI) ionization techniques, (2) chemical derivatization, (3) isotopic labeling (e.g., with D, 15N, 18O ) and chemical marking (introduction to the molecule of substituents that are not released during the fragmentation, e.g., F, OCH3, etc. ); (4) high resolution mass spectrometry, which allows to establish the elemental composition of each ion; (5) metastable ion study to determine the relationship between parent ions and subsidiary ions, (6) use of tandem configurations and study of collision-activated dissociation (CID, Collision-Induced Dissociation); (7) photoionization (PI) and determination of energies (potential) of ionization and ion appearance; (8) quantum mechanical calculations of formation energies and ion structures. Obviously, not all experimental techniques can be performed on a single mass spectrometer, but the whole set will provide sufficient information to correctly set the scheme or even the mechanism for the fragmentation of an organic molecule ionized by electrons. Many references [34,35,36,41,42,48,52], contain information about fragmentation of organic molecules, their mechanisms and the classification of simple rupture or transposition proc‐ esses. Generally, it is considered that the energies involved during the electron ionization are very high (70 eV) and can "level" or mask thermodynamic differences generated by the spatial characteristics in isomeric molecules. However, steric factors exert pronounced effects on activated complex structure and are more relevant to the rearrangement processes. Increasing the internal energy of ion M+• causes a decrease of rearrangement products. Decreasing the energy of bombarding electrons to 10-20 eV, can reduce the internal energy of the ions M+• and

(M-17)+

M+

COOH)+

→ (M - OH)<sup>+</sup>

**4.3. Fragmentation patterns**

and (M-18)+

32 Advances in Gas Chromatography

ions. Fragment ions C6H5

The signal at *m/z* 64 in the naphthalene mass spectrum (M+

indicates that this process does not take place.

daughter (m2) ions according to m\* = m2

and (M - OH)+

+

molecule (C2H2) and produce ions at *m/z* 51 and 65, respectively. The substance´s elemental composition is derived from the relative abundance of isotopic ions. The presence of Cl, Br, S or Si can be easily determined from the isotopic peak pattern. A quality requirement of a mass spectrum is the clear distinction of isotopic ions. Multiply charged ions are found in the mass spectra of a limited class of substances, for example, those containing heteroatoms (N, S, O), aromatic or heteroaromatic rings, high unsaturation level, or when several of these elements are combined in the molecule. Their intensity is relatively low and may have fractional values.

charged molecular ion, M2+. Metastable ions are formed out of the ionization chamber from ions with life time longer than a microsecond, but less than the time required to reach the detector. Their apparent mass m\* (a fractional number) relates the masses of parent (m1) and

spectra obtained with magnetic sector analyzers. A mass spectrum of benzoic acid contains m\* signals corresponding to processes of successive loss of radical OH and the CO molecule:

The fragmentation pattern (*m/z*, the amount, I%, of ions that are recorded in a mass spectrum) shows how a molecule dissociates once ionized; this does not occur randomly, because it is derived from the molecular structure (nature of bonded atoms, chemical bonding strength), their spatial arrangement, ionization potential and internal energy that acquires during its collision with an electron. The fragmentation pattern is unique to each molecular structure and contains clearly detectable differences (often only quantitative), including those for the isomers (positional, geometric or stereoisomers). The mass spectrum provides information on: (1) molecular mass: if the molecular ion does not appear in the EI spectrum, it is necessary to obtain the spectrum via soft ionization, e.g., CI, but sometimes, derivatization of the molecule [35,37] can be used, in order to find its molecular weight and also to increase its volatility (that of the derivative) and the sensitivity with which it can be detected; (2) elemental composition: it is possible to know it, based on the analysis of a mass spectrum obtained by a high resolution mass spectrometer. Isotope ions or typical fragment losses (-HCN,-CO,-OH, -HS,-NH2, etc.) also allow inferences about the elemental composition of the substance; the simplest case is the presence of Cl or Br atoms in the molecule; (3) functional groups in the molecule can be elucidated by noticing in the spectrum signals corresponding ions with characteristic mass, which indicate their presence, e.g., phenyl (*m/z* 77), benzyl (*m/z* 91), benzoyl, C6H5CO, (*m/z* 105), or by detecting fragments, the products of the loss of small neutral molecules, e.g., water, CO2, CO, C2H2, HCN, alkyl radicals (CH3, C2H5, C3H7, etc.); these fragment ions are related to the presence of these structural groups in the molecule; (4) spatial structure: sometimes it is possible to establish the spatial configuration based on the mass spectrum, especially when the spectra of both stereoisomers can be compared or when using low voltage mass spectra (10-20 eV) or chemical ionization, on which the differences in conformational energy (enthalpy

2

→ [(M - OH) - CO]<sup>+</sup>

between these ions. The absence of an m\* ion corresponding to the direct process M+

and C7H7

+

dissociate with the loss of an acetylene

, *m/z* 128) corresponds to a doubly

. These signals confirm the genetic link

→ (M –

/m1. Metastable ions are frequently registered in mass

increase the number of rearrangement products (transpositions). Stereoisomeric effects are demonstrated most notably in the mass spectra of chemical ionization or those taken in tandem configuration instruments when collision activated reactions (CID) are used.

[5] Cabaleiro N, de la Calle I, Bendicho C, Lavilla I. Current Trends in Liquid-Liquid and Solid-Liquid Extraction for Cosmetic Analysis: A Review. Analytical Methods

Gas Chromatography-Mass Spectrometry http://dx.doi.org/10.5772/57492 35

[7] Sairam P, Ghosh S, Jena S, Rao KNV, Banji D. Supercritical Fluid Extraction (SFE)-An Overview. Asian Journal of Research in Pharmaceutical Science 2012; 2(3) 112-120. [8] Ge X, Lv Y, Wang A. Application of Accelerated Solvent Extraction in the Analysis of Organic Contaminants, Bioactive and Nutritional Compounds in Food and Feed.

[9] Risticevic S, Vuckovic D, Pawliszyn J. Solid-Phase Microextraction. In: Pawliszyn J, Lord H. (eds.) Handbook of Sample Preparation. Hoboken, NJ, USA: John Wiley &

[10] Heaven MW, Nash D. Recent Analyses Using Solid Phase Microextraction in Indus‐ tries Related to Food Made into or From Liquids. Food Control 2012; 27(1) 214–227.

[11] Stashenko E, Martínez J. Derivatization and Solid-Phase Microextraction. Trends in

[12] Tholl D, Boland W, Hansel A, Loreto F, Rose USR, Schnitzler JP. Practical Ap‐ proaches to Plant Volatile Analysis. The Plant Journal 2006; 45(4) 540-560.

[13] Kolb B, Ettre L. Static Headspace-Gas Chromatography. New York: Wiley-VCH;

[14] Narendra N, Galvão MDS, Madruga MS. Volatile compounds Captured Through Purge and Trap Technique in Caja-umbu (*Spondias* sp.) Fruits During Maturation.

[15] Musshoff F, Lachenmeier DW, Kroener L, Madea B. Automated Headspace Solid-Phase Dynamic Extraction for the Determination of Cannabinoids in Hair Samples.

[16] Bagheri H, Babanezhad E, Khalilian F. An Interior Needle Electropolymerized Pyr‐ role-Based Coating for Headspace Solid-Phase Dynamic Extraction. Analytica Chimi‐

[17] Bicchi C, Cordero C, Liberto E, Rubiolo P, Sgorbini B, David F, Sandra P. Dual-phase twisters: A new approach to headspace sorptive extraction and stir bar sorptive ex‐

[18] C. Cordero, E. Liberto, P. Rubiolo, B. Sgorbini, P. Sandra. Sorptive tape extraction in the analysis of the volatile fraction emitted from biological solid matrices. Journal of

[6] Fritz JS. Analytical Solid-Phase Extraction. New York: Wiley-VCH; 1999.

Journal of Chromatography A 2012; 1237 1–23.

Sons, Inc. 2010. doi: 10.1002/9780813823621.ch5.

Analytical Chemistry 2004; 23(4) 553-561.

Food Chemistry 2007; 102 726-731.

ca Acta 2009; 634(2) 209-214.

Forensic Science International 2003; 133 32-38.

Chromatography A 2007; 1148(2) 137–144.

traction. Journal of Chromatography A 2005; 1094(1-2) 9-16.

2013; 5(2) 323-340.

1997.

### **5. Conclusion**

The association of separation and mass spectrometric techniques is a key that opens up a rich and multidimensional analytical space for the investigation of complex mixtures with high sensitivity, selectivity and specificity. The variation of chromatographic conditions and the modulation of mass spectrometric data acquisition or the use of tandem spectrometers, permit to focus the analytical methods towards different goals in these investigations. Trace-level contaminant detection, molecular structure characterization, or classification, are just a few of the many applications of gas chromatography coupled to mass spectrometry performed currently with varying degrees of resolution and sensitivity on very complex mixtures.

### **Author details**

Elena Stashenko\* and Jairo René Martínez

\*Address all correspondence to: elena@tucan.uis.edu.co

Research Center for Biomolecules, CIBIMOL, CENIVAM, Universidad Industrial de Santander, Bucaramanga, Colombia

### **References**


increase the number of rearrangement products (transpositions). Stereoisomeric effects are demonstrated most notably in the mass spectra of chemical ionization or those taken in tandem

The association of separation and mass spectrometric techniques is a key that opens up a rich and multidimensional analytical space for the investigation of complex mixtures with high sensitivity, selectivity and specificity. The variation of chromatographic conditions and the modulation of mass spectrometric data acquisition or the use of tandem spectrometers, permit to focus the analytical methods towards different goals in these investigations. Trace-level contaminant detection, molecular structure characterization, or classification, are just a few of the many applications of gas chromatography coupled to mass spectrometry performed currently with varying degrees of resolution and sensitivity on very complex mixtures.

Research Center for Biomolecules, CIBIMOL, CENIVAM, Universidad Industrial de

[1] Chaintreau A. Simultaneous Distillation–Extraction: From Birth to Maturity - Review

[2] Flotron V, Houessou JK, Bosio A, Delteil C, Bermond A, Camel V. Rapid Determina‐ tion of Polycyclic Aromatic Hydrocarbons in Sewage Sludges Using Microwave-As‐ sisted Solvent Extraction. Comparison with Other Extraction Methods. Journal of

[3] Kingston HM, Haswell SJ. Microwave-Enhanced Chemistry. Washington:American

[4] Rice SL, Mitra S. Microwave-Assisted Solvent Extraction of Solid Matrices and Subse‐ quent Detection of Pharmaceuticals and Personal Care Products (Ppcps) Using Gas Chromatography–Mass Spectrometry. Analitica Chimica Acta 2007; 589 125-132.

configuration instruments when collision activated reactions (CID) are used.

and Jairo René Martínez

Flavour and Fragrance Journal 2001; 16(2) 136-148.

Chromatography. A 2003; 999(1-2) 175-84.

\*Address all correspondence to: elena@tucan.uis.edu.co

Santander, Bucaramanga, Colombia

Chemical Society; 1997.

**5. Conclusion**

34 Advances in Gas Chromatography

**Author details**

Elena Stashenko\*

**References**


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[21] Jennings W, Mittlefehldt E, Stremple P. Analytical Gas Chromatography, 2nd Ed,

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[50] Meyer MR, Maurer HH. Current Status of Hyphenated Mass Spectrometry in Studies of the Metabolism of Drugs of Abuse, Including Doping Agents. Analytical and Bioa‐ nalytical Chemistry 2012; 402 195-208.

**Chapter 2**

**Analytical Aspects of the Flame Ionization Detection in**

**Comparison with Mass Spectrometry with Emphasis on**

The initial development of gas chromatography (GC) is deeply interconnected with lipid analysis. This separation technique may be considered as the main contribution for most of knowledgement regarding fatty acid (FA) composition that exists today. Actually, it is possible to convert milligrams of a lipidic sample to fatty acid methyl esters (FAMEs), separate them

Probably, the most important identification method which is coupled with GC is mass spectrometry (MS). It is older than GC, and this fact might be surprising for many readers. However, the basic principles and the first separations of atomic masses were primarily demonstrated in the last part of 19th century, while chromatographic columns appeared during

The possibility of coupling a mass spectrometer in the exit of a chromatographic column, along with the advent of modern computers, introduction of fused silica capillary columns and reduction of interference issues allowed the achievement of analytical results which are more precise/accurate. Allied to these facts, the reduction of gas chromatographs prices enforced their application as a tool for researches with lipidic materials. However, in relation to food analysis with emphasis on fatty acid composition, the flame ionization detector (FID) is the

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

in a gas chromatograph and quantify these fatty acids in a short time [1].

**Fatty Acids and Their Esters**

Jesuí Vergilio Visentainer, Thiago Claus,

Additional information is available at the end of the chapter

Lucas Ulisses Rovigatti Chiavelli and

Oscar Oliveira Santos Jr,

Swami Arêa Maruyama

http://dx.doi.org/10.5772/57333

**1. Introduction**

20th century [2].


## **Analytical Aspects of the Flame Ionization Detection in Comparison with Mass Spectrometry with Emphasis on Fatty Acids and Their Esters**

Jesuí Vergilio Visentainer, Thiago Claus, Oscar Oliveira Santos Jr, Lucas Ulisses Rovigatti Chiavelli and Swami Arêa Maruyama

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57333

**1. Introduction**

[50] Meyer MR, Maurer HH. Current Status of Hyphenated Mass Spectrometry in Studies of the Metabolism of Drugs of Abuse, Including Doping Agents. Analytical and Bioa‐

[51] March R, Todd JFJ. Practical Aspects of Ion Trap Mass Spectrometry: Ion Trap Instru‐

[52] Dawson PH. Quadrupole Mass Spectrometry and Its Applications. Heidelberg:

[53] Brenton AG, Godfrey AR, Alamri M, Stein BK, Williams CM, Hunter AP, Wyatt MF. Analysis of large historical accurate mass data sets on sector mass spectrometers.

[54] Belov ME, Prasad S, Prior DC, Danielson WF, Weitz K, IbrahimYM, Smith RD. Pulsed Multiple Reaction Monitoring Approach to Enhancing Sensitivity of a Tan‐ dem Quadrupole Mass Spectrometer. Analytical Chemistry 2011; 83 2162–2171. [55] Xian F, Hendrickson CL, Marshall AG. High Resolution Mass Spectrometry. Analyti‐

[56] El-Aneed A, Cohen A, Banoub J. Mass Spectrometry, Review of the Basics: Electro‐ spray, MALDI, and Commonly Used Mass Analyzers. Applied Spectroscopy Re‐

[57] Wang X, Wang S, Cai Z. The Latest Developments and Applications of Mass Spec‐ trometry in Food-Safety and Quality Analysis. TrAC Trends in Analytical Chemistry

[58] Eiserbeck C, Nelson RK, Grice K, Curiale J, Reddy CM. Comparison of GC–MS, GC– MRM-MS, and GC × GC to Characterise Higher Plant Biomarkers in Tertiary Oils

and Rock Extracts. Geochimica et Cosmochimica Acta 2012; 87 299–322.

Rapid Communications in Mass Spectrometry 2009; 23(21) 3484–3487.

nalytical Chemistry 2012; 402 195-208.

mentation. Boca Raton: CRC Press; 1995.

cal Chemistry 2012; 84 708−719.

views 2009; 44(3) 210-230.

2013; 52 170–185.

Springer; 1995.

38 Advances in Gas Chromatography

The initial development of gas chromatography (GC) is deeply interconnected with lipid analysis. This separation technique may be considered as the main contribution for most of knowledgement regarding fatty acid (FA) composition that exists today. Actually, it is possible to convert milligrams of a lipidic sample to fatty acid methyl esters (FAMEs), separate them in a gas chromatograph and quantify these fatty acids in a short time [1].

Probably, the most important identification method which is coupled with GC is mass spectrometry (MS). It is older than GC, and this fact might be surprising for many readers. However, the basic principles and the first separations of atomic masses were primarily demonstrated in the last part of 19th century, while chromatographic columns appeared during 20th century [2].

The possibility of coupling a mass spectrometer in the exit of a chromatographic column, along with the advent of modern computers, introduction of fused silica capillary columns and reduction of interference issues allowed the achievement of analytical results which are more precise/accurate. Allied to these facts, the reduction of gas chromatographs prices enforced their application as a tool for researches with lipidic materials. However, in relation to food analysis with emphasis on fatty acid composition, the flame ionization detector (FID) is the

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

most popular one, because it is easy to operate, possesses a wide linearity range, rapid response, and its limit of detection is of 10-12g for alkanes (an almost universal answer) [3].

didactical approach, from now on the discussions will be centered on FAMEs, although this

Analytical Aspects of the Flame Ionization Detection in Comparison with Mass...

http://dx.doi.org/10.5772/57333

41

Instead of responding to solute property, FID is sensitive to the mass flux which passes through it in a determined amount of time. It is important to point out that, despite the fact that a solution is injected, solvent and methyl esters are separated by the column during chromato‐ graphic run. Thus, a certain mass, in gaseous form, of each compound will arrive at the detector togethter with the carrier gas. Inside FID, it undergoes combustion in a flame produced by oxygen and hydrogen, producing ions which will be collected by electrodes. The amount of formed ions when a certain methyl ester is present in carrier gas is greater than the amount of ions which are formed when only carrier gas is being burned, thus the generated current is converted into a voltage, amplified and registered under the form of a chromatogram [5].

ion is unstable and quickly reacts with water in the flame in order to produce

+ e- (Reaction 1)

+ CO (Reaction 2)

book chapter may be extended for every FAE (ethyl esters, propyl esters, etc.) [5].

Reaction (1) demonstrates the chemical ionization that occurs in FID:

hydronium, according with the reaction (2):

**1.4. Mass spectrometric detector**

tography (LC) or capillary electrophoresis (CE) [7].

CHO+

The CHO+

CH + O → CHO<sup>+</sup>

+ H2O → H3O+

FID response is proportional to the number of carbon atoms which are burned.

in a proper manner during combustion, thus it is not considered as active carbon.

molecules to biomolecules such as proteins, lipids and oligonucleotides [7].

This reaction occurs for every 100000 carbon atoms which are introduced in the flame [3]. Thus,

Specifically, the magnitude of the signal generated by FID is proportional to the number of carbon atoms which are bonded to hydrogen atoms (active carbon, C\*). However, there are intrinsic factors of a FAME molecule that alters FID response, such as presence of oxygen, which diminishes such response. The carbon atom of carboxylate group (COO) is not ionized

Mass spectrometry is one of the most versatile and sensitive analytical methods which is employed in many studies, ranging from medical to technological science. This method allows the molecular mass/structure determination of unknown compounds, and to quantify small

In MS analysis, samples may be directly inserted in the mass spectrometer (off-line analysis) or this equipment may be coupled with a separation technique such as GC, liquid chroma‐

A mass spectrometer is basically composed of an ionization source, where the analytes of a sample are ionized and/or fragmented in ions with specific values of mass/charge ratio (m/z). They are immediately accelerated towards a mass analyzer, which has the function of selecting

In the last years, it can be observed a significant increase of published works about FAs which also envolve new discoveries regarding good and bad effects of FAs for human health, new food products derived from fats and oils available for the end user, novel FAs (such *trans* FAs) which do not exist in certain foods, supplementation of FAs in food products and optimization of biodiesel synthesis.

### **1.1. Derivatization of functional groups**

Before analyzing FAs through GC, it is necessary to convert them into compounds with greater volatility and thermal stability (esters, for example). The transformation of FAs into other organic functionalities with lower boiling points enhances their elution through a chromato‐ graphic column. Thus, if correct conditions are used, it is possible to separate compounds with close molar masses or even isomers [3].

The formation of these esters commonly occurs from reactions between lipids with short chain monoalcohols (like (m)ethanol) which provide (m)ethyl radical. The competition between use of methanol versus ethanol originated from factors such as disponibility, reaction yield, toxicity, among others.

A method which is very efficient for fatty acid methylation was proposed in works done by Joseph and Ackman (1992) [4], and it is commonly employed as a fine tool for studies of lipidic composition.

### **1.2. Integration of chromatographic peaks**

After sample injection in a gas chromatograph (GCph) and further separation of analytes through the chromatographic column, an chromatogram of the sample of interest is obtained. Such chromatogram is produced by the integration of the signals which are collected by the detector in a measure related with the amount of each analyte which are detected. In FID case, compounds are burned. Besides, important information such as peak number/area/percen‐ tages, as well as their respective retention times, can be obtained from the chromatogram [5].

These integration measures may be related with the existing FAs or FAMEs concentrations in a food/biodiesel sample. However, in order to apply this relation between chromatographic peak area and analyte concentration, the analyst must understand the principles and execute the necessary corrections. Otherwise, the expressed results will be erroneous and will not be clearly understood [6].

#### **1.3. Flame ionization detector (FID) and the differential response**

The used of FID for quantification of fatty acid esters (FAEs) is advantageous in relation to other detector types, due to the reasons mentioned before (it is easy to operate, possesses a wide linearity range, rapid response, and its limit of detection is of 10-12g for alkanes). For a didactical approach, from now on the discussions will be centered on FAMEs, although this book chapter may be extended for every FAE (ethyl esters, propyl esters, etc.) [5].

Instead of responding to solute property, FID is sensitive to the mass flux which passes through it in a determined amount of time. It is important to point out that, despite the fact that a solution is injected, solvent and methyl esters are separated by the column during chromato‐ graphic run. Thus, a certain mass, in gaseous form, of each compound will arrive at the detector togethter with the carrier gas. Inside FID, it undergoes combustion in a flame produced by oxygen and hydrogen, producing ions which will be collected by electrodes. The amount of formed ions when a certain methyl ester is present in carrier gas is greater than the amount of ions which are formed when only carrier gas is being burned, thus the generated current is converted into a voltage, amplified and registered under the form of a chromatogram [5].

Reaction (1) demonstrates the chemical ionization that occurs in FID:

$$\text{CH} + \text{O} \rightarrow \text{CHO}^{\cdot} + \text{e}^{\cdot} \tag{\text{Reaction 1}}$$

The CHO+ ion is unstable and quickly reacts with water in the flame in order to produce hydronium, according with the reaction (2):

$$\rm{CHO^{+}} + \rm{H\_{2}O} \rightarrow \rm{H\_{3}O^{+}} + \rm{CO} \tag{R reaction 2}$$

This reaction occurs for every 100000 carbon atoms which are introduced in the flame [3]. Thus, FID response is proportional to the number of carbon atoms which are burned.

Specifically, the magnitude of the signal generated by FID is proportional to the number of carbon atoms which are bonded to hydrogen atoms (active carbon, C\*). However, there are intrinsic factors of a FAME molecule that alters FID response, such as presence of oxygen, which diminishes such response. The carbon atom of carboxylate group (COO) is not ionized in a proper manner during combustion, thus it is not considered as active carbon.

#### **1.4. Mass spectrometric detector**

most popular one, because it is easy to operate, possesses a wide linearity range, rapid response, and its limit of detection is of 10-12g for alkanes (an almost universal answer) [3].

In the last years, it can be observed a significant increase of published works about FAs which also envolve new discoveries regarding good and bad effects of FAs for human health, new food products derived from fats and oils available for the end user, novel FAs (such *trans* FAs) which do not exist in certain foods, supplementation of FAs in food products and optimization

Before analyzing FAs through GC, it is necessary to convert them into compounds with greater volatility and thermal stability (esters, for example). The transformation of FAs into other organic functionalities with lower boiling points enhances their elution through a chromato‐ graphic column. Thus, if correct conditions are used, it is possible to separate compounds with

The formation of these esters commonly occurs from reactions between lipids with short chain monoalcohols (like (m)ethanol) which provide (m)ethyl radical. The competition between use of methanol versus ethanol originated from factors such as disponibility, reaction yield,

A method which is very efficient for fatty acid methylation was proposed in works done by Joseph and Ackman (1992) [4], and it is commonly employed as a fine tool for studies of lipidic

After sample injection in a gas chromatograph (GCph) and further separation of analytes through the chromatographic column, an chromatogram of the sample of interest is obtained. Such chromatogram is produced by the integration of the signals which are collected by the detector in a measure related with the amount of each analyte which are detected. In FID case, compounds are burned. Besides, important information such as peak number/area/percen‐ tages, as well as their respective retention times, can be obtained from the chromatogram [5].

These integration measures may be related with the existing FAs or FAMEs concentrations in a food/biodiesel sample. However, in order to apply this relation between chromatographic peak area and analyte concentration, the analyst must understand the principles and execute the necessary corrections. Otherwise, the expressed results will be erroneous and will not be

The used of FID for quantification of fatty acid esters (FAEs) is advantageous in relation to other detector types, due to the reasons mentioned before (it is easy to operate, possesses a wide linearity range, rapid response, and its limit of detection is of 10-12g for alkanes). For a

**1.3. Flame ionization detector (FID) and the differential response**

of biodiesel synthesis.

40 Advances in Gas Chromatography

toxicity, among others.

clearly understood [6].

composition.

**1.1. Derivatization of functional groups**

close molar masses or even isomers [3].

**1.2. Integration of chromatographic peaks**

Mass spectrometry is one of the most versatile and sensitive analytical methods which is employed in many studies, ranging from medical to technological science. This method allows the molecular mass/structure determination of unknown compounds, and to quantify small molecules to biomolecules such as proteins, lipids and oligonucleotides [7].

In MS analysis, samples may be directly inserted in the mass spectrometer (off-line analysis) or this equipment may be coupled with a separation technique such as GC, liquid chroma‐ tography (LC) or capillary electrophoresis (CE) [7].

A mass spectrometer is basically composed of an ionization source, where the analytes of a sample are ionized and/or fragmented in ions with specific values of mass/charge ratio (m/z). They are immediately accelerated towards a mass analyzer, which has the function of selecting ions according to the desired m/z values. Such selection occurs through the application of electric and/or magnetic fields. A detector transforms the ionic (fragment) signals to electrical signals, and the magnitude of these signals in function of m/z ratio is converted by a data processor, generating a corresponding mass spectrum. The electron multipliers are among the most frequently used detectors in mass spectrometers [8].

about 0.720g of FAs. Thus, the determination of F factor (decimal) may be done in the following

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43

Assuming that a total lipid sample contains 65% in mass of TAG and 30% in mass of PL, the

Knowing that the mass percentage of a certain X fatty acid (relative percentage by normaliza‐ tion method) in sample is 26%, the amount in grams of this acid *per* 100g of total lipids (TLs)

The conversion factor may be found for some specific foods. For example, Table 1 shows the F conversion factors (decimal) for different classes of TAGs and PLs in total lipids of fish.

Despite this method being easy and practical, it includes some constant values, often theoret‐ ical, that might result in errors. These constants and other interpretations will be discussed

In order to obtain greater accuracy from quantification, it must be taken into account that FAEs respond to FID in a differential manner. Thus, it is necessary to use the theoretical correction factor (FCT), determined through the number of active carbons (C\*), and the experimental/ empiric correction factor (FCE). Both factors are obtained from comparisons between FAMEs

The internal standard calibration method is considered less sensitive to injection errors and instrumental variations. Besides, the internal standard must have high degree of purity/ stability, must not be component of sample, and it needs to elute separately from other analytes. It is not easy to fulfill all these requirement during a FAEs mixture analysis. However, some esters have been recommended for use as IS, such as methyl tricosanoate (23:0Me) for foods and methyl heptadecanoate (17:0Me) / ethyl oleate (18:1Et) for biodiesel, because they

way [6]:

Where:

= 0.84

will be:

F (decimal) = TAG × 0.956 + PL × 0.720

TAG = Mass amount of TAGs in total lipids.

F factor with application decimal values will be:

g of fatty acid X/100 g of TL = F (decimal) × % Area

**4. Internal standard calibration and correction factors**

PL = Mass amount of PLs in total lipids.

F (decimal) = 0.65 × 0.956 × 0.30 × 0.720

g of fatty acid X/100 g of TL = 0.84 × 26

g of fatty acid X/100 g of TL = 21.76 g.

later in a practical example.

and an internal standard [3].

The advanced methods in MS differ especially in sample ionization modes and mass analyzers. The most suitable ionization mode for an analysis will depend on physicochemical properties of analytes, such as polarity, molecular mass and boiling point [8], etc.

### **2. Methods of relative normalization and absolute quantification**

Also called area normalization, this method is based on the relative area percentage of a certain FA in relation to the total area of all fatty acids which are eluted from the column. Despite the fact it is an obsolete method, a lot of works regarding FAEs analysis in foods/biodiesel still use it, where the results are expressed in relative area percentage. The disadvantages of normali‐ zation are propagation of errors due to results interdependence. Every component of a sample must be detected and, if a certain compound is omitted/estimated, the areas of other substances are affected. Besides, results obtained through this method normally are difficult to interpre‐ tand published in an erroneous form.

However, in the expression of results in form of FAMEs (biodiesel) or FAs (foods), where concentrations are expressed in FAME mass *per* raw material/sample mass, using internal standard (IS) or correction factors for FID will lead to more accurate results which can be used by professionals from different areas without major difficulties. It is noteworthy that, if correction factors are complicated for use, internal standard calibration must be used to express results as concentrations, not as area percentages [5].

### **3. Transformation of methyl ester percentual area in fatty acid concentration: an alternative method**

In this method, conversion factors are used in the transformation of relative percentage of a chromatographic peak from a methylic ester (normalization method) in mass amounts of the corresponding FA. Results commonly are expressed in (milli)grams of FA *per* 100g of food or total lipids. Such a conversion factor is obtained from the amount in mass of FAs (tabulated values) which exists in the different classes of total lipids (triacylglycerols and phospholipids, mainly) and form their different masses in a certain food sample.

Some authors describe that the average percentual mass contribution of FAs in triacylglycerols (TAGs) and phospholipids (PLs) corresponds to 95.6% and 72.0%, respectively. This means that, upon expressing values in decimals, it can be understood that 1g of phospholipids possess about 0.720g of FAs. Thus, the determination of F factor (decimal) may be done in the following way [6]:

F (decimal) = TAG × 0.956 + PL × 0.720

Where:

ions according to the desired m/z values. Such selection occurs through the application of electric and/or magnetic fields. A detector transforms the ionic (fragment) signals to electrical signals, and the magnitude of these signals in function of m/z ratio is converted by a data processor, generating a corresponding mass spectrum. The electron multipliers are among the

The advanced methods in MS differ especially in sample ionization modes and mass analyzers. The most suitable ionization mode for an analysis will depend on physicochemical properties

Also called area normalization, this method is based on the relative area percentage of a certain FA in relation to the total area of all fatty acids which are eluted from the column. Despite the fact it is an obsolete method, a lot of works regarding FAEs analysis in foods/biodiesel still use it, where the results are expressed in relative area percentage. The disadvantages of normali‐ zation are propagation of errors due to results interdependence. Every component of a sample must be detected and, if a certain compound is omitted/estimated, the areas of other substances are affected. Besides, results obtained through this method normally are difficult to interpre‐

However, in the expression of results in form of FAMEs (biodiesel) or FAs (foods), where concentrations are expressed in FAME mass *per* raw material/sample mass, using internal standard (IS) or correction factors for FID will lead to more accurate results which can be used by professionals from different areas without major difficulties. It is noteworthy that, if correction factors are complicated for use, internal standard calibration must be used to express

In this method, conversion factors are used in the transformation of relative percentage of a chromatographic peak from a methylic ester (normalization method) in mass amounts of the corresponding FA. Results commonly are expressed in (milli)grams of FA *per* 100g of food or total lipids. Such a conversion factor is obtained from the amount in mass of FAs (tabulated values) which exists in the different classes of total lipids (triacylglycerols and phospholipids,

Some authors describe that the average percentual mass contribution of FAs in triacylglycerols (TAGs) and phospholipids (PLs) corresponds to 95.6% and 72.0%, respectively. This means that, upon expressing values in decimals, it can be understood that 1g of phospholipids possess

**3. Transformation of methyl ester percentual area in fatty acid**

mainly) and form their different masses in a certain food sample.

most frequently used detectors in mass spectrometers [8].

42 Advances in Gas Chromatography

tand published in an erroneous form.

results as concentrations, not as area percentages [5].

**concentration: an alternative method**

of analytes, such as polarity, molecular mass and boiling point [8], etc.

**2. Methods of relative normalization and absolute quantification**

TAG = Mass amount of TAGs in total lipids.

PL = Mass amount of PLs in total lipids.

Assuming that a total lipid sample contains 65% in mass of TAG and 30% in mass of PL, the F factor with application decimal values will be:

F (decimal) = 0.65 × 0.956 × 0.30 × 0.720

= 0.84

Knowing that the mass percentage of a certain X fatty acid (relative percentage by normaliza‐ tion method) in sample is 26%, the amount in grams of this acid *per* 100g of total lipids (TLs) will be:

g of fatty acid X/100 g of TL = F (decimal) × % Area

g of fatty acid X/100 g of TL = 0.84 × 26

g of fatty acid X/100 g of TL = 21.76 g.

The conversion factor may be found for some specific foods. For example, Table 1 shows the F conversion factors (decimal) for different classes of TAGs and PLs in total lipids of fish.

Despite this method being easy and practical, it includes some constant values, often theoret‐ ical, that might result in errors. These constants and other interpretations will be discussed later in a practical example.

### **4. Internal standard calibration and correction factors**

In order to obtain greater accuracy from quantification, it must be taken into account that FAEs respond to FID in a differential manner. Thus, it is necessary to use the theoretical correction factor (FCT), determined through the number of active carbons (C\*), and the experimental/ empiric correction factor (FCE). Both factors are obtained from comparisons between FAMEs and an internal standard [3].

The internal standard calibration method is considered less sensitive to injection errors and instrumental variations. Besides, the internal standard must have high degree of purity/ stability, must not be component of sample, and it needs to elute separately from other analytes. It is not easy to fulfill all these requirement during a FAEs mixture analysis. However, some esters have been recommended for use as IS, such as methyl tricosanoate (23:0Me) for foods and methyl heptadecanoate (17:0Me) / ethyl oleate (18:1Et) for biodiesel, because they


**Methyl esters of reference as internal standard**

4:0 1.5396 1.4294 1.5257 1.5501 1.5522 1.5742 1.5930 5:0 1.4009 1.3006 1.3883 1.4105 1.4123 1.4324 1.4495 6:0 1.3084 1.2147 1.2966 1.3174 1.3191 1.3378 1.3538 7:0 1.2423 1.1534 1.2311 1.2508 1.2524 1.2702 1.2854 8:0 1.1927 1.1073 1.1820 1.2009 1.2024 1.2195 1.2340 9:0 1.1542 1.0716 1.1438 1.1621 1.1636 1.1802 1.1942 10:0 1.1233 1.0429 1.1132 1.1310 1.1325 1.1486 1.1622 11:0 1.0981 1.0195 1.0882 1.1056 1.1071 1.1228 1.1361 12:0 1.0771 **1.0000** 1.0674 1.0845 1.0859 1.1013 1.1144 12:1 1.0670 0.9906 1.0574 1.0743 1.0757 1.0910 1.1040 13:0 1.0593 0.9835 1.0497 1.0666 1.0680 1.0831 1.0960 14:0 1.0441 0.9694 1.0347 1.0512 1.0526 1.0676 1.0803 14:1 1.0354 0.9613 1.0261 1.0425 1.0439 1.0587 1.0713 15:0 1.0308 0.9570 1.0215 1.0379 1.0392 1.0540 1.0665 15:1 1.0227 0.9495 1.0135 1.0297 1.0311 1.0457 1.0581 16:0 1.0193 0.9463 1.0101 1.0263 1.0276 1.0422 1.0546 16:1 1.0117 0.9393 1.0026 1.0186 1.0200 1.0345 1.0468 16:2 1.0041 0.9322 0.9951 1.0110 1.0123 1.0267 1.0389 16:3 0.9965 0.9252 0.9875 1.0033 1.0046 1.0189 1.0310 16:4 0.9989 0.9181 0.9799 0.9957 0.9970 1.0111 1.0232 17:0 1.0091 0.9369 **1.0000** 1.0160 1.0173 1.0318 1.0440 17:1 1.0019 0.9302 0.9929 1.0088 1.0101 1.0244 1.0366 18:0 **1.0000** 0.9284 0.9910 1.0068 1.0082 1.0225 1.0347 18:1 0.9932 0.9221 0.9842 **1.0000** 1.0013 1.0155 1.0276 18:2 0.9865 0.9195 0.9776 0.9933 0.9946 1.0087 1.0207 18:3 0.9797 0.9096 0.9709 0.9864 0.9877 1.0017 1.0137 18:4 0.9730 0.9034 0.9642 0.9797 0.9809 0.9949 1.0067 19:0 0.9919 0.9209 0.9830 0.9987 **1.0000** 1.0142 1.0263 20:0 0.9846 0.9141 0.9757 0.9913 0.9926 1.0067 1.0187 20:1 0.9785 0.9085 0.9697 0.9852 0.9865 1.0005 1.0124 20:2 0.9724 0.9028 0.9636 0.9791 0.9803 0.9943 1.0061 20:3 0.9663 0.8971 0.9576 0.9729 0.9742 0.9880 0.9998 20:4 0.9603 0.8916 0.9516 0.9669 0.9681 0.9819 0.9936 20:5 0.9542 0.8859 0.9456 0.9607 0.9620 0.9757 0.9878 21:0 0.9780 0.9080 0.9692 0.9847 0.9860 **1.0000** 1.0119 22:0 0.9720 0.9024 0.9632 0.9787 0.9799 0.9939 1.0057 22:1 0.9664 0.8972 0.9577 0.9730 0.9743 0.9881 0.9999 22:2 0.9609 0.8921 0.9522 0.9675 0.9687 0.9825 0.9942 22:3 0.9554 0.8870 0.9468 0.9619 0.9632 0.9769 0.9885 22:4 0.9499 0.8819 0.9413 0.9564 0.9577 0.9713 0.9828 22:5 0.9443 0.8767 0.9358 0.9508 0.9520 0.9655 0.9770 22:6 0.9388 0.8716 0.9303 0.9452 0.9465 0.9599 0.9713 23:0 0.9665 0.8973 0.9578 0.9731 0.9744 0.9882 **1.0000** 24:0 0.9615 0.8927 0.9528 0.9681 0.9694 0.9831 0.9948 24:1 0.9564 0.8879 0.9478 0.9629 0.9642 0.9779 0.9896 \*The symbology represents the principal chain of the FAME in question. The following atomic masses were used: C = 12.0110; H = 1.0079; O = 15.9994. FAMEs positional and geometrical isomers, as well as the ramified methyl esters, possess the same FCT since they show the same C\* number.

**18:1 Methyl oleate**

**19:0 Methyl nonadodecanoate**

Analytical Aspects of the Flame Ionization Detection in Comparison with Mass...

**21:0 Methyl heneicosanoate**

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**23:0 Methyl tricosanoate** 45

**17:0 Methyl heptadodecanoate**

**\*Methyl ester 18:0 Methyl**

**stearate**

**Table 2.** Theoretical correction factor (FCT) for FAMEs

**12:0 Methyl dodecanoate**

% in mass of TL = percentage in mass of total lipids per 100g of food; %TAG and %PL= percentage in mass of triacylgly‐ cerols and phospholipids, respectively; F (decimal)= decimal factor. Source: Kinsella [9] (apud EXLER, 1975, p. 154).

**Table 1.** Conversion factors based on the total lipid content.

are not naturally found in the samples cited above, possess good stability, and do not contain unsaturation in their carbonic chains[9].

### **4.1. Theoretical correction factor (FCT)**

The determination of FCT is based on two facts: (1) that FID proportionally responds to the relative mass percentage of a FAME carbonic chain and (2) this detector does not respond, during combustion, in a proper way to carbon of carboxylate group (COO- ). Thus, randomly assigning a FCT=1 for a fame, for example, methyl stearate (18:0Me), the correction factors of other methyl esters can be calculated, according to Table 2. Thus, FID`s FCT is a constant and it can not be modified due to instrumental errors [3].

### **4.2. Theoretical correction factor (FCT) determination and calculus of active carbon (C\*) percentage**

In order to show the application of active carbon (C\*) percentage determination to FID`s FCT, methyl stearate (C19H38O2, molar mass=298.5080g) was used as internal standard to calculate methyl decanoate`s factor (C11H22O2, molar mass=186.2936g). It is important to remind that the


\*The symbology represents the principal chain of the FAME in question. The following atomic masses were used: C = 12.0110; H = 1.0079; O = 15.9994. FAMEs positional and geometrical isomers, as well as the ramified methyl esters, possess the same FCT since they show the same C\* number.

**Table 2.** Theoretical correction factor (FCT) for FAMEs

are not naturally found in the samples cited above, possess good stability, and do not contain

% in mass of TL = percentage in mass of total lipids per 100g of food; %TAG and %PL= percentage in mass of triacylgly‐ cerols and phospholipids, respectively; F (decimal)= decimal factor. Source: Kinsella [9] (apud EXLER, 1975, p. 154).

**% in mass of TL % TAG % PL F (decimal)** 0.65 - 92.3 0.66 0.70 7.1 85.7 0.68 0.80 18.8 75.0 0.72 0.90 27.8 66.7 0.75 1.00 35.0 60.0 0.77 1.25 48.0 48.0 0.80 1.50 56.7 40.0 0.83 1.75 62.9 34.3 0.85 2.00 67.5 30.0 0.86 2.50 74.0 24.0 0.88 **3.00** 78.3 20.0 0.89 3.50 81.4 17.1 0.90 4.00 83.8 15.0 0.91 4.50 85.6 13.3 0.91 5.00 87.0 12.0 0.92

The determination of FCT is based on two facts: (1) that FID proportionally responds to the relative mass percentage of a FAME carbonic chain and (2) this detector does not respond,

assigning a FCT=1 for a fame, for example, methyl stearate (18:0Me), the correction factors of other methyl esters can be calculated, according to Table 2. Thus, FID`s FCT is a constant and

**4.2. Theoretical correction factor (FCT) determination and calculus of active carbon (C\*)**

In order to show the application of active carbon (C\*) percentage determination to FID`s FCT, methyl stearate (C19H38O2, molar mass=298.5080g) was used as internal standard to calculate methyl decanoate`s factor (C11H22O2, molar mass=186.2936g). It is important to remind that the

). Thus, randomly

during combustion, in a proper way to carbon of carboxylate group (COO-

unsaturation in their carbonic chains[9].

44 Advances in Gas Chromatography

**Table 1.** Conversion factors based on the total lipid content.

**4.1. Theoretical correction factor (FCT)**

**percentage**

it can not be modified due to instrumental errors [3].

COO- group (molar mass=44.0098g) from FAMEs shows negligible response in FID and, only considering the active carbons (C\*) in FAMEs molecules. The following calculations may be done:

**Response factor Coefficient ©**

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47

Analytical Aspects of the Flame Ionization Detection in Comparison with Mass...

**FAMEs Experimental (FCE)a Theoretical (FCT)b C = FCE / FCT** 12:0 1.0535 ± 0.0095 1.1144 0.9454 14:0 1.0653 ± 0.0093 1.0803 0.9861 16:0 1.0491 ± 0.0092 1.0546 0.9948 18:0 1.0282 ± 0.0094 1.0347 0.9937 18:1n-9 1.0329 ± 0.0098 1.0276 1.0052 18:2n-6 1.0524 ± 0.0189 1.0207 1.0311 18:3n-3 1.0505 ± 0.0168 1.0137 1.0363 20:0 1.0274 ± 0.0083 1.0187 1.0085 20:4n-6 1.0484 ± 0.0198 0.9936 1.0552 20:5n-3 1.0443 ± 0.0239 0.9878 1.0572 22:0 0.9905 ± 0.0092 1.0057 0.9849 22:6n-3 1.0442 ± 0.0278 0.9713 1.0751 23:0 1.0000 ± 0.0000 1.0000 1.0000 24:0Me 0.9874 ± 0.0104 0.9948 0.9926

Average values ± estimation of standard deviation (n = 6), bVisentainer *et al*., 2007. FAMEs = fatty acid methyl esters.

In a general manner, the coefficients © between FCE and FCT of saturated FAMEs (Table 3) are closer to 1.000 than that from polyunsaturated FAMEs. Thus, it is recommended the use of a saturated FAME as IS. Coefficients with values equal to 1.000 are preferred, because they indicate that the equipment is working with optimized conditions. A very high coefficient for a saturated fatty acid means that the GCph is not optimized, or there are some inconsistencies with the employed method. It is also important to remind that, every time as possible, fresh

For determination of FAME mass in milligrams, equation 2 should be used. However, in order to increase the degree of accuracy in fatty acid/FAME quantification, it is recommended to use FCT. The instrumental and chemical parameters must be optimized to minimize possible errors from these sources. It has been reported that, due to oxidative instability of polyunsaturated fatty acids (PUFAs), it is virtually impossible to obtain and keep standards of this kind with high degrees of purity, and they recommend the use of FCT as a good approach for PUFAs analysis. Transforming equation 2 in function of a mass from a fatty acid X, equation 3 is

**Table 3.** FCT and FCE in relation to the internal standard methyl tricosanoate (23:0Me)

standards with proper preparation and storage procedures should be used.

**4.4. Determination of fatty acid concentration**

a

obtained:

Methyl stearate = 18 C\* × 12.0110 = 216.1980;

Mehtyl decanoate = 10 C\* × 12.0110 = 120.1100.

Thus, the relative percentage for every FAME will be:

Methyl stearate 298.5080 (100%) and 216.1980 (% of C\*) = 72.4262% of C\*;

Mehtyl decanoate 186.2936 (100%) and 120.1100 (% of C\*) = 64.4735% of C\*.

Dividing methyl stearate`s C\* percetage by methyl decaniate`s C\* percetage:

FCT of methyl decanoate = 72.4262% / 64.4735% = 1.1233.

Thus, the value of 1.1233 must be used as FCT in the quantitative determination of methyl decanoate (10:0Me) to correct FID`s differential response with methyl stearate as IS. Values for other FAMEs are expressed in Table 2.

It is possible to obtain, in a simplified manner, new values of FAEs FCT through the use of another internal standard. Just use the obtained values for methyl stearate as IS (first column-Table 2) and values of the new IS in question. For example, to determine the new FCT value of alpha-linolenic acid methyl ester (18:3Me) using methyl tricosanoate (23:0Me) as IS, just divide 18:3Me FCT by 23:0Me FCT, according to FCT values from Table 2, the calculation is shown in equation 1.

$$\mathbf{F\_{CT}} \text{ of 18:3} = \mathbf{F\_{CT}} \text{18:3} \mid \mathbf{F\_{CT}} \text{23:0} = 0.9797 \mid 0.9665 = 1.0137 \tag{1}$$

Table 2 shows FCT for several FAMEs with different internal standards. Every entry from Table 2 is in accordance with the values published in the literature.

### **4.3. Experimental correction factor (FCE) determination**

Visentainer (2012) determined FID's experimental correction factors (FCE) using a mixture of several FAME's with known amounts of methyl tricosanoate as IS, and from masses and percentual areas of FAME's and IS, the FCE for every FAME was determined. Table 3 shows the average values with the respective standard deviations.The following equation is used for FCE determination:

$$\mathbf{F\_{CE}} = \mathbf{A\_P} \times \mathbf{M\_x} / \,\mathbf{M\_P} \times \mathbf{A\_x} \tag{2}$$

where: *AP* = standard area; *Mx* = FAME mass; *MP* = standard mass; *Ax*= FAME mass.


a Average values ± estimation of standard deviation (n = 6), bVisentainer *et al*., 2007. FAMEs = fatty acid methyl esters.

**Table 3.** FCT and FCE in relation to the internal standard methyl tricosanoate (23:0Me)

In a general manner, the coefficients © between FCE and FCT of saturated FAMEs (Table 3) are closer to 1.000 than that from polyunsaturated FAMEs. Thus, it is recommended the use of a saturated FAME as IS. Coefficients with values equal to 1.000 are preferred, because they indicate that the equipment is working with optimized conditions. A very high coefficient for a saturated fatty acid means that the GCph is not optimized, or there are some inconsistencies with the employed method. It is also important to remind that, every time as possible, fresh standards with proper preparation and storage procedures should be used.

#### **4.4. Determination of fatty acid concentration**

COO- group (molar mass=44.0098g) from FAMEs shows negligible response in FID and, only considering the active carbons (C\*) in FAMEs molecules. The following calculations may be

Thus, the value of 1.1233 must be used as FCT in the quantitative determination of methyl decanoate (10:0Me) to correct FID`s differential response with methyl stearate as IS. Values for

It is possible to obtain, in a simplified manner, new values of FAEs FCT through the use of another internal standard. Just use the obtained values for methyl stearate as IS (first column-Table 2) and values of the new IS in question. For example, to determine the new FCT value of alpha-linolenic acid methyl ester (18:3Me) using methyl tricosanoate (23:0Me) as IS, just divide 18:3Me FCT by 23:0Me FCT, according to FCT values from Table 2, the calculation is shown in

Table 2 shows FCT for several FAMEs with different internal standards. Every entry from Table

Visentainer (2012) determined FID's experimental correction factors (FCE) using a mixture of several FAME's with known amounts of methyl tricosanoate as IS, and from masses and percentual areas of FAME's and IS, the FCE for every FAME was determined. Table 3 shows the average values with the respective standard deviations.The following equation is used for

where: *AP* = standard area; *Mx* = FAME mass; *MP* = standard mass; *Ax*= FAME mass.

CT CT CT F of 18:3 = F 18:3 / F 23:0 = 0.9797 / 0.9665 = 1.0137 (1)

CE P x P x F = A × M / M × A (2)

× 12.0110 = 216.1980;

Methyl stearate 298.5080 (100%) and 216.1980 (% of C\*) = 72.4262% of C\*;

Mehtyl decanoate 186.2936 (100%) and 120.1100 (% of C\*) = 64.4735% of C\*.

Dividing methyl stearate`s C\* percetage by methyl decaniate`s C\* percetage:

Mehtyl decanoate = 10 C\* × 12.0110 = 120.1100.

Thus, the relative percentage for every FAME will be:

FCT of methyl decanoate = 72.4262% / 64.4735% = 1.1233.

2 is in accordance with the values published in the literature.

**4.3. Experimental correction factor (FCE) determination**

other FAMEs are expressed in Table 2.

done:

equation 1.

FCE determination:

Methyl stearate = 18 C\*

46 Advances in Gas Chromatography

For determination of FAME mass in milligrams, equation 2 should be used. However, in order to increase the degree of accuracy in fatty acid/FAME quantification, it is recommended to use FCT. The instrumental and chemical parameters must be optimized to minimize possible errors from these sources. It has been reported that, due to oxidative instability of polyunsaturated fatty acids (PUFAs), it is virtually impossible to obtain and keep standards of this kind with high degrees of purity, and they recommend the use of FCT as a good approach for PUFAs analysis. Transforming equation 2 in function of a mass from a fatty acid X, equation 3 is obtained:

$$\mathbf{M}\_{\mathbf{x}} = \mathbf{M}\_{\mathbf{P}} \times \mathbf{A}\_{\mathbf{x}} \times \mathbf{F}\_{\text{CT}} / \mathbf{A}\_{\mathbf{P}} \tag{3}$$

The FAME of a determined fatty acid possesses methyl group (CH3) in substitution of an atom of hydrogen. Thus, the methyl ester will show greater response in FID in relation to its corresponding FA, because the CH3 contributes to increase C\* number. Table 4 shows MM and

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In case of biodiesel determination (FAMEs), FCEA must be equal to 1, because in biodiesel case

**5. Exemplifying the fatty acid quantification using gas chromatography**

Experimental design, sample preparation, interpretation and processing of generated data are the intrinsic components of a successful GC analysis. In order to assist especially beginner scientists to get started smoothly, the fatty acid quantification of a sardine sample will be demonstrated in a practical manner, using all of the previously described methodologies:

There are several methods of extraction and determination of total lipids in oils and fats from foods. It must be considered that some fatty acids derived from them are unstable, especially the unsaturated ones. The steps that come before FAs analysis must be attended with care, in order to protect the lipidic constituents. Such measures include: (1) the avoidance of metal utensils during sample preparation, (2) packing under vacuum and in a dark place to reduce interactions of light and oxygen with analytes, (3) storing samples in frozen state and (4) use

Among methods that may be used for extracting lipids without system heating, the Bleigh and Dyer method (1959) [10] is one of the most recommended. In this method, three solvents are employed: chloroform (CHCl3), methanol (H3COH) and water (H2O). The obtained extract is

Transfer 100g of homogenized sardine meat to a becker with capacity of 500mL. Add a precise amount of 100mL of chloroform and 200mL of methanol. The contribution of food's water content must be 80%, in order to keep the proportions of chloroform:methanol:water at (1:2:0.8). If it is needed, correct the water content. Stir vigorously during 5 minutes. Add 100mL of chloroform and 100mL of distilled water. Return to vigorous stirring for 5 minutes. The final proportion of solvents, as mentioned above, must be of 1chloroform:2methanol:0.8water. Filter in a Büchner funnel, transfer the filtrate to a separatory funnel and let the aqueous and organic phases to separate. Collect the lower layer (organic phase-chloroform), transfer it to a previ‐ ously weighed flat-bottomed flask, remove the chloroform in excess through rotary evapora‐

C and determine the remaining total lipids through gravimetry. After such

FCEA values for some methyl esters and their respective FAs.

normalization, internal standard calibration, use of FCT, FCE and FCEA [3].

results are expressed in FAME and not in fatty acids.

**5.1. Lipidic extraction (total lipids)**

of lipid extraction methods at room temperature.

more reliable for evaluating FAs compositions.

**5.2. Bligh and Dyer (1959) method – Simplified**

tion at 33-350

where: *Mx* = FAME mass; *MP* = standard mass; *Ax*= FAME area; *FCT* = theoretical correction factor; *AP* = standard area.

#### **4.5. Final equation and FAME conversion factor for foods**

In food analysis, results must be expressed in mass of fatty acids, unlike biodiesel, in which results must be expressed in FAME. Thus, sample mass (MA) and the conversion factor of methyl ester to fatty acid (FCEA) must be added to equation 3, and this new equation (shown below) is called "final equation for determination of fatty acids in mg/g of oil or fat":

$$\mathbf{M\_{x} = M\_{P} \times A\_{x} \times F\_{CT} / A\_{P} \times M\_{A} \times F\_{CEA}}\tag{4}$$

where: MX = mass of fatty acid in mg/g of oil or fat; MP = internal standard mass in mg; AX = FAME area; FCT = theoretical correction factor; AP = internal standard area; MA = sample mass (oil or fat) in g; FCEA = conversion factor of methyl ester to fatty acid.

The FCEA is determined through division of a FAME molecular mass (MM) by the MM of its corresponding fatty acid, according to equation 5:



The following atomic masses were taken in account: H = 1; C = 12 e O = 16. MM = molecular mass, FA = fatty acid, FAME = fatty acid methyl ester.

**Table 4.** Conversion factor of methyl ester to fatty acid FCEA

The FAME of a determined fatty acid possesses methyl group (CH3) in substitution of an atom of hydrogen. Thus, the methyl ester will show greater response in FID in relation to its corresponding FA, because the CH3 contributes to increase C\* number. Table 4 shows MM and FCEA values for some methyl esters and their respective FAs.

In case of biodiesel determination (FAMEs), FCEA must be equal to 1, because in biodiesel case results are expressed in FAME and not in fatty acids.

### **5. Exemplifying the fatty acid quantification using gas chromatography**

Experimental design, sample preparation, interpretation and processing of generated data are the intrinsic components of a successful GC analysis. In order to assist especially beginner scientists to get started smoothly, the fatty acid quantification of a sardine sample will be demonstrated in a practical manner, using all of the previously described methodologies: normalization, internal standard calibration, use of FCT, FCE and FCEA [3].

### **5.1. Lipidic extraction (total lipids)**

M = M × A × F / A x P x CT P (3)

M = M × A × F / A × M × F x P x CT P A CEA (4)

where: *Mx* = FAME mass; *MP* = standard mass; *Ax*= FAME area; *FCT* = theoretical correction

In food analysis, results must be expressed in mass of fatty acids, unlike biodiesel, in which results must be expressed in FAME. Thus, sample mass (MA) and the conversion factor of methyl ester to fatty acid (FCEA) must be added to equation 3, and this new equation (shown

where: MX = mass of fatty acid in mg/g of oil or fat; MP = internal standard mass in mg; AX = FAME area; FCT = theoretical correction factor; AP = internal standard area; MA = sample mass

The FCEA is determined through division of a FAME molecular mass (MM) by the MM of its

CEA F = MM of FAME / MM of the corresponding fatty acid (5)

Tetradecanoic- 14:0 (C14H28O2) 228 242 1.061 Hexadecanoic- 16:0 (C16H32O2) 256 270 1.055 Octadecanoic- 18:0 (C18H36O2) 284 298 1.049 9-Octadecenoic- 18:1n-9 (C18H34O2) 282 296 1.050 9,12-Octadienoic- 18:2n-6 (C18H32O2) 280 294 1.057 9,12,15-Octadecatrienoic- 18:3n-3 (C18H30O2) 278 292 1.050

Eicosanoic- 20:0 (C20H40O2) 312 326 1.045

Docosanoic- 22:0 (C22H44O2) 340 354 1.041

5,8,11,14-Eicosatetraenoic- 20:4n-6 (C20H32O2) 304 318 1.046 5,8,11,14,17-Eicosapentaenoic- 20:5n-3 (C20H30O2) 302 216 1.044

4,7,10,13,16,19-Docosahexaenoic- 22:6n-3 (C22H32O2) 328 342 1.042

The following atomic masses were taken in account: H = 1; C = 12 e O = 16. MM = molecular mass, FA = fatty acid,

**Fatty acids MM of FA MM of FAME FCEA**

below) is called "final equation for determination of fatty acids in mg/g of oil or fat":

factor; *AP* = standard area.

48 Advances in Gas Chromatography

FAME = fatty acid methyl ester.

**Table 4.** Conversion factor of methyl ester to fatty acid FCEA

**4.5. Final equation and FAME conversion factor for foods**

(oil or fat) in g; FCEA = conversion factor of methyl ester to fatty acid.

corresponding fatty acid, according to equation 5:

There are several methods of extraction and determination of total lipids in oils and fats from foods. It must be considered that some fatty acids derived from them are unstable, especially the unsaturated ones. The steps that come before FAs analysis must be attended with care, in order to protect the lipidic constituents. Such measures include: (1) the avoidance of metal utensils during sample preparation, (2) packing under vacuum and in a dark place to reduce interactions of light and oxygen with analytes, (3) storing samples in frozen state and (4) use of lipid extraction methods at room temperature.

Among methods that may be used for extracting lipids without system heating, the Bleigh and Dyer method (1959) [10] is one of the most recommended. In this method, three solvents are employed: chloroform (CHCl3), methanol (H3COH) and water (H2O). The obtained extract is more reliable for evaluating FAs compositions.

### **5.2. Bligh and Dyer (1959) method – Simplified**

Transfer 100g of homogenized sardine meat to a becker with capacity of 500mL. Add a precise amount of 100mL of chloroform and 200mL of methanol. The contribution of food's water content must be 80%, in order to keep the proportions of chloroform:methanol:water at (1:2:0.8). If it is needed, correct the water content. Stir vigorously during 5 minutes. Add 100mL of chloroform and 100mL of distilled water. Return to vigorous stirring for 5 minutes. The final proportion of solvents, as mentioned above, must be of 1chloroform:2methanol:0.8water. Filter in a Büchner funnel, transfer the filtrate to a separatory funnel and let the aqueous and organic phases to separate. Collect the lower layer (organic phase-chloroform), transfer it to a previ‐ ously weighed flat-bottomed flask, remove the chloroform in excess through rotary evapora‐ tion at 33-350 C and determine the remaining total lipids through gravimetry. After such determination, transfer the total lipids from the flask to a amber-type recipient, and cover the new recipient into aluminium foil. Samples must be stored at -18 o C.

**5.5. Chromatographic analysis**

chromatogram.

standard (23:0Me).

be used:

will be detailed below:

software which is used for peak integration).

In the sequence, a GCph is needed for a proper chromatographic run. For the example of this book, 2µL of sample were injected and separated in a GCph from Thermo brand, model 3300, equipped with a flame ionization detector, automatic injector and a capillary column of fused silica CP-7420 SELECT-FAME (100 % bonded cyanopropyl, 100m length, 0.25mm internal diameter and 0.39 µm of stationary phase). Injector temperature was 230°C. Initially, the column temperature was maintained at 165°C for 18 minutes. Then, it was raised to 235°C, at a rate of 4°C/min. The flow rates for the carrier (H2), auxiliary (N2) and detector flame (H2 and synthetic air) gases were 1.2 mL/min, 30 mL/min, 30 mL/min and 300 mL/min, respectively. Sample split ratio was 1/80. Figure 1 is an illustrative chromatogram for the sardine oil sample that was esterified. It can be observed in the chromatogram a peak regarding solvent, which must be removed. After this removal, only the peak areas from esters will compose the

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**Figure 1.** Ilustrative chromatogram of the obtained esters from sardine oil sample with a known amount of internal

Table 5 shows the obtained areas for the different esters (these areas are provided by the

The values in percentage area (%) were calculated through the normalization method, which

Assuming to calculate the percentage of 22:6n-3 (DHA) in sample, the following formula can

### **5.3. Preparation of internal standard solution**

The first step of analysis is to prepare a solution of the ester that will be used as internal standard. Regardless of FA type, the steps described below always will be the same.

In this moment the analyst must know, through bibliographic research, which FAs will be present in this sample, because it must not contain the ester that will be used as IS. For example, animal fat possesses 17:0Me and 19:0Me FAs, thus these two compounds cannot be used for IS calibration. In this case, 23:0Me is used instead.

The internal standard must be of analytical grade (high purity degree) in order to avoid presence of impurities that might compromise future results.

The IS mass must be measured in an analytical balance and transferred to a volumetric flask (for instance, 50 mL for 50 mg IS). A bit of solvent should be added to the flask. Alkanes such as n-hexane, n-heptano and iso-octane are recommended to be used as solvents, although other alkanes may also be employed. In this case, iso-octane is chosen for use. The final solution should be stored in amber flask and under refrigeration. Other volumes might be prepared, according to the number of samples to be analyzed.

#### **5.4. Transesterification of total lipids**

After extraction of total lipids from the sample, they must be derivatized for further FAs analysis through GC. There are several methods for this purpose. For the example of this chapter, the most recommended methodology for fish samples is the one reported by Joseph and Ackman (1992) [4]. This method originated from a collaborative study which involved 21 international laboratories.

In a screw-capped glass tube, 1 mL of the previously prepared IS solution is added. Soon after, solvent is removed by a gentle flow of gaseous N2. Then, approximately 25 mg of total lipids are weighed in the same tube, and to it 1.5 mL of a methanolic solution of NaOH 0.5 mol/L is added. The entire system is double-boiled to 100 0 C for five minutes, and cooled to room temperature.

After cooling, 2.0 mL of solution which corresponds to 12% of BF3 in methanol is added, and the system is once again double-boiled to 100 0 C for thirty minutes and rapidly cooled with running water to room temperature. Immediately after this step, 1 mL of iso-octane is added to the tube, followed by vigorously shaking for 30 seconds and, finally, 5 mL of a saturated NaCl solution is added. The esterified sample is left to rest in a fridge, to allow a better separation of phases. The supernatant is removed and transferred to a 5 mL amber flask. An additional iso-octane amount is further added to the tube. After shaking, the new supernatant is removed and united with the previous fraction in the amber flask, and this final mixture is concentrated to a final volume of approximadetely 1 mL, with the help of gaseous N2.

### **5.5. Chromatographic analysis**

determination, transfer the total lipids from the flask to a amber-type recipient, and cover the

The first step of analysis is to prepare a solution of the ester that will be used as internal

In this moment the analyst must know, through bibliographic research, which FAs will be present in this sample, because it must not contain the ester that will be used as IS. For example, animal fat possesses 17:0Me and 19:0Me FAs, thus these two compounds cannot be used for

The internal standard must be of analytical grade (high purity degree) in order to avoid

The IS mass must be measured in an analytical balance and transferred to a volumetric flask (for instance, 50 mL for 50 mg IS). A bit of solvent should be added to the flask. Alkanes such as n-hexane, n-heptano and iso-octane are recommended to be used as solvents, although other alkanes may also be employed. In this case, iso-octane is chosen for use. The final solution should be stored in amber flask and under refrigeration. Other volumes might be prepared,

After extraction of total lipids from the sample, they must be derivatized for further FAs analysis through GC. There are several methods for this purpose. For the example of this chapter, the most recommended methodology for fish samples is the one reported by Joseph and Ackman (1992) [4]. This method originated from a collaborative study which involved 21

In a screw-capped glass tube, 1 mL of the previously prepared IS solution is added. Soon after, solvent is removed by a gentle flow of gaseous N2. Then, approximately 25 mg of total lipids are weighed in the same tube, and to it 1.5 mL of a methanolic solution of NaOH 0.5 mol/L is

After cooling, 2.0 mL of solution which corresponds to 12% of BF3 in methanol is added, and

running water to room temperature. Immediately after this step, 1 mL of iso-octane is added to the tube, followed by vigorously shaking for 30 seconds and, finally, 5 mL of a saturated NaCl solution is added. The esterified sample is left to rest in a fridge, to allow a better separation of phases. The supernatant is removed and transferred to a 5 mL amber flask. An additional iso-octane amount is further added to the tube. After shaking, the new supernatant is removed and united with the previous fraction in the amber flask, and this final mixture is

concentrated to a final volume of approximadetely 1 mL, with the help of gaseous N2.

standard. Regardless of FA type, the steps described below always will be the same.

C.

C for five minutes, and cooled to room

C for thirty minutes and rapidly cooled with

new recipient into aluminium foil. Samples must be stored at -18 o

**5.3. Preparation of internal standard solution**

50 Advances in Gas Chromatography

IS calibration. In this case, 23:0Me is used instead.

according to the number of samples to be analyzed.

added. The entire system is double-boiled to 100 0

the system is once again double-boiled to 100 0

**5.4. Transesterification of total lipids**

international laboratories.

temperature.

presence of impurities that might compromise future results.

In the sequence, a GCph is needed for a proper chromatographic run. For the example of this book, 2µL of sample were injected and separated in a GCph from Thermo brand, model 3300, equipped with a flame ionization detector, automatic injector and a capillary column of fused silica CP-7420 SELECT-FAME (100 % bonded cyanopropyl, 100m length, 0.25mm internal diameter and 0.39 µm of stationary phase). Injector temperature was 230°C. Initially, the column temperature was maintained at 165°C for 18 minutes. Then, it was raised to 235°C, at a rate of 4°C/min. The flow rates for the carrier (H2), auxiliary (N2) and detector flame (H2 and synthetic air) gases were 1.2 mL/min, 30 mL/min, 30 mL/min and 300 mL/min, respectively. Sample split ratio was 1/80. Figure 1 is an illustrative chromatogram for the sardine oil sample that was esterified. It can be observed in the chromatogram a peak regarding solvent, which must be removed. After this removal, only the peak areas from esters will compose the chromatogram.

**Figure 1.** Ilustrative chromatogram of the obtained esters from sardine oil sample with a known amount of internal standard (23:0Me).

Table 5 shows the obtained areas for the different esters (these areas are provided by the software which is used for peak integration).

The values in percentage area (%) were calculated through the normalization method, which will be detailed below:

Assuming to calculate the percentage of 22:6n-3 (DHA) in sample, the following formula can be used:


% DHA = (% area of DHA) × (conversion factor).

empirical determination of the desired fractions;

all the sample constituents must be present.

of composition with works which use modern quantification methods.

correction factor; FCEA = conversion factor of methyl ester to fatty acid.

MDHA = (68164925).(1).(0.97)/(10889779).(1.04).(0.027)

MDHA = 215.61 mg/g of total lipids.

This calculation may be repeated for the remaining FAs, through use of the equation above,

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This method is very practical and easily done to express results in amount of FAs *pe*r amount

**1.** In the application, FID's differential response for the different FAs are not taken tinto

**2.** The different total lipid fractions are dependent of many variables, and the tabulated values might not correspond to the real values. A way of minimizing this problem is the

**3.** In many foods, total lipids were not fractionated. In this case, the percentage of each fraction must be determined, as mentioned in item II. However, a proper method for

**4.** Calculations involve mass percentage, through the use of normalization method. Thus,

Although the FA quantification with use of internal standard calibration is labor-intensive, especially in relation to its implementation in a laboratory, the obtained results compensate for the spent time, because they are reliable and of easy interpretation, allowing comparison

For this same sardine oil sample, the percentage of DHA will be calculated with this method: Knowing that DHA area from the chromatogram (Figure 1) is 68164925, in this analysis a 23:0Me 1 mg/mL solution was used as PI, with application of 1 mL (mass 1mg and area of 10889779) from this, the 0.024g of total lipids were used for esterification, the FCT for DHA (Table 3) is 0.97 and the FCE for DHA is 1.04. Thus, through the use of the equation cited below:

Mx = Mass of DHA in mg/g of total lipids; MP = Mass of internal standard in milligrams; MA = Mass of total lipids in grams; AX = Area of DHA; AP = Area of internal standard; FCT = theoretical

M = A × M × F /A × F × M DHA x p CT p CE A (6)

but with their respective values of area percentage and correction factors.

= (23.56) x (0.89) = 20.97%.

account;

Where:

Thus :

Finally:

of food. However, it has some critics:

fractionating should be employed;

**Table 5.** Methyl esters which were identified in the chromatogram as well as their respective areas and relative area percentages.

X [%] = Area of 22:6n-3 (DHA) × 100 / Total area (with exception of IS

= 23.56 [%] of DHA.

The procedure is repeated for every peak, and the final sum of percentages is equal to 100%.

The problem of this method is that it considers that every compound present in sample was eluted through the column and detected. This is not always true because some compounds might stay retained on the column. Besides, as mentioned before, the detector must have the same response for every compound. However, this does not happen with FID, because it shows differential responses between the different carbonic chains of methyl esters. Thus, it is not correct to affirm that, in this sardine oil sample, the DHA concentration corresponds to 23.56%.

In order to correct this problem, it is recommended to use conversion factors, as mentioned before. Thus, now it will be exemplified for the same fatty acid (DHA), which would be its percentage after using the correction factor for fish.

For a fish meat sample with 3% of total lipids, the conversion factor is 0.89 (Table 1). Thus, the % of DHA in sample is obtained:

% DHA = (% area of DHA) × (conversion factor).

= (23.56) x (0.89) = 20.97%.

This calculation may be repeated for the remaining FAs, through use of the equation above, but with their respective values of area percentage and correction factors.

This method is very practical and easily done to express results in amount of FAs *pe*r amount of food. However, it has some critics:


Although the FA quantification with use of internal standard calibration is labor-intensive, especially in relation to its implementation in a laboratory, the obtained results compensate for the spent time, because they are reliable and of easy interpretation, allowing comparison of composition with works which use modern quantification methods.

For this same sardine oil sample, the percentage of DHA will be calculated with this method:

Knowing that DHA area from the chromatogram (Figure 1) is 68164925, in this analysis a 23:0Me 1 mg/mL solution was used as PI, with application of 1 mL (mass 1mg and area of 10889779) from this, the 0.024g of total lipids were used for esterification, the FCT for DHA (Table 3) is 0.97 and the FCE for DHA is 1.04. Thus, through the use of the equation cited below:

$$\mathbf{M\_{DHA}} = \mathbf{A\_{x}} \times \mathbf{M\_{p}} \times \mathbf{F\_{CT}} / \mathbf{A\_{p}} \times \mathbf{F\_{CE}} \times \mathbf{M\_{A}} \tag{6}$$

Where:

X [%] = Area of 22:6n-3 (DHA) × 100 / Total area (with exception of IS

percentage after using the correction factor for fish.

% of DHA in sample is obtained:

The procedure is repeated for every peak, and the final sum of percentages is equal to 100%. The problem of this method is that it considers that every compound present in sample was eluted through the column and detected. This is not always true because some compounds might stay retained on the column. Besides, as mentioned before, the detector must have the same response for every compound. However, this does not happen with FID, because it shows differential responses between the different carbonic chains of methyl esters. Thus, it is not correct to affirm that, in this sardine oil sample, the DHA concentration corresponds to 23.56%. In order to correct this problem, it is recommended to use conversion factors, as mentioned before. Thus, now it will be exemplified for the same fatty acid (DHA), which would be its

**Table 5.** Methyl esters which were identified in the chromatogram as well as their respective areas and relative area

**Methyl esters (ME) Area Area in percentage (%)** 14:0 21169396 7.32 14:1n-5 1061405 0.37 15:0 3288731 1.14 16:0 80167690 27.71 16:1n-9 3076733 1.06 16:1n-7 13561700 4.69 17:0 3607101 1.25 18:0 16666471 5.76 18:1n-9 23694148 8.19 18:2n-6 6308497 2.18 18:3n-6 4883567 1.69 18:3n-3 1984215 0.69 20:4n-6 4888882 1.69 22:1n-9 1774930 0.61 20:5n-3 28582886 9.88 **23:0 (IS)** 10889779 - 24:0 2684844 0.93 24:1n-9 3789879 1.31 22:6n-3 68164925 23.56 Total 289356000 100.00

For a fish meat sample with 3% of total lipids, the conversion factor is 0.89 (Table 1). Thus, the

= 23.56 [%] of DHA.

52 Advances in Gas Chromatography

percentages.

Mx = Mass of DHA in mg/g of total lipids; MP = Mass of internal standard in milligrams; MA = Mass of total lipids in grams; AX = Area of DHA; AP = Area of internal standard; FCT = theoretical correction factor; FCEA = conversion factor of methyl ester to fatty acid.

Thus :

MDHA = (68164925).(1).(0.97)/(10889779).(1.04).(0.027)

Finally:

MDHA = 215.61 mg/g of total lipids.


Performing the same calculation for the remaining FAs, it can be obtained the composition of the esterified sardine oil.

for 8 minutes. The flow rate for carrier gas (He) was 1.0 mL/min. Injector temperature was 240°C. Sample split ratio was 1/20. Data from MS detection was obtained in full scan mode, with range of masses 50-650 m/z and 0.8170 scans per second. Temperature of ionization source was 250°C. Since the MS detector does not show differential response between the different carbonic chain of FAMEs, a very reliable quantification is obtained, and the results of this

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The illustrations and results shown here indicate that it is possible to increase accuracy in expression of fatty acids (in foods) or FAEs (in biodiesel), especially when compared with the area normalization method, which is limited and frequently used in an erroneous manner. Besides, results of analyses using internal standard calibration and correction factors, in a sardine oil sample, showed that the expression of concentration results, in mass of fatty acid *per* oil mass, must be used whenever possible, because in this quantification only relevant peaks from a chromatogram are taken into consideration. Thus, the obtained results are reliable and easily interpreted, allowing quantitative comparisons of fatty acids/FAEs in foods/biodiesel. The support of such results were obtained upon comparison of the same sample using GC-FID and GC-MS, and the results were indistinguishable, demonstrating that FID may be used for fatty acid analysis, as long as the proper correction factors are applied. Thus, using FID for such quantifications is highly recommended and, comparing to MS, it has additional advan‐

tages because it is cheaper and easier to operate, while keeping good sensitivities.

Lucas Ulisses Rovigatti Chiavelli and Swami Arêa Maruyama

\*Address all correspondence to: swamimaruyama@yahoo.com.br

Maringá State University, Department of Chemistry, Maringá-PR, Brazil

, Thiago Claus, Oscar Oliveira Santos Jr,

[1] Collins, C. H.; Braga, G. L.; Bonato, P. S.; *Fundamentos de Cromatografia,* Ed. da Uni‐

[2] Santos, F. J.; Galceran, M. T. Modern developments in gas chromatography-mass spectrometry-based environmental analysis. *Journal Chromatography*. *A* 2003; 1000,

analysis can be compared with the ones from GC-FID as given in Table 6.

**6. Conclusion**

**Author details**

**References**

125-151.

Jesuí Vergilio Visentainer\*

camp: Campinas, 2006.

N = values determined through the normalization method; FC = values determined through the correction factor method ; PI = values determined through internal standard calibration method with FID correction factors.

**Table 6.** Obtained values for sardine oil quantification in GC-FID and GC-MS.

For comparison effects, 1µL of the same esterified sardine oil sample was injected in GC-MS. In the example of this book, samples were separated in a GC-MS from Thermo brand, model Focus DSQ II, equipped with automatic injector, and a capillary column DB-5 (5% phenyl and 95% methylpolisiloxane, 30m length, 0.25mm internal diameter and 0.25 µm of stationary phase). Initially, the column temperature was maintained at 165°C for 4 minutes. It was then raised to 200°C, at a rate of 6°C/min, and kept at this temperature for 5 minutes. After this period, it was once again raised to 235°C at a rate of 7°C/min and maintained for 5 minutes. Finally, it was raised for the last time to 290°C at a rate of 20°C/min and kept at this temperature for 8 minutes. The flow rate for carrier gas (He) was 1.0 mL/min. Injector temperature was 240°C. Sample split ratio was 1/20. Data from MS detection was obtained in full scan mode, with range of masses 50-650 m/z and 0.8170 scans per second. Temperature of ionization source was 250°C. Since the MS detector does not show differential response between the different carbonic chain of FAMEs, a very reliable quantification is obtained, and the results of this analysis can be compared with the ones from GC-FID as given in Table 6.

### **6. Conclusion**

Performing the same calculation for the remaining FAs, it can be obtained the composition of

14:0 7.32 6.51 73.29 ± 0.034 73.33 ± 0.031 14:1n-5 0.37 0.33 3.64 ± 0.014 3.62 ± 0.013 15:0 1.14 1.01 11.31 ± 0.017 11.02 ± 0.016 16:0 27.71 24.66 273.95 ± 0.114 271.14 ± 0.103 16:1n-9 1.06 0.95 10.41 ± 0.010 10.78 ± 0.009 16:1n-7 4.69 4.17 45.91 ± 0.016 45.20 ± 0.014 17:0 1.25 1.11 12.13 ± 0.015 9.11 ± 0.014 18:0 5.76 5.13 55.66 ± 0.018 54.91 ± 0.016 18:1n-9 8.19 7.29 79.05 ± 0.032 82.18 ± 0.029 18:2n-6 2.18 1.94 20.84 ± 0.029 17.26 ± 0.026 18:3n-6 1.69 1.50 15.98 ± 0.256 15.28 ± 0.230 18:3n-3 0.69 0.61 6.49 ± 1.287 6.21 ± 1.158 20:4n-6 1.69 1.50 15.81 ± 1.992 14.83 ± 1.792 22:1n-9 0.61 0.55 5.71 ± 0.422 5.91 ± 0.380 20:5n-3 9.88 8.79 91.43 ± 0.288 87.50 ± 0.259 **23:0 (PI)** - - - -

24:0 0.93 0.83 8.71±0.139 8.08 ± 0.126 24:1n-9 1.31 1.17 12.17 ± 0.144 11.11 ± 0.130 22:6n-3 23.56 20.97 215.61 ± 0.302 214.57 ± 0.272 Total 100.00 89.02 958.10 942.04

N = values determined through the normalization method; FC = values determined through the correction factor

For comparison effects, 1µL of the same esterified sardine oil sample was injected in GC-MS. In the example of this book, samples were separated in a GC-MS from Thermo brand, model Focus DSQ II, equipped with automatic injector, and a capillary column DB-5 (5% phenyl and 95% methylpolisiloxane, 30m length, 0.25mm internal diameter and 0.25 µm of stationary phase). Initially, the column temperature was maintained at 165°C for 4 minutes. It was then raised to 200°C, at a rate of 6°C/min, and kept at this temperature for 5 minutes. After this period, it was once again raised to 235°C at a rate of 7°C/min and maintained for 5 minutes. Finally, it was raised for the last time to 290°C at a rate of 20°C/min and kept at this temperature

method ; PI = values determined through internal standard calibration method with FID correction factors.

**Table 6.** Obtained values for sardine oil quantification in GC-FID and GC-MS.

**N (%) FC (%) PI PI**

**GC-FID GC-MS**

the esterified sardine oil.

54 Advances in Gas Chromatography

**Fatty acids**

The illustrations and results shown here indicate that it is possible to increase accuracy in expression of fatty acids (in foods) or FAEs (in biodiesel), especially when compared with the area normalization method, which is limited and frequently used in an erroneous manner. Besides, results of analyses using internal standard calibration and correction factors, in a sardine oil sample, showed that the expression of concentration results, in mass of fatty acid *per* oil mass, must be used whenever possible, because in this quantification only relevant peaks from a chromatogram are taken into consideration. Thus, the obtained results are reliable and easily interpreted, allowing quantitative comparisons of fatty acids/FAEs in foods/biodiesel. The support of such results were obtained upon comparison of the same sample using GC-FID and GC-MS, and the results were indistinguishable, demonstrating that FID may be used for fatty acid analysis, as long as the proper correction factors are applied. Thus, using FID for such quantifications is highly recommended and, comparing to MS, it has additional advan‐ tages because it is cheaper and easier to operate, while keeping good sensitivities.

### **Author details**

Jesuí Vergilio Visentainer\* , Thiago Claus, Oscar Oliveira Santos Jr, Lucas Ulisses Rovigatti Chiavelli and Swami Arêa Maruyama

\*Address all correspondence to: swamimaruyama@yahoo.com.br

Maringá State University, Department of Chemistry, Maringá-PR, Brazil

### **References**


[3] Visentainer, J. V.; Franco, M. R. B.; *Ácidos Graxos em Óleos e Gorduras: Identificação e Quantificação*, 1a ed, Varela: São Paulo, 2006.

**Chapter 3**

**Limit of Detection and Limit of Quantification**

Any year, millions of analyses of any kind are performed around the world, and millions of decisions are made, based on these analyses; have the medicaments, the amount of drug reported in their container?, Can we safely consume this water or these foods?, are these alloys suitable for use in the aircraft construction?, was the driver drunk, when he crashed?, is this sportsman using drugs to enhance his performance?, and if he was punished, there is no doubt, he was using those substances or we are unfair with him; all these questions are answered with the help of a chemical analysis and all have consequences in real life (compensation claims, disease, fines, even prison). Virtually every aspect of society is supported in some way by analytical measurement, consequently, there is a need these analyses would be reliable.

Until the 1970´s, the underlying assumption was that the reports submitted by laboratories accurately described study conduct and precisely reported the study data. Suspicion about this assumption was raised during the review of some studies. Data inconsistencies and evidence of unacceptable laboratory practices came to light [1]. If the result of a test cannot be trusted then it has little value and the test might as well have not been carried out. When a client commissions analytical work from a laboratory, it is assumed that the laboratory has a degree of expertise that the client does not have. The client expects to be able to trust the results reported. Thus, the laboratory and its staff have a clear responsibility to justify the clients trust by providing the right answer to the analytical part of the problem, in other words, results that have demonstrable "fitness for purpose"'[2]. Implicit in this is that the tests carried out are appropriate for the analytical part of the problem that the client wishes solved, and that the final report presents the analytical data in such a way that the client can readily understand it and draw appropriate conclusions. Method validation enables chemists to demonstrate that a method is "fit for purpose" [1]. For an analytical result to be fit for its intended purpose it must

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Determination in Gas Chromatography**

Additional information is available at the end of the chapter

Ernesto Bernal

**1. Introduction**

http://dx.doi.org/10.5772/57341


### **Limit of Detection and Limit of Quantification Determination in Gas Chromatography**

Ernesto Bernal

[3] Visentainer, J. V.; Franco, M. R. B.; *Ácidos Graxos em Óleos e Gorduras: Identificação e*

[4] Joseph, J. D.; Ackman, R. G.; Capillary column gas chromatography method for anal‐ ysis of encapsulated fish oil and fish oil ethyl esters: collaborative study. *Journal of*

[5] Visentainer, J. V. Aspectos analíticos da resposta do detector de ionização em chama

[6] Ackman, R. G.; The analysis of fatty acids and related materials by gas-liquid chro‐ matography. *Progress in the Chemistry of Fats and other Lipids*. 1972; *12*, 165-284.

[7] Feng, X.; Liu, X.; Luo, Q.; Liu, B. F. Mass spectrometry in systems biology: an over‐

[8] Holm, T.; Aspects of the mechanism of the flame ionization detector. *Journal of Chro‐*

[9] Ackman, R.; Sipos, J. C.; Flame ionization detector response for the carbonyl carbon atom in the carboxyl group of fatty acids and esters. *Journal of the American Oil Chem‐*

[10] Bligh, E. G.; Dyer, W.; A rapid method of total lipid extraction and purification. *Cana‐*

para ésteres de ácidos graxos *Quimica. Nova* 2012; 35, 274-279.

view. *Mass Spectrometry Reviews* 2008; 27, 635-660.

*dian journal of biochemistry and physiology.* 1959, *37*; 911-917.

*Quantificação*, 1a ed, Varela: São Paulo, 2006.

*AOAC International.* 1992; *75*, 488-506.

56 Advances in Gas Chromatography

*matography A,* 1999; *842*, 221-227.

*ists Society* 1964; *16*, 298-305.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57341

### **1. Introduction**

Any year, millions of analyses of any kind are performed around the world, and millions of decisions are made, based on these analyses; have the medicaments, the amount of drug reported in their container?, Can we safely consume this water or these foods?, are these alloys suitable for use in the aircraft construction?, was the driver drunk, when he crashed?, is this sportsman using drugs to enhance his performance?, and if he was punished, there is no doubt, he was using those substances or we are unfair with him; all these questions are answered with the help of a chemical analysis and all have consequences in real life (compensation claims, disease, fines, even prison). Virtually every aspect of society is supported in some way by analytical measurement, consequently, there is a need these analyses would be reliable.

Until the 1970´s, the underlying assumption was that the reports submitted by laboratories accurately described study conduct and precisely reported the study data. Suspicion about this assumption was raised during the review of some studies. Data inconsistencies and evidence of unacceptable laboratory practices came to light [1]. If the result of a test cannot be trusted then it has little value and the test might as well have not been carried out. When a client commissions analytical work from a laboratory, it is assumed that the laboratory has a degree of expertise that the client does not have. The client expects to be able to trust the results reported. Thus, the laboratory and its staff have a clear responsibility to justify the clients trust by providing the right answer to the analytical part of the problem, in other words, results that have demonstrable "fitness for purpose"'[2]. Implicit in this is that the tests carried out are appropriate for the analytical part of the problem that the client wishes solved, and that the final report presents the analytical data in such a way that the client can readily understand it and draw appropriate conclusions. Method validation enables chemists to demonstrate that a method is "fit for purpose" [1]. For an analytical result to be fit for its intended purpose it must

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

be sufficiently reliable that any decision based on it can be taken with confidence. Thus the method performance must be validated and the uncertainty on the result, at a given level of confidence, estimated. Uncertainty should be evaluated and quoted in a way that is widely recognized, internally consistent and easy to interpret. Most of the information required to evaluate uncertainty can be obtained during validation of the method [1].

**Parameter Comment** Specificity USP, ICH Selectivity ISO 17025

Accuracy USP, ICH, ISO 17025 Linearity USP, ICH, ISO 17025

Limit of detection USP, ICH, ISO 17025 Limit of quantification USP, ICH, ISO 17025 Robustness USP; ISO 17025

Range USP, ICH

Ruggedness USP

calculation of the LOD. Some of the problems are [15]:

derived from these definitions, differ between them. **•** LOD is confused with other concepts like sensitivity.

**•** Estimates of LOD are subject to quite large random variation.

**Table 1.** Parameters for method validation

**1.2. Limit of detection**

concentrations.

USP, ICH ICH, ISO 17025

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

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59

ICH ICH

From the previous section, it is clear that despite the efforts to standardize concepts, there is still confusion about some terms in method validation, like selectivity and specificity, rug‐ gedness and reproducibility, accuracy and trueness. Nevertheless, the most troublesome concept of all, in method validation is the limit of detection (LOD). LOD remains an ambiguous quantity on analytic chemistry in general and gas chromatography in particular; LOD's differing by orders of magnitude are frequently found for very similar chemical measurement process (CMP). Such discrepancies raise questions about the validity of the concept of the LOD. The limit of detection is the smallest amount or concentration of analyte in the test sample that can be reliably distinguished from zero [20]. Despite the simplicity of the concept, the whole subject of LOD is with problems, translating these into the observed discrepancies in the

**•** There are several conceptual approaches to the subject, each providing a somewhat different definition of the limit, and consequently, the methodology used to calculate the LOD

**•** Statistical determinations of LOD assume normality, which is at least questionable at low

**•** The LOD, which characterizes the whole chemical measurement process (CMP), is mistaken

with concepts that characterize only one aspect of the CMP, the detection.

Precision • Repeatability

• Intermediate precision • Reproducibility

Since then, several agencies like United States Food and Drug Administration (FDA) [3-5], the International Conference for Harmonization (ICH) [6], the United States Pharmacopeia (USP) [7], the International Standards Organization (ISO/IEC) [8], etc. created working groups to ensure the validity and reliability of the studies. They would eventually publish standards for measurement the performance of laboratories and enforcement policy. Good laboratory practice (GLP) regulations were finally proposed in 1976, being method validation an impor‐ tant part of GLP.

### **1.1. Method validation**

ISO [9] defines validation as the confirmation, via the provision of objective evidence, that the requirements for specifically intended use or application have been met, so method validation is the process of defining an analytical requirement, and confirming that the method under consideration has performance capabilities consistent with what the application requires [2].

Therefore, method validation should be an essential component of the measurements that a laboratory makes to allow it to produce reliable analytical data, in consequence, meth‐ od validation should be an important part in the practice of all the chemists around the world. Nevertheless, the knowledge of exactly what needs to be done to validate a method seems to be poor amongst analytical chemists. The origin of the problem is the fact that many of the technical terms used in processes for evaluating methods vary in different sectors of analytical measurement, both in terms of their meaning and also the way they are determined [2].

In order to resolve the previous problem, several protocols and guidelines [2, 7, 10-19] on method validation have been prepared. Method validation consists of a series of tests that both proof any assumptions on which the analytical method is based and established and document the performance characteristics of a method, demonstrating whether the method fits for a particular analytical purpose [15]. Typical performance characteristics of analytical methods are applicability, selectivity or specificity, calibration, accuracy and recovery, precision, range, limit of quantification, limit of detection, sensitivity and ruggedness or robustness [2, 7, 10-19], Unfortunately, some of the definitions vary between the different organizations producing confusion among analysts. Some parameters are summarized in Table 1.

It´s not the purpose of this work to define the previous parameters and the procedure to determine them or the strategies to perform a validation, all of them concerned with these issues are invited to consult the following references [2, 7, 10-19].


**Table 1.** Parameters for method validation

#### **1.2. Limit of detection**

be sufficiently reliable that any decision based on it can be taken with confidence. Thus the method performance must be validated and the uncertainty on the result, at a given level of confidence, estimated. Uncertainty should be evaluated and quoted in a way that is widely recognized, internally consistent and easy to interpret. Most of the information required to

Since then, several agencies like United States Food and Drug Administration (FDA) [3-5], the International Conference for Harmonization (ICH) [6], the United States Pharmacopeia (USP) [7], the International Standards Organization (ISO/IEC) [8], etc. created working groups to ensure the validity and reliability of the studies. They would eventually publish standards for measurement the performance of laboratories and enforcement policy. Good laboratory practice (GLP) regulations were finally proposed in 1976, being method validation an impor‐

ISO [9] defines validation as the confirmation, via the provision of objective evidence, that the requirements for specifically intended use or application have been met, so method validation is the process of defining an analytical requirement, and confirming that the method under consideration has performance capabilities consistent with what the application requires [2].

Therefore, method validation should be an essential component of the measurements that a laboratory makes to allow it to produce reliable analytical data, in consequence, meth‐ od validation should be an important part in the practice of all the chemists around the world. Nevertheless, the knowledge of exactly what needs to be done to validate a method seems to be poor amongst analytical chemists. The origin of the problem is the fact that many of the technical terms used in processes for evaluating methods vary in different sectors of analytical measurement, both in terms of their meaning and also the way they

In order to resolve the previous problem, several protocols and guidelines [2, 7, 10-19] on method validation have been prepared. Method validation consists of a series of tests that both proof any assumptions on which the analytical method is based and established and document the performance characteristics of a method, demonstrating whether the method fits for a particular analytical purpose [15]. Typical performance characteristics of analytical methods are applicability, selectivity or specificity, calibration, accuracy and recovery, precision, range, limit of quantification, limit of detection, sensitivity and ruggedness or robustness [2, 7, 10-19], Unfortunately, some of the definitions vary between the different organizations producing

It´s not the purpose of this work to define the previous parameters and the procedure to determine them or the strategies to perform a validation, all of them concerned with these

confusion among analysts. Some parameters are summarized in Table 1.

issues are invited to consult the following references [2, 7, 10-19].

evaluate uncertainty can be obtained during validation of the method [1].

tant part of GLP.

**1.1. Method validation**

58 Advances in Gas Chromatography

are determined [2].

From the previous section, it is clear that despite the efforts to standardize concepts, there is still confusion about some terms in method validation, like selectivity and specificity, rug‐ gedness and reproducibility, accuracy and trueness. Nevertheless, the most troublesome concept of all, in method validation is the limit of detection (LOD). LOD remains an ambiguous quantity on analytic chemistry in general and gas chromatography in particular; LOD's differing by orders of magnitude are frequently found for very similar chemical measurement process (CMP). Such discrepancies raise questions about the validity of the concept of the LOD.

The limit of detection is the smallest amount or concentration of analyte in the test sample that can be reliably distinguished from zero [20]. Despite the simplicity of the concept, the whole subject of LOD is with problems, translating these into the observed discrepancies in the calculation of the LOD. Some of the problems are [15]:


Theseproblemsaremoreprominentinthefieldofchromatography,where,besides theprevious issues, no standard model for the LOD has ever proposed by any recognized organization. Actually, the International Union of Pure and Applied Chemistry (IUPAC) model for LOD determination was chosen for spectrochemical analysis specifically. Thus, chromatographic conditions are usually not taken in consideration to determine the LOD [15, 21].

the minimum concentration of an analyte that can be identified, measured and reported with

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

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61

On the other hand, the ICH defines LOD as the lowest amount of analyte in a sample which can be detected but not necessarily quantitated as an exact value and LOQ of an individual analytical procedure as the lowest amount of analyte in a sample which can be quantitatively

The American Chemical Society (ACS) committee on environmental improvement states LOD as the lowest concentration of an analyte that the analytical process can reliably detect [27].

The conference for drug evaluation and research (CDER) in its document entitled 'Valida‐ tion of chromatographic method' defines LOD as the lowest concentration of analyte in a sample that can be detected but not necessarily quantitated under the stated experimen‐

The national association of testing authorities (NATA) defines LOD as the smallest amount or concentration that can be readily distinguished from zero and be positively identified accord‐ ing to predetermined criteria and/or level of confidence, while the lowest concentration of an analyte that can be determined with acceptable precision (repeatability) and accuracy under

On the other hand, the AOAC defines limit of detection as the lowest content that can be measured with reasonable statistical certainty and the limit of quantification as the content

Finally, the USP [7] defines LOD as: the lowest amount of analyte that can be detected but not necessarily quantitated under the stated experimental conditions. Table 2 summarizes these

All definitions include terms as reliability, probability, confidence, etc. That implies the use of statistics to calculate them. Some definitions even put inside its text explicitly the degree of reliability and consequently, define it. The others leave the decision to the operator. Some definitions make it clear that they are referring to a CMP and not only to the detection phase of the analysis. Therefore, these terms should not be confused with terms referred only to detection like the Instrumental Detection Limits (IDL) used by EPA. The limit of detection is a parameter set before the measure, and, in other words, is defined a priori; therefore, is not related to the decision whether a measurement detects anything or not. Finally, these terms should not be confused with sensitivity defined as the slope of the

As an example of the wide range of terms used to define detection capabilities of a method used nowadays, it is shown a study performed in 2002 [29], by the American Petroleum Institute (API), to intend to review policies related to analytical detection and quantification limits of ten states of the USA, with particular focus on water quality and wastewater issues in permitting and compliance. Thus, these regulations should follow the EPA recommenda‐ tions. It was found that every state incorporates detection or quantification terms in its

equal to or greater than the lowest concentration point on the calibration curve [2].

99% confidence that the analyte concentration is greater than zero.

the stated conditions of the test is the limit of quantification [2, 27].

terms, symbols and statistical items reported in the literature [28].

determined with suitable precision and accuracy [19, 27].

tal conditions [19, 27].

calibration curve by IUPAC.

The main purpose of this paper is to bring some light to these problems. In order to achieve this goal, the different problems behind LOD and limit of quantification (LOQ) are going to be discussed. The different definitions and conceptual approaches to LOD and LOQ, given by different associations [2,7,20, 22], the different models to calculate LOD and LOQ, and the effect of matrix and particularities related to chromatographic techniques on LOD and LOQ calculations are going to be critically reviewed [23-25], aiming at unifying criteria and estimating LOD and LOQ in a more reliable figure of merit in chromatography.

### **2. Limit of detection and limit of quantification**

### **2.1. Definitions**

Since the seminal work of Currie [26], emphasis has been placed on the negative effect it has had, the large number of terms that have been used through the years regarding the detection capabilities of a method (table 2). A wide range of terminologies and multiple mathematical expressions have been used to define the limit of detection concept. These different terms resulted in different ways of calculating the LOD, leading to numerical values that can span over three orders of magnitude, applied to the same measurement process. Some mathematical definitions involved the standard deviation of the blank, some the standard deviation of the net signal, some authors used two sided confidence intervals, while others used one sided intervals, and some authors even used non statistical definitions; what was missing from these authors was a theoretical basis for the concept that led to an operational definition of the term.

To overcome the previous problem, the ISO and the IUPAC developed documents bringing their nomenclature into essential agreement [15, 20, 22]. As the measure of the detection capability of a CMP, the IUPAC recommends the term Minimum Detectable Value (LD) of the appropriate chemical variable or Detection limit and defines it as the smallest amount or concentration of analyte in the test sample that can be reliably distinguished from zero. And as the measure of the quantification capability of the CMP, the IUPAC recommends the term Minimum quantifiable limit (LQ) or Quantification limit, which is the concentration or amount below which the analytical method cannot operate with an acceptable precision. ISO definition puts emphasis in the statistics; ISO [2, 22] defines the minimum detectable net concentration as the true net concentration or amount of the analyte in the material to be analyzed, which will lead with probability (1-β) to the conclusion that the concentration of the analyte in the analyzed material is larger that of the blank matrix.

In addition, several organizations have introduced terms with similar meaning to LOD. The US Environmental Protection agency (EPA) uses the term method detection limit (MDL) as the minimum concentration of an analyte that can be identified, measured and reported with 99% confidence that the analyte concentration is greater than zero.

Theseproblemsaremoreprominentinthefieldofchromatography,where,besides theprevious issues, no standard model for the LOD has ever proposed by any recognized organization. Actually, the International Union of Pure and Applied Chemistry (IUPAC) model for LOD determination was chosen for spectrochemical analysis specifically. Thus, chromatographic

The main purpose of this paper is to bring some light to these problems. In order to achieve this goal, the different problems behind LOD and limit of quantification (LOQ) are going to be discussed. The different definitions and conceptual approaches to LOD and LOQ, given by different associations [2,7,20, 22], the different models to calculate LOD and LOQ, and the effect of matrix and particularities related to chromatographic techniques on LOD and LOQ calculations are going to be critically reviewed [23-25], aiming at unifying criteria and

Since the seminal work of Currie [26], emphasis has been placed on the negative effect it has had, the large number of terms that have been used through the years regarding the detection capabilities of a method (table 2). A wide range of terminologies and multiple mathematical expressions have been used to define the limit of detection concept. These different terms resulted in different ways of calculating the LOD, leading to numerical values that can span over three orders of magnitude, applied to the same measurement process. Some mathematical definitions involved the standard deviation of the blank, some the standard deviation of the net signal, some authors used two sided confidence intervals, while others used one sided intervals, and some authors even used non statistical definitions; what was missing from these authors was a theoretical basis for the concept that led to an operational definition of the term.

To overcome the previous problem, the ISO and the IUPAC developed documents bringing their nomenclature into essential agreement [15, 20, 22]. As the measure of the detection capability of a CMP, the IUPAC recommends the term Minimum Detectable Value (LD) of the appropriate chemical variable or Detection limit and defines it as the smallest amount or concentration of analyte in the test sample that can be reliably distinguished from zero. And as the measure of the quantification capability of the CMP, the IUPAC recommends the term Minimum quantifiable limit (LQ) or Quantification limit, which is the concentration or amount below which the analytical method cannot operate with an acceptable precision. ISO definition puts emphasis in the statistics; ISO [2, 22] defines the minimum detectable net concentration as the true net concentration or amount of the analyte in the material to be analyzed, which will lead with probability (1-β) to the conclusion that the concentration of the analyte in the

In addition, several organizations have introduced terms with similar meaning to LOD. The US Environmental Protection agency (EPA) uses the term method detection limit (MDL) as

conditions are usually not taken in consideration to determine the LOD [15, 21].

estimating LOD and LOQ in a more reliable figure of merit in chromatography.

**2. Limit of detection and limit of quantification**

analyzed material is larger that of the blank matrix.

**2.1. Definitions**

60 Advances in Gas Chromatography

On the other hand, the ICH defines LOD as the lowest amount of analyte in a sample which can be detected but not necessarily quantitated as an exact value and LOQ of an individual analytical procedure as the lowest amount of analyte in a sample which can be quantitatively determined with suitable precision and accuracy [19, 27].

The American Chemical Society (ACS) committee on environmental improvement states LOD as the lowest concentration of an analyte that the analytical process can reliably detect [27].

The conference for drug evaluation and research (CDER) in its document entitled 'Valida‐ tion of chromatographic method' defines LOD as the lowest concentration of analyte in a sample that can be detected but not necessarily quantitated under the stated experimen‐ tal conditions [19, 27].

The national association of testing authorities (NATA) defines LOD as the smallest amount or concentration that can be readily distinguished from zero and be positively identified accord‐ ing to predetermined criteria and/or level of confidence, while the lowest concentration of an analyte that can be determined with acceptable precision (repeatability) and accuracy under the stated conditions of the test is the limit of quantification [2, 27].

On the other hand, the AOAC defines limit of detection as the lowest content that can be measured with reasonable statistical certainty and the limit of quantification as the content equal to or greater than the lowest concentration point on the calibration curve [2].

Finally, the USP [7] defines LOD as: the lowest amount of analyte that can be detected but not necessarily quantitated under the stated experimental conditions. Table 2 summarizes these terms, symbols and statistical items reported in the literature [28].

All definitions include terms as reliability, probability, confidence, etc. That implies the use of statistics to calculate them. Some definitions even put inside its text explicitly the degree of reliability and consequently, define it. The others leave the decision to the operator. Some definitions make it clear that they are referring to a CMP and not only to the detection phase of the analysis. Therefore, these terms should not be confused with terms referred only to detection like the Instrumental Detection Limits (IDL) used by EPA. The limit of detection is a parameter set before the measure, and, in other words, is defined a priori; therefore, is not related to the decision whether a measurement detects anything or not. Finally, these terms should not be confused with sensitivity defined as the slope of the calibration curve by IUPAC.

As an example of the wide range of terms used to define detection capabilities of a method used nowadays, it is shown a study performed in 2002 [29], by the American Petroleum Institute (API), to intend to review policies related to analytical detection and quantification limits of ten states of the USA, with particular focus on water quality and wastewater issues in permitting and compliance. Thus, these regulations should follow the EPA recommenda‐ tions. It was found that every state incorporates detection or quantification terms in its regulations to some extent. Terms referenced are usually defined in the regulations, but not always. The most frequently used terms are detection limit/level, method detection limit (MDL), limit of detection (LOD), and practical quantitation level (PQL). Minimum Level (ML) is the term used by EPA instead of LOQ; it is defined as the concentration at which the entire analytical system must give a recognizable signal and acceptable calibration point. The ML is the concentration in a sample that is equivalent to the concentration of the lowest calibration standard analyzed by a specific analytical procedure, assuming that all of the method-specified sample weights, volumes, and processing steps have been followed. EPA uses other terms like Interim minimum level (IML) which is a term created by the EPA to describe MLs that are based on 3.18 times the MDL, to distinguish them from MLs that have been promulgated. The EPA defines the PQL as: The lowest level that can be reliably achieved within specified limits of precision and accuracy during routine laboratory operating conditions. Another term used by EPA is the alternative minimum level (AML) which can account for interlaboratory variability and sample matrix effects and finally the Interlaboratory Quantification estimate (IQE) a term developed by the American Society for Testing and Materials (ASTM), which is similar to the AML. Table 3 lists detection and quantification terms used by the ten states.

Therefore, despite the efforts of various agencies to standardize the terms concerning the detection capability of a measurement process, there are still differences between the various

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In 1968, Currie published the hypothesis testing approach to detection decisions and limits in chemistry [26]. This approach has gradually been accepted as the detection limit theory.

Currie´s achievement was in recognizing that there were two different questions under consideration when measurements were performed on a specimen under test, and these two questions have different answers. The first question, which was "does the measurement result indicate detection or not?", is answered by performing measurements on the specimen under test then computing an appropriate measure for comparison with a critical decision level. The second question is "what is the lowest analyte content that will reliably indicate detection?"

**State programs DL IDL IML LOD LOQ MDL ML QL PQL Other§** Alabama

California Drinking water x x DLR

Illinois

Drinking water <sup>x</sup> <sup>x</sup> <sup>x</sup> MQL,

Pesticides x x MQL

ADL

EDL, EQL, MQL

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup>

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup>

Water quality x x x x

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup>

Water quality x x x x x x

Laboratories x

Water quality x x x x

regulations.

**2.2. Theory**

Groundwater soil/

Groundwater soil/

Groundwater soil/

*2.2.1. Hypothesis testing approach*

and the answer is defined as the detection limit.

Health/Safety x


**Table 2.** Terms and symbols reported in the literature related to method detection capabilities

Therefore, despite the efforts of various agencies to standardize the terms concerning the detection capability of a measurement process, there are still differences between the various regulations.

### **2.2. Theory**

regulations to some extent. Terms referenced are usually defined in the regulations, but not always. The most frequently used terms are detection limit/level, method detection limit (MDL), limit of detection (LOD), and practical quantitation level (PQL). Minimum Level (ML) is the term used by EPA instead of LOQ; it is defined as the concentration at which the entire analytical system must give a recognizable signal and acceptable calibration point. The ML is the concentration in a sample that is equivalent to the concentration of the lowest calibration standard analyzed by a specific analytical procedure, assuming that all of the method-specified sample weights, volumes, and processing steps have been followed. EPA uses other terms like Interim minimum level (IML) which is a term created by the EPA to describe MLs that are based on 3.18 times the MDL, to distinguish them from MLs that have been promulgated. The EPA defines the PQL as: The lowest level that can be reliably achieved within specified limits of precision and accuracy during routine laboratory operating conditions. Another term used by EPA is the alternative minimum level (AML) which can account for interlaboratory variability and sample matrix effects and finally the Interlaboratory Quantification estimate (IQE) a term developed by the American Society for Testing and Materials (ASTM), which is similar to the AML. Table 3 lists detection and quantification terms used by the ten states.

**Terms Signal Domain Concentration Domain Reference**

Critical value LC XC Currie, 1995

Limit of guarantee of purity xG cG Kaiser, 1966

Minimum detectable value LD XD Currie, 1995

Minimum Quantifiable level LQ x Q Currie, 1995

**Table 2.** Terms and symbols reported in the literature related to method detection capabilities

Limit of identification xI cI Long & Winefordner, 1983 Detection level LD XD Oppenheimer, et al, 1983 Detection limit yD xD Hubaux & Vos, 1970

Determination Limit LQ xQ Oppenheimer, et al, 1983

Method detection limit MDL EPA

LOD LOD IUPAC 1976

LOD ACS, 1980

LOQ ACS, 1980

LOQ Long & Winefordner, 1983

ML EPA PQL, IML EPA

LC XC Oppenheimer, et al, 1983 yC XC Hubaux & Vos, 1970

Limit of detection

62 Advances in Gas Chromatography

Limit of quantification

Minimum Level

Critical Level

### *2.2.1. Hypothesis testing approach*

In 1968, Currie published the hypothesis testing approach to detection decisions and limits in chemistry [26]. This approach has gradually been accepted as the detection limit theory.

Currie´s achievement was in recognizing that there were two different questions under consideration when measurements were performed on a specimen under test, and these two questions have different answers. The first question, which was "does the measurement result indicate detection or not?", is answered by performing measurements on the specimen under test then computing an appropriate measure for comparison with a critical decision level. The second question is "what is the lowest analyte content that will reliably indicate detection?" and the answer is defined as the detection limit.



**State programs DL IDL IML LOD LOQ MDL ML QL PQL Other§**

Texas

Water quality X MAL Washington

<sup>X</sup> <sup>X</sup> <sup>X</sup> <sup>x</sup> MQL,

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

X X X X X

Water quality X X X X

Drinking water X X x

Air X

Water quality X X X

According to Currie, the IUPAC and ISO [20, 30-31], detection limits (minimum detectable amounts) are based on the theory of hypothesis testing and the probabilities of false positives *α,* and false negatives β. On the other hand, quantification limits are defined in terms of a specified value for the relative standard deviation. It is important to emphasize that both types of limits are CMP performance characteristics, associated with underlying true values of the quantity of interest; they are not associated with any particular outcome or result. The detection decision, on the other hand, is result-specific; it is made by comparing the experimental result with the critical value which is the minimum significantly estimated value of the quantity of interest. In other words it is used to make a posteriori estimate of the detection capabilities of the measurement process, while the limit of detection is used to make a priori estimate.

In order to explain the detection limit theory, let's assume we have an analytical method with known precision along all its concentration levels and that its results follow a normal distri‐ bution, if we test a lot of blanks with the method above, certainly a distribution as in Figure

The blank values will distribute around zero with a standard deviation σ0. In others words if we measure a blank, we can obtain a result different from zero due to the experimental errors of the measure process. Thus we need to establish a point to differentiate blanks measures from non blank measures. That point is the critical value LC, that point allows us to determine

**Table 3.** Summary of detection and quantification terms used by the states

X X X EQL

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SQL

65

Groundwater solid/ hazardous waste

Groundwater solid/ hazardous waste

Groundwater solid/ hazardous waste

§ Definitions on section 4

1 can be obtained.

Air X

Drinking water X


**Table 3.** Summary of detection and quantification terms used by the states

**State programs DL IDL IML LOD LOQ MDL ML QL PQL Other§** Lousiana

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup>

Water quality <sup>x</sup> LOM,

New Jersey

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup> <sup>x</sup> CRDL

Water quality <sup>x</sup> <sup>x</sup> <sup>x</sup> NJQL

Ohio

hazardous waste <sup>x</sup> <sup>x</sup> <sup>x</sup> ADL State programs DL IDL IML LOD LOQ MDL ML QL PQL Other

Water quality X X X X ADL Oklahoma

Drinking water MDL\*

Water quality X MQL\* Pennsylvania

Drinking water X X

X X X

X X

MQL\*\*

LOD\*

ADL, MLD, MQL\*

Air x x x

Drinking water x

Laboratories x x x

Drinking water x x

Laboratories X

Air x

Groundwater soil/

64 Advances in Gas Chromatography

Groundwater soil/

Groundwater solid/

Underground storage

Air X

Groundwater solid/ hazardous waste

tanks

Air

According to Currie, the IUPAC and ISO [20, 30-31], detection limits (minimum detectable amounts) are based on the theory of hypothesis testing and the probabilities of false positives *α,* and false negatives β. On the other hand, quantification limits are defined in terms of a specified value for the relative standard deviation. It is important to emphasize that both types of limits are CMP performance characteristics, associated with underlying true values of the quantity of interest; they are not associated with any particular outcome or result. The detection decision, on the other hand, is result-specific; it is made by comparing the experimental result with the critical value which is the minimum significantly estimated value of the quantity of interest. In other words it is used to make a posteriori estimate of the detection capabilities of the measurement process, while the limit of detection is used to make a priori estimate.

In order to explain the detection limit theory, let's assume we have an analytical method with known precision along all its concentration levels and that its results follow a normal distri‐ bution, if we test a lot of blanks with the method above, certainly a distribution as in Figure 1 can be obtained.

The blank values will distribute around zero with a standard deviation σ0. In others words if we measure a blank, we can obtain a result different from zero due to the experimental errors of the measure process. Thus we need to establish a point to differentiate blanks measures from non blank measures. That point is the critical value LC, that point allows us to determine

**Figure 1.** Value distribution around zero and the critical level.

if a signal corresponds to a blank or if the signal is from an analyte, i.e. to make a posterior decision. Nevertheless, in the critical level there is the probability (blue shadow) that a blank can give a signal above the LC. Therefore we can erroneously conclude that there is analyte, when is not. That probability α, is named type I error or a false positive. The value of α is chosen by the analyst in function of the risk that one wants to take of being wrong. The hypothesis testing theory uses the following definition.

$$\Pr\left(\mathbf{L} \rhd \mathbf{L}\_{\mathbf{C}} | \mathbf{L} = \mathbf{0}\right) \le a \tag{1}$$

that the analyte is not present in the sample, when it actually is. The probability β, is named

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67

Therefore, if a laboratory cannot accept a 50% of error around the limit of detection, the only alternative to reduce the probability of false negative is to set the limit of detection to a bigger

**Figure 2.** Critical level and limit of detection with probability α. The risk of a false negative is 50%.

pothesis testing theory, we obtain the following relation:

Mathematically, the limit of detection is given as:

If equation 2 is substituted in equation 4, we obtain:

fication (LQ) is:

Once LC has been defined, a priori limit of detection LD, may be established by specifying LC, the acceptable level β, for the type II error and the standard deviation, σD, which characterizes the probability distribution of the signal when its true value is equal to LD [26]. By the hy‐

Where Kβ and β, are linked with the one sided tails of the distribution of the limit of detection corresponding to probability levels, 1-β. Finally, the defining relation for the limit of quanti‐

Pr (L≤ LC|L=LD) =β (3)

LD= K*α*σ0+ Kβσ<sup>D</sup> (4)

LD= LC+ Kβσ<sup>D</sup> (5)

type II error or a false negative.

concentration (Figure 3).

Where, L is used, as the generic symbol for the quantity of interest. This is replaced by S, when treating net analyte signal and x, when treating analyte concentrations or amount; mathemat‐ ically, the critical level is given as:

$$\mathbf{L}\_{\mathbf{C}} = \mathbf{K}\_{\alpha} \sigma\_{0} \tag{2}$$

Where Kα and α, are linked with the one sided tails of the distribution of the blank corre‐ sponding to probability levels, 1-α.

Nevertheless, the critical level LC cannot be used as the limit of detection, because if we measure a series of samples with an amount of analyte equal to the LC, the results will follow like the blanks a normal distribution around the LC value (Figure 2). Half of the results would be above the LC and we conclude that the signal is from an analyte, and half would be below the LC and consequently, we would think the sample is a blank. Therefore, if we set the LC as the limit of detection, we would report erroneously half of the results; then, there is the possibility to report that the analyte is not present in the sample, when it actually is. The probability β, is named type II error or a false negative.

Therefore, if a laboratory cannot accept a 50% of error around the limit of detection, the only alternative to reduce the probability of false negative is to set the limit of detection to a bigger concentration (Figure 3).

**Figure 2.** Critical level and limit of detection with probability α. The risk of a false negative is 50%.

Once LC has been defined, a priori limit of detection LD, may be established by specifying LC, the acceptable level β, for the type II error and the standard deviation, σD, which characterizes the probability distribution of the signal when its true value is equal to LD [26]. By the hy‐ pothesis testing theory, we obtain the following relation:

$$\Pr\left\{\mathbf{L} \le \mathbf{L}\_{\mathbf{C}} \,|\, \mathbf{L} = \mathbf{L}\_{\mathbf{D}}\right\} = \boldsymbol{\upbeta} \tag{3}$$

Mathematically, the limit of detection is given as:

if a signal corresponds to a blank or if the signal is from an analyte, i.e. to make a posterior decision. Nevertheless, in the critical level there is the probability (blue shadow) that a blank can give a signal above the LC. Therefore we can erroneously conclude that there is analyte, when is not. That probability α, is named type I error or a false positive. The value of α is chosen by the analyst in function of the risk that one wants to take of being wrong. The

( C|Pr L> L L=0 ) £

Where, L is used, as the generic symbol for the quantity of interest. This is replaced by S, when treating net analyte signal and x, when treating analyte concentrations or amount; mathemat‐

Where Kα and α, are linked with the one sided tails of the distribution of the blank corre‐

Nevertheless, the critical level LC cannot be used as the limit of detection, because if we measure a series of samples with an amount of analyte equal to the LC, the results will follow like the blanks a normal distribution around the LC value (Figure 2). Half of the results would be above the LC and we conclude that the signal is from an analyte, and half would be below the LC and consequently, we would think the sample is a blank. Therefore, if we set the LC as the limit of detection, we would report erroneously half of the results; then, there is the possibility to report

a

(1)

LC= Kασ<sup>0</sup> (2)

hypothesis testing theory uses the following definition.

**Figure 1.** Value distribution around zero and the critical level.

66 Advances in Gas Chromatography

ically, the critical level is given as:

sponding to probability levels, 1-α.

$$\mathbf{L}\_{\rm D} = \mathbf{K}\_{\rm a} \sigma\_{0} + \mathbf{K}\_{\beta} \sigma\_{\rm D} \tag{4}$$

If equation 2 is substituted in equation 4, we obtain:

$$\mathbf{L\_D = L\_C + K\_\beta \sigma\_D} \tag{5}$$

Where Kβ and β, are linked with the one sided tails of the distribution of the limit of detection corresponding to probability levels, 1-β. Finally, the defining relation for the limit of quanti‐ fication (LQ) is:

$$\mathbf{L}\_{\mathbf{Q}} \mathbf{\bar{K}}\_{\mathbf{Q}} \sigma\_{\mathbf{Q}} \tag{6}$$

α, β and K<sup>Q</sup> can be chosen by the analyst according to the detection and quantification needs; of particular interest is when α=β, and σ=constant, in that circumstances Kα= Kβ=K, and σD=σ<sup>0</sup>

In this particular case the limit of detection is twice the critical level. The values of α and β recommended by IUPAC and ISO regulations are 0.05 each, which gives a value of K= 1.645, and since σ=constant, it can choose either the value of σ0 or σD, because LD is not known, in equation 9 the value of σ0 is used. Under these circumstances (normal distribu‐ tion, constant standard deviation and parameters α=β=0.05) the following expressions are

If σ0 is estimated by s0, based on ν degrees of freedom, K can be replaced by Student's t,

Following the procedure developed in the previous paragraphs, equivalent equations are

If L<sup>C</sup> employs an estimate s0, based on ν degrees of freedom, then K can be replaced by δα,β,ν, the non centrality parameter of the non central t distribution. If α=0.05 and four degrees of

And because σ=constant, σ0=σD=σ, relation 7, can be written as:

For α=0.05 and four degrees of freedom, equation 12 is obtained.

obtained [20, 26, 30-31].

resulting in equation:

obtained for the limit of detection.

freedom are used, equation 14 is obtained.

LC=Kσ<sup>0</sup> (7)

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69

LD= LC+ KσD=K(σ0+σD) (8)

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

LD= 2Kσ=2L<sup>C</sup> (9)

LC= Kσ0=1.645 σ<sup>0</sup> (10)

LC= t1-*α*,νs<sup>0</sup> (11)

LC= 2.132 σ<sup>0</sup> (12)

LD= LC+KσD=3.29 σ<sup>0</sup> (13)

LD=δα,β,νσ0=2t1-α,νσ0=4.26 <sup>σ</sup><sup>0</sup> (14)

Where KQ=1/RSDQ, and σQ equals the standard deviation of L when L= LQ.

**Figure 3.** Critical level and limit of detection with α=β.

Summarizing, the levels LC, LD and LQ, are determined by the error structure of the measure‐ ment process, the risks α and β and the maximum acceptable relative standard deviation for quantitative analysis. LC is used to test an experimental result, whereas LD and LQ refer to the capabilities of the measurement process itself. The relations among the three levels and their significance in analysis are shown in Figure 4.

**Figure 4.** The three principal analytical regions [26].

α, β and K<sup>Q</sup> can be chosen by the analyst according to the detection and quantification needs; of particular interest is when α=β, and σ=constant, in that circumstances Kα= Kβ=K, and σD=σ<sup>0</sup>

$$\mathbf{L}\_{\mathbf{C}} \mathbf{\overline{\mathbf{K}}} \sigma\_0 \tag{7}$$

$$\mathbf{L\_D = L\_C + K \sigma\_D = K(\sigma\_0 + \sigma\_D)}\tag{8}$$

And because σ=constant, σ0=σD=σ, relation 7, can be written as:

LQ= KQσ<sup>Q</sup> (6)

Where KQ=1/RSDQ, and σQ equals the standard deviation of L when L= LQ.

Summarizing, the levels LC, LD and LQ, are determined by the error structure of the measure‐ ment process, the risks α and β and the maximum acceptable relative standard deviation for quantitative analysis. LC is used to test an experimental result, whereas LD and LQ refer to the capabilities of the measurement process itself. The relations among the three levels and their

**Figure 3.** Critical level and limit of detection with α=β.

68 Advances in Gas Chromatography

significance in analysis are shown in Figure 4.

**Figure 4.** The three principal analytical regions [26].

$$\mathbf{L}\_{\mathrm{D}} = 2\mathbf{K}\sigma = 2\mathbf{L}\_{\mathrm{C}} \tag{9}$$

In this particular case the limit of detection is twice the critical level. The values of α and β recommended by IUPAC and ISO regulations are 0.05 each, which gives a value of K= 1.645, and since σ=constant, it can choose either the value of σ0 or σD, because LD is not known, in equation 9 the value of σ0 is used. Under these circumstances (normal distribu‐ tion, constant standard deviation and parameters α=β=0.05) the following expressions are obtained [20, 26, 30-31].

$$\mathbf{L}\_{\text{C}} = \mathbf{K} \sigma\_{0} \mathbf{=} 1.645 \quad \sigma\_{0} \tag{10}$$

If σ0 is estimated by s0, based on ν degrees of freedom, K can be replaced by Student's t, resulting in equation:

$$\mathbf{L}\_{\mathbf{C}} = \mathbf{t}\_{1\_{"\tilde{a},\mathbf{v}"}} \mathbf{s}\_0 \tag{11}$$

For α=0.05 and four degrees of freedom, equation 12 is obtained.

$$\mathbf{L\_{C}} \mathbf{=} 2.132 \quad \sigma\_{0} \tag{12}$$

Following the procedure developed in the previous paragraphs, equivalent equations are obtained for the limit of detection.

$$\mathbf{L\_D = L\_C + K \sigma\_D = 3.29 \quad \sigma\_0} \tag{13}$$

If L<sup>C</sup> employs an estimate s0, based on ν degrees of freedom, then K can be replaced by δα,β,ν, the non centrality parameter of the non central t distribution. If α=0.05 and four degrees of freedom are used, equation 14 is obtained.

$$\mathbf{L\_D = \delta\_{a,\beta,\mathbf{v}} \sigma\_0 = 2} \mathbf{t\_{1\_{\hat{a},\mathbf{v}}} \sigma\_0 = 4.26 \quad \sigma\_0 \tag{14}$$

The ability to quantify is expressed in terms of the signal or analyte that will produce estimates having a specified relative standard deviation (RSD), commonly 10%. That is

$$\mathbf{L}\_{\mathbf{Q}} \mathbf{=} \mathbf{K}\_{\mathbf{Q}} \sigma\_{\mathbf{Q}} = 10 \sigma\_0 \tag{15}$$

the calibration curve, the contents of the standards are accurately known and above all, the signals of all the points in the calibration curve have a Gaussian distribution (Figure 5).

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71

Then Hubaux and Vos drew two confident limits on either sides of the regression line with a priori level of confidence (Figure 5), 1−α−β. The regression line and its two confident limits can be used to predict with a 1−α−β probability, likely values for signals. The confidence band can be used in reverse, for a measured signal y of a sample of unknown content, it is possible

To our subject, a signal equal to yC is of interest (Figure 5), where the lower limit of content is zero. Signals equal or lower than yC have a probability bigger than α due to a blank, and hence cannot be distinguished from a blank signal. yC is the lowest measurable signal and therefore corresponds to the critical level LC of Currie. More exactly yC is an estimate of LC. Because this

If we trace a line from yC to the lower confident limit and then to the x axis (Figure 5), the value xD can be obtained, which is the lowest content it can be distinguished from zero. This value is inherent to the CMP and can be used as a priori limit, thus is equivalent to the limit of detection LD of Currie. It is important to clarify that the regression line and its confident limits are estimates of the real values. Consequently, the values of yC and xD are estimates too [32].

**Figure 5.** The linear calibration line, with its upper and lower confidence limits. yc is the decision limit and xD the detec‐

One serious problem with the Hubaux-Vos approach is the non-constant widths of the prediction interval which contradicts the assumption of homoscedasticity; another problem is, because yC and xD are estimates, this method requires the generation of multiple calibration

to predict the range of its content (xmax-xmin) (Figure 5).

limit concerns signals, it is used to posteriori decisions.

tion limit [33].

curves to calculate the mean of yC and xD.

Where, LQ is the limit of quantification, σQ, the standard deviation at the limit of quantification and KQ is the multiplier whose reciprocal equals the selected RSD; the IUPAC default value is 10. It's possible to transform all those expressions from the signal domain to concentration domain, and vice versa through the slope of the calibration curve.

The above relations represent the simplest possible case, based on restrictive assumptions. Actually, some of them are questionable as the assumption of normality in the blank measures and that σ is constant along the region of the critical level and limit of detection. They must not be taken as the defining relations for detection and quantification capabilities; being the defining relations, equation one, three and six for the critical level, the limit of detection and the limit of quantification respectively. Finally, for chemical measurement at least, the Fundamental contributing factor to the detection and quantification performance character‐ istics is the variability of the blank.

Currie's hypothesis testing schema, in spite of being theoretically solid, is very broad in scope, being independent of noise, the methodology to perform the measurements, conditions, etc. In fact his schema does not even have a connection to the calibration curve methodology or even the substance that would be measured.

A particular problem for the calculation of the limit of detection within the field of gas chromatography is the calculation of the deviation of the blank. It has been suggested to use the measurement of 20 blanks and calculate its standard deviation. Other authors suggest measuring the noise at the baseline of one chromatogram in a region near the analyte peak [21]. Nevertheless, questions arise about the sets of the integration parameters, the region of the baseline which should be used to calculate the blank variability and the presence of interfering substances. This makes the determination of s0 subjective and highly variable and has a major drawback in using the IUPAC definition in dynamic systems such as chromatography [23]. In order to introduce the calibration curve in the limit of calculation, other approaches have been developed to calculate the detection capabilities of a CMP.

### *2.2.2. Hubaux- Vos approach*

In 1970, Hubaux and Vos suggested how Currie's schema could be implemented with calibration curve methodology based on CMP, with homoscedastic (σ=contant), Gaussian noise, ordinary least square processing of the calibration curve data and ordinary least square prediction intervals [32]. Since then, Hubaux and Vos' treatment has generally been assumed to be fundamentally correct.

Hubaux and Vos made a series of assumptions to develop their approach, It was assumed that the standards of the calibration curve are independent, that the deviation is constant through the calibration curve, the contents of the standards are accurately known and above all, the signals of all the points in the calibration curve have a Gaussian distribution (Figure 5).

The ability to quantify is expressed in terms of the signal or analyte that will produce estimates

Where, LQ is the limit of quantification, σQ, the standard deviation at the limit of quantification and KQ is the multiplier whose reciprocal equals the selected RSD; the IUPAC default value is 10. It's possible to transform all those expressions from the signal domain to concentration

The above relations represent the simplest possible case, based on restrictive assumptions. Actually, some of them are questionable as the assumption of normality in the blank measures and that σ is constant along the region of the critical level and limit of detection. They must not be taken as the defining relations for detection and quantification capabilities; being the defining relations, equation one, three and six for the critical level, the limit of detection and the limit of quantification respectively. Finally, for chemical measurement at least, the Fundamental contributing factor to the detection and quantification performance character‐

Currie's hypothesis testing schema, in spite of being theoretically solid, is very broad in scope, being independent of noise, the methodology to perform the measurements, conditions, etc. In fact his schema does not even have a connection to the calibration curve methodology or

A particular problem for the calculation of the limit of detection within the field of gas chromatography is the calculation of the deviation of the blank. It has been suggested to use the measurement of 20 blanks and calculate its standard deviation. Other authors suggest measuring the noise at the baseline of one chromatogram in a region near the analyte peak [21]. Nevertheless, questions arise about the sets of the integration parameters, the region of the baseline which should be used to calculate the blank variability and the presence of interfering substances. This makes the determination of s0 subjective and highly variable and has a major drawback in using the IUPAC definition in dynamic systems such as chromatography [23]. In order to introduce the calibration curve in the limit of calculation, other approaches have been

In 1970, Hubaux and Vos suggested how Currie's schema could be implemented with calibration curve methodology based on CMP, with homoscedastic (σ=contant), Gaussian noise, ordinary least square processing of the calibration curve data and ordinary least square prediction intervals [32]. Since then, Hubaux and Vos' treatment has generally been assumed

Hubaux and Vos made a series of assumptions to develop their approach, It was assumed that the standards of the calibration curve are independent, that the deviation is constant through

LQ=KQσQ=10σ<sup>0</sup> (15)

having a specified relative standard deviation (RSD), commonly 10%. That is

domain, and vice versa through the slope of the calibration curve.

istics is the variability of the blank.

70 Advances in Gas Chromatography

*2.2.2. Hubaux- Vos approach*

to be fundamentally correct.

even the substance that would be measured.

developed to calculate the detection capabilities of a CMP.

Then Hubaux and Vos drew two confident limits on either sides of the regression line with a priori level of confidence (Figure 5), 1−α−β. The regression line and its two confident limits can be used to predict with a 1−α−β probability, likely values for signals. The confidence band can be used in reverse, for a measured signal y of a sample of unknown content, it is possible to predict the range of its content (xmax-xmin) (Figure 5).

To our subject, a signal equal to yC is of interest (Figure 5), where the lower limit of content is zero. Signals equal or lower than yC have a probability bigger than α due to a blank, and hence cannot be distinguished from a blank signal. yC is the lowest measurable signal and therefore corresponds to the critical level LC of Currie. More exactly yC is an estimate of LC. Because this limit concerns signals, it is used to posteriori decisions.

If we trace a line from yC to the lower confident limit and then to the x axis (Figure 5), the value xD can be obtained, which is the lowest content it can be distinguished from zero. This value is inherent to the CMP and can be used as a priori limit, thus is equivalent to the limit of detection LD of Currie. It is important to clarify that the regression line and its confident limits are estimates of the real values. Consequently, the values of yC and xD are estimates too [32].

**Figure 5.** The linear calibration line, with its upper and lower confidence limits. yc is the decision limit and xD the detec‐ tion limit [33].

One serious problem with the Hubaux-Vos approach is the non-constant widths of the prediction interval which contradicts the assumption of homoscedasticity; another problem is, because yC and xD are estimates, this method requires the generation of multiple calibration curves to calculate the mean of yC and xD.

#### *2.2.3. Propagation of errors approach*

In the hypothesis testing approach, the value of the limit of detection LD depends only on the variability of the blanks σ0. The propagation of errors approach considers the standard deviation of the concentration sX. This value is calculated by including the standard deviations of the blank s0, slope sm, and intercept si , in the equation [25]. The contribution of the variability of slope, blank and intercept to the variability of x is expressed by the formula:

$$\mathbf{s} = \frac{\left[\mathbf{s}\_o^2 + \mathbf{s}\_i^2 + \left(\frac{\mathbf{1} - \mathbf{y}}{m}\right)^2 \mathbf{s}\_m^2\right]^{1/2}}{m} \tag{16}$$

to calculate the LOD, it is necessary to generate a calibration curve, from which the values of the slope (m) and intersect (i) are obtained. From these values and the equation of the calibra‐ tion curve a predicted response is calculated (yp), and then the error associated with each

Then the sum of the square of the errors is calculated for all the points of the calibration curve,

(Y )

*Y*

*ip*

é ù ê ú -

> *n* =

ê ú <sup>=</sup> ê ú - ê ú ê ú ë û

2

Since the RMSE is calculated from a calibration curve, this approach uses both the variability of the blank and of the measurements. For dynamic systems, such as chromatography with

This method to calculate the MDL analyzed seven fortified samples with amounts of analyte close to an estimated limit of quantitation (ELOQ) or the lowest limit of method validation (LLMV) and its standard deviation sELOQ/LLMV was calculated. This value in a way is a substitute of σD in Currie's definitions. Because this approach was developed by EPA, it is used to

Where, t99n-1 is the one sided Student's t for N-1 observations (six degrees of freedom in our

However, it is extremely important that the ELOQ be accurately determined, because the fortification concentration greatly influenced the final value of MDL and MQL determined by this approach. EPA recommends that if the calculated MQL is significantly different from the ELOQ, the procedure has to be repeated with the calculated MQL as the new ELOQ, and MDL and MQL should be recalculated. This should be done until the calculated values are in the range of the estimated values. This approach is considered a fairly accurate way to determining method detection limits [23]. This approach is similar in some aspects to the so-called empirical

1/2 2

é ù y -y ë û (18)

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Limit of Detection and Limit of Quantification Determination in Gas Chromatography

MDL= t s 99n-1 ELOQ/LLMV (20)

(19)

73

p

1

*i*

autointegration systems, RMSE is easier to measure and more reliable than σ0 [23].

*RMSE*

case) at the 99% confidence level. In this case t99n=1 equals to 3.143.

*n*

å

measurement:

and finally the RMSE.

*2.2.5. The t99sLLMV approach*

determine the MDL with the relation:

The standard deviation of the concentration is equal to sD, and because it was assumed that, the standard deviation is constant along the region of interest s0=sD=s, it can substitute s0 in any of Currie relations. If we substitute equation 16 in equation 13, and we assume the blank´s signal is set to zero the following relation is obtained.

$$L\_D = \frac{k\left\{s\_0^2 + s\_i^2 + \left[\left(\frac{i}{m}\right)^2 s\_m^2\right]\right\}^{1/2}}{m} \tag{17}$$

Where K is a constant related to the degree of error, the analysts assume. The mathematical expressions for s0, si and sm, can be found in [25] and publications specialized in statistics.

Experimentally, it has been found that the IUPAC approach, based exclusively on the blank variability, in most cases, gives lower values of LD than the propagation of error ap‐ proach, which, besides the errors of the blank, takes into account errors in analyte measurement (slope and intersect). Consequently, the propagation of errors approach gives More realistic values of LD and consistent with the reliability of the blank measures and the signal measures of the standards. In the literature, the propagation of errors is preferred in many chemistry fields [25].

In order to calculate the limit of detection with the propagation of errors approach, it is necessary to make a minimum of five calibration curves to be able to measure si and sm properly, All of these calibration curves would have to be prepared by fortifying control samples with the analyte of interest at concentrations around an estimated limit of detection. This would make the procedure cumbersome for dynamic systems such as chromatography [23].

#### *2.2.4. Root mean square error approach*

In this approach, the root mean square error (RMSE) is used instead of the standard deviation of the blank σ0 in equations 7, 9 and 15, corresponding to LC, LD and LQ, respectively. In order to calculate the LOD, it is necessary to generate a calibration curve, from which the values of the slope (m) and intersect (i) are obtained. From these values and the equation of the calibra‐ tion curve a predicted response is calculated (yp), and then the error associated with each measurement:

$$
\begin{bmatrix}
\mathbf{y}\_{\mathsf{P}} \cdot \mathbf{y} \\
\end{bmatrix}
\tag{18}
$$

Then the sum of the square of the errors is calculated for all the points of the calibration curve, and finally the RMSE.

$$RMSE = \left[ \frac{\sum\_{i=1}^{n} (\mathbf{Y}\_{ip} - \overline{\mathbf{Y}})^2}{n - 2} \right]^{1/2} \tag{19}$$

Since the RMSE is calculated from a calibration curve, this approach uses both the variability of the blank and of the measurements. For dynamic systems, such as chromatography with autointegration systems, RMSE is easier to measure and more reliable than σ0 [23].

### *2.2.5. The t99sLLMV approach*

*2.2.3. Propagation of errors approach*

72 Advances in Gas Chromatography

of the blank s0, slope sm, and intercept si

In the hypothesis testing approach, the value of the limit of detection LD depends only on the variability of the blanks σ0. The propagation of errors approach considers the standard deviation of the concentration sX. This value is calculated by including the standard deviations

1/2 <sup>2</sup>

1/2 <sup>2</sup>

and sm, can be found in [25] and publications specialized in statistics.

of slope, blank and intercept to the variability of x is expressed by the formula:

*s*

signal is set to zero the following relation is obtained.

*D*

*L*

expressions for s0, si

in many chemistry fields [25].

*2.2.4. Root mean square error approach*

2 2 1 <sup>2</sup> *o i m <sup>y</sup> ss s m*

é ù æ ö - ê ú + + ç ÷ è ø ë û <sup>=</sup>

*m*

The standard deviation of the concentration is equal to sD, and because it was assumed that, the standard deviation is constant along the region of interest s0=sD=s, it can substitute s0 in any of Currie relations. If we substitute equation 16 in equation 13, and we assume the blank´s

> 22 2 0 *i m*

ì ü é ù ï ï æ ö + + ê ú í ý ç ÷ ï ï ê ú è ø î þ ë û <sup>=</sup>

*m*

Where K is a constant related to the degree of error, the analysts assume. The mathematical

Experimentally, it has been found that the IUPAC approach, based exclusively on the blank variability, in most cases, gives lower values of LD than the propagation of error ap‐ proach, which, besides the errors of the blank, takes into account errors in analyte measurement (slope and intersect). Consequently, the propagation of errors approach gives More realistic values of LD and consistent with the reliability of the blank measures and the signal measures of the standards. In the literature, the propagation of errors is preferred

In order to calculate the limit of detection with the propagation of errors approach, it is

All of these calibration curves would have to be prepared by fortifying control samples with the analyte of interest at concentrations around an estimated limit of detection. This would

In this approach, the root mean square error (RMSE) is used instead of the standard deviation of the blank σ0 in equations 7, 9 and 15, corresponding to LC, LD and LQ, respectively. In order

make the procedure cumbersome for dynamic systems such as chromatography [23].

necessary to make a minimum of five calibration curves to be able to measure si

*m*

*<sup>i</sup> ks s s*

, in the equation [25]. The contribution of the variability

(16)

(17)

and sm properly,

This method to calculate the MDL analyzed seven fortified samples with amounts of analyte close to an estimated limit of quantitation (ELOQ) or the lowest limit of method validation (LLMV) and its standard deviation sELOQ/LLMV was calculated. This value in a way is a substitute of σD in Currie's definitions. Because this approach was developed by EPA, it is used to determine the MDL with the relation:

$$\text{MDL} = \mathbf{t}\_{99n\text{-}1} \; \text{s}\_{\text{ELOQ/LLMV}} \tag{20}$$

Where, t99n-1 is the one sided Student's t for N-1 observations (six degrees of freedom in our case) at the 99% confidence level. In this case t99n=1 equals to 3.143.

However, it is extremely important that the ELOQ be accurately determined, because the fortification concentration greatly influenced the final value of MDL and MQL determined by this approach. EPA recommends that if the calculated MQL is significantly different from the ELOQ, the procedure has to be repeated with the calculated MQL as the new ELOQ, and MDL and MQL should be recalculated. This should be done until the calculated values are in the range of the estimated values. This approach is considered a fairly accurate way to determining method detection limits [23]. This approach is similar in some aspects to the so-called empirical method [24, 33], where increasingly lower concentrations of the analyte are analyzed until the measurement do not satisfy a predetermined criteria.

#### *2.2.6. Baseline noise approach*

The IUPAC and propagation of errors approaches were developed for spectroscopic analysis. Nearly all concepts used in this approach have an equivalent in chromatography, except the interpretation and measurement of S0, It has been proposed that the chromatographic baseline is analogous to a blank and S0 must represent a measure of the baseline fluctuations [21, 23].

Therefore, in order to calculate the LOD and LOQ it is necessary to measure the peak-to-peak noise (Np-p) of the baseline around the analyte retention time. Np-p can be related to the standard deviation of the blank through the relation [21]:

$$s\_0 = \frac{N\_{p-p}}{5} \tag{21}$$

**•** Both Limits are chemical measurement process (CMP) performance characteristics, and therefore, involve all the phases of the analysis. Consequently should not be confused with

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75

terms referred exclusively to the detection capabilities of the instrument like IDL.

**•** Detection limits are not associated with any particular outcome, they are a priori limit

**•** The existence of both type I errors (false positives) and type II errors (false negatives).

**•** Detection decision is based on the other hand on a posteriori limit, the critical value.

curve.

variability [20, 26, 30-31].

not be attainable for the CMP in question.

**•** Detection limits should not be confused with sensibility, which is the slope of the calibration

In developing the limit of detection theory, Currie made a series of assumptions. First, the measurement distribution of the blanks follow a normal distribution, which is questionable at low concentrations, and secondly, in order to obtain simplified relations, the standard deviation is constant over the range of concentrations studied (homoscedasticity). In this specific circumstances the detection capabilities of a method depends exclusively on blank

Even Hubaux-Vos prediction bands with their non-uniform width proves that the assumption of homoscedasticity is false. Currie and other authors [26, 28, 30-31] have addressed this problem, but stated that if the standard deviation increases too sharply, limits of detection may

Although in Currie´s simplified relations, limits of detection exclusively depend on blank variability; other sources of error can be introduced in making the transition from the signal domain to the concentration domain through the uncertainty of the slope and intercept of the calibration curve. To take this into account several approaches have been developed like the propagation error approach, Hubaix –Vos, RMSE, etc [23-26, 32]. Actually, the IUPAC approach which does not account measurement variability, usually gives artificially low values of limit of detection, while methods which account slope and intercept uncertainties, like the propagation error method and Hubaix-Vos method give more realistic estimates, consistent with the reliability of the blank measure and the signal measure of the standards.

In other words, Currie's simplified equations can only be valid, when all their assumptions are met (normal distribution, homoscedasticity, main source of error is the blank). To achieve this, a good knowledge of the blanks is needed to generate confidence in the nature of the blank distribution and some precision in the blank RSD is necessary; therefore an adequate number

Most of the assumptions of the IUPAC method are fulfilled in spectrophotometric analysis, for which it was developed, and where it has been used successfully. However, in the case of gas chromatography, where dynamic measures are carried out, and no practical rules are defined to measure the blank standard deviation; the error associated to the intersect of the calibration curve is not always negligible, and the presence of interferences is important. It is better to use a method that takes into account these sources of error. Therefore, for chromato‐ graphic techniques it is not recommended the IUPAC approach for the calculation of the

of full scale true blanks must be assayed through all the CMP.

In spite of being the simplest path to determine the detection capabilities of a chromatographic method, this approach is not recommended because it is very dependent on analyst interpre‐ tation since, there is no agreement on where to measure the noise and the extension of baseline that has to be measured. Therefore, the obtained results show great variability between laboratories and even between analysts and consequently, they are hard to compare.

### **3. Conclusions**

The limit of detection is an important figure of merit in analytical chemistry. It is of the utmost importance, in the development of methods to test the detection capabilities of a method and although it is not necessary to calculate it in the process of validation of all methods. It finds applications in areas such as environmental analysis, food analysis and areas under great scrutiny such as forensic science, etc.

Although the detection limit concept is deceptively simple, little is understood by the chem‐ istry community. This caused the proliferation of terms relating to the detection capabilities of a method with different approaches for its determination and impeded efforts to harmonize the methodology.

Although various authors and agencies [20-28, 30-32] have published their own defini‐ tions of the detection limit of analytical method, nowadays, the limits of detection and quantification are commonly accepted as that in the hypothesis testing detection limit theory [20, 26, 28, 30-32].

This theory states:

**•** Limits of detection are actual true values, which can be determined.


method [24, 33], where increasingly lower concentrations of the analyte are analyzed until the

The IUPAC and propagation of errors approaches were developed for spectroscopic analysis. Nearly all concepts used in this approach have an equivalent in chromatography, except the interpretation and measurement of S0, It has been proposed that the chromatographic baseline is analogous to a blank and S0 must represent a measure of the baseline fluctuations [21, 23]. Therefore, in order to calculate the LOD and LOQ it is necessary to measure the peak-to-peak noise (Np-p) of the baseline around the analyte retention time. Np-p can be related to the standard

<sup>0</sup> 5

laboratories and even between analysts and consequently, they are hard to compare.

In spite of being the simplest path to determine the detection capabilities of a chromatographic method, this approach is not recommended because it is very dependent on analyst interpre‐ tation since, there is no agreement on where to measure the noise and the extension of baseline that has to be measured. Therefore, the obtained results show great variability between

The limit of detection is an important figure of merit in analytical chemistry. It is of the utmost importance, in the development of methods to test the detection capabilities of a method and although it is not necessary to calculate it in the process of validation of all methods. It finds applications in areas such as environmental analysis, food analysis and areas under great

Although the detection limit concept is deceptively simple, little is understood by the chem‐ istry community. This caused the proliferation of terms relating to the detection capabilities of a method with different approaches for its determination and impeded efforts to harmonize

Although various authors and agencies [20-28, 30-32] have published their own defini‐ tions of the detection limit of analytical method, nowadays, the limits of detection and quantification are commonly accepted as that in the hypothesis testing detection limit theory

**•** Limits of detection are actual true values, which can be determined.

*Np p <sup>s</sup>* - <sup>=</sup> (21)

measurement do not satisfy a predetermined criteria.

deviation of the blank through the relation [21]:

*2.2.6. Baseline noise approach*

74 Advances in Gas Chromatography

**3. Conclusions**

the methodology.

[20, 26, 28, 30-32].

This theory states:

scrutiny such as forensic science, etc.


In developing the limit of detection theory, Currie made a series of assumptions. First, the measurement distribution of the blanks follow a normal distribution, which is questionable at low concentrations, and secondly, in order to obtain simplified relations, the standard deviation is constant over the range of concentrations studied (homoscedasticity). In this specific circumstances the detection capabilities of a method depends exclusively on blank variability [20, 26, 30-31].

Even Hubaux-Vos prediction bands with their non-uniform width proves that the assumption of homoscedasticity is false. Currie and other authors [26, 28, 30-31] have addressed this problem, but stated that if the standard deviation increases too sharply, limits of detection may not be attainable for the CMP in question.

Although in Currie´s simplified relations, limits of detection exclusively depend on blank variability; other sources of error can be introduced in making the transition from the signal domain to the concentration domain through the uncertainty of the slope and intercept of the calibration curve. To take this into account several approaches have been developed like the propagation error approach, Hubaix –Vos, RMSE, etc [23-26, 32]. Actually, the IUPAC approach which does not account measurement variability, usually gives artificially low values of limit of detection, while methods which account slope and intercept uncertainties, like the propagation error method and Hubaix-Vos method give more realistic estimates, consistent with the reliability of the blank measure and the signal measure of the standards.

In other words, Currie's simplified equations can only be valid, when all their assumptions are met (normal distribution, homoscedasticity, main source of error is the blank). To achieve this, a good knowledge of the blanks is needed to generate confidence in the nature of the blank distribution and some precision in the blank RSD is necessary; therefore an adequate number of full scale true blanks must be assayed through all the CMP.

Most of the assumptions of the IUPAC method are fulfilled in spectrophotometric analysis, for which it was developed, and where it has been used successfully. However, in the case of gas chromatography, where dynamic measures are carried out, and no practical rules are defined to measure the blank standard deviation; the error associated to the intersect of the calibration curve is not always negligible, and the presence of interferences is important. It is better to use a method that takes into account these sources of error. Therefore, for chromato‐ graphic techniques it is not recommended the IUPAC approach for the calculation of the detection capabilities of the method. Instead, the propagation of error, Hubaix- Vos, RMSE and t99sLLMV approaches, which take into account the errors of the measurements of the analyte through a calibration curve, are recommended. A brief comparison of the different approaches for the determination of the detection capabilities of a CMP can be found in Table 4.

β Type II error, false negative

DL Detection limit/level

EDL Estimated Detection Limit

ELOQ Estimated limit of quantification

EQL Estimated Quantitation Limit

GLP Good Laboratory Practice

i Intersect of the calibration curve

IDL Instrumental Detection Limit IML Interim Minimum level

L Quantity of interest

LOD Limit of Detection LOD\* Limit of detectability LOM Limit of Measurement LOQ Limit of Quantification

LD Minimum detectable value

LQ Minimum Quantification level/value LLMV Lower limit of method validation

m Sensitivity, slope of calibration curve

MAL Minimum Analytical Level

LC Critical value

CMP Chemical Measurement Process CRQL Contract Required Quantitation Limit

CDER Conference for Drug Evaluation and Research

DLR Detection Limit for Purpose of Reporting

EPA (United States) Environmental Protection Agency

FDA United States Food and Drug Administration

ICH International Conference for Harmonization

IQE Interlaboratory Quantication Estimate ISO/IEC International Standards Organization

IUPAC International Union of Pure and Applied Chemistry

K<sup>α</sup> the one sided tails of the distribution of the blank corresponding to probability levels, 1-. K<sup>β</sup> the one sided tails of the distribution of L, when L=LD corresponding to probability levels, 1-.

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

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77

δα,β,ν the non centrality parameter of the non central t distribution


**Table 4.** Comparison of approaches for calculating detection and quantification limits for analytical method

Since several methods can be used, and could be a difference in the limit of detection calculated by them, it is important that when reporting values of limits of detection, the method used to define these values should be clearly identified in order to have meaningful comparisons.

In order to properly determine the limit of detection and limit of quantification of a method, it is necessary to know the theory behind them, to recognize the scope and limitations of any approach, and be able to choose the method that better suits our CMP. The intention of this chapter is to review the fundamentals of detection limits determination for the purpose of achieving a better understanding of this key element in trace analysis, in particular and analytical chemistry in general; and to achieve a more scientific and less arbitrary use of this figure of merit with a view to their harmonization and avoid the confusion about them, which still prevails in the chemical community.

### **Nomenclature**



detection capabilities of the method. Instead, the propagation of error, Hubaix- Vos, RMSE and t99sLLMV approaches, which take into account the errors of the measurements of the analyte through a calibration curve, are recommended. A brief comparison of the different approaches

> **Considers method efficiency and matrix effect**

**Variability between laboratories and analysts**

for the determination of the detection capabilities of a CMP can be found in Table 4.

**Variability of calibration curve**

Ks0 No No Yes Moderate Np-p Yes No Yes High Propagation of errors No Yes No Low Hubaux - Vos No Yes No Low RMSE Yes Yes No Low **t99sLLMV** Yes Yes Yes Low

**Table 4.** Comparison of approaches for calculating detection and quantification limits for analytical method

Since several methods can be used, and could be a difference in the limit of detection calculated by them, it is important that when reporting values of limits of detection, the method used to define these values should be clearly identified in order to have meaningful comparisons.

In order to properly determine the limit of detection and limit of quantification of a method, it is necessary to know the theory behind them, to recognize the scope and limitations of any approach, and be able to choose the method that better suits our CMP. The intention of this chapter is to review the fundamentals of detection limits determination for the purpose of achieving a better understanding of this key element in trace analysis, in particular and analytical chemistry in general; and to achieve a more scientific and less arbitrary use of this figure of merit with a view to their harmonization and avoid the confusion about them, which

**Method Easy to**

76 Advances in Gas Chromatography

still prevails in the chemical community.

α Type I error, false positive ACS American Chemical Society ADL Analytical Detection Level AML Alternative Minimum Level API American Petroleum Institute

ASTM American Society for Testing and Materials

**Nomenclature**

**apply**


**Author details**

Ernesto Bernal1,2\*

Mexico

**References**

fice; 2012.

USP; 2009.

ISO; 2005

national; 1975

2005

Rockville: ICH; 2001.

Address all correspondence to: ernestob066@gmail.com

1 Faculty of Criminology, University of Ixtlahuaca CUI, Mexico, Ixtlahuaca, Estado de Mexico,

Limit of Detection and Limit of Quantification Determination in Gas Chromatography

http://dx.doi.org/10.5772/57341

79

[1] Hirsch AF. Good laboratory practice regulations. New York: Marcel Dekker; 1989.

Guide to method validation and related topics. Teddington: LGC; 1998.

[2] EURACHEM Guide: The fitness for purpose of analytical methods. A Laboratory

[3] Title 21of the US Code of federal regulations: 21 CFR 211, Current good manufactur‐ ing practice for finished pharmaceuticals. Washington: U.S. Government Printing Of‐

[4] Title 21of the US Code of federal regulations: 21 CFR 58, Good laboratory practice for nonclinical laboratory studies. Washington: U.S. Government Printing Office; 2012.

[5] Title 21of the US Code of federal regulations: 21 CFR 320, Bioavailability and bioe‐ quivalence requirements. Washington: U.S. Government Printing Office; 2012.

[6] ICH Q7A: Good Manufacturing practice guide for active pharmaceutical ingredients.

[7] USP 32- NF27. General chapter 1225, Validation of compendial methods. Rockville:

[8] ISO/IEC 17025, General requirements for the competence of testing and calibration

[9] ISO 9000: Quality management systems – Fundamentals and vocabulary. Geneva:

[11] Youden WJ, Steiner EH. Statistical manual of the AOAC. Gaithersburg: AOAC Inter‐

[12] Validation of analytical procedures: text and methodology Q2(R1). Rockville: ICH;

[10] ISO, Guide to the expression of uncertainty in measurement. Geneva: ISO; 1995

laboratories. 2nd Edition, Switzerland: ISO/IEC; 2005.

2 Chemistry Department, Forensic Science Institute of Mexico City, México

### **Acknowledgements**

I would like to acknowledge to the Chemical Engineer Sarai Cortes for the exchange of ideas and knowledge on this subject and the joint work.

### **Author details**

MDL Method Detection Limit MDL\* Minimum Detection Limit

MLD Minimum Level of Detectability MQL Method quantitative limit MQL\* Minimum quantification level

NJQL New Jersey Quantitation Level PQL Practical Quantification Level

MQL\*\* Minimum analytical quantification level

RSDQ Relative standard deviation when L=LQ

sELOQ/LLMV Standard deviation when L=ELOQ or L=LLMV

t1-α,ν Student's t, with a probability and degrees of freedom

xD The lowest content it can be distinguished from zero

and knowledge on this subject and the joint work.

t99n-1 One sided Student's t, for N-1 observations at the 99% confidence level

I would like to acknowledge to the Chemical Engineer Sarai Cortes for the exchange of ideas

σ<sup>0</sup> Standard deviation of the blank σ<sup>D</sup> Standard deviation when L=LD σ<sup>Q</sup> Standard deviation when L=LQ

SQL Sample Quantitation Limit

USP United States Pharmacopeia x Amount or concentration

yC The lowest measurable signal

yp Predicted response

**Acknowledgements**

ML Minimum Limit

78 Advances in Gas Chromatography

Np-p Peak to peak noise

QL Quantification Level RMSE Root mean square error

Pr Probability

S Analyte signal

Ernesto Bernal1,2\*

Address all correspondence to: ernestob066@gmail.com

1 Faculty of Criminology, University of Ixtlahuaca CUI, Mexico, Ixtlahuaca, Estado de Mexico, Mexico

2 Chemistry Department, Forensic Science Institute of Mexico City, México

### **References**


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[30] Currie LA. Detection and quantification limits: origins and historical overview. Anal.

[31] Currie LA. Detection: international update, and some emerging di-lemmas involving calibration, the blank and multiple detection decisions. Chem. Intell. Lab. Sys. 1997;

[32] Hubaux, A. Vos, G. Decision and detection limits for linear calibrations curves. Anal.

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[13] Validation of analytical methods for food control. Report of a joint FAO/IAEA expert

[14] Thompson M, Ellison RL, Wood R. Harmonized guidelines for single laboratory vali‐

[15] U.S. FDA – Guidance for Industry (draft): Analytical Procedures and Methods vali‐ dation: chemistry, Manufacturing, and Controls and Documentation, Rockville: FDA;

[16] Lopez Garcia P, Buffoni E, Pereira Gomez F, Vilchez Quero JL. Analytical method validation. Wide spectra of quality control. In Akyar I. (ed.), In Tech. Rijeka: In Tech;

[18] Reviewer Guidance, Validation of Chromatographic Methods. Rockville: Center for

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[21] Foley JP, Dorsey JG. Clarification of the limit of detection in chromatography. Chro‐

[22] International Organization for Standardization. Capability of detection. Part 1: Terms

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[24] Armbruster DA, Tillman MD, Hubbs LA. Limit of detection (LOD)/limit of quantifi‐ cation (LOQ): comparison of the empirical and the statistical methods exemplified

[25] Long GL, Winefordner JD. Limit of detection, a closer look at the IUPAC definition.

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2011


**Chapter 4**

**Gas Chromatography in Metabolomics Study**

The metabolome of a cell, tissue, organ, or organism is represented by its small molecular metabolites (molecular weight less than 1000 Da), which are the end products of cellular processes. Variation of the metabolome reflects the interaction of changes in upstream molecules, such as genes and proteins with environmental factors. Metabolic profiling, which is the high-throughput characterization of the metabolome, can be used to assess health status and is a potential diagnostic tool for human diseases. Historically, the sweetness of urine was used to diagnose diabetes. In late 1940s and early 1950s, Roger Williams and colleagues introduced the concept of a "metabolic pattern" to explain the results from their paper chromatography studies that distinguished saliva and urine components among individuals [1]. However, this concept was not widely accepted until the 1960s, when gas chromatography (GC) was successfully used for profiling a complex biological matrix. In the 1960s and 1970s, GC and gas chromatography-mass spectrometry (GC-MS) were successfully used to diagnose metabolic disorders, including maple syrup urine disease [2] and phenylketonuria [3]. In the early 1970s, the term "metabolic profiles" was conceived by Hornings to describe the chro‐

With the improvement of the sensitivity for the instrument and statistical analysis tools, the field of metabolomics (metabonomics) consequently developed at the end of last century and has been widely used in the past decade. In addition to GC-MS, nuclear magnetic resonance (NMR) and other chromatography coupled mass spectrometry, such as liquid chromatogra‐ phy-mass spectrometry (LC-MS) and capillary electrophoresis–mass spectrometry (CE-MS), are widely used as analytical tools for metabolomics. Compared with other analytical tools, GC-MS is one of the most efficient, sensitive, and reliable tools for metabolomics studies. GC-MS produces reproducible molecular fragmentation patterns making it an integral tool for metabolite identification. Although the application of GC is limited to volatile compounds (before or after derivatization), a large portion of small molecular metabolites are within the

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Yunping Qiu and Deborah Reed

http://dx.doi.org/10.5772/57397

**1. Introduction**

Additional information is available at the end of the chapter

matographic pattern associated with bio-fluid analysis [4].

### **Gas Chromatography in Metabolomics Study**

Yunping Qiu and Deborah Reed

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57397

### **1. Introduction**

The metabolome of a cell, tissue, organ, or organism is represented by its small molecular metabolites (molecular weight less than 1000 Da), which are the end products of cellular processes. Variation of the metabolome reflects the interaction of changes in upstream molecules, such as genes and proteins with environmental factors. Metabolic profiling, which is the high-throughput characterization of the metabolome, can be used to assess health status and is a potential diagnostic tool for human diseases. Historically, the sweetness of urine was used to diagnose diabetes. In late 1940s and early 1950s, Roger Williams and colleagues introduced the concept of a "metabolic pattern" to explain the results from their paper chromatography studies that distinguished saliva and urine components among individuals [1]. However, this concept was not widely accepted until the 1960s, when gas chromatography (GC) was successfully used for profiling a complex biological matrix. In the 1960s and 1970s, GC and gas chromatography-mass spectrometry (GC-MS) were successfully used to diagnose metabolic disorders, including maple syrup urine disease [2] and phenylketonuria [3]. In the early 1970s, the term "metabolic profiles" was conceived by Hornings to describe the chro‐ matographic pattern associated with bio-fluid analysis [4].

With the improvement of the sensitivity for the instrument and statistical analysis tools, the field of metabolomics (metabonomics) consequently developed at the end of last century and has been widely used in the past decade. In addition to GC-MS, nuclear magnetic resonance (NMR) and other chromatography coupled mass spectrometry, such as liquid chromatogra‐ phy-mass spectrometry (LC-MS) and capillary electrophoresis–mass spectrometry (CE-MS), are widely used as analytical tools for metabolomics. Compared with other analytical tools, GC-MS is one of the most efficient, sensitive, and reliable tools for metabolomics studies. GC-MS produces reproducible molecular fragmentation patterns making it an integral tool for metabolite identification. Although the application of GC is limited to volatile compounds (before or after derivatization), a large portion of small molecular metabolites are within the

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

range of GC separation. For their different metabolic windows among NMR, LC-MS, CE-MS, and GC-MS, the combination of two or more analytical platforms has been used in many metabolomics studies.

In this chapter we introduce GC-MS-based metabolomics. We will begin with the introduction of GC-MS-based metabolomics procedures, and then describe each section of the procedures in more detail. GC-MS-based metabolomics have been utilized in various areas (such as biomedical research, plant research, and microbial research) as well as for different types of samples. We will also provide some examples of using GC-MS-based metabolomics studies in these described fields.

### **2. Overview of GC-MS-based metabolomics**

Prior to GC-MS analysis, effective sample collection and processing methods are necessary to optimize analyte yield and instrument performance. The collected samples can be liquid, solid, or gas, and different sample pretreatment procedures can be used on different sample types. For complex matrices and solid samples such as blood, tissue, and cell pellets, metabolite extraction is necessary to diminish the matrix effect arising from the complex biological materials. Unlike LC-MS and NMR-based metabolomics, compound derivatization is neces‐ sary to increase the volatility of metabolites containing polar functional groups, such as carboxylic and amino groups. The volatile compounds (pre- or post-derivatization) are subjected to GC-MS analysis and data collection. To understand the complex mass signals, a data processing procedure should be performed to identify the true signals, assign the signals into different compounds, and align these compounds from different samples. The aligned data can then reveal the differential variables with different sample groups based on statistical analysis. After peak annotation, the metabolic pathway associated with certain physiological or pathological variations can be obtained. Therefore, the entire process is comprised of sample collection, metabolites extraction, compound derivatization, instrument analysis, data analysis, and metabolite annotation and pathway analysis (Figure 1).

**3.1. Serum or plasma**

**Figure 1.** The procedure for GC-MS-based metabolomics.

For blood samples, both serum and plasma can be obtained using different biochemical processes after collection. Serum samples are subjected to a clotting process, which mainly removes fibrinogens, while plasma samples need an anticoagulant during the separation from blood cells. Do these different processes cause variation in metabolites? Is serum or plasma better for metabolomic studies? To answer these questions, previous studies were performed using different analytical platforms. The results showed that there were only slight differences between plasma and serum samples. Using NMR, Teahan et al. showed minor peak shifts between serum and plasma (heparin used as the anticoagulant) samples, such as intensity differences in the resonance dominated by lipid/ triglyceride peaks [5]. In an LC-MS-based untargeted metabolomics study, differences between the serum and plasma (heparin used as the anticoagulant) samples were found to be peptide/protein fragments and lysophophatidy‐ linositol. A lower concentration of lysophophatidylinositol in serum samples was due to degradation in the blood clotting cascade [6]. In an LC-MS/MS-based targeted metabolomics study [7], the authors compared 122 metabolites (focus on lipids and amino acids) between serum and plasma (EDTA used as the anticoagulant) samples. That study found a high correlation between serum and plasma concentrations in all of the targeted metabolites and a

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### **3. Sample collection**

Typical samples for GC-MS-based metabolomics can be bio-fluids, tissue, or cell samples. For biomedical or clinical use, bio-fluids are widely used due to the non-invasive collection procedures. Bio-fluids have also been considered to be good sources of diagnostic biomarkers. The most widely used bio-fluids are serum/ plasma and urine. Saliva, tears, and sweat are also used in metabolomics studies. Tissue samples can be obtained from animals, human beings, or plants; and cell samples can be from mammals, plants, or microorganisms. Specific sample collection procedures may impact reproducibility, sensitivity and yield of identified metabo‐ lites. Here, we will use blood sample collection and treatment as an example to show how the procedure can impact the metabolomics results.

**Figure 1.** The procedure for GC-MS-based metabolomics.

#### **3.1. Serum or plasma**

range of GC separation. For their different metabolic windows among NMR, LC-MS, CE-MS, and GC-MS, the combination of two or more analytical platforms has been used in many

In this chapter we introduce GC-MS-based metabolomics. We will begin with the introduction of GC-MS-based metabolomics procedures, and then describe each section of the procedures in more detail. GC-MS-based metabolomics have been utilized in various areas (such as biomedical research, plant research, and microbial research) as well as for different types of samples. We will also provide some examples of using GC-MS-based metabolomics studies in

Prior to GC-MS analysis, effective sample collection and processing methods are necessary to optimize analyte yield and instrument performance. The collected samples can be liquid, solid, or gas, and different sample pretreatment procedures can be used on different sample types. For complex matrices and solid samples such as blood, tissue, and cell pellets, metabolite extraction is necessary to diminish the matrix effect arising from the complex biological materials. Unlike LC-MS and NMR-based metabolomics, compound derivatization is neces‐ sary to increase the volatility of metabolites containing polar functional groups, such as carboxylic and amino groups. The volatile compounds (pre- or post-derivatization) are subjected to GC-MS analysis and data collection. To understand the complex mass signals, a data processing procedure should be performed to identify the true signals, assign the signals into different compounds, and align these compounds from different samples. The aligned data can then reveal the differential variables with different sample groups based on statistical analysis. After peak annotation, the metabolic pathway associated with certain physiological or pathological variations can be obtained. Therefore, the entire process is comprised of sample collection, metabolites extraction, compound derivatization, instrument analysis, data

Typical samples for GC-MS-based metabolomics can be bio-fluids, tissue, or cell samples. For biomedical or clinical use, bio-fluids are widely used due to the non-invasive collection procedures. Bio-fluids have also been considered to be good sources of diagnostic biomarkers. The most widely used bio-fluids are serum/ plasma and urine. Saliva, tears, and sweat are also used in metabolomics studies. Tissue samples can be obtained from animals, human beings, or plants; and cell samples can be from mammals, plants, or microorganisms. Specific sample collection procedures may impact reproducibility, sensitivity and yield of identified metabo‐ lites. Here, we will use blood sample collection and treatment as an example to show how the

metabolomics studies.

84 Advances in Gas Chromatography

these described fields.

**3. Sample collection**

procedure can impact the metabolomics results.

**2. Overview of GC-MS-based metabolomics**

analysis, and metabolite annotation and pathway analysis (Figure 1).

For blood samples, both serum and plasma can be obtained using different biochemical processes after collection. Serum samples are subjected to a clotting process, which mainly removes fibrinogens, while plasma samples need an anticoagulant during the separation from blood cells. Do these different processes cause variation in metabolites? Is serum or plasma better for metabolomic studies? To answer these questions, previous studies were performed using different analytical platforms. The results showed that there were only slight differences between plasma and serum samples. Using NMR, Teahan et al. showed minor peak shifts between serum and plasma (heparin used as the anticoagulant) samples, such as intensity differences in the resonance dominated by lipid/ triglyceride peaks [5]. In an LC-MS-based untargeted metabolomics study, differences between the serum and plasma (heparin used as the anticoagulant) samples were found to be peptide/protein fragments and lysophophatidy‐ linositol. A lower concentration of lysophophatidylinositol in serum samples was due to degradation in the blood clotting cascade [6]. In an LC-MS/MS-based targeted metabolomics study [7], the authors compared 122 metabolites (focus on lipids and amino acids) between serum and plasma (EDTA used as the anticoagulant) samples. That study found a high correlation between serum and plasma concentrations in all of the targeted metabolites and a slightly higher concentration of metabolites in serum samples. There was also better repro‐ ducibility with the plasma samples, but higher sensitivity with the serum samples.

tions of extraction solvents, such as methanol/ chloroform or methanol/ chloroform/ water, are

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Methanol or methanol/chloroform combination is widely used for GC-MS-based metabolo‐ mics studies of blood samples (serum/ plasma). Using the combination of a D-optimal experimental design and partial least squares projections to latent structures analysis, Jiye and colleagues developed a methanol/water-based metabolites extraction procedure from plasma [10]. In this study, the authors compared different solvents and their combinations including water, methanol, ethanol, acetonitrile, acetone, and chloroform. Using methoxyamine and N-Methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) derivatization, the authors resolved 501 peaks in a gas chromatography-Time-of-Flight mass spectrometry (GC-TOF-MS) analysis and identified 80 metabolites from these peaks. Their study showed 100 µL of plasma mixed with 900 µL of methanol and water (8:1, v/v) provided optimal metabolites extraction results. Our laboratory has found that the combination of methanol and chloroform (3:1, v/v) is better for serum metabolite extraction than methanol alone [11]. Alternative ratios of methanol and chloroform have also been used in other metabolomics studies. For example, Nishiumi and colleagues used 250 µl of a solvent mixture (MeOH: H2O: CHCl3 = 2.5:1:1) for extracting 50 µl

Liquid extraction is necessary for metabolomics studies with solid samples, such as tissue and cell pellets. Tissue samples (from mammalian or plant) stored in liquid nitrogen need to be ground to a fine powder prior to solvent extraction or homogenized in a solvent with blenders. A combination of chloroform/methanol/water has been used extensively in metabolite extraction from tissue. Denkert et al. used a combination of chloroform/methanol/water (2:5:2, v/v/v) for colon tissue extraction [13]. Pan et al. developed a two-step metabolite extraction procedure with a mixed solvent of chloroform/methanol/water (1:2:1, v/v/v) in the first step and methanol in the second step for GC-TOFMS-based metabonomics study of liver tissue [14]. In addition, Gullberg et al. optimized an extraction protocol that yielded 66 metabolites from *Arabidopsis thaliana* samples. The authors found that the optimal extraction mixture for plant tissue was 200 µL of chloroform in a vibration mill followed by 800 µl of a methanol/water

The extraction procedures for collected cell pellets have been widely tested with different solvents and various types of cells. Yeast is one of the most investigated species used for targeted metabolites analysis. One commonly used intracellular procedure for metabolite extraction is based on boiling ethanol, which was firstly described by Entian et al. in 1977 [16]. However, boiling ethanol may be harmful for some metabolites. Villas-Boas et al. tested 6 extraction conditions for yeast, including chloroform/methanol/buffer, boiling ethanol, perchloric acid, potassium hydroxide, methanol/water, and pure methanol. Their results indicated that pure methanol is the most suitable solvent and perchloric acid is the least suitable for metabolite extraction [17]. After testing five extraction protocols, including methanol, methanol/chloroform, perchloric acid, boiling ethanol and potassium hydroxide, Winder et al. also found that cold methanol with repeated freeze/thaw cycles was most effective for extracting intracellular metabolites from *E. coli*. [18]. For mammalian cells, methanol, metha‐ nol/water, and methanol/chloroform have been identified as suitable agents for metabolite

used in many metabolite extraction procedures.

mixture (75:25, v/v) to form a monophase [15].

of serum [12].

In addition to NMR and LC-MS, GC-MS-based metabolomics has also been used to evaluate the differences between serum and plasma samples. Dettmer et al. showed slightly higher concentrations in most of the 26 quantified metabolites (19 amino acids, 6 organic acids, and glucose), with the exception of pyruvate, malate, citrate, and glucose in serum samples compared to plasma samples (EDTA used as the anticoagulant) [8]. Only lactate and citrate had variation greater than 15%, which could be attributed to serum and plasma sample preparation. The clotting time for serum preparation may result in a higher level of lactate in the serum due to glycolysis occurring in the blood cells. The conjugation of cations (such as Ca2+ and Mg2+) with EDTA (the anticoagulant) lowers the concentration of cations in the plasma, which conjugate with citrate during the metabolite extraction process [8]. Slightly higher metabolite concentrations in the serum samples were observed using different analyt‐ ical platforms, which may be due to lower protein levels in the serum samples as a result of fibrinogen removal during the clotting procedure. Taken together, data from multiple analytical platforms indicate that serum and plasma samples generate similar metabolomics results.

### **3.2. Pre-analytical variations in serum and plasma samples**

In both serum and plasma samples, metabolite variations can be derived during the sample collection and preparation procedures. Therefore in order to acquire high quality samples for metabolomics studies, a suitable protocol for sample collection is very important. In a NMRbased metabolomics study, Teahan et al. analyzed metabolite variations under different processing conditions, and found slight differences in serum samples processed with different clotting times (up to 180 min) regardless of the temperature used (ice or room temperature) [5]. NMR spectrometry has also detected variations between samples caused by freeze-thaw cycles, and therefore it is important to minimize the number of freeze-thaw cycles during sample handling. Serum samples were found to be stable for 24 hours at 40 C and 4 hours at room temperature [9].

### **4. Metabolite extraction**

Due to the complexity of metabolites with respect to their chemical structures and properties, molecular weight, and concentrations, it is impossible to extract all metabolites using a single extraction procedure. Different extraction procedures target different metabolites. Although many extraction procedures are currently used, such as liquid-liquid extraction, solid-phase extraction, and supercritical fluid extraction, liquid-liquid extraction is used more universally for metabolomic studies. For polar metabolites, commonly used extraction solvents include water, isopropanol, methanol, and acetonitrile, while for lipophilic metabolites, chloroform and ethyl acetate are commonly used. To increase the extracted metabolites range, combina‐ tions of extraction solvents, such as methanol/ chloroform or methanol/ chloroform/ water, are used in many metabolite extraction procedures.

slightly higher concentration of metabolites in serum samples. There was also better repro‐

In addition to NMR and LC-MS, GC-MS-based metabolomics has also been used to evaluate the differences between serum and plasma samples. Dettmer et al. showed slightly higher concentrations in most of the 26 quantified metabolites (19 amino acids, 6 organic acids, and glucose), with the exception of pyruvate, malate, citrate, and glucose in serum samples compared to plasma samples (EDTA used as the anticoagulant) [8]. Only lactate and citrate had variation greater than 15%, which could be attributed to serum and plasma sample preparation. The clotting time for serum preparation may result in a higher level of lactate in the serum due to glycolysis occurring in the blood cells. The conjugation of cations (such as Ca2+ and Mg2+) with EDTA (the anticoagulant) lowers the concentration of cations in the plasma, which conjugate with citrate during the metabolite extraction process [8]. Slightly higher metabolite concentrations in the serum samples were observed using different analyt‐ ical platforms, which may be due to lower protein levels in the serum samples as a result of fibrinogen removal during the clotting procedure. Taken together, data from multiple analytical platforms indicate that serum and plasma samples generate similar metabolomics

In both serum and plasma samples, metabolite variations can be derived during the sample collection and preparation procedures. Therefore in order to acquire high quality samples for metabolomics studies, a suitable protocol for sample collection is very important. In a NMRbased metabolomics study, Teahan et al. analyzed metabolite variations under different processing conditions, and found slight differences in serum samples processed with different clotting times (up to 180 min) regardless of the temperature used (ice or room temperature) [5]. NMR spectrometry has also detected variations between samples caused by freeze-thaw cycles, and therefore it is important to minimize the number of freeze-thaw cycles during

Due to the complexity of metabolites with respect to their chemical structures and properties, molecular weight, and concentrations, it is impossible to extract all metabolites using a single extraction procedure. Different extraction procedures target different metabolites. Although many extraction procedures are currently used, such as liquid-liquid extraction, solid-phase extraction, and supercritical fluid extraction, liquid-liquid extraction is used more universally for metabolomic studies. For polar metabolites, commonly used extraction solvents include water, isopropanol, methanol, and acetonitrile, while for lipophilic metabolites, chloroform and ethyl acetate are commonly used. To increase the extracted metabolites range, combina‐

C and 4 hours at

sample handling. Serum samples were found to be stable for 24 hours at 40

ducibility with the plasma samples, but higher sensitivity with the serum samples.

**3.2. Pre-analytical variations in serum and plasma samples**

results.

86 Advances in Gas Chromatography

room temperature [9].

**4. Metabolite extraction**

Methanol or methanol/chloroform combination is widely used for GC-MS-based metabolo‐ mics studies of blood samples (serum/ plasma). Using the combination of a D-optimal experimental design and partial least squares projections to latent structures analysis, Jiye and colleagues developed a methanol/water-based metabolites extraction procedure from plasma [10]. In this study, the authors compared different solvents and their combinations including water, methanol, ethanol, acetonitrile, acetone, and chloroform. Using methoxyamine and N-Methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) derivatization, the authors resolved 501 peaks in a gas chromatography-Time-of-Flight mass spectrometry (GC-TOF-MS) analysis and identified 80 metabolites from these peaks. Their study showed 100 µL of plasma mixed with 900 µL of methanol and water (8:1, v/v) provided optimal metabolites extraction results. Our laboratory has found that the combination of methanol and chloroform (3:1, v/v) is better for serum metabolite extraction than methanol alone [11]. Alternative ratios of methanol and chloroform have also been used in other metabolomics studies. For example, Nishiumi and colleagues used 250 µl of a solvent mixture (MeOH: H2O: CHCl3 = 2.5:1:1) for extracting 50 µl of serum [12].

Liquid extraction is necessary for metabolomics studies with solid samples, such as tissue and cell pellets. Tissue samples (from mammalian or plant) stored in liquid nitrogen need to be ground to a fine powder prior to solvent extraction or homogenized in a solvent with blenders. A combination of chloroform/methanol/water has been used extensively in metabolite extraction from tissue. Denkert et al. used a combination of chloroform/methanol/water (2:5:2, v/v/v) for colon tissue extraction [13]. Pan et al. developed a two-step metabolite extraction procedure with a mixed solvent of chloroform/methanol/water (1:2:1, v/v/v) in the first step and methanol in the second step for GC-TOFMS-based metabonomics study of liver tissue [14]. In addition, Gullberg et al. optimized an extraction protocol that yielded 66 metabolites from *Arabidopsis thaliana* samples. The authors found that the optimal extraction mixture for plant tissue was 200 µL of chloroform in a vibration mill followed by 800 µl of a methanol/water mixture (75:25, v/v) to form a monophase [15].

The extraction procedures for collected cell pellets have been widely tested with different solvents and various types of cells. Yeast is one of the most investigated species used for targeted metabolites analysis. One commonly used intracellular procedure for metabolite extraction is based on boiling ethanol, which was firstly described by Entian et al. in 1977 [16]. However, boiling ethanol may be harmful for some metabolites. Villas-Boas et al. tested 6 extraction conditions for yeast, including chloroform/methanol/buffer, boiling ethanol, perchloric acid, potassium hydroxide, methanol/water, and pure methanol. Their results indicated that pure methanol is the most suitable solvent and perchloric acid is the least suitable for metabolite extraction [17]. After testing five extraction protocols, including methanol, methanol/chloroform, perchloric acid, boiling ethanol and potassium hydroxide, Winder et al. also found that cold methanol with repeated freeze/thaw cycles was most effective for extracting intracellular metabolites from *E. coli*. [18]. For mammalian cells, methanol, metha‐ nol/water, and methanol/chloroform have been identified as suitable agents for metabolite extraction. Methanol/chloroform and boiling methanol with tricine buffer have been shown to sufficiently extract metabolites involved in cellular energy metabolism from canine kidney cells [19]. To increase the range of extracted metabolites, Sellick et al. reported a procedure using two extractions in Chinese hamster ovary cells: the first with pure ethanol and the second with water [20]. In addition, Dettmer et al. found that a methanol/water extraction yielded the best results compared to pure methanol, pure acetone, acetone/water, methanol/chloroform/ water, methanol/isopropanol/water, or acid–base methanol in a colorectal cancer cell line (SW480). They also found that direct scraping with methanol/water extraction markedly improved the concentration of metabolites compared to trypsin/EDTA detachment [21].

An alternative to TMS silylation is chloroformate derivatization, which has also been used in metabolomic studies. Compare to silylation, the reaction with chloroformates can be conduct‐ ed in aqueous media, which is very convenient for bio-fluids such urine, serum, and saliva. Reaction in the aqueous phase allows derivatization and analysis of certain volatile polar metabolites, such as short chain fatty acids [23], which would evaporate from the sample during the drying procedure prior to TMS derivatization. In addition, the reaction with chloroformates is very quick; under ultra-sonication, the reaction can be finished in seconds [24]. Chloroformate derivatization is widely used in quantitative measurements of amino acids, organic acids, and amines prior to GC-MS analysis [24]. Qiu et al. successfully adapted an ethyl chloroformate derivatization procedure for urine metabolomics analysis, which was validated with 25 standards (amino acids, amines, and organic acids) and urine samples from a precancerous rat model [25]. The authors used a two-step derivatization to increase the metabolite range and intensity, and found the method to be efficient and robust. Tao et al. subsequently optimized an ethyl chloroformate derivatization procedure for a serum metab‐ olomics study and successfully discriminated between uremic patients and normal subjects [26]. However, only 50 metabolites were identified in that analysis, and thus this method has

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Chloroformate derivatization-based metabolomic analysis has also been used for microbial cells. As reported by Smart et al., a methyl chloroformate (MCF) derivatization-based GC-MS metabolomics analysis of microbial cells was able to profile the end products and/or inter‐ mediates from specific metabolic pathways, including glycolysis, the Kreb cycle, amino acid metabolism, and fatty acid metabolism [27]. The authors described the advantages of MCF derivatization as being an economical reagent with a fast derivatization procedure (1 min) and

Silylation and chloroformate derivatization are the two predominantly used procedures for GC-MS-based metabolomic studies. Figure 2 illustrates examples of some of the derivatization

Data analysis for metabolomics study includes two key steps. The first step is to acquire a readable dataset from mass spectrometry signals. The other step is to perform statistical analysis, which allows comparisons of the same data generated from different analytical platforms. However, different types of instruments generate different signal patterns. There‐ fore, in order to obtain better interpretation of the signals, specific computational programs

The field of metabolomics studies the entire metabolome in a given biological sample. Compared to conventional quantitative analytical studies, which target a limited number of compounds, interpretation of mass signals from metabolomics is substantially more complex

less damage of the GC capillary column by the extraction solvent (chloroform).

not been widely used in serum metabolomic studies.

**6. Data analysis in GC-MS-based metabolomics**

**6.1. Signal interpretation for GC-MS-based metabolomics**

agents react with glycine.

should be designed.

### **5. Derivatization**

GC-MS is designed to analyze volatile compounds that can pass through a gas chromatogra‐ phy column heated to approximately 300°C. Most metabolites have polar functional groups, such as hydroxyl, amino, or carboxyl groups, and therefore are not volatile at the highest temperature allowed by the GC system. Although some of the metabolites may pass through the GC system, their peak shapes and/or recoveries are often unacceptable due to column absorption. Therefore, protection of those polar functional groups from metabolites with chemical derivatization is necessary.

To analyze as many metabolites as possible, wide range silylation is still the most popular derivatization procedure prior to GC-MS metabolomics analysis. Silylation agents can react with nearly all polar functional groups, including –COOH, –OH, –NH, and –SH. In general, the silylation reaction and stability of the derivatized metabolites decrease in the following order: –hydroxy > hydroxyl (phenol) > carboxylic acid > amine > amide; and primary > secondary > tertiary for alcohols and amines. After silylation, metabolites are more volatile and stable to high temperature. The most commonly used silylation agents for metabolomics include N,O-bis-(trimethylsilyl)-trifluoroacetamide (BSTFA), MSTFA, and N-methyl-N-tertbutyldimethylsilyltrifluoroacetamide (MTBSTFA) (Fig. 2). Typically, 1% trimethylchlorosi‐ lane (TMCS) is added as a catalyst for the reaction. Fiehn et al. recommended MSTFA instead of BSTFA for metabolomics studies to avoid high volatility and volatile by-products, including trifluoroacetamide, which causes some interference with early eluting peaks [22]. For keto ( oxo) groups, direct derivatization with silylation will result in multiple products via enoliza‐ tion reactions, which can result in complicated chromatograms with quantification and identification problems. Methoximation is always used to protect keto groups prior to silylation in metabolomic studies, although it will generate two peaks with *syn* and *anti* isoforms for the same keto-containing metabolite. For better separation between the *syn* and *anti* isoforms, Fiehn et al. recommended alkoxyamines instead of hydroxylamines [22].

Since silylation agents are sensitive to moisture, it is important to dry the samples thoroughly prior to the derivatization and prevent moisture exposure during the derivatization procedure. The methoximation reaction can be completed in 1-2 hours with heat or overnight at room temperature [10, 15]. Silylation reactions are usually finished in an hour at room temperature or with heat at 60-70 0 C.

An alternative to TMS silylation is chloroformate derivatization, which has also been used in metabolomic studies. Compare to silylation, the reaction with chloroformates can be conduct‐ ed in aqueous media, which is very convenient for bio-fluids such urine, serum, and saliva. Reaction in the aqueous phase allows derivatization and analysis of certain volatile polar metabolites, such as short chain fatty acids [23], which would evaporate from the sample during the drying procedure prior to TMS derivatization. In addition, the reaction with chloroformates is very quick; under ultra-sonication, the reaction can be finished in seconds [24]. Chloroformate derivatization is widely used in quantitative measurements of amino acids, organic acids, and amines prior to GC-MS analysis [24]. Qiu et al. successfully adapted an ethyl chloroformate derivatization procedure for urine metabolomics analysis, which was validated with 25 standards (amino acids, amines, and organic acids) and urine samples from a precancerous rat model [25]. The authors used a two-step derivatization to increase the metabolite range and intensity, and found the method to be efficient and robust. Tao et al. subsequently optimized an ethyl chloroformate derivatization procedure for a serum metab‐ olomics study and successfully discriminated between uremic patients and normal subjects [26]. However, only 50 metabolites were identified in that analysis, and thus this method has not been widely used in serum metabolomic studies.

Chloroformate derivatization-based metabolomic analysis has also been used for microbial cells. As reported by Smart et al., a methyl chloroformate (MCF) derivatization-based GC-MS metabolomics analysis of microbial cells was able to profile the end products and/or inter‐ mediates from specific metabolic pathways, including glycolysis, the Kreb cycle, amino acid metabolism, and fatty acid metabolism [27]. The authors described the advantages of MCF derivatization as being an economical reagent with a fast derivatization procedure (1 min) and less damage of the GC capillary column by the extraction solvent (chloroform).

Silylation and chloroformate derivatization are the two predominantly used procedures for GC-MS-based metabolomic studies. Figure 2 illustrates examples of some of the derivatization agents react with glycine.

### **6. Data analysis in GC-MS-based metabolomics**

extraction. Methanol/chloroform and boiling methanol with tricine buffer have been shown to sufficiently extract metabolites involved in cellular energy metabolism from canine kidney cells [19]. To increase the range of extracted metabolites, Sellick et al. reported a procedure using two extractions in Chinese hamster ovary cells: the first with pure ethanol and the second with water [20]. In addition, Dettmer et al. found that a methanol/water extraction yielded the best results compared to pure methanol, pure acetone, acetone/water, methanol/chloroform/ water, methanol/isopropanol/water, or acid–base methanol in a colorectal cancer cell line (SW480). They also found that direct scraping with methanol/water extraction markedly improved the concentration of metabolites compared to trypsin/EDTA detachment [21].

GC-MS is designed to analyze volatile compounds that can pass through a gas chromatogra‐ phy column heated to approximately 300°C. Most metabolites have polar functional groups, such as hydroxyl, amino, or carboxyl groups, and therefore are not volatile at the highest temperature allowed by the GC system. Although some of the metabolites may pass through the GC system, their peak shapes and/or recoveries are often unacceptable due to column absorption. Therefore, protection of those polar functional groups from metabolites with

To analyze as many metabolites as possible, wide range silylation is still the most popular derivatization procedure prior to GC-MS metabolomics analysis. Silylation agents can react with nearly all polar functional groups, including –COOH, –OH, –NH, and –SH. In general, the silylation reaction and stability of the derivatized metabolites decrease in the following order: –hydroxy > hydroxyl (phenol) > carboxylic acid > amine > amide; and primary > secondary > tertiary for alcohols and amines. After silylation, metabolites are more volatile and stable to high temperature. The most commonly used silylation agents for metabolomics include N,O-bis-(trimethylsilyl)-trifluoroacetamide (BSTFA), MSTFA, and N-methyl-N-tertbutyldimethylsilyltrifluoroacetamide (MTBSTFA) (Fig. 2). Typically, 1% trimethylchlorosi‐ lane (TMCS) is added as a catalyst for the reaction. Fiehn et al. recommended MSTFA instead of BSTFA for metabolomics studies to avoid high volatility and volatile by-products, including trifluoroacetamide, which causes some interference with early eluting peaks [22]. For keto ( oxo) groups, direct derivatization with silylation will result in multiple products via enoliza‐ tion reactions, which can result in complicated chromatograms with quantification and identification problems. Methoximation is always used to protect keto groups prior to silylation in metabolomic studies, although it will generate two peaks with *syn* and *anti* isoforms for the same keto-containing metabolite. For better separation between the *syn* and *anti* isoforms, Fiehn et al. recommended alkoxyamines instead of hydroxylamines [22].

Since silylation agents are sensitive to moisture, it is important to dry the samples thoroughly prior to the derivatization and prevent moisture exposure during the derivatization procedure. The methoximation reaction can be completed in 1-2 hours with heat or overnight at room temperature [10, 15]. Silylation reactions are usually finished in an hour at room temperature

**5. Derivatization**

88 Advances in Gas Chromatography

or with heat at 60-70 0

C.

chemical derivatization is necessary.

Data analysis for metabolomics study includes two key steps. The first step is to acquire a readable dataset from mass spectrometry signals. The other step is to perform statistical analysis, which allows comparisons of the same data generated from different analytical platforms. However, different types of instruments generate different signal patterns. There‐ fore, in order to obtain better interpretation of the signals, specific computational programs should be designed.

### **6.1. Signal interpretation for GC-MS-based metabolomics**

The field of metabolomics studies the entire metabolome in a given biological sample. Compared to conventional quantitative analytical studies, which target a limited number of compounds, interpretation of mass signals from metabolomics is substantially more complex

In 2008, Kopka and colleagues from the Max Planck Institute published a data processing procedure for GC-MS-based metabolomics called TagFinder [31], which is based on the Java programming language. In their process, the raw chromatography files with interchanged format (such as the NetCDF format) or pre-processed peak lists are acceptable. Based on the retention index or retention time, the fragment ions from different chromatograms are binned to "mass tags". After testing of the time groups, the selective mass tags can then be extracted. This process supports both non-targeted fingerprinting and targeting profiling, and the software is freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-

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The Automated Data Analysis Pipeline (ADAP; available at http://www.du-lab.org) was developed by Wenxin et al. in 2010. ADAP uses data generated by GC-TOFMS and contains features such as peak detection, deconvolution, peak alignment, and library search [32]. This software is written in standard C++ and R language. To increase the speed of the peak detection and deconvolution steps, parallel computing via Message Passing Interface (MPI) was used. The software was subsequently modified with improved algorithms mainly for peak decon‐ volution of coeluting metabolites, and substantial improvements in ADAP-GC 2.0 compared

In addition to software designed for GC-MS-based metabolomic studies, some software originally developed for LC-MS data has also been used for GC-MS data analysis. XCMS is one of the most popular software programs for MS-based metabolomic studies, and it was originally developed based on LC-MS data [34]. This software was built using an R environ‐ ment. After data input, a filter and peak identification are performed. The identified peaks are subjected to peak matching across samples. In this step, a group of metabolites are used as standards for a nonlinear retention time correction. Missing values during the peak matching step are filled in during the following step. Statistical analysis and peak visualization are the last two steps. XCMS is freely available under an open-source license at http:// metlin.scripps.edu/download/. An online version includes all the same features and is available at https://xcmsonline.scripps.edu [35]. Based on the results from XCMS, a metacomparison among different groups or complex subgroups can be subsequently performed

Other LC-MS-based software programs originally designed for LC-MS metabolomics data can also be used for GC-MS-based metabolomic data analysis, including xMSanalyzer [37], MZmine [38], and metAlign [39]. Vendors have also improved their software by adding metabolomic tool kits. For example, ChromaTOF (LECO, St. Joseph, MI) now provides an

Metabolomic studies typically contain different groups with multiple samples in each group. The aim of metabolomics is to identify metabolites (variables) that are highly correlated with different conditions, such as gender, stimuli, and pathological status by making comparisons among groups. Due to the complex dataset generated from the chromatography processing, multivariate statistical analysis is typically used in addition to traditional univariate statistics.

research/researchGroups/01-dept1/Root\_Metabolism/smp/TagFinder/index.html.

to ADAP 1.0 have been reported [33].

with metaXCMS (http://metlin.scripps.edu/metaxcms/) [36].

alignment function with its "Statistical Compare" section.

**6.2. Statistical analysis**

**Figure 2.** Silylation and chloroformates derivatization with glycine.

and difficult. This complexity lies in two main aspects: deconvolution of coeluting metabolites and alignment of metabolites across multiple samples. An automated mass spectral deconvo‐ lution and identification system (AMDIS) developed by Stephen E. Stein is regarded as one of the most powerful software programs for peak deconvolution [28]. This software is designed to deconvolute coeluting compounds from a single sample and does not provide alignment across samples. More recently, several software programs have been designed for GC-MSbased metabolomic data analysis.

Hierarchical Multivariate Curve Resolution (H-MCR) is one of the pioneering software programs that focuses on GC-MS-based metabolomics data [29]. Unlike conventional proce‐ dures, this software used a multiprocessing approach to process all interested samples simultaneously. The generated results include the aligned peak information for all samples. However, it will slow down the speed or even exceed computer's capacity when simultane‐ ously processing a large size of samples. An improved procedure was subsequently developed that allows the software to process a subset of representative training samples and generate H-MCR parameters. Using the same parameters, the remaining samples can then be processed very quickly [30].

In 2008, Kopka and colleagues from the Max Planck Institute published a data processing procedure for GC-MS-based metabolomics called TagFinder [31], which is based on the Java programming language. In their process, the raw chromatography files with interchanged format (such as the NetCDF format) or pre-processed peak lists are acceptable. Based on the retention index or retention time, the fragment ions from different chromatograms are binned to "mass tags". After testing of the time groups, the selective mass tags can then be extracted. This process supports both non-targeted fingerprinting and targeting profiling, and the software is freely available for academic use from http://www-en.mpimp-golm.mpg.de/03 research/researchGroups/01-dept1/Root\_Metabolism/smp/TagFinder/index.html.

The Automated Data Analysis Pipeline (ADAP; available at http://www.du-lab.org) was developed by Wenxin et al. in 2010. ADAP uses data generated by GC-TOFMS and contains features such as peak detection, deconvolution, peak alignment, and library search [32]. This software is written in standard C++ and R language. To increase the speed of the peak detection and deconvolution steps, parallel computing via Message Passing Interface (MPI) was used. The software was subsequently modified with improved algorithms mainly for peak decon‐ volution of coeluting metabolites, and substantial improvements in ADAP-GC 2.0 compared to ADAP 1.0 have been reported [33].

In addition to software designed for GC-MS-based metabolomic studies, some software originally developed for LC-MS data has also been used for GC-MS data analysis. XCMS is one of the most popular software programs for MS-based metabolomic studies, and it was originally developed based on LC-MS data [34]. This software was built using an R environ‐ ment. After data input, a filter and peak identification are performed. The identified peaks are subjected to peak matching across samples. In this step, a group of metabolites are used as standards for a nonlinear retention time correction. Missing values during the peak matching step are filled in during the following step. Statistical analysis and peak visualization are the last two steps. XCMS is freely available under an open-source license at http:// metlin.scripps.edu/download/. An online version includes all the same features and is available at https://xcmsonline.scripps.edu [35]. Based on the results from XCMS, a metacomparison among different groups or complex subgroups can be subsequently performed with metaXCMS (http://metlin.scripps.edu/metaxcms/) [36].

Other LC-MS-based software programs originally designed for LC-MS metabolomics data can also be used for GC-MS-based metabolomic data analysis, including xMSanalyzer [37], MZmine [38], and metAlign [39]. Vendors have also improved their software by adding metabolomic tool kits. For example, ChromaTOF (LECO, St. Joseph, MI) now provides an alignment function with its "Statistical Compare" section.

### **6.2. Statistical analysis**

and difficult. This complexity lies in two main aspects: deconvolution of coeluting metabolites and alignment of metabolites across multiple samples. An automated mass spectral deconvo‐ lution and identification system (AMDIS) developed by Stephen E. Stein is regarded as one of the most powerful software programs for peak deconvolution [28]. This software is designed to deconvolute coeluting compounds from a single sample and does not provide alignment across samples. More recently, several software programs have been designed for GC-MS-

Hierarchical Multivariate Curve Resolution (H-MCR) is one of the pioneering software programs that focuses on GC-MS-based metabolomics data [29]. Unlike conventional proce‐ dures, this software used a multiprocessing approach to process all interested samples simultaneously. The generated results include the aligned peak information for all samples. However, it will slow down the speed or even exceed computer's capacity when simultane‐ ously processing a large size of samples. An improved procedure was subsequently developed that allows the software to process a subset of representative training samples and generate H-MCR parameters. Using the same parameters, the remaining samples can then be processed

based metabolomic data analysis.

90 Advances in Gas Chromatography

**Figure 2.** Silylation and chloroformates derivatization with glycine.

very quickly [30].

Metabolomic studies typically contain different groups with multiple samples in each group. The aim of metabolomics is to identify metabolites (variables) that are highly correlated with different conditions, such as gender, stimuli, and pathological status by making comparisons among groups. Due to the complex dataset generated from the chromatography processing, multivariate statistical analysis is typically used in addition to traditional univariate statistics. Multivariate analyses based on projection methods are popular in metabolomics studies [40, 41]. An unsupervised Principal Component Analysis (PCA) is usually applied to provide a general overview of the acquired dataset and check for outliers. For more specific correlations between the observations and variables, supervised pattern recognitions may be used, such as Partial least-squares (PLS), Orthogonal-PLS (OPLS), PLS-discriminant analysis (PLS-DA), and OPLS-DA. By setting a specific Y matrix, such as gender and pathological status, the variables with high impact on the selected observations can be identified. Those metabolites with high variable importance in the projection (VIP) value can then be selected as differenti‐ ating variables. In addition to projection-based methods, other pattern recognition technolo‐ gies, such as support vector machines (SVM) [42], hierarchical cluster analysis (HCA) [43], and random forest (RF) [44] have also been used in metabolomic data processing.

Chart, is available online (http://www.sigmaaldrich.com/img/assets/4202/MetabolicPath‐ ways\_6\_17\_04\_.pdf). Other online sources for the metabolic pathway analysis include Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg), BioCyc (http:// www.biocyc.org/), Pathway Commons (PC, http://www.pathwaycommons.org/pc/), Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca/), Reactome (http://www.reac‐ tome.org/), The Arabidopsis Information Resource (TAIR, http://www.arabidopsis.org), Expert Protein Analysis System (ExPASy, http://web.expasy.org/pathways/), the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/),

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Advanced GC-MS-based metabolic profiling techniques, which evolved with the development of GC-MS instrumentation, were originally applied in biomedical research. However, in the past several decades metabolic profiling has been widely used in plant, microorganism, and environment research as well. In this section, we will introduce applications of GC-MS-based

Variations in the metabolome are associated with both genetic alterations and environmental interactions. Therefore, the field of metabolomics is an effective tool for biomedical research. GC-MS-based metabolomics approaches are extensively used in biomarker discoveries and biological understandings for human diseases, such as cancers, psychosis, and cardiovascular

Colorectal cancer (CRC) is among the most common types of cancer worldwide. GC-MS-based metabolomics approaches have been used with various types of CRC samples, including urine, serum, tissue, and stool. Qiu et al. used GC-MS analysis of urine from CRC patients and a 1,2 dimethylhydrazine (DMH)-induced precancerous colon lesion rat model and their healthy control counterparts [46]. Good separation was observed between CRC patients and healthy controls, pre- and post-operative patients, and between precancerous colon lesion rats and control rats. The authors observed significantly increased tryptophan metabolism as well as disturbed tricarboxylic acid (TCA) cycle and gut microflora metabolism in both the CRC patients and the rat model. In particular, recovery of tryptophan metabolism toward a healthy state was observed in post-surgical samples. A similar study assessed sera metabolomics from CRC patients and found perturbations in glycolysis, arginine and proline metabolism, fatty acid metabolism, and oleamide metabolism [45]. Ong et al. detected enhancement of the purine salvage pathway in CRC tumor tissues compared to adjacent normal epithelia [49]. Higher concentrations of purines and pyrimidines in CRC tissues were also observed by Denkert et al. based on GC-MS analysis [13]. They also observed higher levels of amino acids and lower levels of intermediates of the TCA cycle and lipids in CRC tissues compared to adjacent normal colon mucosa. In another study, GC-MS analysis was used for metabolic profiling of stool

**8. Applications of GC-MS-based metabolic profiling**

**8.1. GC-MS-based metabolomics used in biomedical research**

diseases. Samples can be obtained from both patients and animal models.

and others.

metabolomics in these fields.

To further characterize differentiating metabolites, univariate statistics, such as a Student's *t*test, analysis of variance (ANOVA) test, and non-parametric Mann-Whitney V-tests, are used to compare the selected differential variables from the multivariate statistics [45, 46]. For a large number of distinct variables in the metabolomics data, the false discovery rate (FDR) is used to correct *p* values from the univariate statistics [47].

### **7. Metabolite identification and interpretation**

Accurate and reproducible compound identification is one of the most important reasons for the popularity of GC-MS in metabolomics studies. However, because of the diversity in chemical structures and concentrations, identification of metabolites remains a challenge. A comparison with commercial libraries, such as the NIST and Wiley libraries, can tentatively identify some of the metabolites. As discussed above, coeluting peaks are a major problem for metabolite identification, and improvements in the separation conditions and advanced deconvolution software (such as ADMIS and ChromaTOF) aid in the accuracy of compound identification. In addition, due to the limited number of metabolite entries in current com‐ mercial libraries, even well separated metabolites cannot be identified. Many metabolomic laboratories have developed their own metabolite libraries, which are also used to confirm the identifications based on the retention time (or retention index). FiehnLib is one of the major metabolomic databases and uses a fatty acid methyl ester retention index system. Using GC-MS and GC-TOFMS (LECO, St. Joseph, MI), more than 1,000 primary metabolites, including lipids, amino acids, fatty acids, amines, alcohols, sugars, amino-sugars, sugar alcohols, sugar acids, organic phosphates, hydroxyl acids, aromatics, purines, and sterols have been entered into this library [48].

In addition to biomarker discovery, metabolomics also aims to understand the metabolic variations behind physiological or pathological changes. To obtain the entire picture of metabolic changes, the detected metabolic variations are connected in the metabolic pathways. In a pathway analysis, differential metabolites can be linked with each other as well as to the corresponding genes and enzymes. One of the widely used integrated maps for metabolic pathways, called the 22nd (2003) edition of the IUBMB-Sigma-Nicholson Metabolic Pathways Chart, is available online (http://www.sigmaaldrich.com/img/assets/4202/MetabolicPath‐ ways\_6\_17\_04\_.pdf). Other online sources for the metabolic pathway analysis include Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg), BioCyc (http:// www.biocyc.org/), Pathway Commons (PC, http://www.pathwaycommons.org/pc/), Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca/), Reactome (http://www.reac‐ tome.org/), The Arabidopsis Information Resource (TAIR, http://www.arabidopsis.org), Expert Protein Analysis System (ExPASy, http://web.expasy.org/pathways/), the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.msi.umn.edu/), and others.

### **8. Applications of GC-MS-based metabolic profiling**

Multivariate analyses based on projection methods are popular in metabolomics studies [40, 41]. An unsupervised Principal Component Analysis (PCA) is usually applied to provide a general overview of the acquired dataset and check for outliers. For more specific correlations between the observations and variables, supervised pattern recognitions may be used, such as Partial least-squares (PLS), Orthogonal-PLS (OPLS), PLS-discriminant analysis (PLS-DA), and OPLS-DA. By setting a specific Y matrix, such as gender and pathological status, the variables with high impact on the selected observations can be identified. Those metabolites with high variable importance in the projection (VIP) value can then be selected as differenti‐ ating variables. In addition to projection-based methods, other pattern recognition technolo‐ gies, such as support vector machines (SVM) [42], hierarchical cluster analysis (HCA) [43], and

To further characterize differentiating metabolites, univariate statistics, such as a Student's *t*test, analysis of variance (ANOVA) test, and non-parametric Mann-Whitney V-tests, are used to compare the selected differential variables from the multivariate statistics [45, 46]. For a large number of distinct variables in the metabolomics data, the false discovery rate (FDR) is

Accurate and reproducible compound identification is one of the most important reasons for the popularity of GC-MS in metabolomics studies. However, because of the diversity in chemical structures and concentrations, identification of metabolites remains a challenge. A comparison with commercial libraries, such as the NIST and Wiley libraries, can tentatively identify some of the metabolites. As discussed above, coeluting peaks are a major problem for metabolite identification, and improvements in the separation conditions and advanced deconvolution software (such as ADMIS and ChromaTOF) aid in the accuracy of compound identification. In addition, due to the limited number of metabolite entries in current com‐ mercial libraries, even well separated metabolites cannot be identified. Many metabolomic laboratories have developed their own metabolite libraries, which are also used to confirm the identifications based on the retention time (or retention index). FiehnLib is one of the major metabolomic databases and uses a fatty acid methyl ester retention index system. Using GC-MS and GC-TOFMS (LECO, St. Joseph, MI), more than 1,000 primary metabolites, including lipids, amino acids, fatty acids, amines, alcohols, sugars, amino-sugars, sugar alcohols, sugar acids, organic phosphates, hydroxyl acids, aromatics, purines, and sterols have been entered

In addition to biomarker discovery, metabolomics also aims to understand the metabolic variations behind physiological or pathological changes. To obtain the entire picture of metabolic changes, the detected metabolic variations are connected in the metabolic pathways. In a pathway analysis, differential metabolites can be linked with each other as well as to the corresponding genes and enzymes. One of the widely used integrated maps for metabolic pathways, called the 22nd (2003) edition of the IUBMB-Sigma-Nicholson Metabolic Pathways

random forest (RF) [44] have also been used in metabolomic data processing.

used to correct *p* values from the univariate statistics [47].

**7. Metabolite identification and interpretation**

into this library [48].

92 Advances in Gas Chromatography

Advanced GC-MS-based metabolic profiling techniques, which evolved with the development of GC-MS instrumentation, were originally applied in biomedical research. However, in the past several decades metabolic profiling has been widely used in plant, microorganism, and environment research as well. In this section, we will introduce applications of GC-MS-based metabolomics in these fields.

### **8.1. GC-MS-based metabolomics used in biomedical research**

Variations in the metabolome are associated with both genetic alterations and environmental interactions. Therefore, the field of metabolomics is an effective tool for biomedical research. GC-MS-based metabolomics approaches are extensively used in biomarker discoveries and biological understandings for human diseases, such as cancers, psychosis, and cardiovascular diseases. Samples can be obtained from both patients and animal models.

Colorectal cancer (CRC) is among the most common types of cancer worldwide. GC-MS-based metabolomics approaches have been used with various types of CRC samples, including urine, serum, tissue, and stool. Qiu et al. used GC-MS analysis of urine from CRC patients and a 1,2 dimethylhydrazine (DMH)-induced precancerous colon lesion rat model and their healthy control counterparts [46]. Good separation was observed between CRC patients and healthy controls, pre- and post-operative patients, and between precancerous colon lesion rats and control rats. The authors observed significantly increased tryptophan metabolism as well as disturbed tricarboxylic acid (TCA) cycle and gut microflora metabolism in both the CRC patients and the rat model. In particular, recovery of tryptophan metabolism toward a healthy state was observed in post-surgical samples. A similar study assessed sera metabolomics from CRC patients and found perturbations in glycolysis, arginine and proline metabolism, fatty acid metabolism, and oleamide metabolism [45]. Ong et al. detected enhancement of the purine salvage pathway in CRC tumor tissues compared to adjacent normal epithelia [49]. Higher concentrations of purines and pyrimidines in CRC tissues were also observed by Denkert et al. based on GC-MS analysis [13]. They also observed higher levels of amino acids and lower levels of intermediates of the TCA cycle and lipids in CRC tissues compared to adjacent normal colon mucosa. In another study, GC-MS analysis was used for metabolic profiling of stool samples from 11 CRC patients and 10 healthy controls, and, higher concentrations of amino acids and lower concentrations of fatty acids and ursodeoxycholic acid were observed in stool samples from CRC patients compared to those from healthy adults[50].

enzymes involved in existing metabolic pathways, or discovery of a new metabolic pathway are widely investigated with metabolomics technology. One example of metabolomics in genetically-modified plants was published in 2000 [61]. Fiehn et al. analyzed 326 distinct compounds in *Arabidopsis thaliana* leaf extracts with GC-MS technology and successfully distinguished four *Arabidopsis* genotypes (two homozygous ecotypes and a mutant of each ecotype) from each other. In another study, Weckwerth et al. applied GC-TOFMS-based metabolomics to a potato plant line with suppressed sucrose synthase isoform II expression levels and found metabolic variations in carbohydrate and amino acid metabolism compared

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A more specific link between metabolic regulation and gene or protein expression levels was achieved with the combination of metabolomics and transcriptomic analysis. Using a GC– TOFMS-based metabolomics technique, Albinsky et al. screened the metabolome of *Arabidop‐ sis* lines that overexpressed rice full-length (FL) cDNAs (rice FOX Arabidopsis lines) [63]. Combined with the transcriptome data, they found that Os-LBD37/ASL39 is a new transcrip‐ tional regulator for nitrogen metabolism in rice. Kusano et al. also revealed the importance of cytosolic glutamine synthetase 1;1 (*OsGS1*) in the metabolic balance of rice plant growth [64]. In addition, Sulpice et al. attempted to link enzyme activities and metabolite levels to biomass in *Arabidopsis* accessions [65]. Although the association was weak, it is a good example of a

Plant metabolism is largely influenced by genes and genetic modifications; however, the environment can also impact changes in metabolism. Metabolomics is an effective tool for investigating plant adaptive responses against environmental changes. One good example of a link between environment changes and genetic modification was shown in a study using metabolomics technology in *Arabidopsis*. Urano et al. analyzed metabolic variations of *Arabidopsis* with a knock-out mutant of *NCED3* gene wide type compared to the wide type one*s* under dehydration stress using the combination of GC-MS and CE-MS [66]. Their results showed the dependence of the accumulation of amino acids to abscisic acid production, and the independence of the level of raffinose to abscisic acid production under dehydration stress. Metabolomics technology has also been used to reveal how plants adapt to defend against insects. Using GC-MS analysis, Kant et al. detected a significant increase in the emission of

Microorganisms are an essential component of both environmental and human intestinal ecosystems, and they exhibit marked similarities with human beings in terms of metabolism. Microbial metabolomic approaches are important tools for understanding the interactions

*Escherichia coli* represent a commonly found microorganism in the human gut and thus have received extensive attention in previous metabolomics studies. In one time course response study, *E. coli* were exposed to five different conditions (cold, heat, oxidative stress, lactose diauxie, and stationary phase) [68]. The GC-MS-based metabolomic results showed similar global impact on cell metabolism under different stresses, and metabolic signatures revealed

to its parental background line [62].

metabolomics study using enzyme analysis.

volatile terpenoids in spider mite-infected tomato plants [67].

**8.3. GC-MS-based metabolomics used in microbial research**

between human and environmental changes as well as cellular functions.

Budczies et al.reportedaGC-TOFMS-basedmetabolicprofiling approachfor comparing breast cancer tissues to normal ones [51]. That study detected changes in several metabolic path‐ ways, including glycolysis, TCA cycle, nucleotide metabolism, and catabolism of amino acids. They also found that the ratio of cytidine-5-monophosphate to pentadecanoic acid was able to discriminate breast cancer tissues from controls with a sensitivity of 94.8% and a specificity of 93.9%. Using GC-MS analysis, Nam et al. detected four metabolic biomarkers (homovanillate, 4-hydroxyphenylacetate, 5-hydroxyindoleacetate, and urea) in the urine of breast cancer patients, which were validated with gene expression data from the NCBI GEO database [52]. GC-MS-based metabolomics approaches have also been used in several other cancer types, including ovarian carcinoma [53], hepatocellular cancer [54], and osteosarcoma [55].

Several GC-MS-based metabolomics studies on diabetes have also been reported in recent years. To identify metabolic signatures for a relatively new type of diabetes, fulminant type 1 diabetes (FT1DM), Lu et al. profiled the serum metabolome in FT1DM patients together with healthy control subjects, type 2 diabetes, classic type 1 diabetes, and diabetic ketoacidosis patients. Their study found that three metabolite markers (homocysteine, 5-oxoproline, and glutamate) had diagnostic potential for diagnosing FT1DM [56]. GC-MS was also used to evaluate metabolic variations of three drugs, including rosiglitazone, metformin, and repa‐ glinide, in type 2 diabetic patients using multivariate statistical analysis [57]. In another study, two-dimensional GC-TOFMS-based metabolomics coupled with data from LC-MS analysis implicated involvement of the gut microbiota in the development of type 1 diabetes [58].

In addition to cancer and diabetes, GC-MS-based metabolomics approaches have been used in other diseases such as cardiovascular disease and psychosis. To identify metabolite biomarkers for heart failure, Dunn et al. used a GC-MS technique to profile serum metabolites from 52 patients with systolic heart failure and 57 controls [59]. Their observations revealed two promising biomarkers for the failing heart: pseudouridine and 2-oxoglutarate. Yang et al. profiled the serum metabolome in schizophrenia patients and healthy controls using GC-TOFMS and found that fatty acid catabolism was up-regulated with elevated fatty acids and beta-hydroxybutyrate levels in schizophrenia patients [60].

### **8.2. GC-MS-based metabolomics used in plant research**

The plant cell metabolome has not been completely described due to the complexity of primary and secondary metabolites in plants. New metabolites are continuously reported with the development of advanced technologies. GC-MS is widely used in plant metabolomics studies with its powerful separation and compound identification abilities. For example, Fiehn et al. identified 15 relatively new metabolites in *Arabidopsis* extracts by calculating the elemental compositions, and some were found to be completely novel in plant metabolism [22].

One focus in plant metabolomics is the elucidation of the correlation between genotypes and metabolomic phenotypes. The effect of gene modification, identification of key genes and enzymes involved in existing metabolic pathways, or discovery of a new metabolic pathway are widely investigated with metabolomics technology. One example of metabolomics in genetically-modified plants was published in 2000 [61]. Fiehn et al. analyzed 326 distinct compounds in *Arabidopsis thaliana* leaf extracts with GC-MS technology and successfully distinguished four *Arabidopsis* genotypes (two homozygous ecotypes and a mutant of each ecotype) from each other. In another study, Weckwerth et al. applied GC-TOFMS-based metabolomics to a potato plant line with suppressed sucrose synthase isoform II expression levels and found metabolic variations in carbohydrate and amino acid metabolism compared to its parental background line [62].

samples from 11 CRC patients and 10 healthy controls, and, higher concentrations of amino acids and lower concentrations of fatty acids and ursodeoxycholic acid were observed in stool

Budczies et al.reportedaGC-TOFMS-basedmetabolicprofiling approachfor comparing breast cancer tissues to normal ones [51]. That study detected changes in several metabolic path‐ ways, including glycolysis, TCA cycle, nucleotide metabolism, and catabolism of amino acids. They also found that the ratio of cytidine-5-monophosphate to pentadecanoic acid was able to discriminate breast cancer tissues from controls with a sensitivity of 94.8% and a specificity of 93.9%. Using GC-MS analysis, Nam et al. detected four metabolic biomarkers (homovanillate, 4-hydroxyphenylacetate, 5-hydroxyindoleacetate, and urea) in the urine of breast cancer patients, which were validated with gene expression data from the NCBI GEO database [52]. GC-MS-based metabolomics approaches have also been used in several other cancer types,

including ovarian carcinoma [53], hepatocellular cancer [54], and osteosarcoma [55].

Several GC-MS-based metabolomics studies on diabetes have also been reported in recent years. To identify metabolic signatures for a relatively new type of diabetes, fulminant type 1 diabetes (FT1DM), Lu et al. profiled the serum metabolome in FT1DM patients together with healthy control subjects, type 2 diabetes, classic type 1 diabetes, and diabetic ketoacidosis patients. Their study found that three metabolite markers (homocysteine, 5-oxoproline, and glutamate) had diagnostic potential for diagnosing FT1DM [56]. GC-MS was also used to evaluate metabolic variations of three drugs, including rosiglitazone, metformin, and repa‐ glinide, in type 2 diabetic patients using multivariate statistical analysis [57]. In another study, two-dimensional GC-TOFMS-based metabolomics coupled with data from LC-MS analysis implicated involvement of the gut microbiota in the development of type 1 diabetes [58].

In addition to cancer and diabetes, GC-MS-based metabolomics approaches have been used in other diseases such as cardiovascular disease and psychosis. To identify metabolite biomarkers for heart failure, Dunn et al. used a GC-MS technique to profile serum metabolites from 52 patients with systolic heart failure and 57 controls [59]. Their observations revealed two promising biomarkers for the failing heart: pseudouridine and 2-oxoglutarate. Yang et al. profiled the serum metabolome in schizophrenia patients and healthy controls using GC-TOFMS and found that fatty acid catabolism was up-regulated with elevated fatty acids and

The plant cell metabolome has not been completely described due to the complexity of primary and secondary metabolites in plants. New metabolites are continuously reported with the development of advanced technologies. GC-MS is widely used in plant metabolomics studies with its powerful separation and compound identification abilities. For example, Fiehn et al. identified 15 relatively new metabolites in *Arabidopsis* extracts by calculating the elemental

One focus in plant metabolomics is the elucidation of the correlation between genotypes and metabolomic phenotypes. The effect of gene modification, identification of key genes and

compositions, and some were found to be completely novel in plant metabolism [22].

beta-hydroxybutyrate levels in schizophrenia patients [60].

**8.2. GC-MS-based metabolomics used in plant research**

samples from CRC patients compared to those from healthy adults[50].

94 Advances in Gas Chromatography

A more specific link between metabolic regulation and gene or protein expression levels was achieved with the combination of metabolomics and transcriptomic analysis. Using a GC– TOFMS-based metabolomics technique, Albinsky et al. screened the metabolome of *Arabidop‐ sis* lines that overexpressed rice full-length (FL) cDNAs (rice FOX Arabidopsis lines) [63]. Combined with the transcriptome data, they found that Os-LBD37/ASL39 is a new transcrip‐ tional regulator for nitrogen metabolism in rice. Kusano et al. also revealed the importance of cytosolic glutamine synthetase 1;1 (*OsGS1*) in the metabolic balance of rice plant growth [64]. In addition, Sulpice et al. attempted to link enzyme activities and metabolite levels to biomass in *Arabidopsis* accessions [65]. Although the association was weak, it is a good example of a metabolomics study using enzyme analysis.

Plant metabolism is largely influenced by genes and genetic modifications; however, the environment can also impact changes in metabolism. Metabolomics is an effective tool for investigating plant adaptive responses against environmental changes. One good example of a link between environment changes and genetic modification was shown in a study using metabolomics technology in *Arabidopsis*. Urano et al. analyzed metabolic variations of *Arabidopsis* with a knock-out mutant of *NCED3* gene wide type compared to the wide type one*s* under dehydration stress using the combination of GC-MS and CE-MS [66]. Their results showed the dependence of the accumulation of amino acids to abscisic acid production, and the independence of the level of raffinose to abscisic acid production under dehydration stress. Metabolomics technology has also been used to reveal how plants adapt to defend against insects. Using GC-MS analysis, Kant et al. detected a significant increase in the emission of volatile terpenoids in spider mite-infected tomato plants [67].

### **8.3. GC-MS-based metabolomics used in microbial research**

Microorganisms are an essential component of both environmental and human intestinal ecosystems, and they exhibit marked similarities with human beings in terms of metabolism. Microbial metabolomic approaches are important tools for understanding the interactions between human and environmental changes as well as cellular functions.

*Escherichia coli* represent a commonly found microorganism in the human gut and thus have received extensive attention in previous metabolomics studies. In one time course response study, *E. coli* were exposed to five different conditions (cold, heat, oxidative stress, lactose diauxie, and stationary phase) [68]. The GC-MS-based metabolomic results showed similar global impact on cell metabolism under different stresses, and metabolic signatures revealed a consistent decrease in the levels of metabolites related to glycolysis, the pentose phosphate pathway, and the TCA cycle, which were in agreement with the transcriptomic data. In another example, *E. coli* were used to investigate metabolic responses to different biofuel products, including ethanol, butanol, and isobutanol [69]. The authors observed variations in amino acids and osmoprotectants, including isoleucine, valine, glycine, glutamate, and trehalose in *E. coli* cells exposed to biofuel stress. Stress-derived metabolic variations were also investigated with other microorganisms including yeast. Using GC-MS analysis, Aggio et al. investigated the metabolic changes in yeast cells exposed to different frequencies of sonic vibration and silence and found that metabolic perturbations occurred at different sound frequencies [70].

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[2] Melvin Greera, C.M.W., *Diagnosis of branched-chain ketonuria (maple syrup urine disease)*

[3] Blau, K., H.H. Cameron, and G.K. Summer, *Diagnosis of phenylketonuria by gas chroma‐*

[4] E. C. Horning, M.G.H., *Human Metabolic Profiles Obtained by GC and GC/MS.* Journal

[5] Teahan, O., et al., *Impact of analytical bias in metabonomic studies of human blood serum*

[6] Denery, J.R., A.A. Nunes, and T.J. Dickerson, *Characterization of differences between blood sample matrices in untargeted metabolomics.* Anal Chem, 2011. 83(3): p. 1040-7.

[7] Yu, Z., et al., *Differences between human plasma and serum metabolite profiles.* PLoS One,

[8] Dettmer, K., et al., *Comparison of serum versus plasma collection in gas chromatography- mass spectrometry-based metabolomics.* Electrophoresis, 2010. 31(14): p. 2365-73.

[9] Fliniaux, O., et al., *Influence of common preanalytical variations on the metabolic profile of*

[10] A, J., et al., *Extraction and GC/MS analysis of the human blood plasma metabolome.* Ana‐

[11] Qiu, Y., *Metabonomics Study on Colorectal Cancer Using Combined Chromatography-mass Spectrometry Strategy* 2008, Shanghai Jiao Tong University: Shanghai. p. 92-95.

[12] Nishiumi, S., et al., *A novel serum metabolomics-based diagnostic approach for colorectal*

[13] Denkert, C., et al., *Metabolite profiling of human colon carcinoma--deregulation of TCA cy‐*

[14] Pan, L., et al., *An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry.* J Pharm Biomed Anal, 2010. 52(4): p.

[15] Gullberg, J., et al., *Design of experiments: an efficient strategy to identify factors influenc‐ ing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry.* Anal Biochem, 2004. 331(2): p. 283-95.

*serum samples in biobanks.* J Biomol NMR, 2011. 51(4): p. 457-65.

*by gas chromatography.* Biochemical Medicine, 1967. 1(1): p. 87–91.

*phy.* Clin Chem, 1978. 24(10): p. 1663-73.

*tography.* Methods Med Res, 1970. 12: p. 100-5.

of Chromatographic Science, 1971. 9(3): p. 129-140.

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2011. 6(7): p. e21230.

589-96.

GC-MS-based metabolomics technology can also be used to rapidly detect microorganisms present in the environment or food. For example, Cevallos-Cevallos et al. developed a rapid GC-MS-based procedure that could simultaneously detect 4 microorganisms in ground beef or chicken: *Escherichia coli O157:H7, Salmonella Typhimurium, Salmonella Muenchen,* and *Salmonella Hartford* [71]. In addition, a study by Sue et al. rapidly detected microbial contami‐ nation in fermentation processes using a metabolic footprint analysis approach [72].

### **9. Conclusions**

With its powerful separation and identification ability, GC-MS is widely used in metabolomic studies today. The technology is capable of analyzing non-polar metabolites directly and polar metabolites through derivatization. Many GC-MS-based metabolomics protocols have been developed and validated with various applications, and these applications have resulted in new knowledge regarding metabolic networks as well as enhanced systemic understanding of diseases, metabolic regulations, metabolic response to stresses, and cellular functions. Promising diagnostic biomarkers have also been identified in biomedical research studies, and novel microorganism detection procedures have been developed based on microbial metab‐ olomics. Although great achievements have been made with this technology, challenges still remain. For instance, the processing of huge datasets generated from high-throughput metabolomic studies and identification of those unknown peaks are two of the top challenges for GC-MS-based metabolomics. With continuous development and application, GC-MSbased metabolomics will be a powerful tool that benefits to our daily life.

### **Author details**

Yunping Qiu\* and Deborah Reed

\*Address all correspondence to: qyp29@163.com

Center for Translational Biomedical Research at University of North Carolina at Greensboro, Kannapolis, NC, USA

### **References**

a consistent decrease in the levels of metabolites related to glycolysis, the pentose phosphate pathway, and the TCA cycle, which were in agreement with the transcriptomic data. In another example, *E. coli* were used to investigate metabolic responses to different biofuel products, including ethanol, butanol, and isobutanol [69]. The authors observed variations in amino acids and osmoprotectants, including isoleucine, valine, glycine, glutamate, and trehalose in *E. coli* cells exposed to biofuel stress. Stress-derived metabolic variations were also investigated with other microorganisms including yeast. Using GC-MS analysis, Aggio et al. investigated the metabolic changes in yeast cells exposed to different frequencies of sonic vibration and silence and found that metabolic perturbations occurred at different sound frequencies [70].

GC-MS-based metabolomics technology can also be used to rapidly detect microorganisms present in the environment or food. For example, Cevallos-Cevallos et al. developed a rapid GC-MS-based procedure that could simultaneously detect 4 microorganisms in ground beef or chicken: *Escherichia coli O157:H7, Salmonella Typhimurium, Salmonella Muenchen,* and *Salmonella Hartford* [71]. In addition, a study by Sue et al. rapidly detected microbial contami‐

With its powerful separation and identification ability, GC-MS is widely used in metabolomic studies today. The technology is capable of analyzing non-polar metabolites directly and polar metabolites through derivatization. Many GC-MS-based metabolomics protocols have been developed and validated with various applications, and these applications have resulted in new knowledge regarding metabolic networks as well as enhanced systemic understanding of diseases, metabolic regulations, metabolic response to stresses, and cellular functions. Promising diagnostic biomarkers have also been identified in biomedical research studies, and novel microorganism detection procedures have been developed based on microbial metab‐ olomics. Although great achievements have been made with this technology, challenges still remain. For instance, the processing of huge datasets generated from high-throughput metabolomic studies and identification of those unknown peaks are two of the top challenges for GC-MS-based metabolomics. With continuous development and application, GC-MS-

Center for Translational Biomedical Research at University of North Carolina at Greensboro,

nation in fermentation processes using a metabolic footprint analysis approach [72].

based metabolomics will be a powerful tool that benefits to our daily life.

and Deborah Reed

\*Address all correspondence to: qyp29@163.com

**9. Conclusions**

96 Advances in Gas Chromatography

**Author details**

Kannapolis, NC, USA

Yunping Qiu\*


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**Chapter 5**

**The Utilization of Gas Chromatography/Mass**

**Botanicals**

**1. Introduction**

radical O2

NO2, nitrosyl cation NO+

Serban Moldoveanu

http://dx.doi.org/10.5772/57292

Additional information is available at the end of the chapter

**1.1. Introductory information regarding antioxidants in botanicals**

reactions (where ROO• is a free radical and AH an antioxidant):

**Spectrometry in the Profiling of Several Antioxidants in**

Antioxidants are chemicals that inhibit oxidation, and certain antioxidant molecules from fruits and vegetables are thought to alleviate oxidative stress in biological systems. Oxidative stress is a process generated by excessive reactive oxygen species (ROS) in organisms, and it is considered to be involved in a number of illnesses such as cancer, arteriosclerosis, heart diseases, etc. Among these reactive oxygen species are hydroxyl radical OH•, superoxide

in organisms, although at lower levels. The RNS include nitric oxide NO•, nitrogen dioxide

these are ascorbic acid, glutathione, and uric acid, and these molecules maintain a limited level of ROS in the organism. The enhancement of endogenous antioxidant capability of the human body is thought to be achieved by: 1) ingesting exogenous antioxidants either as food or as dietary supplements, 2) inducing the body production of antioxidant enzymes such as catalase, glutathione peroxidase, and superoxide dismutase, also with ingesting certain compounds, 3) inhibiting lipid peroxidation. The use of specific botanicals, either as food or as dietary supplements, has been intensively investigated for potential health benefits (see e.g. [1-5]). The reaction of antioxidants that interact with free radicals on a one-to-one basis takes place through various mechanisms. Among these are the hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET or ET-PT), and sequential proton loss electron transfer (SPLET). For example, the HAT mechanism can be described by the following

• • and also hydrogen peroxide H2O2. Reactive nitrogen species (RNS) are also present

, etc. Reducing agents are also present in aerobic organisms. Among

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals**

Serban Moldoveanu

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57292

### **1. Introduction**

#### **1.1. Introductory information regarding antioxidants in botanicals**

Antioxidants are chemicals that inhibit oxidation, and certain antioxidant molecules from fruits and vegetables are thought to alleviate oxidative stress in biological systems. Oxidative stress is a process generated by excessive reactive oxygen species (ROS) in organisms, and it is considered to be involved in a number of illnesses such as cancer, arteriosclerosis, heart diseases, etc. Among these reactive oxygen species are hydroxyl radical OH•, superoxide radical O2 • • and also hydrogen peroxide H2O2. Reactive nitrogen species (RNS) are also present in organisms, although at lower levels. The RNS include nitric oxide NO•, nitrogen dioxide NO2, nitrosyl cation NO+ , etc. Reducing agents are also present in aerobic organisms. Among these are ascorbic acid, glutathione, and uric acid, and these molecules maintain a limited level of ROS in the organism. The enhancement of endogenous antioxidant capability of the human body is thought to be achieved by: 1) ingesting exogenous antioxidants either as food or as dietary supplements, 2) inducing the body production of antioxidant enzymes such as catalase, glutathione peroxidase, and superoxide dismutase, also with ingesting certain compounds, 3) inhibiting lipid peroxidation. The use of specific botanicals, either as food or as dietary supplements, has been intensively investigated for potential health benefits (see e.g. [1-5]). The reaction of antioxidants that interact with free radicals on a one-to-one basis takes place through various mechanisms. Among these are the hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET or ET-PT), and sequential proton loss electron transfer (SPLET). For example, the HAT mechanism can be described by the following reactions (where ROO• is a free radical and AH an antioxidant):

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ROO•+ AH → ROOH + A•

ROO•+ A•→ROOA

The mechanism by which antioxidant enzymes are stimulated in the human body is less well understood, but specific botanicals with "antioxidant character" are also recommended for this purpose.

**14.** Benzodioxoles, such as myristicin, piperine, safrole.

**16.** Other compounds, such as gambogic acid, gingerol, ar-turmerone, antioxidant enzymes. Most antioxidants molecules are relatively large, and in addition, these molecules are fre‐ quently polar with groups such as OH and COOH. The high molecular weight and the high polarity of many antioxidant molecules are not conducive to the use of gas chromatography (GC) as the preferred analytical tool. If the molecule is also thermally unstable, such as lutein and carotene, the use of GC is definitely inadequate. For this reason, the analysis of many antioxidant compounds has been performed using high performance liquid chromatography (HPLC) methods [12-24]. However, GC methods can also be used for the identification of antioxidant compounds [25-27]. The use of mass spectrometric detection with its excellent capability for the determination of compound chemical formula makes GC/MS an irreplace‐ able tool when antioxidant analysis requires compound identification. Although significant progress has been made in using LC/MS (and LC/MS/MS) for compound identification, these techniques still remain more adequate for quantitation and not for qualitative analysis. Various procedures for the GC/MS analysis of certain antioxidants in botanicals are further described

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

http://dx.doi.org/10.5772/57292

105

**2. Experimental procedures for extending the GC analysis to larger**

The GC/MS analysis has considerable advantages compared to other analytical techni‐ ques. Besides the simplicity of the procedure, the technique can be used for definite identification of each compound based on its MS spectrum. Also, GC/MS provides separation with excellent resolution of the compounds, and is suitable for quantitation when standards are available. The area counts in the chromatograms can be measured and expressed as normalized area counts reported to the peak area of an internal standard. This type of presentation of results, does not provide quantitative levels for compounds, but allows for the determination of which sample has a higher or a lower level of a given compound. The disadvantage of the technique is caused by the need for volatility and certain thermal stability for the compounds to be analyzed. These restrictions limit the use of GC to larger and non-volatile molecules. However, several procedures are used for extending the capability of gas chromatography for the analysis of these types of mole‐ cules. Among these procedures are specific techniques for sample preparation, in particu‐ lar the derivatization of the analytes. Other procedures include certain GC instrument settings such as the use of hydrogen as carrier gas, selection of appropriate chromatograph‐ ic column, selection of the type of injection port, and a GC oven gradient with high final temperatures, etc. Derivatization of analytes can be beneficial in a variety of circumstan‐ ces in GC, such as when the polarity of the analyte is too high and does not elute from the column, when a desired separation is not achievable, when the peak shape of a com‐

**15.** Unsaturated lipids.

in this chapter.

**molecules**

Several procedures have been reported in the literature for the characterization of antioxidant properties of a material (typically food or dietary supplement). Among these are parameters such as "oxygen radical absorbance capacity" or ORAC [6-8], "ferric ion reducing antioxidant power" (FRAP) [9], "Folin-Ciocalteu reducing capacity assay" (FCR) [10], etc. The ORAC parameter can be measured by two versions of the same procedure, one indicated as hydro‐ philic ORAC and the other as lipophilic ORAC [6] and is expressed as µM of Trolox (TE) per g of sample. FRAP values are expressed in µM Fe2+ per g of sample [9]. The chemical nature of the antioxidants from different sources can vary considerably. Each compound may have different antioxidant properties, and may be considered useful for specific health benefits. Also, beneficial synergistic effects were reported for specific associations of compounds [11]. For these reasons, the analysis of individual antioxidant molecules including their identifica‐ tion and quantitation is important. Antioxidants from botanicals belong to different classes of molecules. Among these are the following:


ROO•+ AH → ROOH + A•

molecules. Among these are the following:

epigallocatechin gallate, gossypin.

**11.** Carotenoids, β-carotene, lutein.

**13.** Ascorbic acid, ascorbyl palmitate.

**12.** Ubiquinone, CoQ10.

gallate, tannins.

The mechanism by which antioxidant enzymes are stimulated in the human body is less well understood, but specific botanicals with "antioxidant character" are also recommended for

Several procedures have been reported in the literature for the characterization of antioxidant properties of a material (typically food or dietary supplement). Among these are parameters such as "oxygen radical absorbance capacity" or ORAC [6-8], "ferric ion reducing antioxidant power" (FRAP) [9], "Folin-Ciocalteu reducing capacity assay" (FCR) [10], etc. The ORAC parameter can be measured by two versions of the same procedure, one indicated as hydro‐ philic ORAC and the other as lipophilic ORAC [6] and is expressed as µM of Trolox (TE) per g of sample. FRAP values are expressed in µM Fe2+ per g of sample [9]. The chemical nature of the antioxidants from different sources can vary considerably. Each compound may have different antioxidant properties, and may be considered useful for specific health benefits. Also, beneficial synergistic effects were reported for specific associations of compounds [11]. For these reasons, the analysis of individual antioxidant molecules including their identifica‐ tion and quantitation is important. Antioxidants from botanicals belong to different classes of

**1.** Monoterpenoid phenols and alcohols such as: thymol, carvacol, menthol.

acid, p-coumaric acid, resveratrol, curcumin, eugenol, cinnamaladehyde.

**3.** Hydroxycinnamic type compounds such as: caffeic acid, chlorogenic acid, rosmarinic

**4.** Hydroxybenzoic acids and derivatives such as: gallic acid, protocatechuic acid, propyl

**5.** Benzopyrones (2- and 4-) and xanthones such as: scopoletin, coumarin, quercetin,

**6.** Flavones and their derivatives such as: epicatechin, epigallocatechin, epicatechin gallate,

**8.** Anthocyanins and anthocyanidins, such as cyanidin, pelargonidin, cyanidin glucosides.

**2.** Diterpene phenols, such as: carnosic acid, carnosol, rosmanol.

genistein, naringenin, diosmin, rutin, mangiferin.

**7.** Dihydrochalcones, such as aspalathin, notophagin.

**9.** Triterpene acids such as ursolic acid, oleanolic acid, betulinic acid.

**10.** Tocopherols, such as α, β, γ, δ-tocopherols, tocotrienols.

ROO•+ A•→ROOA

104 Advances in Gas Chromatography

this purpose.

**16.** Other compounds, such as gambogic acid, gingerol, ar-turmerone, antioxidant enzymes.

Most antioxidants molecules are relatively large, and in addition, these molecules are fre‐ quently polar with groups such as OH and COOH. The high molecular weight and the high polarity of many antioxidant molecules are not conducive to the use of gas chromatography (GC) as the preferred analytical tool. If the molecule is also thermally unstable, such as lutein and carotene, the use of GC is definitely inadequate. For this reason, the analysis of many antioxidant compounds has been performed using high performance liquid chromatography (HPLC) methods [12-24]. However, GC methods can also be used for the identification of antioxidant compounds [25-27]. The use of mass spectrometric detection with its excellent capability for the determination of compound chemical formula makes GC/MS an irreplace‐ able tool when antioxidant analysis requires compound identification. Although significant progress has been made in using LC/MS (and LC/MS/MS) for compound identification, these techniques still remain more adequate for quantitation and not for qualitative analysis. Various procedures for the GC/MS analysis of certain antioxidants in botanicals are further described in this chapter.

### **2. Experimental procedures for extending the GC analysis to larger molecules**

The GC/MS analysis has considerable advantages compared to other analytical techni‐ ques. Besides the simplicity of the procedure, the technique can be used for definite identification of each compound based on its MS spectrum. Also, GC/MS provides separation with excellent resolution of the compounds, and is suitable for quantitation when standards are available. The area counts in the chromatograms can be measured and expressed as normalized area counts reported to the peak area of an internal standard. This type of presentation of results, does not provide quantitative levels for compounds, but allows for the determination of which sample has a higher or a lower level of a given compound. The disadvantage of the technique is caused by the need for volatility and certain thermal stability for the compounds to be analyzed. These restrictions limit the use of GC to larger and non-volatile molecules. However, several procedures are used for extending the capability of gas chromatography for the analysis of these types of mole‐ cules. Among these procedures are specific techniques for sample preparation, in particu‐ lar the derivatization of the analytes. Other procedures include certain GC instrument settings such as the use of hydrogen as carrier gas, selection of appropriate chromatograph‐ ic column, selection of the type of injection port, and a GC oven gradient with high final temperatures, etc. Derivatization of analytes can be beneficial in a variety of circumstan‐ ces in GC, such as when the polarity of the analyte is too high and does not elute from the column, when a desired separation is not achievable, when the peak shape of a com‐

pound is not good, or when the analyte is not stable in the injection port of the GC. Many antioxidants fit this scenario, and for this reason derivatization is frequently used in GC/MS analysis of antioxidants from botanicals. A variety of chemical reactions are utilized for analytes derivatization. These reactions can be alkylations, arylations, silylation, acylation, additions to carbon-heteroatom multiple bonds, etc. Hydrolysis and formation of smaller molecules (e.g. from lipids) is another type of chemical reaction used as sample prepara‐ tion step for GC analysis. Of particular interest for the derivatization of many antioxidant molecules is silylation. Many antioxidant molecules contain OH and COOH groups, and these can be easy derivatized using silylation. For this reason, silylation is a preferred technique used for extending the range of analysis by GC/MS of antioxidants. However, in spite of the utility of GC/MS for antioxidant analysis it must be emphasized that it offers only a limited window in the whole range of antioxidant compounds present in botani‐ cals, and heavier molecules may still need to be analyzed using HPLC methods.

on high resolution mass spectra. The spectrum with high resolution can be obtained using MS instruments that were initially recommended for spectra in unit resolution, by using specific post acquisition programs and internal calibration (e.g. MassWorks, Cerno Bioscience, Danbury, CT 06810 USA). Such programs allow the determination of the

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107

Common procedures for the quantitation of specific compounds, such as calibration curves using standards can be applied for the quantitation in case the compound derivatization is not strongly influenced by the sample matrix. In some cases, the standard addition technique for quantitation (see e.g. [30]) gives better results as compared to external calibration. In both cases,

probable empirical molecular formula of unknown compounds.

**molecules**

the unavailability of standards may limit the possibilities for quantitation.

Wilmington, Delaware 19808). The GC/MS conditions are given in Table 1.

**3. Examples of GC/MS analysis of botanicals containing antioxidant**

A large number of botanicals contain antioxidant compounds, and their properties are extensively reported in the literature (see e.g. [31,32]). Also numerous studies were dedicated to individual botanical composition and content of antioxidants. Two examples of botanicals studied using GC/MS of directly silylated natural material (e.g. leaves) are further described. The silylation technique starts with 50 mg solid sample which is weighed (with 0.1 mg precision) in GC vials (2 mL screw top vials with screw caps with septa, Agilent, Wilmington, Delaware 19808). The silylation is done to all the compounds containing active hydrogens, such as acids, alcohols, or amines. The result is the formation of various trimethylsilyl (TMS) derivatives. A reagent and a solvent are used for the silylation process, and the procedure does not require a separate extraction step. From various available reagents, it was determined that bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% trimethylchloro-silane (TMCS) gives the best results. The preferred solvent was found to be N,N-dimethylformamide (DMF). The solvent used in this study contained as internal standard *tert*-butylhydroquinone. The DMF solution with internal standards was prepared using 100 mL DMF and 40 mg *tert*- butylhy‐ droquinone (all compounds from Aldich/Sigma Saint Louis, MO 63178-9916). The final DMF solution contained 0.4 mg/mL *tert*-butylhydroquinone. For the analysis, to each vial were added 0.4 mL DMF with internal standards and 0.8 mL BSTFA with 1% TMCS (Aldrich/Sigma Saint Louis, MO 63178-9916). The vials were kept at 78o C (in a heating block) for 30 min., and were allowed to cool at room temperature for another 30 min. After cooling the solution, each vial was filtered through a 0.45 µm PTFE filter (VWR Suwanee, GA 30024) into screw top vials with screw caps and septa, which were used for the GC/MS analysis. This procedure can be scaled down when either the amount of sample is small, or when using the derivatization reagent d9-BSTFA (available from CDN Isotopes, Pointe Claire, Canada H9R 1H1). The analysis was done using a GC/MS instrument (such as Agilent 6890/5973 system from Agilent,

Larger molecules, even after derivatization typically require specific conditions for the GC separation, such as temperature gradient up to a relatively high temperature. Modern GC ovens are designed to be able to reach temperatures as high as 400 o C, but the limiting factor regarding the oven temperature is typically the stability of the stationary phase of the column. Depending on the nature of the stationary phase, the columns may be stable up to 360 o C, and only special ones may stand higher temperatures. Such temperatures are necessary in certain instances for the elution of heavier compounds from the chromatograph‐ ic column. The typical split/splitless injection port, with relatively high temperatures (e.g. around 300 o C) is frequently adequate for the analysis of larger molecules. However, some compounds decompose in the standard split/splitless injection port and "cold on-column" injection is necessary for obtaining acceptable results [28,29].

The identification of the compounds in the chromatogram is typically performed using the library search capability of the GC/MS instrument and a mass spectral library (e.g. NIST8, NIST11, Wiley\_9THL, Wiley Registry 10th Ed., etc.). However, the mass spectra of most antioxidant molecules, in particular in silylated form, are not available in standard mass spectral libraries. For this reason, the identification of unknown antioxidant molecules in a natural product material may be difficult. Valuable information can be obtained from separate analysis of standard compounds (if available), or from the comparison of spectra of unknowns with those of expected similar molecules that are available as standards and can be directly analyzed. Special procedures can be used that help with identification of silylated compounds, by using derivatization with deuterated silylating reagents. As an example, the use of d9-BSTFA [d9-bis(trimethylsilyl)-trifluoroacetamide] that generates deuterated TMS (trimethylsilyl) derivatives allows the detection of the number of silyl groups in a compound (and implicitly of the number of OH or COOH groups). This can be done by comparing the masses of the same compound when silylated with the deuter‐ ated reagent and when silylated with non-deuterated reagent. For each TMS group a difference of 9 a.m.u. is noticed between the two types of silylated compounds. One additional procedure that helps with the identification of an unknown compound is based on high resolution mass spectra. The spectrum with high resolution can be obtained using MS instruments that were initially recommended for spectra in unit resolution, by using specific post acquisition programs and internal calibration (e.g. MassWorks, Cerno Bioscience, Danbury, CT 06810 USA). Such programs allow the determination of the probable empirical molecular formula of unknown compounds.

pound is not good, or when the analyte is not stable in the injection port of the GC. Many antioxidants fit this scenario, and for this reason derivatization is frequently used in GC/MS analysis of antioxidants from botanicals. A variety of chemical reactions are utilized for analytes derivatization. These reactions can be alkylations, arylations, silylation, acylation, additions to carbon-heteroatom multiple bonds, etc. Hydrolysis and formation of smaller molecules (e.g. from lipids) is another type of chemical reaction used as sample prepara‐ tion step for GC analysis. Of particular interest for the derivatization of many antioxidant molecules is silylation. Many antioxidant molecules contain OH and COOH groups, and these can be easy derivatized using silylation. For this reason, silylation is a preferred technique used for extending the range of analysis by GC/MS of antioxidants. However, in spite of the utility of GC/MS for antioxidant analysis it must be emphasized that it offers only a limited window in the whole range of antioxidant compounds present in botani‐

cals, and heavier molecules may still need to be analyzed using HPLC methods.

ovens are designed to be able to reach temperatures as high as 400 o

injection is necessary for obtaining acceptable results [28,29].

up to 360 o

106 Advances in Gas Chromatography

around 300 o

Larger molecules, even after derivatization typically require specific conditions for the GC separation, such as temperature gradient up to a relatively high temperature. Modern GC

factor regarding the oven temperature is typically the stability of the stationary phase of the column. Depending on the nature of the stationary phase, the columns may be stable

necessary in certain instances for the elution of heavier compounds from the chromatograph‐ ic column. The typical split/splitless injection port, with relatively high temperatures (e.g.

compounds decompose in the standard split/splitless injection port and "cold on-column"

The identification of the compounds in the chromatogram is typically performed using the library search capability of the GC/MS instrument and a mass spectral library (e.g. NIST8, NIST11, Wiley\_9THL, Wiley Registry 10th Ed., etc.). However, the mass spectra of most antioxidant molecules, in particular in silylated form, are not available in standard mass spectral libraries. For this reason, the identification of unknown antioxidant molecules in a natural product material may be difficult. Valuable information can be obtained from separate analysis of standard compounds (if available), or from the comparison of spectra of unknowns with those of expected similar molecules that are available as standards and can be directly analyzed. Special procedures can be used that help with identification of silylated compounds, by using derivatization with deuterated silylating reagents. As an example, the use of d9-BSTFA [d9-bis(trimethylsilyl)-trifluoroacetamide] that generates deuterated TMS (trimethylsilyl) derivatives allows the detection of the number of silyl groups in a compound (and implicitly of the number of OH or COOH groups). This can be done by comparing the masses of the same compound when silylated with the deuter‐ ated reagent and when silylated with non-deuterated reagent. For each TMS group a difference of 9 a.m.u. is noticed between the two types of silylated compounds. One additional procedure that helps with the identification of an unknown compound is based

C, and only special ones may stand higher temperatures. Such temperatures are

C) is frequently adequate for the analysis of larger molecules. However, some

C, but the limiting

Common procedures for the quantitation of specific compounds, such as calibration curves using standards can be applied for the quantitation in case the compound derivatization is not strongly influenced by the sample matrix. In some cases, the standard addition technique for quantitation (see e.g. [30]) gives better results as compared to external calibration. In both cases, the unavailability of standards may limit the possibilities for quantitation.

### **3. Examples of GC/MS analysis of botanicals containing antioxidant molecules**

A large number of botanicals contain antioxidant compounds, and their properties are extensively reported in the literature (see e.g. [31,32]). Also numerous studies were dedicated to individual botanical composition and content of antioxidants. Two examples of botanicals studied using GC/MS of directly silylated natural material (e.g. leaves) are further described. The silylation technique starts with 50 mg solid sample which is weighed (with 0.1 mg precision) in GC vials (2 mL screw top vials with screw caps with septa, Agilent, Wilmington, Delaware 19808). The silylation is done to all the compounds containing active hydrogens, such as acids, alcohols, or amines. The result is the formation of various trimethylsilyl (TMS) derivatives. A reagent and a solvent are used for the silylation process, and the procedure does not require a separate extraction step. From various available reagents, it was determined that bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% trimethylchloro-silane (TMCS) gives the best results. The preferred solvent was found to be N,N-dimethylformamide (DMF). The solvent used in this study contained as internal standard *tert*-butylhydroquinone. The DMF solution with internal standards was prepared using 100 mL DMF and 40 mg *tert*- butylhy‐ droquinone (all compounds from Aldich/Sigma Saint Louis, MO 63178-9916). The final DMF solution contained 0.4 mg/mL *tert*-butylhydroquinone. For the analysis, to each vial were added 0.4 mL DMF with internal standards and 0.8 mL BSTFA with 1% TMCS (Aldrich/Sigma Saint Louis, MO 63178-9916). The vials were kept at 78o C (in a heating block) for 30 min., and were allowed to cool at room temperature for another 30 min. After cooling the solution, each vial was filtered through a 0.45 µm PTFE filter (VWR Suwanee, GA 30024) into screw top vials with screw caps and septa, which were used for the GC/MS analysis. This procedure can be scaled down when either the amount of sample is small, or when using the derivatization reagent d9-BSTFA (available from CDN Isotopes, Pointe Claire, Canada H9R 1H1). The analysis was done using a GC/MS instrument (such as Agilent 6890/5973 system from Agilent, Wilmington, Delaware 19808). The GC/MS conditions are given in Table 1.


2000000 4000000 6000000 8000000 1e+07 1.2e+07 1.4e+07

at 29.65 min).

8.11 12.97

16.63 25.76

29.65 33.13

Abundance

10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 Time-->

**Figure 1.** Chromatogram of silylated green tea dry leaf. The identification of main peaks is given in Table 2. (I.S. elutes

**Antioxidant compounds Ret. Time Other main compounds Ret. Time**

Caffeine 37.79 Phosphate 16.63

Gallic acid 42.23 Malic acid 25.76

Epicatechin 63.83 5-Oxoproline 26.42

Catechin 64.32 Fructose 37.20

Epigallocatechin (EGC) 65.18 Quinic acid 39.03

α-Tocoferol (trace) 68.03 Glucose 43.23

Chlorogenic acid (trace) 68.11 Myoinositol 45.93

Epicatechin gallate 78.14 Sucrose 59.80

Epigallocatechin gallate (EGCG) 78.83 Disaccharide 61.77

Gallocatechin gallate 79.41 Disaccharide ? 72.18

Fragmentation indicated in Figures 2 to 5 can be verified using silylation with d9-BSTFA. Figure

The masses of different ions are explained in Figure 6 in comparison with those shown in Figure 5. For example, the mass 693 a.m.u. is obtained from the ion with mass 648 a.m.u. by adding 5 x 9 a.m.u. resulting from d9 groups, which indicates 5 TMS groups on this fragment. This spectrum is in agreement with the suggested fragmentation from Figure 5. A similar result

**Table 2.** Compound identification for green tea chromatogram shown in Figure 1

6 shows the spectrum of d9-silylated epigallocatechin gallate.

54.45

60.79

63.83

72.18 75.30

78.14

83.76

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109

78.83

65.18

61.77

59.80

42.23 45.93

37.79

39.03

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

\*Note: equivalent columns can be used, such as ZB-5 HT Inferno, etc.

**Table 1.** GC/MS operating parameters for silylated compounds analysis.

As shown in Table 1, the separation used hydrogen as a carrier gas, and a relatively high final oven temperature.

### **3.1. Example of green tea analysis**

Green tea (leaves of *Camellia sinensis*) is a well known botanical with antioxidant properties [33-35]. For the green tea evaluated in this study (commercially available from Shanghai Tiantan Intern. Trading Co., Ltd.) the ORAC values (both lipophilic and hydrophilic) were 1150 ± 20 µM TE/g, and FRAP was 2200 ± 15 µM Fe2+/g. The chemical composition of green tea leaf can be studied using the GC/MS technique after direct silylation of the dry leaf. The chromatogram of silylated green tea is shown in Figure 1.

The identification of the main peaks from Figure 1 can be viewed in Table 2 where the retention times for individual compounds are listed.

Some of the spectra of the silylated compounds are not available in common mass spectral libraries. The spectra of silylated epigallocatechin (EGC), epicatechin gallate (ECG), epigallo‐ catechin gallate(EGCG), and chlorogenic acid, as obtained using standards, are shown in Figures 2 to 5.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 109

**Parameter Description Parameter Description** GC column DB-5MS\* Carrier gas Hydrogen Column dimensions 30 m long, 0.25 mm id. Flow mode Constant flow Film thickness 0.25 μm Flow rate 1.0 mL/min Initial oven temp. 50°C Nominal initial pressure 7.57 psi Initial time 0.5 min Split ratio 30 : 1

Oven ramp rate 3°C/min Split flow 29.8 mL/min

Injection volume 1.0 μL Mass range 33 to 1050 a.m.u.

As shown in Table 1, the separation used hydrogen as a carrier gas, and a relatively high final

Green tea (leaves of *Camellia sinensis*) is a well known botanical with antioxidant properties [33-35]. For the green tea evaluated in this study (commercially available from Shanghai Tiantan Intern. Trading Co., Ltd.) the ORAC values (both lipophilic and hydrophilic) were 1150 ± 20 µM TE/g, and FRAP was 2200 ± 15 µM Fe2+/g. The chemical composition of green tea leaf can be studied using the GC/MS technique after direct silylation of the dry leaf. The

The identification of the main peaks from Figure 1 can be viewed in Table 2 where the retention

Some of the spectra of the silylated compounds are not available in common mass spectral libraries. The spectra of silylated epigallocatechin (EGC), epicatechin gallate (ECG), epigallo‐ catechin gallate(EGCG), and chlorogenic acid, as obtained using standards, are shown in

\*Note: equivalent columns can be used, such as ZB-5 HT Inferno, etc.

oven temperature.

108 Advances in Gas Chromatography

Figures 2 to 5.

**3.1. Example of green tea analysis**

**Table 1.** GC/MS operating parameters for silylated compounds analysis.

chromatogram of silylated green tea is shown in Figure 1.

times for individual compounds are listed.

Oven final first ramp 200°C GC outlet MSD Final time first ramp 0 min Outlet pressure Vacuum Oven ramp rate 4°C/min Transfer line heater 300°C Oven final temp. 320°C Ion source temp. 230°C Final time 10 min Quadrupole temp. 150°C Total run time 90.5 min MSD EM offset 100 V Inlet temp. 300°C MSD solvent delay 7.0 min Inlet mode Split MSD acquisition mode scan

**Figure 1.** Chromatogram of silylated green tea dry leaf. The identification of main peaks is given in Table 2. (I.S. elutes at 29.65 min).


**Table 2.** Compound identification for green tea chromatogram shown in Figure 1

Fragmentation indicated in Figures 2 to 5 can be verified using silylation with d9-BSTFA. Figure 6 shows the spectrum of d9-silylated epigallocatechin gallate.

The masses of different ions are explained in Figure 6 in comparison with those shown in Figure 5. For example, the mass 693 a.m.u. is obtained from the ion with mass 648 a.m.u. by adding 5 x 9 a.m.u. resulting from d9 groups, which indicates 5 TMS groups on this fragment. This spectrum is in agreement with the suggested fragmentation from Figure 5. A similar result as shown for the spectrum of epigallocatechin gallate, can be obtained for any other silylated compound.

Besides the similarity in the spectrum profile, the d9-silylated compounds have a similar retention time as those silylated with non-isotopically labeled BSTFA, and the chromatogram also has a similar profile, as shown in Figure 7, that displays two time windows between 58 min and 80 min from a green tea water extract derivatized with BSTFA and for the same extract derivatized with d9-BSTFA.

50 100 150 200 250 300 350 400 450 500 550 600 650

**Figure 4.** Spectrum of silylated epicatechin gallate (ECG) (from standard compound), ret. time 78.14 min, MW =

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750

369

O

TMS

O

648

O

O

O TMS

TMS

O

TMS

TMS

O

TMS

O TMS

<sup>179</sup> <sup>559</sup> <sup>458</sup>

0 708 740

**Figure 5.** Spectrum of silylated epigallocatechin gallate (EGCG) (from standard compound), ret. time 78.83 min, MW = 1034.40. At higher instrument sensitivity, small peaks at 1034 a.m.u. and 1019 a.m.u. (loss of 15 a.m.u.) can be seen.

281

TMS

O

<sup>239</sup> <sup>147</sup>

100 OO

281

OO

TMS

O

TMS

<sup>147</sup> <sup>239</sup> <sup>45</sup> <sup>471</sup>

179

369

O

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

O

560

O

O

TMS

O

TMS

TMS

O

TMS

O TMS 560

648

634

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111

m/z-->

Abundance

73

m/z-->

20

40

60

80

45

946.37.

0

20

40

60

80

100

Abundance

73

**Figure 2.** Spectrum of silylated epigallocatechin (EGC) (from standard compound), ret. time 65.18 min, MW = 738.31 (Note: The molecular weight does not consider the natural isotope distribution of elements and it is based only on the nuclidic masses of a single isotope).

**Figure 3.** Spectrum of silylated chlorogenic acid (from standard compound), ret. time 68.11 min, MW = 786.33.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 111

as shown for the spectrum of epigallocatechin gallate, can be obtained for any other silylated

Besides the similarity in the spectrum profile, the d9-silylated compounds have a similar retention time as those silylated with non-isotopically labeled BSTFA, and the chromatogram also has a similar profile, as shown in Figure 7, that displays two time windows between 58 min and 80 min from a green tea water extract derivatized with BSTFA and for the same extract

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750

<sup>648</sup> <sup>325</sup> <sup>738</sup> <sup>193</sup> <sup>45</sup>

**Figure 2.** Spectrum of silylated epigallocatechin (EGC) (from standard compound), ret. time 65.18 min, MW = 738.31 (Note: The molecular weight does not consider the natural isotope distribution of elements and it is based only on the

345

255 307

<sup>50</sup> <sup>100</sup> <sup>150</sup> <sup>200</sup> <sup>250</sup> <sup>300</sup> <sup>350</sup> <sup>400</sup> <sup>450</sup> <sup>500</sup> <sup>550</sup> <sup>600</sup> <sup>650</sup> <sup>700</sup> <sup>750</sup> m/z-->

**Figure 3.** Spectrum of silylated chlorogenic acid (from standard compound), ret. time 68.11 min, MW = 786.33.

397

O

TMS

O

355

147 267

OO

648

TMS

456

O

TMS

462 786

O

TMS

345

O

TMS

O

TMS O

O

800

O

TMS

O

TMS

670

O

456

TMS

O

O

TMS

TMS

O

TMS

compound.

110 Advances in Gas Chromatography

derivatized with d9-BSTFA.

Abundance

73

0

m/z-->

nuclidic masses of a single isotope).

Abundance

0

20

40

60

80

100

73

147

219

20

40

60

80

100

**Figure 4.** Spectrum of silylated epicatechin gallate (ECG) (from standard compound), ret. time 78.14 min, MW = 946.37.

**Figure 5.** Spectrum of silylated epigallocatechin gallate (EGCG) (from standard compound), ret. time 78.83 min, MW = 1034.40. At higher instrument sensitivity, small peaks at 1034 a.m.u. and 1019 a.m.u. (loss of 15 a.m.u.) can be seen.

The quantitation of epigallocatechin and epigallocatechin gallate in the green tea was also evaluated in this study. For this purpose, the initial peak area was measured in the chroma‐ togram of the silylated green tea sample. This was followed by the addition of 500 µg and 1000 µg of the two compounds (as solution in DMF) to 50 mg green tea sample with silylation. In order to avoid peak overloading, the silylated solution that was filtered through a 0.45 µm PTFE filter. The samples were analyzed by GC/MS and the peak areas were measured. The results are illustrated in Figure 8 that represents the peak area measurement normalized by the internal standard area (0.04 mg/mL *tert*-butylhydroquinone after 1/10 dilution) versus the

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

y = 1.8229E‐02x + 4.1823E+01 R² = 9.9222E‐01

0 200 400 600 800 1000

**ug added compound**

From the trendline equations, it can be calculated that the green tea contained about 2.307 mg EGCG/50 mg sample, and 1.720 mg EGC/50 mg sample. This is equivalent to 46.14 mg/g EGCG and 34.41 mg/g ECG. These levels are in the range reported in other studies for green tea [34]. Green tea from different sources may have different levels of antioxidants, and silylation

Figure 8. Peak area for epigallocatechin (EGC) and epigallocatechin gallate (EGCG) for a green tea sample, and for the same type of sample with

EGC EGCG

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113

y = 1.6588E‐02x + 2.8541E+01 R² = 9.8728E‐01

From the trendline equations, it can be calculated that the green tea contained about 2.307 mg EGCG/50 mg sample, and 1.720 mg EGC/50 mg sample. This is equivalent to 46.14 mg/g EGCG and 34.41 mg/g ECG. These levels are in the range reported in other studies for green tea [34]. Green tea from different sources may have different levels of antioxidants, and silylation followed by

Rosemary (dry leaf) (*Rosmarinus officinalis*) is another botanical with antioxidant properties [22,23]. For rosemary (dry leaf) evaluated in this study (commercially available from American Spice Trading Co.) the ORAC values (both lipophilic and hydrophilic) were 620 ± 20 μM TE/g, and FRAP was 800 ± 15 μM Fe2+/g. The chemical composition of rosemary leaf can be studied using the GC/MS technique after direct silylation of the dry leaf similarly to green tea. The chromatogram of silylated rosemary is shown in Figure 9, and the identification of the main peaks can be viewed in Table 3 where the retention times for individual

Figure 9. Chromatogram of silylated rosemary dry leaf. The identification of main peaks is given in Table 3. (I.S. elutes at 29.67 min).

43.24 47.60 55.68

10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

**Antioxidant compounds Ret. Time Other main compounds Ret. Time** 

69.90

72.69

74.02 74.35

75.23

83.78

Time-->

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6

Abundance

72.69

74.02 74.35

73.0 74.0 75.0

74.77

75.23

74.77

57.54

59.79

compound addition.

500 μg and 1000 μg of the two compounds added.

same type of sample with 500 μg and 1000 μg of the two compounds added.

**3.2. Example of rosemary analysis** 

compounds are listed.

**3.2. Example of rosemary analysis**

Abundance

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6 1.0e+07 1.1e+07

11.09 12.89

14.21 25.77

29.67

37.24

39.01

6e+6**Figure 8.** Peak area for epigallocatechin (EGC) and epigallocatechin gallate (EGCG) for a green tea sample, and for the

Rosemary (dry leaf) (*Rosmarinus officinalis*) is another botanical with antioxidant properties [22,23]. For rosemary (dry leaf) evaluated in this study (commercially available from American Spice Trading Co.) the ORAC values (both lipophilic and hydrophilic) were 620 ± 20 µM TE/g, and FRAP was 800 ± 15 µM Fe2+/g. The chemical composition of rosemary leaf can be studied using the GC/MS technique after direct silylation of the dry leaf similarly to green tea.

40.05

Time-->

0.0E+00

1.0E+01

2.0E+01

3.0E+01

**Normalized area count**

4.0E+01

5.0E+01

6.0E+01

7.0E+01

GC/MS analysis is an excellent tool for comparing these levels.

followed by GC/MS analysis is an excellent tool for comparing these levels.

**Figure 6.** Spectrum of d9-silylated epigallocatechin gallate. Masses of fragments are shown as related to those in the spectrum of non-deuterated compound displayed in Figure 5.

**Figure 7.** Two time windows between 58 min and 80 min from a green tea water extract derivatized with BSTFA (A) and for the same extract derivatized with d9-BSTFA (B).

The quantitation of epigallocatechin and epigallocatechin gallate in the green tea was also evaluated in this study. For this purpose, the initial peak area was measured in the chroma‐ togram of the silylated green tea sample. This was followed by the addition of 500 µg and 1000 µg of the two compounds (as solution in DMF) to 50 mg green tea sample with silylation. In order to avoid peak overloading, the silylated solution that was filtered through a 0.45 µm PTFE filter. The samples were analyzed by GC/MS and the peak areas were measured. The results are illustrated in Figure 8 that represents the peak area measurement normalized by the internal standard area (0.04 mg/mL *tert*-butylhydroquinone after 1/10 dilution) versus the compound addition.

Figure 8. Peak area for epigallocatechin (EGC) and epigallocatechin gallate (EGCG) for a green tea sample, and for the same type of sample with 500 μg and 1000 μg of the two compounds added. same type of sample with 500 μg and 1000 μg of the two compounds added.

From the trendline equations, it can be calculated that the green tea contained about 2.307 mg EGCG/50 mg sample, and 1.720 mg EGC/50 mg sample. This is equivalent to 46.14 mg/g EGCG and 34.41 mg/g ECG. These levels are in the range reported in other studies for green tea [34]. Green tea from different sources may have different levels of antioxidants, and silylation followed by GC/MS analysis is an excellent tool for comparing these levels. From the trendline equations, it can be calculated that the green tea contained about 2.307 mg EGCG/50 mg sample, and 1.720 mg EGC/50 mg sample. This is equivalent to 46.14 mg/g EGCG and 34.41 mg/g ECG. These levels are in the range reported in other studies for green tea [34]. Green tea from different sources may have different levels of antioxidants, and silylation followed by GC/MS analysis is an excellent tool for comparing these levels.

### **3.2. Example of rosemary analysis**

Abundance

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6 1.0e+07 1.1e+07

11.09 12.89

14.21 25.77

29.67

37.24

39.01

40.05

Time-->

**3.2. Example of rosemary analysis** 

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800

**Figure 6.** Spectrum of d9-silylated epigallocatechin gallate. Masses of fragments are shown as related to those in the

*494 = 458 + 4x9*

72.19

B

A

58.0 60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 76.0 78.0 80.0

58.0 60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 76.0 78.0 <sup>0</sup>

**Figure 7.** Two time windows between 58 min and 80 min from a green tea water extract derivatized with BSTFA (A)

396

*396 = 369 + 3x9*

*693 = 648 + 5x9*

*794 = 740 + 6x9*

78.14 78.83

79.41

77.66 78.27

78.85

80.0

*595 = 559 + 4x9*

162 251 595 <sup>494</sup> <sup>794</sup> <sup>0</sup>

Abundance <sup>82</sup> <sup>693</sup>

296

*296 = 281 + 9 + 6*

m/z-->

0 2000000 4000000 6000000 8000000 1e+07 1.2e+07 1.4e+07 1.6e+07

Time-->

2000000

Time-->

and for the same extract derivatized with d9-BSTFA (B).

4000000

6000000

8000000

1e+07

Abundance 59.06

100 90

100

112 Advances in Gas Chromatography

*82 = 73 + 9*

spectrum of non-deuterated compound displayed in Figure 5.

60.82

61.05

61.79 63.83

64.32

64.70

63.38

63.86

65.18

Abundance 59.87

Rosemary (dry leaf) (*Rosmarinus officinalis*) is another botanical with antioxidant properties [22,23]. For rosemary (dry leaf) evaluated in this study (commercially available from American Spice Trading Co.) the ORAC values (both lipophilic and hydrophilic) were 620 ± 20 μM TE/g, and FRAP was 800 ± 15 μM Fe2+/g. The chemical composition of rosemary leaf can be studied using the GC/MS technique after direct silylation of the dry leaf similarly to green tea. The chromatogram of silylated rosemary is shown in Figure 9, and the identification of the main peaks can be viewed in Table 3 where the retention times for individual compounds are listed. 6e+6**Figure 8.** Peak area for epigallocatechin (EGC) and epigallocatechin gallate (EGCG) for a green tea sample, and for the Rosemary (dry leaf) (*Rosmarinus officinalis*) is another botanical with antioxidant properties [22,23]. For rosemary (dry leaf) evaluated in this study (commercially available from American Spice Trading Co.) the ORAC values (both lipophilic and hydrophilic) were 620 ± 20 µM TE/g, and FRAP was 800 ± 15 µM Fe2+/g. The chemical composition of rosemary leaf can be studied using the GC/MS technique after direct silylation of the dry leaf similarly to green tea.

Figure 9. Chromatogram of silylated rosemary dry leaf. The identification of main peaks is given in Table 3. (I.S. elutes at 29.67 min).

43.24 47.60 55.68

10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

**Antioxidant compounds Ret. Time Other main compounds Ret. Time** 

69.90

72.69

74.02 74.35

75.23

83.78

Time-->

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6

Abundance

72.69

74.02 74.35

73.0 74.0 75.0

74.77

75.23

74.77

57.54

59.79

The chromatogram of silylated rosemary is shown in Figure 9, and the identification of the main peaks can be viewed in Table 3 where the retention times for individual compounds are listed.

Some of the spectra of the silylated compounds are not available in common mass spectral libraries.The spectra of silylatedrosmaricin, carnosic acid, carnosol,rosmanol,rosmarinic acid, oleanolic acid, betulinic acid, ursolic acid, and betulonic acid are shown in Figures 10 to 18.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

373

431

490

http://dx.doi.org/10.5772/57292

115

548

460 505

500

50 100 150 200 250 300 350 400 450

**Figure 10.** Spectrum of silylated rosmaircin, ret. time 55.68 min, MW = 489.27 (Note: the presence of two silyl groups

CH3

TMS

CH3

50 100 150 200 250 300 350 400 450 500 550

147 361

<sup>45</sup> <sup>271</sup> <sup>229</sup> <sup>299</sup> <sup>389</sup>

O

431

**Figure 11.** Spectrum of silylated carnosic acid, ret. time 57.54 min, MW = 548.32.

H3C CH3

O O

TMS

O

TMS

335

<sup>342</sup> <sup>45</sup> <sup>204</sup> <sup>229</sup> <sup>147</sup> <sup>417</sup> <sup>447</sup>

277

CH3 CH3

303

m/z-->

Abundance

73

m/z-->

0

20

40

60

80

100

0

20

40

60

80

100

Abundance

73

H3C

O

TMS

on this molecule has been verified using silylation with d9-BSTFA).

O TMS

NH2

O O

CH3

**Figure 9.** Chromatogram of silylated rosemary dry leaf. The identification of main peaks is given in Table 3. (I.S. elutes at 29.67 min).


**Table 3.** Compound identification for rosemary chromatogram shown in Figure 9

Some of the spectra of the silylated compounds are not available in common mass spectral libraries.The spectra of silylatedrosmaricin, carnosic acid, carnosol,rosmanol,rosmarinic acid, oleanolic acid, betulinic acid, ursolic acid, and betulonic acid are shown in Figures 10 to 18.

The chromatogram of silylated rosemary is shown in Figure 9, and the identification of the main peaks can be viewed in Table 3 where the retention times for individual compounds are

listed.

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6 1.0e+07 1.1e+07

11.09 12.89

14.21 25.77

29.67

37.24

**Table 3.** Compound identification for rosemary chromatogram shown in Figure 9

39.01

40.05 43.24 47.60 55.68

10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

**Antioxidant compounds Ret. Time Other main compounds Ret. Time** Catechol lactate 45.33 Camphor 11.09 Caffeic acid 47.60 Borneol 14.21 Rosmaricin 55.68 Malic acid 25.77 Carnosic acid 57.54 Pentose (ribose ?) 34.71 Carnosol 58.90 Fructose 37.24 Rosmanol 60.19 Quinic acid 39.01 Rosmarinic acid 72.69 Glucose 40.05, 43.24 Oleanolic acid 74.02 Myoinositol 45.91 Betulinic acid 74.35 Sucrose 59.79 Ursolic acid 74.77 Disaccharide 69.90 Betulonic acid 75.23 Disaccharide ? 83.78

**Figure 9.** Chromatogram of silylated rosemary dry leaf. The identification of main peaks is given in Table 3. (I.S. elutes

69.90

72.69

74.02 74.35

75.23

83.78

Time-->

1e+6 2e+6 3e+6 4e+6 5e+6 6e+6 7e+6 8e+6 9e+6

1e+6

Abundance

72.69

74.02 74.35

73.0 74.0 75.0

74.77

75.23

74.77

57.54

59.79

Abundance

114 Advances in Gas Chromatography

Time-->

at 29.67 min).

**Figure 10.** Spectrum of silylated rosmaircin, ret. time 55.68 min, MW = 489.27 (Note: the presence of two silyl groups on this molecule has been verified using silylation with d9-BSTFA).

**Figure 11.** Spectrum of silylated carnosic acid, ret. time 57.54 min, MW = 548.32.

O

TMS

O

OO

TMS

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117

396 +2H

O

TMS

O TMS

600

O

483

O

H3C CH3

O

O

396

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

50 100 150 200 250 300 350 400 450 500 550 600 650 700

50 100 150 200 250 300 350 400 450 500 550 600

**Figure 15.** Spectrum of silylated oleanolic acid or 3β-hydroxyolean-12-en-28-oic acid (from standard), ret. time 74.02

393

CH3 CH3

TMS TMS

CH3

320 129 482 45 279 585

O

H3C CH3

45 147 705 <sup>469</sup> <sup>615</sup> <sup>720</sup> <sup>0</sup>

219 324

<sup>179</sup> <sup>267</sup>

**Figure 14.** Spectrum of silylated rosmarinic acid, ret. time 72.69 min, MW = 720.28.

203

189

m/z-->

Abundance

73

m/z-->

min, MW = 600.44.

0

20

40

60

80

100

20

40

60

80

100

Abundance

73

TMS

**Figure 12.** Spectrum of silylated carnosol, ret. time 58.90 min, MW = 474.26.

**Figure 13.** Spectrum of silylated rosmanol, ret. time 60.19 min, MW = 562.30.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 117

**Figure 14.** Spectrum of silylated rosmarinic acid, ret. time 72.69 min, MW = 720.28.

<sup>50</sup> <sup>100</sup> <sup>150</sup> <sup>200</sup> <sup>250</sup> <sup>300</sup> <sup>350</sup> <sup>400</sup> <sup>450</sup> m/z-->

O

O

O

TMS

O

O

O

TMS

O

H3C CH3

<sup>229</sup> <sup>45</sup> <sup>147</sup> <sup>387</sup>

O

271

50 100 150 200 250 300 350 400 450 500 550

147 359 403 472

O

TMS

CH3

CH3

TMS

CH3

CH3

TMS

430

474


<sup>518</sup> <sup>562</sup>


429

348

Abundance

0

m/z-->

0

20

40

60

80

100

Abundance

73

20

40

60

80

100

116 Advances in Gas Chromatography

73

100

**Figure 12.** Spectrum of silylated carnosol, ret. time 58.90 min, MW = 474.26.

<sup>45</sup> <sup>191</sup> <sup>217</sup> <sup>271</sup> <sup>317</sup>

**Figure 13.** Spectrum of silylated rosmanol, ret. time 60.19 min, MW = 562.30.

H3C CH3

**Figure 15.** Spectrum of silylated oleanolic acid or 3β-hydroxyolean-12-en-28-oic acid (from standard), ret. time 74.02 min, MW = 600.44.

CH2

O

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119

O

481

CH3

391 583

C, for two hours. The variation in normalized area

H3C

CH3 CH3

TMS TMS

H3C CH3

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

50 100 150 200 250 300 350 400 450 500 550 600

O

305 <sup>481</sup> <sup>598</sup> <sup>45</sup>

**Figure 18.** Spectrum (tentative) of silylated betulonic acid or 3-oxo-lup-20(29)-en-28-oic acid, ret. time 75.23 min,

The spectrum of silylated betulonic acid has a similar pattern to that of betulinic acid, except for several fragments being lower by two a.m.u. It was assumed that during silylation, the

The GC/MS analysis with direct derivatization of dry leaf of a botanical has various specific advantages compared to other analysis techniques. Besides its simplicity, the technique allows a detailed identification of the compounds seen in the chromatogram, allows a comparison of peak intensity between different types of botanicals, and quantitation when standards are available. An example of the application of this technique is the study of stability upon heating of rosemary regarding its antioxidant level. Starting from room temperature the heating was

counts in the chromatograms of silylated leaf heated at different temperatures is shown in Figure 19. The results show that carnosic acid and rosmaricin have the tendency to decrease as the leaves are heated, while other antioxidant compounds are not affected by the heating

A variety of other botanicals (leaves, rhizomes, or other plant parts) containing antioxidant molecules form silyl derivatives can be analyzed by GC/MS. Among the compounds that can be identified by sylilation and GC/MS are: vitexin, isoorientin, mangiferin, gossypin, delphi‐ nidin and other cyanidins, quercetin, tocoferol, coumaroyl quinic acid, ar-turmerone, curcu‐ min, leucocyanidin gallate, etc. Some of the mass spectra of these molecules are easily identifiable, but in other cases, the identification is less obvious. In cases of glucosides (and Cglucosides), for example, the set of ions 147, 204, 217, 305 that are characteristic for the carbohydrate (glucose) moiety may lead to the conclusion that the chromatographic peak

201

171

129

carbonyl group in position 3 is enolised and silylated.

**3.3. Other applications of direct silylation and GC/MS analysis**

performed at three intervals up to 120 o

m/z-->

in the indicated range.

MW = 598.42.

0

20

40

60

80

100

Abundance

73

**Figure 16.** Spectrum of silylated betulinic acid, or (3β)-3-Hydroxy-lup-20(29)-en-28-oic acid (from standard), ret. time 74.35 min, MW = 600.44.

**Figure 17.** Spectrum of silylated ursolic acid or 3-β-hydroxy-urs-12-en-28-oic acid (from standard), ret. time 74.77 min, MW = 600.44.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 119

**Figure 18.** Spectrum (tentative) of silylated betulonic acid or 3-oxo-lup-20(29)-en-28-oic acid, ret. time 75.23 min, MW = 598.42.

The spectrum of silylated betulonic acid has a similar pattern to that of betulinic acid, except for several fragments being lower by two a.m.u. It was assumed that during silylation, the carbonyl group in position 3 is enolised and silylated.

The GC/MS analysis with direct derivatization of dry leaf of a botanical has various specific advantages compared to other analysis techniques. Besides its simplicity, the technique allows a detailed identification of the compounds seen in the chromatogram, allows a comparison of peak intensity between different types of botanicals, and quantitation when standards are available. An example of the application of this technique is the study of stability upon heating of rosemary regarding its antioxidant level. Starting from room temperature the heating was performed at three intervals up to 120 o C, for two hours. The variation in normalized area counts in the chromatograms of silylated leaf heated at different temperatures is shown in Figure 19. The results show that carnosic acid and rosmaricin have the tendency to decrease as the leaves are heated, while other antioxidant compounds are not affected by the heating in the indicated range.

#### **3.3. Other applications of direct silylation and GC/MS analysis**

50 100 150 200 250 300 350 400 450 500 550 600

**Figure 16.** Spectrum of silylated betulinic acid, or (3β)-3-Hydroxy-lup-20(29)-en-28-oic acid (from standard), ret. time

50 100 150 200 250 300 350 400 450 500 550 600

**Figure 17.** Spectrum of silylated ursolic acid or 3-β-hydroxy-urs-12-en-28-oic acid (from standard), ret. time 74.77 min,

<sup>175</sup> <sup>279</sup> <sup>482</sup>

O

<sup>585</sup> <sup>393</sup> <sup>600</sup>

CH3

H3C

O

483

O

CH3

CH3 CH3

TMS TMS

H3C CH3

320

<sup>292</sup> <sup>320</sup> <sup>45</sup> <sup>483</sup> <sup>585</sup> <sup>393</sup> <sup>600</sup>

O

H3C CH3

CH2

O

O

483

CH3

H3C

CH3 CH3

TMS TMS

m/z-->

74.35 min, MW = 600.44.

Abundance

73

133

m/z-->

0

MW = 600.44.

20

40

60

80

100

0

20

40

60

80

100

118 Advances in Gas Chromatography

Abundance <sup>73</sup>

189

203

129

A variety of other botanicals (leaves, rhizomes, or other plant parts) containing antioxidant molecules form silyl derivatives can be analyzed by GC/MS. Among the compounds that can be identified by sylilation and GC/MS are: vitexin, isoorientin, mangiferin, gossypin, delphi‐ nidin and other cyanidins, quercetin, tocoferol, coumaroyl quinic acid, ar-turmerone, curcu‐ min, leucocyanidin gallate, etc. Some of the mass spectra of these molecules are easily identifiable, but in other cases, the identification is less obvious. In cases of glucosides (and Cglucosides), for example, the set of ions 147, 204, 217, 305 that are characteristic for the carbohydrate (glucose) moiety may lead to the conclusion that the chromatographic peak

**4. GC/MS analysis of triglycerides with antioxidant character**

groups [37], or with silyl groups [29,38].

**4.1. Triglyceride hydrolysis and fatty acids methylation**

by GC. The reactions taking place are described as follows:

in CH3OH CH2

OCOR OCOR OCOR

CH CH2 NaOH

Some triglycerides present in botanicals, usually from the fruits or from seeds, are known to have antioxidant character. This character is caused by the presence of polyunsaturation in the long chain hydrocarbon moiety of the fatty acids (PUFAs) that are typically part of the triglyceride molecules. PUFAs (free or as triglyceride) have a scavenging potential toward reactive oxygen/nitrogen (ROS/RNS) species [36]. Several nomenclature systems are used for the fatty acids, a common one being omega-x (ω - x, or n - x). The value of x indicates the position of the double bond which is the closest to the terminal methyl of the hydrocarbon chain of the acid, with counting from the terminal methyl. For example, linoleic acid is a n - 6 or an omega-6 acid. Triglycerides formed from omega-3 acids, besides the antioxidant character, are considered essential fatty acids, since they cannot be synthesized by the human body and are related to additional health benefits. Common analysis of triglycerides is done either for the intact compound, or after hydrolysis and derivatization of the acid with methyl

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

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121

The formation of methyl esters from triglycerides is typically done in one operation that produces both the hydrolysis of the triglyceride and the methylation of the free acids formed in the hydrolysis. Methyl esters of the fatty acids (FAME) can be obtained using various reagents [30,39]. Common procedures use methanol and H2SO4, methanol and BF3 [40-42] or methanol and HCl. One standard procedure [43] starts with the addition to 100 mg lipid in a 50 mL round bottom flask with condenser. To the flask is added 4 mL of a 0.5 M methanolic solution of NaOH. The solution is boiled until fat globules disappear. Then, 5 mL solution of BF3 in methanol (125 g BF3/L) is added and the boiling is continued for 2-3 min. Then about 5 mL of heptane is added and boiled for another minute. The mixture is allowed to cool and 15 mL saturated solution of NaCl is added. About 1 mL heptane is collected from the upper layer and is dried over anhydrous Na2SO4. The solution is diluted with heptane if necessary for the GC analysis. Detection for the GC can be either flame ionnization (FID) or mass spectrometry (MS). A number of variants of this methylation procedure are reported in the literature (e.g. [44]). For example, one variant starts with 200–500 mg lipid which is boiled with 5 mL 0.5 N NaOH or KOH in methanol for 3–5 min. To this mixture is added 15 mL of an esterification solution, and the mixture is refluxed for 3 min. The esterification solution is prepared by adding 2 g NH4Cl to 60 mL methanol and 3 mL conc. H2SO4 which are than refluxed together for 15 min. The esterified acids are transferred into a separation funnel containing 25 mL petroleum ether and 50 mL water. The water is discarded and the organic phase is washed twice with 25 mL water. The resulting organic phase can be concentrated, dried with Na2SO4, and analyzed

RCOO-CH3

 CH3OH + H2SO4 / NH4Cl

RCOONa

**Figure 19.** Variation with temperature in the normalized area counts of several antioxidant compounds (rosmaricin level is shown x 10 for being in scale) in rosemary heated between 20 oC and 120 oC, for two hours.

belongs to a carbohydrate, since carbohydrates are frequently present in plant extracts. As an example, the spectrum of silylated isoorientin (luteolin-6-C-glucoside) is given in Figure 20. In this spectrum, the presence of MW - 15 ion caused by the loss of a CH3 from the silyl group, which is typical for silyl derivatives is a good indication of the parent molecule isoorientin which has MW =1024.41.

**Figure 20.** Spectrum of silylated isoorientin, (luteolin-6-C-glucoside) ret. time 82.68 min, MW = 1024.41. The peak with 1009 a.m.u. resulting from the loss of CH3 is seen in the spectrum.

### **4. GC/MS analysis of triglycerides with antioxidant character**

Some triglycerides present in botanicals, usually from the fruits or from seeds, are known to have antioxidant character. This character is caused by the presence of polyunsaturation in the long chain hydrocarbon moiety of the fatty acids (PUFAs) that are typically part of the triglyceride molecules. PUFAs (free or as triglyceride) have a scavenging potential toward reactive oxygen/nitrogen (ROS/RNS) species [36]. Several nomenclature systems are used for the fatty acids, a common one being omega-x (ω - x, or n - x). The value of x indicates the position of the double bond which is the closest to the terminal methyl of the hydrocarbon chain of the acid, with counting from the terminal methyl. For example, linoleic acid is a n - 6 or an omega-6 acid. Triglycerides formed from omega-3 acids, besides the antioxidant character, are considered essential fatty acids, since they cannot be synthesized by the human body and are related to additional health benefits. Common analysis of triglycerides is done either for the intact compound, or after hydrolysis and derivatization of the acid with methyl groups [37], or with silyl groups [29,38].

### **4.1. Triglyceride hydrolysis and fatty acids methylation**

belongs to a carbohydrate, since carbohydrates are frequently present in plant extracts. As an example, the spectrum of silylated isoorientin (luteolin-6-C-glucoside) is given in Figure 20. In this spectrum, the presence of MW - 15 ion caused by the loss of a CH3 from the silyl group, which is typical for silyl derivatives is a good indication of the parent molecule isoorientin

**Figure 19.** Variation with temperature in the normalized area counts of several antioxidant compounds (rosmaricin

**C**

0 50 100 150 **Temperature o**

level is shown x 10 for being in scale) in rosemary heated between 20 oC and 120 oC, for two hours.

O

O

**Figure 20.** Spectrum of silylated isoorientin, (luteolin-6-C-glucoside) ret. time 82.68 min, MW = 1024.41. The peak

<sup>O</sup> <sup>O</sup>

TMS TMS

TMS

O

O

TMS

TMS

100 200 300 400 500 600 700 800 900 1000

O

<sup>587</sup> <sup>305</sup> <sup>501</sup> <sup>645</sup> <sup>715</sup> <sup>1009</sup> <sup>772</sup> <sup>829</sup> <sup>919</sup>

O

O

TMS

Rosmaricin x 10 Carnosic acid Rosmarinic acid Oleanolic acid Betulinic acid Ursolic acid Betulonic acid

O

O TMS

TMS

which has MW =1024.41.

0

0.5

1

1.5

**Normalized area counts**

2

2.5

3

3.5

120 Advances in Gas Chromatography

Abundance

73

147 204

217

with 1009 a.m.u. resulting from the loss of CH3 is seen in the spectrum.

0

m/z-->

20

40

60

80

100

The formation of methyl esters from triglycerides is typically done in one operation that produces both the hydrolysis of the triglyceride and the methylation of the free acids formed in the hydrolysis. Methyl esters of the fatty acids (FAME) can be obtained using various reagents [30,39]. Common procedures use methanol and H2SO4, methanol and BF3 [40-42] or methanol and HCl. One standard procedure [43] starts with the addition to 100 mg lipid in a 50 mL round bottom flask with condenser. To the flask is added 4 mL of a 0.5 M methanolic solution of NaOH. The solution is boiled until fat globules disappear. Then, 5 mL solution of BF3 in methanol (125 g BF3/L) is added and the boiling is continued for 2-3 min. Then about 5 mL of heptane is added and boiled for another minute. The mixture is allowed to cool and 15 mL saturated solution of NaCl is added. About 1 mL heptane is collected from the upper layer and is dried over anhydrous Na2SO4. The solution is diluted with heptane if necessary for the GC analysis. Detection for the GC can be either flame ionnization (FID) or mass spectrometry (MS). A number of variants of this methylation procedure are reported in the literature (e.g. [44]). For example, one variant starts with 200–500 mg lipid which is boiled with 5 mL 0.5 N NaOH or KOH in methanol for 3–5 min. To this mixture is added 15 mL of an esterification solution, and the mixture is refluxed for 3 min. The esterification solution is prepared by adding 2 g NH4Cl to 60 mL methanol and 3 mL conc. H2SO4 which are than refluxed together for 15 min. The esterified acids are transferred into a separation funnel containing 25 mL petroleum ether and 50 mL water. The water is discarded and the organic phase is washed twice with 25 mL water. The resulting organic phase can be concentrated, dried with Na2SO4, and analyzed by GC. The reactions taking place are described as follows:

$$\begin{array}{ccccc} \text{C\#-} & \text{NaOH} & \text{C\#-} & \text{O} \text{C\#-} & \text{O} \text{N} \\ \text{C\#-} & \text{COOR} & & \text{H}\_{2}\text{SO}\_{4} & \text{N-H}\_{4}\text{Cl} \\ \text{C\#-} & \text{COOR} & & & \\ \text{CH}\_{2}\text{-} & \text{COOR} & & & \\ \end{array}$$

The analysis of FAME can be performed following various procedures. One such procedure uses a SP2560 100 m x 0.25 mm column with 0.2 µm film for separation. This is a highly polar biscyanopropyl column specifically designed to separate geometric position isomers of fatty acid methyl esters. The recommended GC conditions are given in Table 4.

Other procedures to generate methyl esters are also reported in the literature [38].

Hydrolysis and formation of silyl derivatives of fatty acids is another procedure used for lipid analysis. The analysis starts with the hydrolysis of the triglycerides. For this purpose, 0.3 to 0.5 mg lipid (precisely weighed) was treated with 50 µL solution of 2M KOH in ethanol. The

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

potassium salts of the fatty acids. After that, the cap of the vial was removed, and the ethanol evaporated. Complete evaporation of ethanol, which takes 3-5 min, is necessary to avoid the formation of small proportions of ethyl esters when HCl is further added. To the vial, 25 µL solution of 6M HCl was added to neutralize the base and change the organic acid potassium salts into free acids. Then, 750 µL of *n*-nonane was added to extract the free acids. The nonane solution was treated in the vial with about 0.2 g of anhydrous Na2SO4 for drying. From the dry nonane solution, 500 µL were transferred into a separate 2 mL vial, treated with 25 µL of pyridine, 100 µL of dimethylformamide (DMF) that contains 400 µg/mL of tert-butylhydro‐ quinone (TBHQ), and with 300 µL bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS). The TBHQ is used as a chromatographic standard. The vials

of the samples was performed using a GC/MS instrument (Agilent 7890/5975 system, Wil‐ mington, DE, USA), equipped with a Zebron ZB-50 column (Phenomenex, Torrence, CA 90501-1430, USA) that was 60 m long, 0.25 mm i.d., and 0.50 µm film thickness. The recom‐

C in a heating block, to generate

http://dx.doi.org/10.5772/57292

123

C for 30 min, followed by GC/MS analysis. The analysis

**4.2. Triglyceride hydrolysis and fatty acids silylation**

mixture was heated in a 2 mL capped vial for 30 min at 78 o

mended parameters for the GC/MS analysis are given in Table 5

**Parameter Description Parameter Description** Initial oven temp. 50°C Carrier gas Hydrogen Initial time 0.5 min Flow mode Constant flow Oven first ramp rate 10°C/min Flow rate 0.71 mL/min Final oven temp. first ramp 200°C Nominal initial pressure 12.05 psi Final time first ramp 0 min Split ratio 20 : 1 Oven second ramp rate 3°C/min Split flow 14.20 mL/min

Final oven temp. second ramp 250°C GC outlet MSD Final time second ramp 0 min Outlet pressure Vacuum Oven third ramp rate 20°C/min Transfer line heater 300°C Final oven temp. third ramp 300°C Ion source temp. 230°C Final time third ramp 2 min Quadrupole temp. 150°C Total run time 36.66 min MSD EM gain 2.0 Inlet temp. 300°C MSD solvent delay 8.0 min Inlet mode Split MSD acquisition mode scan

Injection volume 0.5 μL Mass range 33 to 550 a.m.u.

**Table 5.** GC/MS operating parameters for silylated acids analysis.

with the samples were heated at 78 o


**Table 4.** GC operating parameters for methyl ester analysis.

The procedure allows the separation of over 60 FAME. Other columns and shorter run times can be utilized if a less detailed separation is desired. For example, a SP2380 30 m x 0.25 mm column with 0.2 µm film can be used, with oven starting at 150 o C and gradient to 250 o C at 4 o C/min, helium carrier gas at 20 cm/s (at 150 o C), and FID detector at 260 o C. In these conditions, a typical GC/MS chromatogram obtained for linseed oil is shown in Figure 21.

**Figure 21.** GC/MS chromatogram for methyl esters in linseed oil.

Other procedures to generate methyl esters are also reported in the literature [38].

### **4.2. Triglyceride hydrolysis and fatty acids silylation**

The analysis of FAME can be performed following various procedures. One such procedure uses a SP2560 100 m x 0.25 mm column with 0.2 µm film for separation. This is a highly polar biscyanopropyl column specifically designed to separate geometric position isomers of fatty

The procedure allows the separation of over 60 FAME. Other columns and shorter run times can be utilized if a less detailed separation is desired. For example, a SP2380 30 m x 0.25 mm

> 0 2 4 6 8 10 12 14 **Time (min)**

Stearic

Oleic

Linoleic

Linolenic

Palmitic

C), and FID detector at 260 o

C and gradient to 250 o

C. In these conditions,

C at 4

acid methyl esters. The recommended GC conditions are given in Table 4.

**Parameter Description Parameter Description** Initial oven temp. 100°C Injection volume 1.0 μL Initial time 4.0 min Carrier gas Helium

Oven ramp rate 3°C/min Flow mode Constant flow Oven final temp. 240°C Flow rate 0.75 mL/min Final time 15 min Linear flow rate 18 cm/s Total run time 65.6 min Split ratio 200 : 1 Inlet temp. 225°C GC outlet FID Inlet mode Split Detector temperature 300°C

**Table 4.** GC operating parameters for methyl ester analysis.

C/min, helium carrier gas at 20 cm/s (at 150 o

0

**Figure 21.** GC/MS chromatogram for methyl esters in linseed oil.

0.5

1

1.5

**Relative height**

2

2.5

3

3.5

122 Advances in Gas Chromatography

o

column with 0.2 µm film can be used, with oven starting at 150 o

Solvent

a typical GC/MS chromatogram obtained for linseed oil is shown in Figure 21.

Hydrolysis and formation of silyl derivatives of fatty acids is another procedure used for lipid analysis. The analysis starts with the hydrolysis of the triglycerides. For this purpose, 0.3 to 0.5 mg lipid (precisely weighed) was treated with 50 µL solution of 2M KOH in ethanol. The mixture was heated in a 2 mL capped vial for 30 min at 78 o C in a heating block, to generate potassium salts of the fatty acids. After that, the cap of the vial was removed, and the ethanol evaporated. Complete evaporation of ethanol, which takes 3-5 min, is necessary to avoid the formation of small proportions of ethyl esters when HCl is further added. To the vial, 25 µL solution of 6M HCl was added to neutralize the base and change the organic acid potassium salts into free acids. Then, 750 µL of *n*-nonane was added to extract the free acids. The nonane solution was treated in the vial with about 0.2 g of anhydrous Na2SO4 for drying. From the dry nonane solution, 500 µL were transferred into a separate 2 mL vial, treated with 25 µL of pyridine, 100 µL of dimethylformamide (DMF) that contains 400 µg/mL of tert-butylhydro‐ quinone (TBHQ), and with 300 µL bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS). The TBHQ is used as a chromatographic standard. The vials with the samples were heated at 78 o C for 30 min, followed by GC/MS analysis. The analysis of the samples was performed using a GC/MS instrument (Agilent 7890/5975 system, Wil‐ mington, DE, USA), equipped with a Zebron ZB-50 column (Phenomenex, Torrence, CA 90501-1430, USA) that was 60 m long, 0.25 mm i.d., and 0.50 µm film thickness. The recom‐ mended parameters for the GC/MS analysis are given in Table 5


**Table 5.** GC/MS operating parameters for silylated acids analysis.

Time-->

The peak identification was performed using both standards (when available) and mass spectra library searches (on NIST 08 library). The chromatography allows excellent separation of acids in the range C6 to C27, and differentiate isomers such as oleic and elaidic acid. Quantitation of fatty acids was obtained using calibration curves. A typical total ion chroma‐ togram (TIC) for the fatty acids as TMS derivatives from a commercial vegetable cooking oil hydrolysate sample is shown in Figure 22.

**4.3. Analysis of intact triglycerides**

lipid in n-nonane (b.p. 151 o

detectors are shown in Table 8.

Inlet Cold on column

**Table 7.** GC operating parameters.

MS operating mode Scan EI+

**Table 8.** ID and MS operating parameters.

Figure 24.

Mass range a.m.u. 50 – 800 a.m.u.

For the analysis of triglycerides as whole molecules, a solution containing about 0.5 mg/mL

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

directly by GC, in conditions described in Table 7. The GC was equipped with a Rtx®-65TG column, 30 m x 0.25 mm, with 0.1 µm film thickness (Restek, Bellefonte, PA 16823, USA). Similar separation was obtained using a CP-Tap column, 25 m x 0.25 mm, 0.1 µm film (Varian, Walnut Creek, CA 94598, USA) in the same conditions as in Table 7. The GC can be used either with FID detection or MS detection. The conditions for the MS and FID

**Parameter Description Parameter Description** Initial oven temperature 130 oC Inlet mode Ramped Initial time 1.0 min Inlet initial temperature 130 oC Oven temp. rate first ramp 30 oC/min Initial time 0.1 min Final temperature first ramp 300 oC Inlet temperature rate 150 oC/min Final time 0.0 min Final temperature 300 oC Oven temp. rate second ramp 4.0 oC/min Injection volume 0.2 μL Final temperature second ramp 365 oC Carrier gas H2

Final time 7.0 min Flow mode Constant flow Total run time 29.92 min Flow rate 0.8 mL/min

**MS Parameter Description FID Parameter Description** MSD transfer line 300 oC Detector temperature 300 oC Ion source temperature 230 oC H2 flow 30 mL/min MSD EM gain 2.0 Air flow 400 mL/min

MSD solvent delay 3.0 min Make up flow N2 25 mL

Using the conditions previously described, the chromatogram of a commercial vegetable cooking oil with FID detection is shown in Figure 23, and with MS detection is shown in

C) was made from each sample. This solution was analyzed

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125

**Figure 22.** GC/MS chromatogram of TMS derivatives of fatty acids from a commercial cooking oil hydrolysate sample. Peak identification can be obtained using the data from Table 6.


**Table 6.** Peak identification and relative peak area for the chromatogram of TMS derivatives of fatty acids from a commercial vegetable cooking oil hydrolysate sample.

### **4.3. Analysis of intact triglycerides**

The peak identification was performed using both standards (when available) and mass spectra library searches (on NIST 08 library). The chromatography allows excellent separation of acids in the range C6 to C27, and differentiate isomers such as oleic and elaidic acid. Quantitation of fatty acids was obtained using calibration curves. A typical total ion chroma‐ togram (TIC) for the fatty acids as TMS derivatives from a commercial vegetable cooking oil

14.0 16.0 18.0 20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0

**Identifying ions**

**Figure 22.** GC/MS chromatogram of TMS derivatives of fatty acids from a commercial cooking oil hydrolysate sample.

2 Unknown 14.61 ? 192, 163 ? 0.24

 Palmitic acid TMS 23.13 328.613 313, 328 C19H40O2Si C16:0 9.52 Palmitoleic acid TMS 23.30 326.597 311, 326 C19H38O2Si C16:1 Z-9 0.07 Stearic acid TMS 27.27 356.667 341, 356 C21H44O2Si C18:0 2.56 Oleic acid TMS 27.35 354.651 339, 354 C21H42O2Si C18:1 Z-9 20.33

 Linoleic acid TMS 27.82 352.635 337, 352 C21H40O2Si C18:2 Z,Z-9,12 59.72 Linolenic acid TMS 28.50 350.62 335, 350 C21H38O2Si C18:3 Z,Z,Z-6,9,12 4.99 Arachidic acid TMS 31.70 384.721 369, 384 C23H48O2Si C20:0 0.13 11-Eicosenoic acid TMS 31.80 382.705 367, 382 C23H46O2Si C20:1 Z-11 0.06 Docosanoic acid TMS (behenic) 34.63 412.78 397, 412 C25H52O2Si C22:0 0.14

**Table 6.** Peak identification and relative peak area for the chromatogram of TMS derivatives of fatty acids from a

23.30

27.27

28.50

10.74 308.64 205, 218 C12H32O3Si3 0.15

27.49 354.651 339, 354 C21H42O2Si C18:1 E-9 2.09

31.70 34.63

**Formula Symbol Area %**

27.35

27.82

23.13

21.03

hydrolysate sample is shown in Figure 22.

14.61

16.41

Peak identification can be obtained using the data from Table 6.

**No. Compound Ret. time MW**

commercial vegetable cooking oil hydrolysate sample.

3 Internal standard (I.S.) 16.41 4 Column bleed 21.03

Abundance

124 Advances in Gas Chromatography

Time-->

0

2000000 4000000 6000000 8000000 1e+07 1.2e+07 1.4e+07 1.6e+07 1.8e+07

Glycerin 3TMS (not shown)

Elaidic acid TMS (trans-9-C18:1)

1

9

For the analysis of triglycerides as whole molecules, a solution containing about 0.5 mg/mL lipid in n-nonane (b.p. 151 o C) was made from each sample. This solution was analyzed directly by GC, in conditions described in Table 7. The GC was equipped with a Rtx®-65TG column, 30 m x 0.25 mm, with 0.1 µm film thickness (Restek, Bellefonte, PA 16823, USA). Similar separation was obtained using a CP-Tap column, 25 m x 0.25 mm, 0.1 µm film (Varian, Walnut Creek, CA 94598, USA) in the same conditions as in Table 7. The GC can be used either with FID detection or MS detection. The conditions for the MS and FID detectors are shown in Table 8.


**Table 7.** GC operating parameters.


**Table 8.** ID and MS operating parameters.

Using the conditions previously described, the chromatogram of a commercial vegetable cooking oil with FID detection is shown in Figure 23, and with MS detection is shown in Figure 24.

**Compound Formula**

for a commercial vegetable cooking oil.

55

<sup>41</sup> <sup>69</sup>

81

95

40m/z-->

m/z-->

**Ret. time in MS**

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

 Dipalmitin olein C53H100O6 16.34 833.380 551, 577, 339 0.51 0.27 Dipalmitin linolein C53H98O6 16.60 831.364 551, 575, 335 1.42 0.85 Palmitin stearin olein C55H104O6 17.55 861.434 579, 605, 341 0.34 0.19 Palmitin diolein C55H102O6 17.73 859.418 577, 603, 339 4.14 2.65 Palmitin stearin linolein C55H102O6 17.81 859.418 579, 603, 341 2.14 1.37 Palmitin olein linolein C55H100O6 18.00 857.402 577, 601, 339 12.51 9.19 Palmitin dilinolein C55H98O6 18.28 855.386 575, 599, 337 15.93 13.44 Palmitin linolein linolenin C55H96O6 18.61 853.370 573, 575, 335, 1.40 1.36 Linolein distearin C57H106O6 18.93 887.472 605, 341, 264 1.03 0.70 Triolein C57H104O6 19.13 885.456 603, 339, 264 5.34 4.56 Distearin olein C57H108O6 19.23 889.488 605, 341, 262 5.31 3.17 Diolein linolein C57H102O6 19.44 883.440 603, 339, 262 8.36 8.63 Stearin olein linolein C57H104O6 19.53 885.456 603, 341, 262 8.68 6.87 Dilinolein olein C57H100O6 19.77 881.424 601, 339, 262 16.37 20.21 Trilinolein C57H98O6 20.11 879.408 599, 337,262 12.90 19.39 Dilinolein linolenin C57H96O6 20.53 877.392 597, 599, 337 3.62 6.59

**Table 9.** Peak identification, relative peak area for the MS chromatogram and % triglyceride from FID measurement

The peak identifications can be done based on the mass spectra of each compound. For

50 100 150 200 250 300 350 400 450 500 550 600

<sup>109</sup> <sup>577</sup> 313 339

O

O

O

O

O

O

367

393 449 503

palm ityl

linoleyl

oleyl

example, the mass spectrum of palmito-linoleo-olein is given in Figure 25.

Abundance 262

123

135

164 239

**Figure 25.** Mass spectrum of palmito-linoleo-olein (the correct position of substituents is unknown).

**MW Identifying ions**

**Area % from MS**

http://dx.doi.org/10.5772/57292

601

**Triglyc. % from FID**

127

**Figure 23.** Chromatogram of a commercial vegetable cooking oil generated using FID detection.

**Figure 24.** Chromatogram of a commercial vegetable cooking oil generated using MS detection (total ion chromato‐ gram or TIC). Peak identification following retention times as given in Table 9.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 127


16.00 17.00 18.00 19.00 20.00 21.00 22.00

16.0 17.0 18.0 19.0 20.0 21.0

**Figure 24.** Chromatogram of a commercial vegetable cooking oil generated using MS detection (total ion chromato‐

18.6018.93

19.12 19.22

19.44

19.77

20.11

20.56

19.53

18.98 19.16 19.38

19.63

19.71

19.97

20.05

20.31

20.65

21.03

16.25 16.34

16.56

FID

17.71

17.93

18.04

**Figure 23.** Chromatogram of a commercial vegetable cooking oil generated using FID detection.

17.55

gram or TIC). Peak identification following retention times as given in Table 9.

17.73

17.81

18.00 18.28

18.27

18.60

Response

126 Advances in Gas Chromatography

Time min

Abundance

Abundance

16.34 16.60

MS

Time min

**Table 9.** Peak identification, relative peak area for the MS chromatogram and % triglyceride from FID measurement for a commercial vegetable cooking oil.

The peak identifications can be done based on the mass spectra of each compound. For example, the mass spectrum of palmito-linoleo-olein is given in Figure 25.

**Figure 25.** Mass spectrum of palmito-linoleo-olein (the correct position of substituents is unknown).

The structures of several diagnostic ions in the spectrum of a triglyceride species that contains palmityl, linoleyl, and oleyl fatty acids in the molecule is given in Figure 26.

**Compound Formula**

**5. Conclusions**

**Author details**

**References**

Serban Moldoveanu\*

analysis of antioxidants in botanicals.

Address all correspondence to: moldovs@rjrt.com

R.J. Reynolds Tobacco Co., Winston-Salem, USA

*ric. Food Chem*., 53 (2005) 1841-1856.

44 (1996) 701-705.

**Ret. time in MS**

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

 Palmito oleino linolenin C55H98O6 18.33 855.386 577, 599, 573 Dioleino linolenin? C57H100O6 19.82 881.424 604, 599, 339 Stearo linoleo linolenin C57H100O6 20.28 881.424 603, 601, 597 Oleino linoleo linolenin C57H98O6 21.45 879.408 597, 599, 601 Oleino dilinolenin C57H96O6 21.95 877.392 595, 599, 335

Note: Ions with m/z value in bold result from the loss of a fatty acid residue from the triglyceride molecule.

GC/MS is a very useful technique for the analysis of antioxidants in botanicals, although many antioxidant molecules are large and/or contain numerous polar groups. GC methods have limitations regarding their capability to be used for the analysis of heavier and less volatile molecules. However, the use of derivatization of the analytes, and special selection of the GC settings allow the extension of the applicability for this technique. The unique capability to identify molecular species based on EI+ mass spectra makes GC/MS an invaluable tool in the

[1] D. Huang, B. Ou, R. L. Prior, The chemistry behind antioxidant capacity assays, *J. Ag‐*

[2] H. Wang, G. Cao, R. L. Prior, Total antioxidant capacity of fruits, *J. Agric. Food Chem.,*

**Table 10.** Peak Identification, for Extra Peaks in the MS Chromatogram for Linseed Oil.

**MW Identifying ions**

http://dx.doi.org/10.5772/57292

129

**Figure 26.** The structures of several diagnostic ions in the spectrum of palmito-linoleo-olein.

Heavier triglycerides are less amenable for direct GC analysis. For example, direct measure‐ ment of triglycerides esterified with more than two linolenic acids is not possible in the chromatographic conditions previously described. As an example, the TIC trace for a sample of linseed oil generated in the same conditions as the chromatogram from Figure 25 is given in Figure 27 [29].

**Figure 27.** Chromatogram of linseed oil generated using MS detection. Peak identification following retention times as given in Table 9 and in Table 10.

Many peaks from this chromatogram are identical to those described in Table 9. However, a few additional triglycerides were identified (some tentatively) in linseed oil and they are given in Table 10.

The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals http://dx.doi.org/10.5772/57292 129


Note: Ions with m/z value in bold result from the loss of a fatty acid residue from the triglyceride molecule.

**Table 10.** Peak Identification, for Extra Peaks in the MS Chromatogram for Linseed Oil.

### **5. Conclusions**

The structures of several diagnostic ions in the spectrum of a triglyceride species that contains

.+ 2C OH

O

Heavier triglycerides are less amenable for direct GC analysis. For example, direct measure‐ ment of triglycerides esterified with more than two linolenic acids is not possible in the chromatographic conditions previously described. As an example, the TIC trace for a sample of linseed oil generated in the same conditions as the chromatogram from Figure 25 is given

19.12

18.93 19.43

19.22

16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0

19.76

19.52

20.11 20.64

19.82 20.16

21.45

20.87

21.95

. <sup>+</sup> OH

O

O O

.

+

OH CH2

O

ion m/z = 575

ion m/z = 339

ion m/z = 262

palmityl, linoleyl, and oleyl fatty acids in the molecule is given in Figure 26.

**Figure 26.** The structures of several diagnostic ions in the spectrum of palmito-linoleo-olein.

17.73 18.00

16.60 17.55

18.28 18.33

18.60

**Figure 27.** Chromatogram of linseed oil generated using MS detection. Peak identification following retention times

Many peaks from this chromatogram are identical to those described in Table 9. However, a few additional triglycerides were identified (some tentatively) in linseed oil and they are given

.

<sup>O</sup> <sup>+</sup>

128 Advances in Gas Chromatography

O

in Figure 27 [29].

Time-->

as given in Table 9 and in Table 10.

in Table 10.

0

16.34

Abundance

1800000

ion m/z = 601

.+ ion m/z = 577

O CH2

O

O

O

CH O

O

GC/MS is a very useful technique for the analysis of antioxidants in botanicals, although many antioxidant molecules are large and/or contain numerous polar groups. GC methods have limitations regarding their capability to be used for the analysis of heavier and less volatile molecules. However, the use of derivatization of the analytes, and special selection of the GC settings allow the extension of the applicability for this technique. The unique capability to identify molecular species based on EI+ mass spectra makes GC/MS an invaluable tool in the analysis of antioxidants in botanicals.

#### **Author details**

Serban Moldoveanu\*

Address all correspondence to: moldovs@rjrt.com

R.J. Reynolds Tobacco Co., Winston-Salem, USA

#### **References**


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[16] M. Parvu, A. Toiu, L. Vlase, P. E. Alina, Determination of some polyphenolic com‐ pounds from Allium species by HPLC-UV-MS, *Nat. Prod. Res*. 24 (2010) 1318–2134.

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[14] S. Y. Li, Y. Yu, S. P. Li, Identification of antioxidants in essential oil of radix Angeli‐ cae sinensis using HPLC coupled with DAD-MS and ABTS-based assay, *J. Agric. Food*

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The Utilization of Gas Chromatography/Mass Spectrometry in the Profiling of Several Antioxidants in Botanicals

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**Chapter 6**

**Influence of Culture Conditions on the Fatty Acids**

**Composition of Green and Purple Photosynthetic**

As was published in [1], based on 16S rRNA sequences, prokaryotes are divided into two primary groups: Archaebacteria (Archaea) and Eubacteria (Bacteria). With the exception of Cyanobacteria, all the main lineages of photosynthetic organisms belong to the Eubacterial

Within Eubacteria, photosynthetic organisms are found according to the following divisions: (i) gram-positive bacteria belonging to the family Heliobacteriaceae, (ii) green non-sulphur bacteria (also known as filamentous green bacteria) such as *Chloroflexus aurantiacus,* (iii) green sulphur bacteria such as *Chlorobium tepidum,* (iv) cyanobacteria, (v) and several genera of

Anoxygenic green and purple photosynthetic sulphur bacteria (GPSB/PPSB) are anaerobes. They do not release O2 by photosynthesis, and as electron donors for this process, they use sulphur and its derivatives. These microorganisms have bacteriochlorophyll (Bchl) and accessory carotene pigments that include spirilloxanthin, okenone and chlorobactene pigment

Photosynthetic pigments in green photosynthetic sulphur bacteria (GPSB) include Bchl c, d and e, as well as chlorobactene and isorenieratene. PPSB may contain Bchl *a* or *b.* In general, these photosynthetic microorganisms present one kind of Bchl, except in GPSB - which could have small quantities of Bchl *a* - they belong to the Chlorobiaceae family. PPSB comprise the families Chromatiaceae (e.g., *Chromatium or Halochromatium)* and Ectothiorhodospiraceae.

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Sulphur Bacteria**

María Teresa Núñez-Cardona

http://dx.doi.org/10.5772/57389

**1. Introduction**

series.

Additional information is available at the end of the chapter

Phyla and the unrelated halobacterial species.

proteobacteria (or purple bacteria) [1].

## **Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria**

María Teresa Núñez-Cardona

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57389

### **1. Introduction**

As was published in [1], based on 16S rRNA sequences, prokaryotes are divided into two primary groups: Archaebacteria (Archaea) and Eubacteria (Bacteria). With the exception of Cyanobacteria, all the main lineages of photosynthetic organisms belong to the Eubacterial Phyla and the unrelated halobacterial species.

Within Eubacteria, photosynthetic organisms are found according to the following divisions: (i) gram-positive bacteria belonging to the family Heliobacteriaceae, (ii) green non-sulphur bacteria (also known as filamentous green bacteria) such as *Chloroflexus aurantiacus,* (iii) green sulphur bacteria such as *Chlorobium tepidum,* (iv) cyanobacteria, (v) and several genera of proteobacteria (or purple bacteria) [1].

Anoxygenic green and purple photosynthetic sulphur bacteria (GPSB/PPSB) are anaerobes. They do not release O2 by photosynthesis, and as electron donors for this process, they use sulphur and its derivatives. These microorganisms have bacteriochlorophyll (Bchl) and accessory carotene pigments that include spirilloxanthin, okenone and chlorobactene pigment series.

Photosynthetic pigments in green photosynthetic sulphur bacteria (GPSB) include Bchl c, d and e, as well as chlorobactene and isorenieratene. PPSB may contain Bchl *a* or *b.* In general, these photosynthetic microorganisms present one kind of Bchl, except in GPSB - which could have small quantities of Bchl *a* - they belong to the Chlorobiaceae family. PPSB comprise the families Chromatiaceae (e.g., *Chromatium or Halochromatium)* and Ectothiorhodospiraceae.

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Morphological patterns are limited in bacteria, and phenotypical together with molecular properties have been used for the identification of these microorganisms. Chemotaxonomy takes into account fingerprint methods using proteins, nucleic acids, sugars isoprenoids and fatty acids, among other molecules. For example, an average of fatty acids can be easily obtained by gas chromatography (or by gas-liquid chromatography). This average is called a 'fatty acid profile' and a part of this profile is a pattern [2]. The pattern is generated by cleaning and adapting the fatty acid profile. Both are highly reproducible when growth conditions, the physiological age of the cells and analysis are well standardized. Subsequently, the fatty acid patterns can be used for the identification of bacteria, library generation, taxonomy, epide‐ miological and ecological studies [2], and obviously it is very useful for detecting bacteria in samples of different origins.

species of these genera; however, C18:1 may be present with more than 50% (e.g., *Chroma‐ tium salexigens* DSM4395). The C16:1 contributes at least with 16 % (e. g., *Chromatium tepidum*, DSM 3771) and 36% as maximum (e. g., *Thiocystis gelatinosa*) [cited in 7], and the C16:0 fatty acid has been reported in quantities of 10.4% in *Chromatium* sp. (strain T7s9) to 25.8% in *Chromatium tepidum* (DSM 3771); it is important to note that in *Chr. tepidum*, the main fatty acids C18:1, C16:0 and C16:1 contribute with 46.38%, 25.86% and 16.17%, respectively, so the pattern for this strain is C18:1>C16:0>C16:1. Other fatty acids detected in minor quantities in members of the Chromatiaceae family include: C12:0, C13:0, C14:0, aC17:0,

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

http://dx.doi.org/10.5772/57389

137

The other family of PPSB, the Ectothiorhodospiraceae, has C18:1 as the main fatty acid, which contributes with 74.7% of the total extracted (96.7%) in *Ectothiorhodospira shaposhnikovii* [cited in 7]. The fatty acid C16:0 is the next in abundance, and C16:1 or C18:0 might be the third. The relative abundance of these major fatty acids as well as the less abundant are very different among the strains of this family (e.g., *Ec. halophila, Ec. halochloris, Ec. abdelmaleki, Ec. shaposh‐ nikovii, Ec. vacuolata, Ec. salini, Ec. magna*, *Halorhodospira halophila* and *Thiorhodospira sibirica*) [cited in 7]. These minor fatty acids are: C12:0 [10] in *Ec. shaposhnikovii*; C19:0d8,9 (a phospho‐ lipid detected in *Halorhodospira halophila*) and 19:0Cy (detected in different strains of *Ectothio‐ rhodospira halophila*, *Ec. Mobilis*, *Ec. salini* and *Ec. Abdelmaleki*). Also, C21:0 has been detected in the phospholipids of *Ec. shaposhnikovii* and *Halorhodospira halophila* DSM 244, while the

eicosanoic (C20:0) fatty acid was found in *Halorhodospira halophila* BN9626 [cited in 7].

This chapter describes the influence of MgSO4 and NaCl on three cultures of GPSB (*Chlorobi‐ um* genus) as well as the effect of culture age (15-30 days) in two strains of *Chromatium* (*Halochromatium*). Some considerations about the uses of fatty acids other than chemotaxono‐ my as well as new applications of fatty acid analyses like lipidomics by mass spectrometry are

The analysis of fatty acids as methyl esters has been used to identify bacteria species. Their composition and the ratios of these compounds are useful in identifying microorganisms. They are conserved if bacteria are grown in similar conditions [11]. Also, it has been pointed out that information such as phylogenetic data and similarities derived from the fatty acid analysis are in broad agreement with 16S rDNA results, and appear to accurately distinguish between

The number of bacterial species evaluated is a limitation in comparing the information available about the fatty acid composition from bacteria [12]. GPSB and PPSB are also not an exception compared to the fatty acid studies on chemoheterotrophic bacteria. There are very few investigations about the influence on fatty acid compositions of the culture medium for bacterial growth (e.g., salinity), the age of the cells or the physical factors (e.g., temperature)

C18:1w9. C17:0, C18:0 and C20:1 [7-9].

also included.

most species [12].

**2. Fatty acids analysis**

used to culture the microorganisms.

Photosynthetic prokaryotes have a great variety of fatty acids, and they are useful for distin‐ guishing these microorganisms; actually, they are recommended for the description and identification of new species ([3, 4]). For example, *Chloroflexus aurantiacus*, a green photosyn‐ thetic non-sulphur bacterium (GPNSB), contains straight-chain, saturated and monounsatu‐ rated fatty acids as its main constituents, but its pattern is distinctly different from the pattern of GSPB [5].

Fatty acids from whole cells of *Chlorobium* are within the range of C12-C18, and the main ones are: n-tetradecanoic (C14:0), hexadecenoic (C16:1) and n-hexadecanoic (C16:0) [5-7].

Currently, studies have reported 12 fatty acids in complete cells from GPSB, and the fatty acid C14:0 accounts for 14% [6] to 27.10% [5]; the fatty acid C16:1 accounts for at least 37.3% in these bacteria [5].

Hexadecanoic is the third main fatty acid in GPSB [6] in quantities from 10% in *Cb limicola v. thiosulfatophilum* to 29% in *Cb. phaeovibroides*; finally, the fatty acid C18:1 could be the other main one in GPSB, where it has been detected in quantities between 2.9% and 10.33% in the genus *Chlorobium* [5-7].

Minor fatty acids have been reported in these microorganisms and they include: C12:0, 12-Me-C14:0, 5-Me-C16:0, 14-Me-C16:0, C15:0, 15-Me-C16:1, C17:0, C18:0 and a cyclic fatty acid [5-7]. GPNSB (like *Chloroflexus*) are characterized by the presence of C17 and C18-C20 as the main fatty acids, and these are not detected in GPSB [5].

In oxygenic photosynthetic cyanobacteria - for example, in *Nostoc* and *Anabaena* - it has been reported [8] the presence of 17 major fatty acids (≥0.9%) that included saturated even-carbon, straight-chain C16:0 and C18:0; the first one was higher for *Anabaena* (27.7-34.72%) and lower for *Nostoc* isolates (18.5 to 26.1%). 26 minor components (at least 0.03%) and 10 trace-level components (average ≤ 0.03%) were also recorded.

Fatty acids of the Chromatiaceae family have been more studied than those from GPSB, and analysed strains of this family include members of the genera *Amoebobacter, Halochro‐ matium* (e.g. *Chromatium salexigens*), *Thiocapsa*, *Allochromatium* (*Allochromatium vinosum* = *Chromatium vinosum*) and *Thiocystis.* As gram-negative bacteria, they have as main fatty acids: C18:1*ω*7>C16:1>C16:0. The proportions of these compounds are variable among species of these genera; however, C18:1 may be present with more than 50% (e.g., *Chroma‐ tium salexigens* DSM4395). The C16:1 contributes at least with 16 % (e. g., *Chromatium tepidum*, DSM 3771) and 36% as maximum (e. g., *Thiocystis gelatinosa*) [cited in 7], and the C16:0 fatty acid has been reported in quantities of 10.4% in *Chromatium* sp. (strain T7s9) to 25.8% in *Chromatium tepidum* (DSM 3771); it is important to note that in *Chr. tepidum*, the main fatty acids C18:1, C16:0 and C16:1 contribute with 46.38%, 25.86% and 16.17%, respectively, so the pattern for this strain is C18:1>C16:0>C16:1. Other fatty acids detected in minor quantities in members of the Chromatiaceae family include: C12:0, C13:0, C14:0, aC17:0, C18:1w9. C17:0, C18:0 and C20:1 [7-9].

The other family of PPSB, the Ectothiorhodospiraceae, has C18:1 as the main fatty acid, which contributes with 74.7% of the total extracted (96.7%) in *Ectothiorhodospira shaposhnikovii* [cited in 7]. The fatty acid C16:0 is the next in abundance, and C16:1 or C18:0 might be the third. The relative abundance of these major fatty acids as well as the less abundant are very different among the strains of this family (e.g., *Ec. halophila, Ec. halochloris, Ec. abdelmaleki, Ec. shaposh‐ nikovii, Ec. vacuolata, Ec. salini, Ec. magna*, *Halorhodospira halophila* and *Thiorhodospira sibirica*) [cited in 7]. These minor fatty acids are: C12:0 [10] in *Ec. shaposhnikovii*; C19:0d8,9 (a phospho‐ lipid detected in *Halorhodospira halophila*) and 19:0Cy (detected in different strains of *Ectothio‐ rhodospira halophila*, *Ec. Mobilis*, *Ec. salini* and *Ec. Abdelmaleki*). Also, C21:0 has been detected in the phospholipids of *Ec. shaposhnikovii* and *Halorhodospira halophila* DSM 244, while the eicosanoic (C20:0) fatty acid was found in *Halorhodospira halophila* BN9626 [cited in 7].

This chapter describes the influence of MgSO4 and NaCl on three cultures of GPSB (*Chlorobi‐ um* genus) as well as the effect of culture age (15-30 days) in two strains of *Chromatium* (*Halochromatium*). Some considerations about the uses of fatty acids other than chemotaxono‐ my as well as new applications of fatty acid analyses like lipidomics by mass spectrometry are also included.

### **2. Fatty acids analysis**

Morphological patterns are limited in bacteria, and phenotypical together with molecular properties have been used for the identification of these microorganisms. Chemotaxonomy takes into account fingerprint methods using proteins, nucleic acids, sugars isoprenoids and fatty acids, among other molecules. For example, an average of fatty acids can be easily obtained by gas chromatography (or by gas-liquid chromatography). This average is called a 'fatty acid profile' and a part of this profile is a pattern [2]. The pattern is generated by cleaning and adapting the fatty acid profile. Both are highly reproducible when growth conditions, the physiological age of the cells and analysis are well standardized. Subsequently, the fatty acid patterns can be used for the identification of bacteria, library generation, taxonomy, epide‐ miological and ecological studies [2], and obviously it is very useful for detecting bacteria in

Photosynthetic prokaryotes have a great variety of fatty acids, and they are useful for distin‐ guishing these microorganisms; actually, they are recommended for the description and identification of new species ([3, 4]). For example, *Chloroflexus aurantiacus*, a green photosyn‐ thetic non-sulphur bacterium (GPNSB), contains straight-chain, saturated and monounsatu‐ rated fatty acids as its main constituents, but its pattern is distinctly different from the pattern

Fatty acids from whole cells of *Chlorobium* are within the range of C12-C18, and the main ones

Currently, studies have reported 12 fatty acids in complete cells from GPSB, and the fatty acid C14:0 accounts for 14% [6] to 27.10% [5]; the fatty acid C16:1 accounts for at least 37.3% in these

Hexadecanoic is the third main fatty acid in GPSB [6] in quantities from 10% in *Cb limicola v. thiosulfatophilum* to 29% in *Cb. phaeovibroides*; finally, the fatty acid C18:1 could be the other main one in GPSB, where it has been detected in quantities between 2.9% and 10.33% in the

Minor fatty acids have been reported in these microorganisms and they include: C12:0, 12-Me-C14:0, 5-Me-C16:0, 14-Me-C16:0, C15:0, 15-Me-C16:1, C17:0, C18:0 and a cyclic fatty acid [5-7]. GPNSB (like *Chloroflexus*) are characterized by the presence of C17 and C18-C20 as the main

In oxygenic photosynthetic cyanobacteria - for example, in *Nostoc* and *Anabaena* - it has been reported [8] the presence of 17 major fatty acids (≥0.9%) that included saturated even-carbon, straight-chain C16:0 and C18:0; the first one was higher for *Anabaena* (27.7-34.72%) and lower for *Nostoc* isolates (18.5 to 26.1%). 26 minor components (at least 0.03%) and 10 trace-level

Fatty acids of the Chromatiaceae family have been more studied than those from GPSB, and analysed strains of this family include members of the genera *Amoebobacter, Halochro‐ matium* (e.g. *Chromatium salexigens*), *Thiocapsa*, *Allochromatium* (*Allochromatium vinosum* = *Chromatium vinosum*) and *Thiocystis.* As gram-negative bacteria, they have as main fatty acids: C18:1*ω*7>C16:1>C16:0. The proportions of these compounds are variable among

are: n-tetradecanoic (C14:0), hexadecenoic (C16:1) and n-hexadecanoic (C16:0) [5-7].

samples of different origins.

136 Advances in Gas Chromatography

of GSPB [5].

bacteria [5].

genus *Chlorobium* [5-7].

fatty acids, and these are not detected in GPSB [5].

components (average ≤ 0.03%) were also recorded.

The analysis of fatty acids as methyl esters has been used to identify bacteria species. Their composition and the ratios of these compounds are useful in identifying microorganisms. They are conserved if bacteria are grown in similar conditions [11]. Also, it has been pointed out that information such as phylogenetic data and similarities derived from the fatty acid analysis are in broad agreement with 16S rDNA results, and appear to accurately distinguish between most species [12].

The number of bacterial species evaluated is a limitation in comparing the information available about the fatty acid composition from bacteria [12]. GPSB and PPSB are also not an exception compared to the fatty acid studies on chemoheterotrophic bacteria. There are very few investigations about the influence on fatty acid compositions of the culture medium for bacterial growth (e.g., salinity), the age of the cells or the physical factors (e.g., temperature) used to culture the microorganisms.

Fatty acid analysis may also depend on the extraction technique; in some studies, different procedures have been applied to investigate the fatty acid compositions of *Pseudomonas diminuta* and *P. maltophilia*, and the results have revealed that the qualitative and quantitative features of these compounds are different depending on the techniques used. Nevertheless, there are no changes in their profiles. As such, the main as well as the minor fatty acids are very useful in distinguishing among these strains [13].

allowing the separation and quantification of alcohols and volatile and non-volatile fatty acids. This method simplifies the procedure for fatty acid identification and quantification [18].

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

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139

Pyrolysis-MS is another technique that allows the direct analysis of bacteria (depending on the organism). With pyrolysis, the bacteria are broken (in an inert atmosphere) using heat to produce volatile, low molecular weight substances that are detected and quantified. This technique involves the analysis of the total cellular composition of the bacteria, and a single

This technique can be used to provide GC data from non-volatile/solid materials. A GC with

Lipids are defined as natural products that may be isolated from biological materials by extraction with organic solvents and which are usually insoluble in water. This definition includes sterols, terpenoids, isoprenoids, waxes and pigments, many of which are not present in bacteria [21], and the simplest are the fatty acids, as in Table 1. These are the fundamental building blocks of complex lipids, like fatty acyls, glycerolipids, glycerophospholipids,

Microbial fatty acids are typically 12-24 carbons long and the common membrane fatty acids are 14-20 carbons long. These compounds are either ester- or ether-linked. The latter is rare, and has been found in the phospholipids of Archaea [23]. In general, bacteria contain fatty acids between C10 and C20 in length, but there are some eubacteria species that have poly‐ unsaturated fatty acids, like *Shewanella gelidimarina*, *Shewanella hanedai*, *Colwellia psychrery‐*

Fatty acids are essential to cells and their metabolic functions. These compounds are typically associated with energy storage and the structural fluidity of membranes; they form com‐ pounds that play important roles in cell signalling processes [26]. In addition, they function as metabolic intermediates and form part of the higher molecular weight lipids [27]. These carbon-based 'backbone' chains, may be elongated, shortened or altered by the activity of the elongase or desaturase enzymic activity, respectively, and longer fatty acids are results of lipogenesis. Moreover, the size of the fatty acid structure depends upon the addition or

Structurally, fatty acids are long chain monocarboxylic acids of the general formula R.COOH. Usually, the R-group has a long chain - commonly unbranched - consisting of an even number between eight and 24 carbon atoms [27]. The chain has a hydrophobic aliphatic tail that is either saturated or unsaturated [22], depending on the presence of double bounds in the R-group. Acids with one double bound are known as 'mono-enoic', those with two as 'di-enoic', and so

Fatty acids are structurally diverse and contain distinct classes that are defined at the molecular level, based on the degree of branching, the number and position of double bonds, the chain length, the *cis*-*trans* isomer conformations, and the presence of functional groups [22].

an ion trap mass spectrometer (IT-MS) could be used for this kind of analysis [20].

sphingolipids, sterol lipids, prenol lipids, saccharolipids and polyketides [22].

*thraea* [24], *Shewanella donghaensis* [25], cyanobacteria and mycobacteria.

removal of double hydrogen bonds and catabolic activity [26].

bacterial colony is sufficient for analysis [19].

**2.1. Fatty acid definition**

on [27].

Different techniques and methods for fatty acid analysis have been developed since Abel et al. (1963) [14] proposed quantitative and qualitative analyses for characterizing microorganisms, where it was indicated that gas chromatography (GC) has the necessary sensitivity, rapidity and selectivity for such analyses. They considered that this is an extremely selective method, such that a single normal-sized colony of microorganisms is sufficient. This method requires a short time between preparation to examination, and organic and inorganic nutrients do not interfere. The fatty acid methyl esters (FAMEs) from cell membranes have been analysed using GC. These fatty acids were extracted from cell hydrolysates and derivatized to volatile methyl esters for further detection by GC [14].

Other detection methods for fatty acid analysis have also been applied. These include gasliquid chromatography and mass spectrometry (MS), which is a rapid, automated method for analysing mixed populations, and the various configurations of MS offer combined advan‐ tages of speed, sensitivity and selectivity. The method also requires that molecules be vapor‐ ized into the gas phase before they can be ionized and analysed.

Gas chromatographic or high pressure liquid chromatographic separation increases the capability for analysing complex mixtures, and they together provide two complementary kinds of characterization. Such chromatographic separation could even distinguish chirality, which is usually invisible to MS [15]. Fatty acids analysis and quantification by GC is carried out by the extraction of lipids from microorganisms, followed by hydrolysis and methylation. The resulting FAME profiles are used for the identification and differentiation of these microorganisms [16]. Actually, there are standardized and automated techniques to asses these compounds; using the Microbial Identification System (MIS) it is a possibility, in a short time, for acquiring precise information about bacteria fatty acid compositions. This system is applied most frequently in the clinical market and it is limited in the number or environmental species they can identify. Nevertheless, microbial fatty acid profiles are unique from one species to another [2].

The MIS system – also known as the MIDI Sherlock Holmes System – which identifies microorganisms using FAMEs and GC, consists of large microbial libraries; the procedure for FAME analysis [17] may be summarized by the following steps: 1) harvesting (the removal cells from the culture media), 2) saponification (the lysis of the cells to liberate fatty acids), 3) derivatization (methylation, the formation of FAMEs), 4) extraction (the transfer of the FAMEs from the aqueous phase to the organic phase by the use of an organic solvent), 5) base wash (an aqueous wash or the organic extract prior to chromatographic analysis).

Alternative procedures have also been proposed for the extraction and separation of FAMEs without the previously described steps, such as by the direct injection of the culture broth, allowing the separation and quantification of alcohols and volatile and non-volatile fatty acids. This method simplifies the procedure for fatty acid identification and quantification [18].

Pyrolysis-MS is another technique that allows the direct analysis of bacteria (depending on the organism). With pyrolysis, the bacteria are broken (in an inert atmosphere) using heat to produce volatile, low molecular weight substances that are detected and quantified. This technique involves the analysis of the total cellular composition of the bacteria, and a single bacterial colony is sufficient for analysis [19].

This technique can be used to provide GC data from non-volatile/solid materials. A GC with an ion trap mass spectrometer (IT-MS) could be used for this kind of analysis [20].

### **2.1. Fatty acid definition**

Fatty acid analysis may also depend on the extraction technique; in some studies, different procedures have been applied to investigate the fatty acid compositions of *Pseudomonas diminuta* and *P. maltophilia*, and the results have revealed that the qualitative and quantitative features of these compounds are different depending on the techniques used. Nevertheless, there are no changes in their profiles. As such, the main as well as the minor fatty acids are

Different techniques and methods for fatty acid analysis have been developed since Abel et al. (1963) [14] proposed quantitative and qualitative analyses for characterizing microorganisms, where it was indicated that gas chromatography (GC) has the necessary sensitivity, rapidity and selectivity for such analyses. They considered that this is an extremely selective method, such that a single normal-sized colony of microorganisms is sufficient. This method requires a short time between preparation to examination, and organic and inorganic nutrients do not interfere. The fatty acid methyl esters (FAMEs) from cell membranes have been analysed using GC. These fatty acids were extracted from cell hydrolysates and derivatized to volatile methyl

Other detection methods for fatty acid analysis have also been applied. These include gasliquid chromatography and mass spectrometry (MS), which is a rapid, automated method for analysing mixed populations, and the various configurations of MS offer combined advan‐ tages of speed, sensitivity and selectivity. The method also requires that molecules be vapor‐

Gas chromatographic or high pressure liquid chromatographic separation increases the capability for analysing complex mixtures, and they together provide two complementary kinds of characterization. Such chromatographic separation could even distinguish chirality, which is usually invisible to MS [15]. Fatty acids analysis and quantification by GC is carried out by the extraction of lipids from microorganisms, followed by hydrolysis and methylation. The resulting FAME profiles are used for the identification and differentiation of these microorganisms [16]. Actually, there are standardized and automated techniques to asses these compounds; using the Microbial Identification System (MIS) it is a possibility, in a short time, for acquiring precise information about bacteria fatty acid compositions. This system is applied most frequently in the clinical market and it is limited in the number or environmental species they can identify. Nevertheless, microbial fatty acid profiles are unique from one species to

The MIS system – also known as the MIDI Sherlock Holmes System – which identifies microorganisms using FAMEs and GC, consists of large microbial libraries; the procedure for FAME analysis [17] may be summarized by the following steps: 1) harvesting (the removal cells from the culture media), 2) saponification (the lysis of the cells to liberate fatty acids), 3) derivatization (methylation, the formation of FAMEs), 4) extraction (the transfer of the FAMEs from the aqueous phase to the organic phase by the use of an organic solvent), 5) base wash

Alternative procedures have also been proposed for the extraction and separation of FAMEs without the previously described steps, such as by the direct injection of the culture broth,

(an aqueous wash or the organic extract prior to chromatographic analysis).

very useful in distinguishing among these strains [13].

ized into the gas phase before they can be ionized and analysed.

esters for further detection by GC [14].

138 Advances in Gas Chromatography

another [2].

Lipids are defined as natural products that may be isolated from biological materials by extraction with organic solvents and which are usually insoluble in water. This definition includes sterols, terpenoids, isoprenoids, waxes and pigments, many of which are not present in bacteria [21], and the simplest are the fatty acids, as in Table 1. These are the fundamental building blocks of complex lipids, like fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids and polyketides [22].

Microbial fatty acids are typically 12-24 carbons long and the common membrane fatty acids are 14-20 carbons long. These compounds are either ester- or ether-linked. The latter is rare, and has been found in the phospholipids of Archaea [23]. In general, bacteria contain fatty acids between C10 and C20 in length, but there are some eubacteria species that have poly‐ unsaturated fatty acids, like *Shewanella gelidimarina*, *Shewanella hanedai*, *Colwellia psychrery‐ thraea* [24], *Shewanella donghaensis* [25], cyanobacteria and mycobacteria.

Fatty acids are essential to cells and their metabolic functions. These compounds are typically associated with energy storage and the structural fluidity of membranes; they form com‐ pounds that play important roles in cell signalling processes [26]. In addition, they function as metabolic intermediates and form part of the higher molecular weight lipids [27]. These carbon-based 'backbone' chains, may be elongated, shortened or altered by the activity of the elongase or desaturase enzymic activity, respectively, and longer fatty acids are results of lipogenesis. Moreover, the size of the fatty acid structure depends upon the addition or removal of double hydrogen bonds and catabolic activity [26].

Structurally, fatty acids are long chain monocarboxylic acids of the general formula R.COOH. Usually, the R-group has a long chain - commonly unbranched - consisting of an even number between eight and 24 carbon atoms [27]. The chain has a hydrophobic aliphatic tail that is either saturated or unsaturated [22], depending on the presence of double bounds in the R-group. Acids with one double bound are known as 'mono-enoic', those with two as 'di-enoic', and so on [27].

Fatty acids are structurally diverse and contain distinct classes that are defined at the molecular level, based on the degree of branching, the number and position of double bonds, the chain length, the *cis*-*trans* isomer conformations, and the presence of functional groups [22].

The major groups of fatty acids are: a) straight chain, b) branched chain, c) unsaturated, and d) cyclopropane. Straight chain fatty acids are found elsewhere in nature (e.g., C12, C14, C16, C18 and C20). However, the corresponding β− hydroxymyristic acid 3-OH and C12 are not normally found in higher organisms, but are common components of the boundary of lipids present in the walls of gram-negative bacteria. Branched fatty acids comprise two types: the iso-form (the methyl group is located on the penultimate carbon atom) and the anteiso (the methyl group is located in the antepenultimate carbon atom). The unsaturated acids found in bacteria are monounsaturated, whereby the double bound is most frequently found between carbon 11 and 12. The cyclopropane fatty acids - rarely found in higher organisms - are frequently encountered in bacteria. The biosynthetic precursors of these acids are the corre‐ sponding monounsaturated fatty acids; as a consequence, the major example is the cis-11-12 methylene hexadecanoid acid [21]. Table 1 contains some examples of fatty acids commonly found in bacteria.

was grown in a limited concentration of ammonium salts, these bacteria produced high

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

The analysis of salinity effects on the fatty acid composition of photosynthetic bacteria has been done in PPSB, such as *Chromatium purpuratum* (or *Halochromatium purpuratum*), *Ectothio‐ rhodospira mobilis* and *Ec. halophila* [29], using different salt concentrations; the results showed

The age of the cells is another factor that may change the fatty acid composition of bacteria as well as the cells of all living organisms. In many techniques, it has been suggested that it is very important to use bacterial cells of the same age for fatty acid analysis, especially for

In order to study the influence of MgSO4 and NaCl and the age of the cells in the fatty acid composition of GPSB and PPSB, respectively, the bacterial strains included in Table 2 were used. This table contains the origin, the phenotypical characteristics and optimal culture

DSMZ (=DSM) = Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH; GSC = Giraud Saltens Camargue; TL = Tampamachoco Lagoon (Veracruz); THS = Tassajara Hot Spring; LB = Lake Blankvann; *Bchl* bacteriochlorophyll, *spx*

Marine origin strains. The genus *Chromatium* was reclassified into *Halochromatium*. \*\*Chlorobium limicola f. thiosulfa‐

For all the experiment, two wild strains were used: a PPSB strain T9s642 (*Chromatium* sp.) and a GPSB strain T11s (*Chlorobium* sp.). These photosynthetic bacteria were isolated from super‐ ficial water samples collected in Tampamachoco, a coastal lagoon located in Tuxpan (Veracruz,

For culturing these bacteria, a basal medium enriched with water samples was used. This was also used for the isolation and growth of these photosynthetic bacteria and contained the

0.50 g, and NaCl 2.0% (only for marine strains). It was sterilized by autoclave and, once cooled, was supplemented with a 1.0 mL SL10 solution (GPSB) and SL12 (PPSB), Vitamin B12 1.0 mL

.

7H2O 0.40 g, CaCl2

http://dx.doi.org/10.5772/57389

141

. 2H2O

spirilloxanthin, *ly* lycopene, *cb* chlorobactene, *rh* rhodopinal; \*NaCl (%) = optimal salinity requirement.

only quantitative alterations, but their fatty acid pattern did not witness changes.

quantities of cyclopropane fatty acids [28].

conditions (NaCl) of the microorganisms.

*tophilum*=actually *Chlorobaculum thiosulfatifilum*

**Table 2.** List of strains used in this work: their origins and optimal salinity

following: distilled water 950 ml, KH2PO4 1.0 g, NH4Cl 0.50 g, MgSO4

chemotaxonomical purposes.

a

Mexico) [7].


**Table 1.** Examples of fatty acids found in bacteria: formula, common and systematic names [modified from 27]

#### **2.2. Nomenclature of fatty acids**

Fatty acids are designated by the total number of carbon atoms followed by the total number of double bonds (e.g., a 16 carbon alkanoic acid is 16:0), beginning with the position of the double bound closest to the methyl-end (*ω*) of the molecule. The double bond position is indicated by the number closest to the carboxyl-end of the fatty acid molecule with the geometry of either c (*cis*) or t (*trans*). For example, 16:1ω9*c* is a phospholipid fatty acid with a total of 16 carbons and with one double bond located between nine and 10 carbons from the methyl-end of the molecule in the *cis* configuration [45].

#### **2.3. Analysis of fatty acids from photosynthetic bacteria**

Numerous papers have been published about the influence of the culture media in growing bacterial cells as well as the salinity in the bacteria fatty acid compositions. These have been done especially in heterotrophic bacteria, and it has been observed that when *Escherichia coli*

was grown in a limited concentration of ammonium salts, these bacteria produced high quantities of cyclopropane fatty acids [28].

The major groups of fatty acids are: a) straight chain, b) branched chain, c) unsaturated, and d) cyclopropane. Straight chain fatty acids are found elsewhere in nature (e.g., C12, C14, C16, C18 and C20). However, the corresponding β− hydroxymyristic acid 3-OH and C12 are not normally found in higher organisms, but are common components of the boundary of lipids present in the walls of gram-negative bacteria. Branched fatty acids comprise two types: the iso-form (the methyl group is located on the penultimate carbon atom) and the anteiso (the methyl group is located in the antepenultimate carbon atom). The unsaturated acids found in bacteria are monounsaturated, whereby the double bound is most frequently found between carbon 11 and 12. The cyclopropane fatty acids - rarely found in higher organisms - are frequently encountered in bacteria. The biosynthetic precursors of these acids are the corre‐ sponding monounsaturated fatty acids; as a consequence, the major example is the cis-11-12 methylene hexadecanoid acid [21]. Table 1 contains some examples of fatty acids commonly

**Fatty acid Simplified formula Common name Systematic name**

C16 H30 O2 C16:1ω9 Palmitoleic Hexadec-9-enoic C18 H34 O2 C18:1ω9 Oleic Octadec-9-enoic

**Table 1.** Examples of fatty acids found in bacteria: formula, common and systematic names [modified from 27]

Fatty acids are designated by the total number of carbon atoms followed by the total number of double bonds (e.g., a 16 carbon alkanoic acid is 16:0), beginning with the position of the double bound closest to the methyl-end (*ω*) of the molecule. The double bond position is indicated by the number closest to the carboxyl-end of the fatty acid molecule with the geometry of either c (*cis*) or t (*trans*). For example, 16:1ω9*c* is a phospholipid fatty acid with a total of 16 carbons and with one double bond located between nine and 10 carbons from the

Numerous papers have been published about the influence of the culture media in growing bacterial cells as well as the salinity in the bacteria fatty acid compositions. These have been done especially in heterotrophic bacteria, and it has been observed that when *Escherichia coli*

C10 H20 O2 C16:0 Capric Decanoic C12 H24 O2 C12:0 Lauric Dodecanoic C14 H28 O2 C14:0 Myristic Tetradecanoic C16 H32 O2 C16:0 Palmitic Hexadecanoic C17 H34 O2 C17:0 Margaric Heptadecanoic C18 H36 O2 C18:0 Stearic Octadecanoic C20 H40 O2 C20:0 Arachidic Eicosadecanoic

found in bacteria.

140 Advances in Gas Chromatography

**SATURATED**

**UNSATURATED**

**2.2. Nomenclature of fatty acids**

methyl-end of the molecule in the *cis* configuration [45].

**2.3. Analysis of fatty acids from photosynthetic bacteria**

The analysis of salinity effects on the fatty acid composition of photosynthetic bacteria has been done in PPSB, such as *Chromatium purpuratum* (or *Halochromatium purpuratum*), *Ectothio‐ rhodospira mobilis* and *Ec. halophila* [29], using different salt concentrations; the results showed only quantitative alterations, but their fatty acid pattern did not witness changes.

The age of the cells is another factor that may change the fatty acid composition of bacteria as well as the cells of all living organisms. In many techniques, it has been suggested that it is very important to use bacterial cells of the same age for fatty acid analysis, especially for chemotaxonomical purposes.

In order to study the influence of MgSO4 and NaCl and the age of the cells in the fatty acid composition of GPSB and PPSB, respectively, the bacterial strains included in Table 2 were used. This table contains the origin, the phenotypical characteristics and optimal culture conditions (NaCl) of the microorganisms.


DSMZ (=DSM) = Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH; GSC = Giraud Saltens Camargue; TL = Tampamachoco Lagoon (Veracruz); THS = Tassajara Hot Spring; LB = Lake Blankvann; *Bchl* bacteriochlorophyll, *spx* spirilloxanthin, *ly* lycopene, *cb* chlorobactene, *rh* rhodopinal; \*NaCl (%) = optimal salinity requirement.

a Marine origin strains. The genus *Chromatium* was reclassified into *Halochromatium*. \*\*Chlorobium limicola f. thiosulfa‐ *tophilum*=actually *Chlorobaculum thiosulfatifilum*

**Table 2.** List of strains used in this work: their origins and optimal salinity

For all the experiment, two wild strains were used: a PPSB strain T9s642 (*Chromatium* sp.) and a GPSB strain T11s (*Chlorobium* sp.). These photosynthetic bacteria were isolated from super‐ ficial water samples collected in Tampamachoco, a coastal lagoon located in Tuxpan (Veracruz, Mexico) [7].

For culturing these bacteria, a basal medium enriched with water samples was used. This was also used for the isolation and growth of these photosynthetic bacteria and contained the following: distilled water 950 ml, KH2PO4 1.0 g, NH4Cl 0.50 g, MgSO4 . 7H2O 0.40 g, CaCl2 . 2H2O 0.50 g, and NaCl 2.0% (only for marine strains). It was sterilized by autoclave and, once cooled, was supplemented with a 1.0 mL SL10 solution (GPSB) and SL12 (PPSB), Vitamin B12 1.0 mL (2.0 mg/100 mL distilled water), and 30 mL of sodium bicarbonate solution 5% [30]. 6.0 mL and 10.0 mL of Na2S. 9H2O 5% (v/v distilled water) were added as an electron donor for *Chromatium* and *Chlorobium*, respectively. The pH was adjusted to 7.5 for the culture medium of *Chromatium* and 6.5 for *Chlorobium.*

temperature of 80°C (using a cover bath), over four hours. Once the sample was cooled to room

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

FAMEs were prepared by adding to each sample 2.0 g N-Nitroso-N-methylurea (Sigma) dissolved in a pre-cooled solution of 30.0 mL diethyl ether, 2.0 g KOH and 6.0 mL distilled water. This mixture was stirred for 5-10 minutes and the supernatant was removed and placed in a new tube containing KOH pellets cooled on ice. Finally, diazomethane was added and the dried lipids were achieved within 10-20 min and the content was evaporated at 40°C in a water

The FAMEs were analysed by injecting 2.0 µL of the sample, previously dissolved with nhexane (0.04-1.0 mL), in a HP-5890A gas chromatograph equipped with a flame ionization detector and a silica capillary column (15 m x 0.25 mm I.D.) with cross-linked methyl silicone (HP-1, Hewlett-Packard). The column was programmed from 175°C to 300°C within 15 minutes. The injector and detector temperature were 275°C and 300°C, respectively. Helium was used as a carrier gas with a flow rate of approximately 1.0 mL/min, and the split ratio was approximately 1:50. Finally, a HP3396 integrator was used for the chromatogram integration and the identification of the FAMEs was done by comparing the retention time of each fatty

For the GC-MS analysis, an HP-5890 gas chromatograph attached to a HP5989X quadripole mass spectrometer was used with a methyl polysiloxane column TRB1 (30 m). The injector and detector temperatures were both 225°C, and two ramps were used: one from the initial temperature 10°C/min to 240°C and the other with 40°C/min to 270°C. The injection mode was

The standard deviations and means were calculated based on the integration areas of the fatty

Strain T11S was isolated from superficial water samples from Tampamahoco lagoon (Vera‐ cruz, México). One of the characteristics of this bacterium is that it is able to tolerate physical and chemical environmental changes. For this reason, it was selected to investigate the changes in fatty acid composition due to salinity (NaCl and MgSO4). However, no changes of the fatty

strains analysed, 12 fatty acids comprising saturated, unsaturated, branched and cyclic acids

All the fatty acids detected in the GPSB had chains with no more than 18 carbons (Table 2). In the *Chlorobium* collection strains (DSMZ266, DSMZ249) and in T11S, the same fatty acids were detected. The standard deviations for almost all the fatty acids identified were very small, with

7H2O changes. In all the

http://dx.doi.org/10.5772/57389

143

temperature, it was acidified to pH = 1 with a H2SO4 solution (20% v/v).

bath.

splitless.

acid peaks.

**3. Results**

acid, using a standard mixture of FAMEs.

**3.1. Influence of the MgSO4 and NaCl cultures on GPSB**

were detected, as has been reported previously [7].

the exception for the C18:0 fatty acid.

acid profile have been observed as a response to NaCl and MgSO.

### *2.3.1. Influence of MgSO4 and NaCl on cultures of GPSB*

The influence of MgSO4 and NaCl on cultures of GPSB (T11s) were observed by growing the bacteria in the basal medium, as described before, and modified as follows: condition T11S = NaCl (2.0%) and MgSO4 . 7H2O 0.3% g; condition C11S = NaCl (0.0%), MgSO4 . 7H2O 0.04% g; and condition MG11S = NaCl (0.0%) and MgSO4 . 7H2O 0.3% g, respectively. Conditions T11S and C11S are optimal for marine and freshwater GPSB, respectively. In all cases, the pH was adjusted to 6.5 using a diluted H2S sterilized solution. Strains of GPSB, DSMZ249 and DSMZ 266 were used as references, and they were cultured under the condition C11S (optimal for freshwater strains).

### *2.3.2. Influence of cell age on the fatty acid composition of PPSB*

In order to investigate the influence of bacterial cell age on the fatty acid composition of PPSB, a wild strain of *Chromatium* sp. (*Halochromatium* sp.), isolated from Tampamachoco lagoon, and a collection strain of *Chromatium salexigens* DSMZ4395, were used. The fatty acids of the cultures of these bacteria were analysed after 15, 20 and 30 days of growth, and they were incubated under the standard conditions as described previously.

All the bacteria cultures (green and purple) were grown in 1.5 L glass flasks, the temperature of incubation was 23ºC and, as the light source, bulbs of incandescent (for *Chromatium* strains) and fluorescent light (*Chlorobium* strains) were used. In all cases, the light intensity was 2000 Lux.

The mass cultures of GPSB and PPSB were obtained by adding a previously sterilized and neutralized solution of Na2S. 9H2O. It was added to the cultures when the sulphur source (electron donor) was depleted, which was detected using an acetate paper pH tester. Cultures were grown for 15 days (with the exception of *Chromatium* cultures, as indicated before) under the conditions described. Once the absence of sulphur in the bacterial cultures was detected, they were centrifuged (5,000-1000 rpm) and afterwards freeze-dried for further methylation and fatty acid analysis.

### *2.3.3. Fatty acid extraction and analysis by GC and GC-MS*

The procedure for total fatty acid analysis has been described previously in [7], and the lyophilized cells of GPSB and PPSB (250 mg and 500 mg, respectively) were used for fatty acid extraction by saponification followed by diazomethane derivation. The resulting FAMEs were identified using a standard methyl ester mixture and the final identification was confirmed using GC and MS (GC-MS). Cell lipids from each sample were saponified by adding 2.0 mL benzene and 8.0 mL KOH solution (5.0 g/100 mL methanol), containing 5% (w/v) under a temperature of 80°C (using a cover bath), over four hours. Once the sample was cooled to room temperature, it was acidified to pH = 1 with a H2SO4 solution (20% v/v).

FAMEs were prepared by adding to each sample 2.0 g N-Nitroso-N-methylurea (Sigma) dissolved in a pre-cooled solution of 30.0 mL diethyl ether, 2.0 g KOH and 6.0 mL distilled water. This mixture was stirred for 5-10 minutes and the supernatant was removed and placed in a new tube containing KOH pellets cooled on ice. Finally, diazomethane was added and the dried lipids were achieved within 10-20 min and the content was evaporated at 40°C in a water bath.

The FAMEs were analysed by injecting 2.0 µL of the sample, previously dissolved with nhexane (0.04-1.0 mL), in a HP-5890A gas chromatograph equipped with a flame ionization detector and a silica capillary column (15 m x 0.25 mm I.D.) with cross-linked methyl silicone (HP-1, Hewlett-Packard). The column was programmed from 175°C to 300°C within 15 minutes. The injector and detector temperature were 275°C and 300°C, respectively. Helium was used as a carrier gas with a flow rate of approximately 1.0 mL/min, and the split ratio was approximately 1:50. Finally, a HP3396 integrator was used for the chromatogram integration and the identification of the FAMEs was done by comparing the retention time of each fatty acid, using a standard mixture of FAMEs.

For the GC-MS analysis, an HP-5890 gas chromatograph attached to a HP5989X quadripole mass spectrometer was used with a methyl polysiloxane column TRB1 (30 m). The injector and detector temperatures were both 225°C, and two ramps were used: one from the initial temperature 10°C/min to 240°C and the other with 40°C/min to 270°C. The injection mode was splitless.

The standard deviations and means were calculated based on the integration areas of the fatty acid peaks.

### **3. Results**

(2.0 mg/100 mL distilled water), and 30 mL of sodium bicarbonate solution 5% [30]. 6.0 mL

*Chromatium* and *Chlorobium*, respectively. The pH was adjusted to 7.5 for the culture medium

The influence of MgSO4 and NaCl on cultures of GPSB (T11s) were observed by growing the bacteria in the basal medium, as described before, and modified as follows: condition T11S =

C11S are optimal for marine and freshwater GPSB, respectively. In all cases, the pH was adjusted to 6.5 using a diluted H2S sterilized solution. Strains of GPSB, DSMZ249 and DSMZ 266 were used as references, and they were cultured under the condition C11S (optimal for

In order to investigate the influence of bacterial cell age on the fatty acid composition of PPSB, a wild strain of *Chromatium* sp. (*Halochromatium* sp.), isolated from Tampamachoco lagoon, and a collection strain of *Chromatium salexigens* DSMZ4395, were used. The fatty acids of the cultures of these bacteria were analysed after 15, 20 and 30 days of growth, and they were

All the bacteria cultures (green and purple) were grown in 1.5 L glass flasks, the temperature of incubation was 23ºC and, as the light source, bulbs of incandescent (for *Chromatium* strains) and fluorescent light (*Chlorobium* strains) were used. In all cases, the light intensity was 2000

The mass cultures of GPSB and PPSB were obtained by adding a previously sterilized and

(electron donor) was depleted, which was detected using an acetate paper pH tester. Cultures were grown for 15 days (with the exception of *Chromatium* cultures, as indicated before) under the conditions described. Once the absence of sulphur in the bacterial cultures was detected, they were centrifuged (5,000-1000 rpm) and afterwards freeze-dried for further methylation

The procedure for total fatty acid analysis has been described previously in [7], and the lyophilized cells of GPSB and PPSB (250 mg and 500 mg, respectively) were used for fatty acid extraction by saponification followed by diazomethane derivation. The resulting FAMEs were identified using a standard methyl ester mixture and the final identification was confirmed using GC and MS (GC-MS). Cell lipids from each sample were saponified by adding 2.0 mL benzene and 8.0 mL KOH solution (5.0 g/100 mL methanol), containing 5% (w/v) under a

9H2O. It was added to the cultures when the sulphur source

.

7H2O 0.3% g; condition C11S = NaCl (0.0%), MgSO4

9H2O 5% (v/v distilled water) were added as an electron donor for

.

7H2O 0.3% g, respectively. Conditions T11S and

7H2O 0.04% g; and

and 10.0 mL of Na2S.

142 Advances in Gas Chromatography

NaCl (2.0%) and MgSO4

freshwater strains).

Lux.

neutralized solution of Na2S.

and fatty acid analysis.

of *Chromatium* and 6.5 for *Chlorobium.*

*2.3.1. Influence of MgSO4 and NaCl on cultures of GPSB*

*2.3.2. Influence of cell age on the fatty acid composition of PPSB*

incubated under the standard conditions as described previously.

*2.3.3. Fatty acid extraction and analysis by GC and GC-MS*

.

condition MG11S = NaCl (0.0%) and MgSO4

### **3.1. Influence of the MgSO4 and NaCl cultures on GPSB**

Strain T11S was isolated from superficial water samples from Tampamahoco lagoon (Vera‐ cruz, México). One of the characteristics of this bacterium is that it is able to tolerate physical and chemical environmental changes. For this reason, it was selected to investigate the changes in fatty acid composition due to salinity (NaCl and MgSO4). However, no changes of the fatty acid profile have been observed as a response to NaCl and MgSO. 7H2O changes. In all the strains analysed, 12 fatty acids comprising saturated, unsaturated, branched and cyclic acids were detected, as has been reported previously [7].

All the fatty acids detected in the GPSB had chains with no more than 18 carbons (Table 2). In the *Chlorobium* collection strains (DSMZ266, DSMZ249) and in T11S, the same fatty acids were detected. The standard deviations for almost all the fatty acids identified were very small, with the exception for the C18:0 fatty acid.

Wild and reference strains of *Chlorobium* have C14:0 (6.14-21.3%), C16:0 (11.9-22.9%) and C16:1 (26.9-31%) as the main fatty acids, a profile that was reported for the same strains, cultured in similar conditions [7]. These results confirm the use of FAMEs to characterize bacteria, because they are conserved in composition and proportion if they are grown in similar conditions [11].

All the strains were incubated in similar physical (temperature and illumination) conditions, but specific culture conditions were used for each one. The culture medium for DSMZ266 and DSMZ249 was the same as with C11S (this is without NaCl and 0.04 g MgSO4 . 7H2O), but it is evident that the condition C11S conserves the same fatty acid pattern as T11S and MG11S.

The collection strains have less saturated fatty acids than T11S, while MG11S and C11S have similar quantities of these fatty acids; inversely, the collection strains have more unsaturated fatty acids than the experimental cultures (T11S, MG11S and C11S). It is very important to note that the branched fatty acids recorded were similar to all the strains cultured in the same conditions (DSMZ266, DSMZ249 and C11S); it is evident that the bacterium of marine origin (T11S) cultured in optimal conditions produced more branched fatty acids than the strains cultured in freshwater conditions as in DSMZ266 and DSMZ249. It has been pointed out that the presence of branched chain fatty acids confers to the membrane a greater degree of flexibility [31].

The cyclic fatty acid was present in greater quantities in DSMZ249 than in DSMZ266 and T11S. When there was an increase in NaCl or Mg salts, the production of C17Cy fatty acid decreased too. It is evident that in optimal conditions for T11S, there was less production of C17Cy fatty acid, so the presence of this fatty acid is as follows: MG11S>C11S>T11S. Cyclopropane acid production in cells is a mechanism for preserving membrane integrity, preventing the formation of abnormal structures [32].

The values of the standard deviation for each fatty acid of T11S, MG11S and C11S were between 0.009 and 1.828, which is evidence that the changes in the fatty acid compositions of *Chlorobi‐ um* sp. due to salinity (NaCl and MgSO4) were not so large to loss its identity (Table 3).

DSM4395 (*Halochromatium salexigens*) and *Chromatium* sp. T9s642 are shown in Table 4 and

**Table 3.** Influence of NaCl and MgSO47 H2O on the fatty acid composition of a wild strain of GPSB (T11s)

TFAE = Total fatty acid extracted; X = mean (%); SD = Standard deviation; Strain (number of independent cultures of each strain

TFAE = Total fatty acid extracted; X = mean (%); SD = Standard deviation; Experimental culture (number of independent

**17Cy 3.336 0.146 1.780 0.271 0.827 0.038 2.014 1.897 1.426 0.051 TFAE (%) 68.657 71.090 81.289 77.288 80.181**

The fatty acid analysis of these bacteria revealed the presence of nine of these compounds. All these fatty acids have been reported before for members of the Chromatiaceae family [7, 9]. The major fatty acids were C18:1, C16:1 and C16:0 and less quantities of: C12:0, C14:0 and C17:0, C18:0 and C20:1. These conformed between 92% and 93% of the total compounds extracted from the cells of two *Chromatium* strains (DSMZ4395 and T9s642). In this analysis, fatty acids less than 0.1% were also taken into account because minor fatty acids are very important in identifying microorganisms, as reported by Núñez-Cardona [7, 8]. For some strains of Cyanobacteria,it has been reported trace quantities of fatty acids (less than 0.03%) [8],

There were no qualitative changes in the fatty acid compositions of DSMZ4395 and T9s642 due to cell ages, and the quantitative changes were minimal: both strains conserved the fatty acid pattern that it is characteristic for members of the Chromatiaceae family, but strain DSMZ4395 produced almost the same quantities of C16:0 (16.2-16.8%) and C16:1 (17.5-18.2%), which is characteristic of halophilic microorganisms like *Halochromatium purpuratum* [29] and

and it has been indicated that minor fatty acids could be useful as biomarkers.

Figures 1 and 2, respectively.

cultures analysed) ND = not detected.

\*It was detected only in one of the three cultures of the same analysed strain.

Table 3. Influence of NaCl and MgSO4.6H2O on the fatty acid compositions of a wild strain of GPSB (T11S)

\*It was detected only in one of the three cultures of the same analysed strain.

**3. Results** 

MgSO4.

identity (Table 3).

**Fatty acids** 

**UNSATURATED** 

**BRANCHED**

**CYCLIC** 

analysed); ND = not detected.

**3.1. Influence of the MgSO4 and NaCl cultures on GPSB** 

identified were very small, with the exception for the C18:0 fatty acid.

unsaturated, branched and cyclic acids were detected, as has been reported previously [7].

have been observed as a response to NaCl and MgSO.

Strain T11S was isolated from superficial water samples from Tampamahoco lagoon (Veracruz, México). One of the characteristics of this bacterium is that it is able to tolerate physical and chemical environmental changes. For this reason, it was selected to investigate the changes in fatty acid composition due to salinity (NaCl and MgSO4). However, no changes of the fatty acid profile

All the fatty acids detected in the GPSB had chains with no more than 18 carbons (Table 2). In the *Chlorobium* collection strains (DSMZ266, DSMZ249) and in T11S, the same fatty acids were detected. The standard deviations for almost all the fatty acids

Wild and reference strains of *Chlorobium* have C14:0 (6.14-21.3%), C16:0 (11.9-22.9%) and C16:1 (26.9-31%) as the main fatty acids, a profile that was reported for the same strains, cultured in similar conditions [7]. These results confirm the use of FAMEs to

All the strains were incubated in similar physical (temperature and illumination) conditions, but specific culture conditions were used for each one. The culture medium for DSMZ266 and DSMZ249 was the same as with C11s (this is without NaCl and 0.04 g

The collection strains have less saturated fatty acids than T11S, while MG11S and C11S have similar quantities of these fatty acids; inversely, the collection strains have more unsaturated fatty acids than the experimental cultures (T11S, MG11S and C11S). It is very important to note that the branched fatty acids recorded were similar to all the strains cultured in the same conditions (DSMZ266, DSMZ249 and C11S); it is evident that the bacteria of marine origin (T11S) cultured in optimal conditions produced more branched fatty acids than the strains cultured in freshwater conditions as in DSMZ266 and DSMZ249 .. It has been pointed

The cyclic fatty acid was present in greater quantities in DSMZ249 than in DSMZ266 and T11S. When there was an increase in NaCl or Mg salts, the production of C17Cy fatty acid decreased too. It is evident that in optimal conditions for T11S, there was less production of 17Cy fatty acid, so the presence of this fatty acid is as follows: MG11S>C11>T11S. Cyclopropane acid production in

The values of the standard deviation for each fatty acid of T11S, MG11S and C11S were between 0.009 and 1.828, which is evidence that the changes in the fatty acid compositions of *Chlorobium* sp. due to salinity (NaCl and MgSO4) were not so large to loss its

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

**SATURATED X SD X SD X SD X SD X SD C12:0 1.489 0.342 0.970 0.414 1.943 0.156 1.881 0.910 2.384 0.264 C14:0 6.114 2.353 13.626 0.406 20.606 1.001 17.366 1.371 21.305 0.860 C15:0 0.757 0.076 1.119 0.532 0.191 0.093 0.441 0.096 0.278 0.162 C16:0 11.932 0.621 11.960 0.622 22.973 1.828 21.435 0.492 19.949 0.696 C17:0 0.230 0.158 0.246 0.050 0.202 0.035 0.429 0.009 0.224 0.015 C18:0 0.838 0.066 0.663 0.252 0.660 0.255 0.334 0.364 0.325 0.018 Sum (%) 21.360 28.584 46.574 41.888 44.465**

**DSMZ249 (3) DSMZ266 (2) T11S (4) MG11S (4) C11S (3)**

**C16:1(9) 31.034 0.293 36.009 0.009 26.912 1.172 26.912 0.922 27.215 1.524 C18:1 10.298 0.055 2.689 2.147 2.972 0.188 3.255 0.107 4.643 1.754 Sum (%) 41.332 38.697 29.884 30.167 31.858**

**12-Me-C14:0 0.516 0.016 0.421 0.070 0.458 0.296 0.973 0.070 0.272 0.013 5-Me-C16:0 0.632 0.078 0.538 0.100 1.163 0.041 1.477 0.335 1.326 0.076 aC17:0 0.862 0.097 0.608 0.011 1.383 0.053 0.770 0.042 0.595 1.123 15-Me-C16:1 0.620 0.176 0.461 0.061 ND - ND - 0.239\* - Sum (%) 2.629 2.029 3.004 3.220 2.432**

characterize bacteria, because they are conserved in composition and proportion if they are grown in similar conditions [11].

7H2O), but it is evident that the condition C11s conserves the same fatty acid pattern as T11S and MG11S.

out that the presence of branched chain fatty acids confers to the membrane a greater degree of flexibility [31].

cells is a mechanism for preserving membrane integrity, preventing the formation of abnormal structures [32].

7H2O changes. In all the strains analysed, 12 fatty acids comprising saturated,

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145

### **3.2. Influence of the culture age (20 and 30 days of growth) on the fatty acids composition of PPSB**

It has been proposed that cell age is among the factors that affect the fatty acid compositions of living organisms. The influence of this factor in the fatty acid composition of bacterial cells has been studied in heterotrophic bacteria, and cultures of 24 and 48 hours incubation have been proposed as the optimal age (Microbial Identification System) for the fatty acid analysis of these bacteria. However, the exponential phase has been proposed for fatty acid analysis in photosynthetic bacteria [33]. In addition, stationary or low stationary phases, after three and six days of growth, have been indicated too [34]. Nevertheless, and frequently, the age of the cells that they used for fatty acid analysis was not mentioned.

In the present experiment, the cells of two strains of PPSB from marine origin were used. For fatty acid analysis, the cells of these bacteria were collected after 15, 20 and 30 days of growth in optimal conditions according to their origins. They were also incubated under the same light and temperature conditions. The results of the fatty acid analysis of *Chromatium salexigens*


Strain T11S was isolated from superficial water samples from Tampamahoco lagoon (Veracruz, México). One of the characteristics of this bacterium is that it is able to tolerate physical and chemical environmental changes. For this reason, it was selected to investigate the changes in fatty acid composition due to salinity (NaCl and MgSO4). However, no changes of the fatty acid profile

All the fatty acids detected in the GPSB had chains with no more than 18 carbons (Table 2). In the *Chlorobium* collection strains (DSMZ266, DSMZ249) and in T11S, the same fatty acids were detected. The standard deviations for almost all the fatty acids

Wild and reference strains of *Chlorobium* have C14:0 (6.14-21.3%), C16:0 (11.9-22.9%) and C16:1 (26.9-31%) as the main fatty acids, a profile that was reported for the same strains, cultured in similar conditions [7]. These results confirm the use of FAMEs to

All the strains were incubated in similar physical (temperature and illumination) conditions, but specific culture conditions were used for each one. The culture medium for DSMZ266 and DSMZ249 was the same as with C11s (this is without NaCl and 0.04 g

The collection strains have less saturated fatty acids than T11S, while MG11S and C11S have similar quantities of these fatty acids; inversely, the collection strains have more unsaturated fatty acids than the experimental cultures (T11S, MG11S and C11S). It is very important to note that the branched fatty acids recorded were similar to all the strains cultured in the same conditions (DSMZ266, DSMZ249 and C11S); it is evident that the bacteria of marine origin (T11S) cultured in optimal conditions produced more branched fatty acids than the strains cultured in freshwater conditions as in DSMZ266 and DSMZ249 .. It has been pointed

The cyclic fatty acid was present in greater quantities in DSMZ249 than in DSMZ266 and T11S. When there was an increase in NaCl or Mg salts, the production of C17Cy fatty acid decreased too. It is evident that in optimal conditions for T11S, there was less production of 17Cy fatty acid, so the presence of this fatty acid is as follows: MG11S>C11>T11S. Cyclopropane acid production in

characterize bacteria, because they are conserved in composition and proportion if they are grown in similar conditions [11].

7H2O), but it is evident that the condition C11s conserves the same fatty acid pattern as T11S and MG11S.

out that the presence of branched chain fatty acids confers to the membrane a greater degree of flexibility [31].

cells is a mechanism for preserving membrane integrity, preventing the formation of abnormal structures [32].

7H2O changes. In all the strains analysed, 12 fatty acids comprising saturated,


Table 3. Influence of NaCl and MgSO4.6H2O on the fatty acid compositions of a wild strain of GPSB (T11S) TFAE = Total fatty acid extracted; X = mean (%); SD = Standard deviation; Experimental culture (number of independent cultures analysed) ND = not detected.

TFAE = Total fatty acid extracted; X = mean (%); SD = Standard deviation; Strain (number of independent cultures of each strain analysed); ND = not detected. \*It was detected only in one of the three cultures of the same analysed strain.

Wild and reference strains of *Chlorobium* have C14:0 (6.14-21.3%), C16:0 (11.9-22.9%) and C16:1 (26.9-31%) as the main fatty acids, a profile that was reported for the same strains, cultured in similar conditions [7]. These results confirm the use of FAMEs to characterize bacteria, because they are conserved in composition and proportion if they are grown in similar conditions [11]. All the strains were incubated in similar physical (temperature and illumination) conditions, but specific culture conditions were used for each one. The culture medium for DSMZ266 and

evident that the condition C11S conserves the same fatty acid pattern as T11S and MG11S. The collection strains have less saturated fatty acids than T11S, while MG11S and C11S have similar quantities of these fatty acids; inversely, the collection strains have more unsaturated fatty acids than the experimental cultures (T11S, MG11S and C11S). It is very important to note that the branched fatty acids recorded were similar to all the strains cultured in the same conditions (DSMZ266, DSMZ249 and C11S); it is evident that the bacterium of marine origin (T11S) cultured in optimal conditions produced more branched fatty acids than the strains cultured in freshwater conditions as in DSMZ266 and DSMZ249. It has been pointed out that the presence of branched chain fatty acids confers to the membrane a greater degree of

The cyclic fatty acid was present in greater quantities in DSMZ249 than in DSMZ266 and T11S. When there was an increase in NaCl or Mg salts, the production of C17Cy fatty acid decreased too. It is evident that in optimal conditions for T11S, there was less production of C17Cy fatty acid, so the presence of this fatty acid is as follows: MG11S>C11S>T11S. Cyclopropane acid production in cells is a mechanism for preserving membrane integrity, preventing the

The values of the standard deviation for each fatty acid of T11S, MG11S and C11S were between 0.009 and 1.828, which is evidence that the changes in the fatty acid compositions of *Chlorobi‐ um* sp. due to salinity (NaCl and MgSO4) were not so large to loss its identity (Table 3).

**3.2. Influence of the culture age (20 and 30 days of growth) on the fatty acids composition**

It has been proposed that cell age is among the factors that affect the fatty acid compositions of living organisms. The influence of this factor in the fatty acid composition of bacterial cells has been studied in heterotrophic bacteria, and cultures of 24 and 48 hours incubation have been proposed as the optimal age (Microbial Identification System) for the fatty acid analysis of these bacteria. However, the exponential phase has been proposed for fatty acid analysis in photosynthetic bacteria [33]. In addition, stationary or low stationary phases, after three and six days of growth, have been indicated too [34]. Nevertheless, and frequently, the age of the

In the present experiment, the cells of two strains of PPSB from marine origin were used. For fatty acid analysis, the cells of these bacteria were collected after 15, 20 and 30 days of growth in optimal conditions according to their origins. They were also incubated under the same light and temperature conditions. The results of the fatty acid analysis of *Chromatium salexigens*

cells that they used for fatty acid analysis was not mentioned.

.

7H2O), but it is

**3. Results** 

MgSO4.

identity (Table 3).

**3.1. Influence of the MgSO4 and NaCl cultures on GPSB** 

identified were very small, with the exception for the C18:0 fatty acid.

unsaturated, branched and cyclic acids were detected, as has been reported previously [7].

have been observed as a response to NaCl and MgSO.

DSMZ249 was the same as with C11S (this is without NaCl and 0.04 g MgSO4

flexibility [31].

144 Advances in Gas Chromatography

**of PPSB**

formation of abnormal structures [32].

\*It was detected only in one of the three cultures of the same analysed strain. **Table 3.** Influence of NaCl and MgSO47 H2O on the fatty acid composition of a wild strain of GPSB (T11s)

> DSM4395 (*Halochromatium salexigens*) and *Chromatium* sp. T9s642 are shown in Table 4 and Figures 1 and 2, respectively.

> The fatty acid analysis of these bacteria revealed the presence of nine of these compounds. All these fatty acids have been reported before for members of the Chromatiaceae family [7, 9]. The major fatty acids were C18:1, C16:1 and C16:0 and less quantities of: C12:0, C14:0 and C17:0, C18:0 and C20:1. These conformed between 92% and 93% of the total compounds extracted from the cells of two *Chromatium* strains (DSMZ4395 and T9s642). In this analysis, fatty acids less than 0.1% were also taken into account because minor fatty acids are very important in identifying microorganisms, as reported by Núñez-Cardona [7, 8]. For some strains of Cyanobacteria,it has been reported trace quantities of fatty acids (less than 0.03%) [8], and it has been indicated that minor fatty acids could be useful as biomarkers.

> There were no qualitative changes in the fatty acid compositions of DSMZ4395 and T9s642 due to cell ages, and the quantitative changes were minimal: both strains conserved the fatty acid pattern that it is characteristic for members of the Chromatiaceae family, but strain DSMZ4395 produced almost the same quantities of C16:0 (16.2-16.8%) and C16:1 (17.5-18.2%), which is characteristic of halophilic microorganisms like *Halochromatium purpuratum* [29] and

1 2

5

13 the oldest.


**Table 4.** Average (%) and standard deviation (SD) of each fatty acid identified in whole cells of *Chromatium* strains, concentrated at 15, 20 and 30 days of growth

*Thiocapsa halophila* [7]. Nevertheless, in *Thiohalospira halophila*, a chemolithoautotrophic, halophilic, sulphur-oxidizing gammaproteobacteria member of *Ectothiorhodospiraceae*, fatty acids such as 10MeC16:0, C16:0 and C18:0 are predominant (more than 80%). Furthermore, it has been pointed out that C16:0 is often the main fatty acid in halophilic bacteria [35]. However, this was not applicable for DSMZ4395. In this strain, the major fatty acids are the unsaturated acids (C16:1 and C18:1). This property was characteristic of other halophilic chemolithoauto‐ trophic bacteria, like *Thiohalomonas denitrificans* (DSM15841), where C18:1 is less than 18.58% (Σ:C18:1ω9, 18:1ω7, C18:1ω5) [36]. In contrast, in the *Chromatium salexigens* strain (DSMZ4395) this is the main fatty acid (48.9-50.5%).

12 Book Title

3 **Fig. 1 Fatty acids (%) of the wild culture T9s642-15 and T9s642-20 fromobtained in Figure 1.** Fatty acids (%) of the wild culture T9s642-15 and T9s642-20 from cells centrifuged after 15 and 20 days of growth, respectively.

**Formatted:** figures/tables caption

**Figure 2.** Fatty acids (%) of DSMZ4395-15 and DSMZ4395-30, obtained in cells centrifuged after 15 and 30 days of

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

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On the other hand, in the wild *Chromatium* sp. strain T9s642, as in some mesophilic eubacteria, the fatty acid C16:1 was present in greater quantities than C16:0 and *Chromatium salexigens* (DSMZ4395) produced more saturated fatty acids (Figures 1 and 2). Nevertheless, it is evident that for the fatty acid compositions in both bacteria, the age factor causes a increase of C18:1

The influence of age on the fatty acid compositions of bacteria has also been studied in microorganisms like *Serratia marcescens* and *Escherichia coli*, which have more C16:0, C16:1 and C18:1 as well as more cyclic fatty acids. When these cultures are in stationary phases (24-48 hours), monounsaturated fatty acids (e.g., C16:1 and C18:1) are transformed to cyclic forms

The changes in branched fatty acids, such as iC15:0 and C15:0 (e. g , increases and decreases, respectively), are the result of aged cells in a strain of *Bacillus* [38]. This microorganism, as in many other gram-positive bacteria, is characterized by high quantities of branched fatty acids,

Two goals of chemotaxonomy are to measure the number of changed molecules as a descriptor of the chemical relevance of two bacteria and also to provide a list of changed molecules to

Bacteria contain lipids in concentrations of 0.2-50% of dry weight, and for fatty acid analysis lipid compositions are important. These are mainly: phospholipids (structural elements of the cell membrane), glycolipids (elements of cell membrane structures but less common than phospholipids; abundant in Actinomycetes), lipid A (present in the outer membrane of gram-

and C16:0 in younger cells, and also a small increase of C16:1, in the oldest.

which are responsible to maintain the fluidity and the membrane structure.

**3.3. Uses of fatty acids analysis (other than chemotaxonomy)**

growth under standard conditions, respectively.

(17Cy and 19Cy) and C16:0 increases too [37].

discover biomarkers [39].

6 **Fig. 2 Fatty acids (%) of DSMZ4395-15 and DSMZ4395-30, obtained in cells centrifuged** 

 On the other hand, in the wild *Chromatium* sp. strain T9s642, as in some mesophilic eubacteria, the fatty acid C16:1 was present in greater quantities than C16:0 and *Chromatium salexigens* (DSMZ4395) produced more saturated fatty acids (Figures 1 and 2). Nevertheless, it is evident that for the fatty acid compositions in both bacteria, the age factor causes a decrease of C18:1 and C16:0 in younger cells, and a small decrease of C16:1 in

7 **after 15 and 30 days of growth underin standard conditions, respectively.**

4 **cells centrifuged after 15 and 20 days of growth, respectively.**

**Figure 2.** Fatty acids (%) of DSMZ4395-15 and DSMZ4395-30, obtained in cells centrifuged after 15 and 30 days of growth under standard conditions, respectively.

On the other hand, in the wild *Chromatium* sp. strain T9s642, as in some mesophilic eubacteria, the fatty acid C16:1 was present in greater quantities than C16:0 and *Chromatium salexigens* (DSMZ4395) produced more saturated fatty acids (Figures 1 and 2). Nevertheless, it is evident that for the fatty acid compositions in both bacteria, the age factor causes a increase of C18:1 and C16:0 in younger cells, and also a small increase of C16:1, in the oldest.

The influence of age on the fatty acid compositions of bacteria has also been studied in microorganisms like *Serratia marcescens* and *Escherichia coli*, which have more C16:0, C16:1 and C18:1 as well as more cyclic fatty acids. When these cultures are in stationary phases (24-48 hours), monounsaturated fatty acids (e.g., C16:1 and C18:1) are transformed to cyclic forms (17Cy and 19Cy) and C16:0 increases too [37].

The changes in branched fatty acids, such as iC15:0 and C15:0 (e. g , increases and decreases, respectively), are the result of aged cells in a strain of *Bacillus* [38]. This microorganism, as in many other gram-positive bacteria, is characterized by high quantities of branched fatty acids, which are responsible to maintain the fluidity and the membrane structure.

### **3.3. Uses of fatty acids analysis (other than chemotaxonomy)**

*Thiocapsa halophila* [7]. Nevertheless, in *Thiohalospira halophila*, a chemolithoautotrophic, halophilic, sulphur-oxidizing gammaproteobacteria member of *Ectothiorhodospiraceae*, fatty acids such as 10MeC16:0, C16:0 and C18:0 are predominant (more than 80%). Furthermore, it has been pointed out that C16:0 is often the main fatty acid in halophilic bacteria [35]. However, this was not applicable for DSMZ4395. In this strain, the major fatty acids are the unsaturated acids (C16:1 and C18:1). This property was characteristic of other halophilic chemolithoauto‐ trophic bacteria, like *Thiohalomonas denitrificans* (DSM15841), where C18:1 is less than 18.58% (Σ:C18:1ω9, 18:1ω7, C18:1ω5) [36]. In contrast, in the *Chromatium salexigens* strain (DSMZ4395)

**Table 4.** Average (%) and standard deviation (SD) of each fatty acid identified in whole cells of *Chromatium* strains,

12 Book Title

27.714

27.414

44.674

45.893

3 **Fig. 1 Fatty acids (%) of the wild culture T9s642-15 and T9s642-20 fromobtained in**

T9s642-15 T9s462-20

0 10 20 30 40

**Figure 1.** Fatty acids (%) of the wild culture T9s642-15 and T9s642-20 from cells centrifuged after 15 and 20 days of

10.498

11.605

6 **Fig. 2 Fatty acids (%) of DSMZ4395-15 and DSMZ4395-30, obtained in cells centrifuged** 

 On the other hand, in the wild *Chromatium* sp. strain T9s642, as in some mesophilic eubacteria, the fatty acid C16:1 was present in greater quantities than C16:0 and *Chromatium salexigens* (DSMZ4395) produced more saturated fatty acids (Figures 1 and 2). Nevertheless, it is evident that for the fatty acid compositions in both bacteria, the age factor causes a decrease of C18:1 and C16:0 in younger cells, and a small decrease of C16:1 in

7 **after 15 and 30 days of growth underin standard conditions, respectively.**

4 **cells centrifuged after 15 and 20 days of growth, respectively.**

1.049 0.201

c12:0 C14:0 C16:1 C16:0 C17:0 C18.1 w9 C18:1w11 C18:0 C20:1

0.705 0.237

0.289

0.413

2.168 0.634

1.928 0.627

5.757

5.622

this is the main fatty acid (48.9-50.5%).

concentrated at 15, 20 and 30 days of growth

146 Advances in Gas Chromatography

1 2

growth, respectively.

5

13 the oldest.

Two goals of chemotaxonomy are to measure the number of changed molecules as a descriptor of the chemical relevance of two bacteria and also to provide a list of changed molecules to discover biomarkers [39].

**Formatted:** figures/tables caption Bacteria contain lipids in concentrations of 0.2-50% of dry weight, and for fatty acid analysis lipid compositions are important. These are mainly: phospholipids (structural elements of the cell membrane), glycolipids (elements of cell membrane structures but less common than phospholipids; abundant in Actinomycetes), lipid A (present in the outer membrane of gramnegative bacteria; part of the lipopolysaccharides) and lipoteichoic acids (present in grampositive bacteria) [2].

*f. minutissima*, lipid metabolomic changes in the different growth phases of these microorgan‐ isms have been studied. Some biomarkers have been selected and identified, including free fatty acids and lipids such as harderoporphyrin, phosphatidylglycerol, 1,2-diacylglycerol-3- O-40-(N,N-trimethyl)-homoserine, triacylglycerol, cholesterol, sulfoquinovosyldiacylglycer‐ ol, lyso-sulfoquinovosyldiacylglycerol, monogalactosyldiacylglycerol,

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

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149

Lipidomics has revealed that under cold stress, *Stephanodiscus* sp. produces the following compounds as biomarkers: triacylglycerol, phosphatidylcholine, phosphatidylglycerol, sulfoquinovosyldiacylglycerol, monogalactosyldiacylglycerol, lyso-phosphatidylglycerol, lyso-phosphatidylcholine, lyso-monogalactosyldiacylglycerol and lyso-sulfoquinovosyldia‐

In addition, lipidomics have been applied to the understanding of the lipogenesis of free fatty acids from the symbiosis between cnidarian-dinoflagellate [26] as well as the study of the lipidomics of human pathogenic mycobacteria. It is possible to get the lipidomics datasets to assess lipid changes during infection or else among clinical strains of mycobacteria tubercu‐

As was mentioned before, the influence of NaCl in the culture medium increases the concen‐ tration of the saturated fatty acids C16:0 and decreases the production of the unsaturated C16:1 and C18:1. The presence in the culture medium of both MgSO4 and NaCl reduces the produc‐ tion of the saturated fatty acids C14:0 and C18:0 and increases the production of the unsatu‐ rated C16:1 and C18:1. The change of the relative abundances of the fatty acids due to the changes of MgSO4 and NaCl in the culture media of *Chlorobium* is not sufficient to alter the fatty acid pattern of the *Chlorobium* strains analysed. Minimal quantitative alterations were observed by the cell age, the pattern and profile of the fatty acids in PPSB and GPSB are very conservative in these microorganisms could be used for chemotaxonomic approaches. Nevertheless it is very important to describe the culture conditions as well as the techniques

Although advances in the technologies for fatty acid analysis have been reported, the isolation and culturing of novel strains as well as the fatty acid analysis of photosynthetic prokaryotes remains scarce, and it is very important to encourage the investigation of these microorgan‐ isms. With new sources of genetic materials from photosynthetic bacteria and the application of new technologies - for example lipidomics - which are more precise, it will be possible to discover new fatty acid structures and to evaluate the sources of these compounds of biotech‐ nological interest and novel chemotaxonomic biomarkers. In addition, the physiological role of fatty acids in the cell membranes of photosynthetic bacteria and their role in environmental

digalactosyldiacylglycerol and lyso-digalactosyldiacylglycerol [47].

cylglycerol [48].

losis [39].

**4. Conclusions**

for fatty acid analysis.

trophic chains could be explored.

Profiles of fatty acids have been used to distinguish pathogenic bacteria species for fish, like the genus *Tenacibaculum* (agents of flexibacteriosis in marine fish) whose cells are characterized by the presence of large amounts of branched (36.1–40.2%) and hydroxylated (29.6–31.7%) fatty acids [40].

The presence of bacteria in sponges has been detected because they produce large quantities of iso-, anteiso-cyclopropyl- and monomethyl-branched fatty acids. Using fatty acid analysis, it is possible to distinguish different symbionts (photosynthetic and non-photosynthetic) present in marine sponges and provide estimates of bacterial symbiont abundances [41].

Special fatty acids from phospholipids have been used for characterizing bacteria and photosynthetic life forms. Fatty acids linked to these molecules in the members of Chroma‐ tiaceae and Ectothiorhodospiraceae were analysed, and it is evident that the fatty acid pattern does not change. As has been indicated before, these compounds have great value in deter‐ mining bacterial phylogeny, providing a useful set of features for characterizing strains, and they give important information about microbial communities in environmental samples.

Phospholipid fatty acids could be used as biomarkers because they degrade rapidly after cell death and are not present in storage lipids or in anthropogenic contaminants. Bacteria contain phospholipids as relatively constant proportions in their biomass [41], and with these it is possible to estimate the microbial biomass in environmental samples where the bacteria of subsets in microbial communities contain specific signature fatty acids in their phospholipids [42]. Using phospholipid fatty acids (as methyl esters) analysis, it is possible to measure attributes or microbial communities, such viable biomass and microbial structures as well as nutritional and physiological status [41, 43]; these compounds, as well as bacterial fatty acid ratios, have been used to disclose bacterial connections in the marine food web and their importance in supplying material and energy to the higher trophic levels [44].

In addition, lipids have been used to establish possible links between modern and ancient microbial communities and to add to the understanding of the evolutionary histories of the organisms in the various environments. The analysis of phospholipids provides information about the viable biomass and biological diversity, and on the other hand information about the glycolipids of the activity of photosynthetic microorganisms in the environment. In addition, sterols and hopanoids - which are better preserved in the geological records - offer insights into the presence of microorganisms in paleoenvironments [45]. In bacterial mem‐ branes, the fatty acids of phospholipids are linked by ester bonds to glycerol and form a phospholipid bilayer, while archaeal membranes have phospholipids containing two polar heads linked by isoprenoid chains and ether linkages to glycerol [44]. As such, these linkages are markers for detecting archaeal bacteria.

### **3.4. Lipidomics studies: some examples**

Lipidomics has been applied in physiological studies, such as the change in the lipidomic profiling of *Chlamydomonas nivalis* as a response to nutritional stress [46]. In *Nitzschia closterium* *f. minutissima*, lipid metabolomic changes in the different growth phases of these microorgan‐ isms have been studied. Some biomarkers have been selected and identified, including free fatty acids and lipids such as harderoporphyrin, phosphatidylglycerol, 1,2-diacylglycerol-3- O-40-(N,N-trimethyl)-homoserine, triacylglycerol, cholesterol, sulfoquinovosyldiacylglycer‐ ol, lyso-sulfoquinovosyldiacylglycerol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol and lyso-digalactosyldiacylglycerol [47].

Lipidomics has revealed that under cold stress, *Stephanodiscus* sp. produces the following compounds as biomarkers: triacylglycerol, phosphatidylcholine, phosphatidylglycerol, sulfoquinovosyldiacylglycerol, monogalactosyldiacylglycerol, lyso-phosphatidylglycerol, lyso-phosphatidylcholine, lyso-monogalactosyldiacylglycerol and lyso-sulfoquinovosyldia‐ cylglycerol [48].

In addition, lipidomics have been applied to the understanding of the lipogenesis of free fatty acids from the symbiosis between cnidarian-dinoflagellate [26] as well as the study of the lipidomics of human pathogenic mycobacteria. It is possible to get the lipidomics datasets to assess lipid changes during infection or else among clinical strains of mycobacteria tubercu‐ losis [39].

### **4. Conclusions**

negative bacteria; part of the lipopolysaccharides) and lipoteichoic acids (present in gram-

Profiles of fatty acids have been used to distinguish pathogenic bacteria species for fish, like the genus *Tenacibaculum* (agents of flexibacteriosis in marine fish) whose cells are characterized by the presence of large amounts of branched (36.1–40.2%) and hydroxylated (29.6–31.7%) fatty

The presence of bacteria in sponges has been detected because they produce large quantities of iso-, anteiso-cyclopropyl- and monomethyl-branched fatty acids. Using fatty acid analysis, it is possible to distinguish different symbionts (photosynthetic and non-photosynthetic) present in marine sponges and provide estimates of bacterial symbiont abundances [41].

Special fatty acids from phospholipids have been used for characterizing bacteria and photosynthetic life forms. Fatty acids linked to these molecules in the members of Chroma‐ tiaceae and Ectothiorhodospiraceae were analysed, and it is evident that the fatty acid pattern does not change. As has been indicated before, these compounds have great value in deter‐ mining bacterial phylogeny, providing a useful set of features for characterizing strains, and they give important information about microbial communities in environmental samples. Phospholipid fatty acids could be used as biomarkers because they degrade rapidly after cell death and are not present in storage lipids or in anthropogenic contaminants. Bacteria contain phospholipids as relatively constant proportions in their biomass [41], and with these it is possible to estimate the microbial biomass in environmental samples where the bacteria of subsets in microbial communities contain specific signature fatty acids in their phospholipids [42]. Using phospholipid fatty acids (as methyl esters) analysis, it is possible to measure attributes or microbial communities, such viable biomass and microbial structures as well as nutritional and physiological status [41, 43]; these compounds, as well as bacterial fatty acid ratios, have been used to disclose bacterial connections in the marine food web and their

importance in supplying material and energy to the higher trophic levels [44].

are markers for detecting archaeal bacteria.

**3.4. Lipidomics studies: some examples**

In addition, lipids have been used to establish possible links between modern and ancient microbial communities and to add to the understanding of the evolutionary histories of the organisms in the various environments. The analysis of phospholipids provides information about the viable biomass and biological diversity, and on the other hand information about the glycolipids of the activity of photosynthetic microorganisms in the environment. In addition, sterols and hopanoids - which are better preserved in the geological records - offer insights into the presence of microorganisms in paleoenvironments [45]. In bacterial mem‐ branes, the fatty acids of phospholipids are linked by ester bonds to glycerol and form a phospholipid bilayer, while archaeal membranes have phospholipids containing two polar heads linked by isoprenoid chains and ether linkages to glycerol [44]. As such, these linkages

Lipidomics has been applied in physiological studies, such as the change in the lipidomic profiling of *Chlamydomonas nivalis* as a response to nutritional stress [46]. In *Nitzschia closterium*

positive bacteria) [2].

148 Advances in Gas Chromatography

acids [40].

As was mentioned before, the influence of NaCl in the culture medium increases the concen‐ tration of the saturated fatty acids C16:0 and decreases the production of the unsaturated C16:1 and C18:1. The presence in the culture medium of both MgSO4 and NaCl reduces the produc‐ tion of the saturated fatty acids C14:0 and C18:0 and increases the production of the unsatu‐ rated C16:1 and C18:1. The change of the relative abundances of the fatty acids due to the changes of MgSO4 and NaCl in the culture media of *Chlorobium* is not sufficient to alter the fatty acid pattern of the *Chlorobium* strains analysed. Minimal quantitative alterations were observed by the cell age, the pattern and profile of the fatty acids in PPSB and GPSB are very conservative in these microorganisms could be used for chemotaxonomic approaches. Nevertheless it is very important to describe the culture conditions as well as the techniques for fatty acid analysis.

Although advances in the technologies for fatty acid analysis have been reported, the isolation and culturing of novel strains as well as the fatty acid analysis of photosynthetic prokaryotes remains scarce, and it is very important to encourage the investigation of these microorgan‐ isms. With new sources of genetic materials from photosynthetic bacteria and the application of new technologies - for example lipidomics - which are more precise, it will be possible to discover new fatty acid structures and to evaluate the sources of these compounds of biotech‐ nological interest and novel chemotaxonomic biomarkers. In addition, the physiological role of fatty acids in the cell membranes of photosynthetic bacteria and their role in environmental trophic chains could be explored.

### **Acknowledgements**

We thank the Universitat Autónoma de Barcelona (UAB), Spain, where all the experimental elements of the present work were performed with academic and technical support from Doctors Marina Luquin, Jodi Mas and Manuel Muñoz. Financial support from the Consejo Nacional de Ciencia y Tecnología (CONACyT, México) and the Universidad Autónoma Metropolitana-Xochimilco are gratefully acknowledged.

[7] Núñez-Cardona, MT (2012). Fatty acids analysis of photosynthetic sulfur bacteria by gas chromatography. Gas chromatography-Biochemicals, Narcotics and Essential Oils. In Tech Publications, 117-138. http://cdn.intechopen.com/pdfs/31528/InTech-Fatty\_acids\_analysis\_of\_photosynthetic\_sulfur\_bacteria\_by\_gas\_chromatogra‐

Influence of Culture Conditions on the Fatty Acids Composition of Green and Purple Photosynthetic Sulphur Bacteria

http://dx.doi.org/10.5772/57389

151

[8] Caudales R, Wells J. Differentiation of Free-Living *Anabaena* and *Nostoc* Cyanobacte‐ ria on the basis of fatty acid composition. International Journal of Systematic Bacteri‐

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[10] Bryantseva IA, Tourova TP, Kovaleva OL, Kostrikina NA, and Gorlenko VM. *Ecto‐ thiorhodospira magna* sp. nov., a new large alkaliphilic purple sulfur bacterium. Micro‐

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### **Author details**

María Teresa Núñez-Cardona

Universidad Autónoma Metropolitana-Xochimilco. Calzada del Hueso, México, Distrito Federal, Mexico

### **References**


[7] Núñez-Cardona, MT (2012). Fatty acids analysis of photosynthetic sulfur bacteria by gas chromatography. Gas chromatography-Biochemicals, Narcotics and Essential Oils. In Tech Publications, 117-138. http://cdn.intechopen.com/pdfs/31528/InTech-Fatty\_acids\_analysis\_of\_photosynthetic\_sulfur\_bacteria\_by\_gas\_chromatogra‐ phy.pdf.

**Acknowledgements**

150 Advances in Gas Chromatography

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1365-2958.1999.01417.x.

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Metropolitana-Xochimilco are gratefully acknowledged.

We thank the Universitat Autónoma de Barcelona (UAB), Spain, where all the experimental elements of the present work were performed with academic and technical support from Doctors Marina Luquin, Jodi Mas and Manuel Muñoz. Financial support from the Consejo Nacional de Ciencia y Tecnología (CONACyT, México) and the Universidad Autónoma

Universidad Autónoma Metropolitana-Xochimilco. Calzada del Hueso, México, Distrito

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**Chapter 7**

**Applications of Modern Pyrolysis Gas Chromatography**

**for the Study of Degradation and Aging in Complex**

**Silicone Elastomers**

http://dx.doi.org/10.5772/57509

**1. Introduction**

James P. Lewicki and Robert S. Maxwell

Additional information is available at the end of the chapter

physically diverse structural architectures. (See Figure 1)

performance silicone systems. [7-10]

**1.1. Silicones — Complex and challenging synthetic polymeric materials**

Silicone based materials (polymers, polymeric network elastomers, composites and hybrid materials) are a ubiquitous class of synthetic polymeric system – encountered commonly in the research, industrial and commercial areas [1]. Silicones find use in diverse areas, ranging from industrial power distribution [1], as biomedical implants [2], in the advanced electronics sector [3, 4] and the aerospace industry [5]. No one material can be considered a representative 'silicone' as the definition ranges from low viscosity liquids through compliant elastomers and gums, to intractable high modulus solids. In general however, silicones can be considered to be polymeric materials based upon an [(R2Si)-O] repeating unit and they are most often structurally complex, multi-component systems which incorporate both chemically and

It is important to understand from the outset in this chapter that gaining an understanding of the relationship between the multi-scale structure of a complex silicone system such as a commercial silicone elastomer and the resultant macroscopic properties and performance must be the goal of any informed approach to both performance and lifetime prediction of current service materials [6] and the rational development of the next generation of high

Of all the silicone based materials in use today, it is arguably *silicone elastomers* that are both the most complex in overall structure and challenging in their analysis: Polymer structure, cure chemistry, network functionality, filler type and loading levels are just some of the

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


## **Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex Silicone Elastomers**

James P. Lewicki and Robert S. Maxwell

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57509

### **1. Introduction**

form for Chemotaxonomic Analysis of Mycobacterium tuberculosis. Chemistry and

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2013; 9 300–310. DOI: 10.1007/s11306-012-0445-1.

nal/marinedrugs.

1472-765X.2008.02348.x.

154 Advances in Gas Chromatography

### **1.1. Silicones — Complex and challenging synthetic polymeric materials**

Silicone based materials (polymers, polymeric network elastomers, composites and hybrid materials) are a ubiquitous class of synthetic polymeric system – encountered commonly in the research, industrial and commercial areas [1]. Silicones find use in diverse areas, ranging from industrial power distribution [1], as biomedical implants [2], in the advanced electronics sector [3, 4] and the aerospace industry [5]. No one material can be considered a representative 'silicone' as the definition ranges from low viscosity liquids through compliant elastomers and gums, to intractable high modulus solids. In general however, silicones can be considered to be polymeric materials based upon an [(R2Si)-O] repeating unit and they are most often structurally complex, multi-component systems which incorporate both chemically and physically diverse structural architectures. (See Figure 1)

It is important to understand from the outset in this chapter that gaining an understanding of the relationship between the multi-scale structure of a complex silicone system such as a commercial silicone elastomer and the resultant macroscopic properties and performance must be the goal of any informed approach to both performance and lifetime prediction of current service materials [6] and the rational development of the next generation of high performance silicone systems. [7-10]

Of all the silicone based materials in use today, it is arguably *silicone elastomers* that are both the most complex in overall structure and challenging in their analysis: Polymer structure, cure chemistry, network functionality, filler type and loading levels are just some of the

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Figure 1.** Left: The basic repeating structure of a polysiloxane (silicone) polymer. Right: A representation of some of the main factors that make up the overall, complex structural architecture of a silicone material.

variables that go into defining a final 3-dimensional, multi-scaled silicone 'network' elastomer. However there is a very real lack of data available on the specific structure of many important classes of silicone elastomer, due to both the proprietary nature of many industrial formula‐ tions and the simple fact that these materials are difficult to analyze at high fidelity: Silicone elastomer systems are most often chemically crosslinked networks with an effective infinite molecular weight and are as such, insoluble & *intractable.* Therefore they are inaccessible to the majority of spectroscopic and chromatographic techniques which are commonly employed for chemical Identification, characterization of structure-property relationships in polymeric systems and powerful techniques such as GCMS cannot be directly employed for the analysis of silicone elastomers. In the past these issues were not as prevalent as they are today due to the less intensive requirements of, and demands on silicone elastomers in application [11]. Simple empirically based additive methodologies were often relied upon for investigating structure-property relationships in silicone elastomers for the elucidation of chemical structure and the prediction of materials performance. [12-15] However, using these comparatively basic approaches, all but the most coarse grain information on network structure were obtained and the specifics of the 'network architecture' were largely overlooked. Today, it is accepted that the macroscopic properties and dynamic performance of a silicone elastomer system over time are subject to the multi-scale structure of the material over a range of size scales and that even comparatively small alterations to the network structure can lead to significant changes in materials properties [16, 17] (see Figure 2).

structure-property relations in complex siloxanes. Many researchers have made extensive use of solid state Nuclear Magnetic Resonance (NMR) both as a method of determining the chemical content/makeup of the polymer backbone in commercial silicone formulations [18] and as a tool for probing segmental dynamics of silicone networks in the solid state. [6, 19, 20] While NMR in particular has been shown extensively [21-23] as a powerful tool for elucidating the chemical identity and network architecture of complex siloxane networks, the methods employed are typically non-trivial, can be time intensive and potentially costly;

**Figure 2.** *Main*: Illustration of the complex relationship between materials properties/performance and structure over a range of size scale, from atomistic to a macro-scale. *Inset*: the time dependencies of various 'aging' processes that

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157

*Degradative thermal analysis* (the thermally induced de-polymerization of a polymer or polymer network) offers an alternate route to the analysis of such complex materials. Through a destructive analysis of silicone networks and the characterization of the resultant products of thermal degradation, a surprising amount of information on the chemical identity, structural architecture and even service history of the material can be gleaned. And indeed, various incarnations of pyrolytic analysis and gravimetry have been used extensively for the analysis of 'unknown' polymeric materials for many years. [24-27] The temperature at which a

requiring both significant investment in instrumentation and expertise.

effect silicone systems in a service environment.

With this information in hand, it is clear that there is a need to move beyond basic 'bulk' empirical approaches to defining structure property relationships in silicones. Instead, we must employ higher resolution analytical methodologies to these materials - in order to relate the underlying network structure, (from an atomistic to a meso-scale level) directly to macro‐ scopic materials behavior.

In spite of the intractability of silicone elastomers towards standard spectroscopic and analytical techniques, much progress has been made towards enhancing understanding of Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex… http://dx.doi.org/10.5772/57509 157

variables that go into defining a final 3-dimensional, multi-scaled silicone 'network' elastomer. However there is a very real lack of data available on the specific structure of many important classes of silicone elastomer, due to both the proprietary nature of many industrial formula‐ tions and the simple fact that these materials are difficult to analyze at high fidelity: Silicone elastomer systems are most often chemically crosslinked networks with an effective infinite molecular weight and are as such, insoluble & *intractable.* Therefore they are inaccessible to the majority of spectroscopic and chromatographic techniques which are commonly employed for chemical Identification, characterization of structure-property relationships in polymeric systems and powerful techniques such as GCMS cannot be directly employed for the analysis of silicone elastomers. In the past these issues were not as prevalent as they are today due to the less intensive requirements of, and demands on silicone elastomers in application [11]. Simple empirically based additive methodologies were often relied upon for investigating structure-property relationships in silicone elastomers for the elucidation of chemical structure and the prediction of materials performance. [12-15] However, using these comparatively basic approaches, all but the most coarse grain information on network structure were obtained and the specifics of the 'network architecture' were largely overlooked. Today, it is accepted that the macroscopic properties and dynamic performance of a silicone elastomer system over time are subject to the multi-scale structure of the material over a range of size scales and that even comparatively small alterations to the network structure can lead to significant changes in

**Figure 1.** Left: The basic repeating structure of a polysiloxane (silicone) polymer. Right: A representation of some of

the main factors that make up the overall, complex structural architecture of a silicone material.

With this information in hand, it is clear that there is a need to move beyond basic 'bulk' empirical approaches to defining structure property relationships in silicones. Instead, we must employ higher resolution analytical methodologies to these materials - in order to relate the underlying network structure, (from an atomistic to a meso-scale level) directly to macro‐

In spite of the intractability of silicone elastomers towards standard spectroscopic and analytical techniques, much progress has been made towards enhancing understanding of

materials properties [16, 17] (see Figure 2).

scopic materials behavior.

156 Advances in Gas Chromatography

**Figure 2.** *Main*: Illustration of the complex relationship between materials properties/performance and structure over a range of size scale, from atomistic to a macro-scale. *Inset*: the time dependencies of various 'aging' processes that effect silicone systems in a service environment.

structure-property relations in complex siloxanes. Many researchers have made extensive use of solid state Nuclear Magnetic Resonance (NMR) both as a method of determining the chemical content/makeup of the polymer backbone in commercial silicone formulations [18] and as a tool for probing segmental dynamics of silicone networks in the solid state. [6, 19, 20] While NMR in particular has been shown extensively [21-23] as a powerful tool for elucidating the chemical identity and network architecture of complex siloxane networks, the methods employed are typically non-trivial, can be time intensive and potentially costly; requiring both significant investment in instrumentation and expertise.

*Degradative thermal analysis* (the thermally induced de-polymerization of a polymer or polymer network) offers an alternate route to the analysis of such complex materials. Through a destructive analysis of silicone networks and the characterization of the resultant products of thermal degradation, a surprising amount of information on the chemical identity, structural architecture and even service history of the material can be gleaned. And indeed, various incarnations of pyrolytic analysis and gravimetry have been used extensively for the analysis of 'unknown' polymeric materials for many years. [24-27] The temperature at which a polymeric material degrades, the mechanism and the products of the thermal degradation are all a function of its *underlying chemical structure* and even physical morphology. Different polymeric materials can therefore be 'fingerprinted' by their thermal or thermo-oxidative degradation behavior. Importantly for the purposes of this discussion, such degradative analyses also re-open the possibility of utilizing GC/MS as a characterization tool. However it must be noted that the complex speciation of degradation products from silicone elastomers can make the interpretation of the data with respect to the original structure challenging.

thermal and thermo-oxidative degradation of a range of linear silicone polymers using a combination of Thermal Volatilization Analysis (TVA) and Thermogravimetric Analysis (TGA). Building upon earlier work by Pantode and Wilcock [36] and Thomas and Kendrick, [37, 38] Grassie demonstrated through an in-depth analysis of the products of thermal degradation that silicones, in general, at elevated temperatures degrade via a de-polymeriza‐ tion reaction to yield cyclic oligomeric siloxanes. This thermal de-polymerization reaction proceeds from both free chain ends and as a result of intra-molecular backbiting reactions of continuous chain segments (see Figure 4). For linear poly(dimethylsiloxane) (PDMS) Grassie and Macfarlane reported the major degradation products to be cyclic oligomeric siloxanes of

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**Figure 4.** Intramolecular chain backbiting mechanisms. Upper scheme illustrates the attack of a chain by its own OH terminated free end to form a cyclic siloxane. Lower scheme illustrates the case were a closed, continuous chain folds

Uncatalyzed backbiting and cyclization reactions such as those proposed by Grassie in Figure 4 are typically reported to occur at temperatures of 350-400 °C and are in essence, the reverse of the ring-opening polymerization reactions that are employed to synthesize high molar mass silicones from cyclic oligomeric silicone pre-cursors. From a thermodynamic standpoint the ceiling temperature for the polymerization of cyclic silicones to form PDMS is relatively low (~110 °C). The implication is therefore that the reverse reaction – de-polymerization to reform

Grassie demonstrated [28] in the late 1970s even linear unmodified PDMS in the absence of catalyst residues or impurities is stable up to a temperature of 350°C and in practice, a silicone can rarely be expected to de-polymerize at a significant rate (unaided) at temperatures below 250 °C. It is therefore reasonable to assume that the influence of kinetic, steric and other factors

C. However as

back upon itself and re-arrangement occurs to form a new Si-O bond and a free cyclic siloxane

cyclic oligomers is thermodynamically favored above temperatures of 110o

ring sizes D3–D12 and higher oligomeric siloxane species.

### **2. A silicone degradation primer**

Silicones, as with all polymeric materials will chemically degrade into a range of other compounds as a consequence of exposure to thermal, radiative or chemical processes with energy sufficient to scission, oxidize, hydrolyze or otherwise break the bonds of the polymer. It is the thermal stability of a silicone system however that is most often the defining factor in its application, performance and lifetime during service. Temperature broadly affects the rates and energetic favorability of all of the major degradation processes that occur in silicone systems (with the exception of non-oxidative ionizing radiation induced scission). For example, a system that is stable to acidic hydrolysis for years at room temperature may only last weeks before failing at 75°C (see Figure 3).

**Figure 3.** The primary mechanisms of silicone degradation as a function of temperature. Differing mechanisms are dominant over specific temperature ranges with more energetic process becoming dominant (within a specific timeframe) at higher temperatures.

It is unsurprising therefore, that since their introduction as usable materials some 60 years ago, there has been interest in the thermal stability and the mechanisms by which silicones degrade at elevated temperatures. The most influential work on the subject of the thermal degradation of the silicone polymers was carried out by Grassie et al. [28-35] who studied in detail the thermal and thermo-oxidative degradation of a range of linear silicone polymers using a combination of Thermal Volatilization Analysis (TVA) and Thermogravimetric Analysis (TGA). Building upon earlier work by Pantode and Wilcock [36] and Thomas and Kendrick, [37, 38] Grassie demonstrated through an in-depth analysis of the products of thermal degradation that silicones, in general, at elevated temperatures degrade via a de-polymeriza‐ tion reaction to yield cyclic oligomeric siloxanes. This thermal de-polymerization reaction proceeds from both free chain ends and as a result of intra-molecular backbiting reactions of continuous chain segments (see Figure 4). For linear poly(dimethylsiloxane) (PDMS) Grassie and Macfarlane reported the major degradation products to be cyclic oligomeric siloxanes of ring sizes D3–D12 and higher oligomeric siloxane species.

polymeric material degrades, the mechanism and the products of the thermal degradation are all a function of its *underlying chemical structure* and even physical morphology. Different polymeric materials can therefore be 'fingerprinted' by their thermal or thermo-oxidative degradation behavior. Importantly for the purposes of this discussion, such degradative analyses also re-open the possibility of utilizing GC/MS as a characterization tool. However it must be noted that the complex speciation of degradation products from silicone elastomers can make the interpretation of the data with respect to the original structure challenging.

Silicones, as with all polymeric materials will chemically degrade into a range of other compounds as a consequence of exposure to thermal, radiative or chemical processes with energy sufficient to scission, oxidize, hydrolyze or otherwise break the bonds of the polymer. It is the thermal stability of a silicone system however that is most often the defining factor in its application, performance and lifetime during service. Temperature broadly affects the rates and energetic favorability of all of the major degradation processes that occur in silicone systems (with the exception of non-oxidative ionizing radiation induced scission). For example, a system that is stable to acidic hydrolysis for years at room temperature may only

**Figure 3.** The primary mechanisms of silicone degradation as a function of temperature. Differing mechanisms are dominant over specific temperature ranges with more energetic process becoming dominant (within a specific time-

It is unsurprising therefore, that since their introduction as usable materials some 60 years ago, there has been interest in the thermal stability and the mechanisms by which silicones degrade at elevated temperatures. The most influential work on the subject of the thermal degradation of the silicone polymers was carried out by Grassie et al. [28-35] who studied in detail the

**2. A silicone degradation primer**

158 Advances in Gas Chromatography

last weeks before failing at 75°C (see Figure 3).

frame) at higher temperatures.

**Figure 4.** Intramolecular chain backbiting mechanisms. Upper scheme illustrates the attack of a chain by its own OH terminated free end to form a cyclic siloxane. Lower scheme illustrates the case were a closed, continuous chain folds back upon itself and re-arrangement occurs to form a new Si-O bond and a free cyclic siloxane

Uncatalyzed backbiting and cyclization reactions such as those proposed by Grassie in Figure 4 are typically reported to occur at temperatures of 350-400 °C and are in essence, the reverse of the ring-opening polymerization reactions that are employed to synthesize high molar mass silicones from cyclic oligomeric silicone pre-cursors. From a thermodynamic standpoint the ceiling temperature for the polymerization of cyclic silicones to form PDMS is relatively low (~110 °C). The implication is therefore that the reverse reaction – de-polymerization to reform cyclic oligomers is thermodynamically favored above temperatures of 110o C. However as Grassie demonstrated [28] in the late 1970s even linear unmodified PDMS in the absence of catalyst residues or impurities is stable up to a temperature of 350°C and in practice, a silicone can rarely be expected to de-polymerize at a significant rate (unaided) at temperatures below 250 °C. It is therefore reasonable to assume that the influence of kinetic, steric and other factors in real materials all contribute to higher experimentally observed thermal stabilities of silicones.

of a silicone material coupled with the subsequent analysis of the products of degradation may allow the elucidation of an unknown network structure and even enable forensic identification (fingerprinting) of unknown engineering elastomers by their degradation profiles. [53]

Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex…

Analytical pyrolysis (the controlled thermal degradation of organic or inorganic materials with the subsequent identification of the gas-phase products of degradation) has long been employed for the study of polymeric materials degradation. [26] In general, analytical pyrolytic methodologies involve the rapid thermal degradation (>100 °C/min) of a polymeric sample to complete thermal degradation under a controlled atmosphere. The effluent gas stream is analyzed by a secondary chromatographic and/or spectroscopic technique to identify the products of the thermal decomposition of the material. Pyrolytic methodologies rely on small sample masses, high analyte detection sensitivity and operate in a non-diffusion limited

Diffusion limited Non-diffusion limited

**Figure 5.** In bulk degradation analyses (5-50 mg sample size) (left) the sample mass requirements and the limitations of experimental geometry give rise to significant diffusion and thermal gradients, rates of diffusion for reactants into the materials and products out (*dy/dt* and *dx/dt*) are often ignored despite their importance in any kinetic or mass transport model derived from the data. A technique that allows the use sub-milligram samples or thin films, sidesteps

Pyrolytic analysis methodologies are therefore typically regarded as more versatile, less biased, chemical information rich techniques which can rapidly and more reliably access the mechanistic degradation behavior of polymeric materials. Early applications of what would later become analytical pyrolysis of silicone systems include work by Wacholtz et. al [54] in which the pyrolysis products of the thermal degradation of a silicone system were sampled and analyzed in-line using a combination of FTIR and GC/MS. Such early in-line pyrolysis studies of silicone degradation would later form the basis of modern micro-analytical pyrolysis methodologies for the analysis of silicone degradation. However, until comparatively recent times the field of analytical degradative analysis of silicones was almost entirely dominated

many of these complications - simply on the basis of surface to volume ratio (right).

by the vacuum pyrolysis technique –Thermal Volatilization Analysis (TVA).

δy/δt -> 0 δx/δt -> 0 ∆T -> 0

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**3.2. Vacuum pyrolysis-mass spectrometry**

regime (See Figure 5).

Backbiting cyclization reactions are recognized in the literature [28, 39] as one of the primary thermal degradation mechanisms that occur in silicone systems. Importantly, however these backbiting reactions become significantly more favorable in the presence of acid or base. [28, 40] The presence of catalytic levels of Lewis acids or bases is now known to accelerate the depolymerization reaction and lower the degradation temperature of silicones significantly. Such catalyzed backbiting reactions are dominant over a temperature range of 110-260 °C. Silicones are also highly susceptible to hydrolysis: The ([R2Si]-O) bond is comparatively strong, having an average bond disassociation energy of 452 KJ mol-1, it is strongly polar and sterically unhindered, making the Si center highly susceptible to nucleophillic attack. A practical consequence of this nucleophillic susceptibility is that silicones are readily hydrolyzed under mildly acidic or basic conditions. At temperatures below 110 °C, thermal hydrolysis is one of the most significant processes that contribute to the long term aging and failure of silicone elastomers. [41]

Today, the thermal degradation chemistry of simple, linear siloxane systems is relatively well understood. However, many commercial siloxane polymer systems are significantly more complex. Such systems may contain inorganic fillers, crosslinking agents, catalysts, processing aids, synthesis residues, curing reaction by-products and other chemical 'complications' – all of which can alter their degradation behavior significantly. [42-46] Such systems are non-trivial to both analyze structurally, predict the properties of long-term and are the subject of much research today. [5, 27, 47-49]

### **3. Analytical degradative thermal analysis**

#### **3.1. Introduction**

There are a number of degradative thermal analysis techniques available for the analysis of silicones which include Thermal-Gravimetric Analysis (TGA), Differential Scanning Calorim‐ etry (DSC) Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS). These methods (and others), their application and the broader field of thermal analysis of polymers have been reviewed thoroughly in the excellent text by Wunderlich. [50] However, for the purposes of discussing analytical thermal methods for the determination of structural information from complex silicone networks, we will focus ourselves only on those techniques capable of providing chemical information on the products of silicone degradation as a function of temperature with the potential for quantification. Specifically, pyrolysis methods coupled with chromatography/spectrometry/scopy - such as Py-GC/MS.

Silicones degrade to form a range of degradation products and it has been established that both gross alterations in the chemical nature of the silicone polymer [28-35] and relatively subtle alterations in the network architecture of a silicone material [51, 52] can alter the speciation of products formed. It follows therefore that the purposeful, analytical degradation of a silicone material coupled with the subsequent analysis of the products of degradation may allow the elucidation of an unknown network structure and even enable forensic identification (fingerprinting) of unknown engineering elastomers by their degradation profiles. [53]

### **3.2. Vacuum pyrolysis-mass spectrometry**

in real materials all contribute to higher experimentally observed thermal stabilities of

Backbiting cyclization reactions are recognized in the literature [28, 39] as one of the primary thermal degradation mechanisms that occur in silicone systems. Importantly, however these backbiting reactions become significantly more favorable in the presence of acid or base. [28, 40] The presence of catalytic levels of Lewis acids or bases is now known to accelerate the depolymerization reaction and lower the degradation temperature of silicones significantly. Such catalyzed backbiting reactions are dominant over a temperature range of 110-260 °C. Silicones are also highly susceptible to hydrolysis: The ([R2Si]-O) bond is comparatively strong, having an average bond disassociation energy of 452 KJ mol-1, it is strongly polar and sterically unhindered, making the Si center highly susceptible to nucleophillic attack. A practical consequence of this nucleophillic susceptibility is that silicones are readily hydrolyzed under mildly acidic or basic conditions. At temperatures below 110 °C, thermal hydrolysis is one of the most significant processes that contribute to the long term aging and failure of silicone

Today, the thermal degradation chemistry of simple, linear siloxane systems is relatively well understood. However, many commercial siloxane polymer systems are significantly more complex. Such systems may contain inorganic fillers, crosslinking agents, catalysts, processing aids, synthesis residues, curing reaction by-products and other chemical 'complications' – all of which can alter their degradation behavior significantly. [42-46] Such systems are non-trivial to both analyze structurally, predict the properties of long-term and are the subject of much

There are a number of degradative thermal analysis techniques available for the analysis of silicones which include Thermal-Gravimetric Analysis (TGA), Differential Scanning Calorim‐ etry (DSC) Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS). These methods (and others), their application and the broader field of thermal analysis of polymers have been reviewed thoroughly in the excellent text by Wunderlich. [50] However, for the purposes of discussing analytical thermal methods for the determination of structural information from complex silicone networks, we will focus ourselves only on those techniques capable of providing chemical information on the products of silicone degradation as a function of temperature with the potential for quantification. Specifically, pyrolysis methods coupled with

Silicones degrade to form a range of degradation products and it has been established that both gross alterations in the chemical nature of the silicone polymer [28-35] and relatively subtle alterations in the network architecture of a silicone material [51, 52] can alter the speciation of products formed. It follows therefore that the purposeful, analytical degradation

silicones.

160 Advances in Gas Chromatography

elastomers. [41]

**3.1. Introduction**

research today. [5, 27, 47-49]

**3. Analytical degradative thermal analysis**

chromatography/spectrometry/scopy - such as Py-GC/MS.

Analytical pyrolysis (the controlled thermal degradation of organic or inorganic materials with the subsequent identification of the gas-phase products of degradation) has long been employed for the study of polymeric materials degradation. [26] In general, analytical pyrolytic methodologies involve the rapid thermal degradation (>100 °C/min) of a polymeric sample to complete thermal degradation under a controlled atmosphere. The effluent gas stream is analyzed by a secondary chromatographic and/or spectroscopic technique to identify the products of the thermal decomposition of the material. Pyrolytic methodologies rely on small sample masses, high analyte detection sensitivity and operate in a non-diffusion limited regime (See Figure 5).

**Figure 5.** In bulk degradation analyses (5-50 mg sample size) (left) the sample mass requirements and the limitations of experimental geometry give rise to significant diffusion and thermal gradients, rates of diffusion for reactants into the materials and products out (*dy/dt* and *dx/dt*) are often ignored despite their importance in any kinetic or mass transport model derived from the data. A technique that allows the use sub-milligram samples or thin films, sidesteps many of these complications - simply on the basis of surface to volume ratio (right).

Pyrolytic analysis methodologies are therefore typically regarded as more versatile, less biased, chemical information rich techniques which can rapidly and more reliably access the mechanistic degradation behavior of polymeric materials. Early applications of what would later become analytical pyrolysis of silicone systems include work by Wacholtz et. al [54] in which the pyrolysis products of the thermal degradation of a silicone system were sampled and analyzed in-line using a combination of FTIR and GC/MS. Such early in-line pyrolysis studies of silicone degradation would later form the basis of modern micro-analytical pyrolysis methodologies for the analysis of silicone degradation. However, until comparatively recent times the field of analytical degradative analysis of silicones was almost entirely dominated by the vacuum pyrolysis technique –Thermal Volatilization Analysis (TVA).

TVA is essentially an evolved gas analysis technique which is based upon the principle of accurate measurement of the pressure of volatile species evolved from a material undergoing a heating regime. TVA effectively monitors the evolution of volatile degradation products of a sample as a function of pressure vs. temperature as the sample is subjected to a linear heating ramp under vacuum. The technique was originally developed in the 1960s by Ian McNeill and co-workers [55] as a tool for studying polymer degradation. Being related to other vacuum based thermal analysis techniques used at this period to study polymer degradation, [56, 57] it rapidly became popular amongst polymer degradation groups and was utilized in many of the seminal studies of polymer degradation. [28] There were several incarnations of the basic technique and it saw its most advanced development in sub-ambient thermal volatilization analysis (SATVA) - which combined the differential monitoring of degradation behavior as a function of pressure, with the collection and separation of condensable volatile products by cryo-trapping and sub-ambient differential distillation. A general schematic diagram of a TVA system is shown in Figure 6.

nitrogen cooled cold traps by controlled heating of the primary trap to ambient or elevated temperatures. A mass spectrometer samples the gas stream continuously at the exit of the primary cryo-trap, providing a means of identifying non-condensable species such as methane or hydrogen evolved from a sample in addition to mass spectral identification of products in as they are separated during the differential distillation stage. Data from the mass spectrometer can be correlated with the sub-ambient distillation pressure peak plots and greatly aids in the identification of volatile species. Distilled product fractions can be subsequently removed into gas-phase cells for offline FTIR and GC/MS analysis. A series of secondary pressure gauges are placed at the entrance and exits of all secondary traps to monitor the distillation of specific

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The TVA technique is a vacuum pyrolysis method, coupled with a thermal trap/desorption system for volatile product separation and in many ways it is a predecessor to the modern pyrolysis-gas chromatography systems commonly in use today. The relative complexity both in design and operation of the method, along with the molecular weight range and resolution limitations of vacuum distillation separation prevented TVA and related vacuum pyrolysis 'line' systems from ever becoming more than a niche technique. And with the introduction of commercial gas chromatography systems directly coupled to fast quadrupolar mass filters throughout the 1970-1980s – the technique has been largely superseded by simpler and more versatile in-line pyrolysis GC/MS systems. That being said, a limited number of academics still utilize TVA as a means of studying polymer degradation [58-60] and indeed, it has even been used in recent years for the study of complex silicone nanocomposite elastomer systems. [51,

**Figure 7.** Sub-ambient differential distillation traces of the captured volatiles from the thermal degradation of an un‐ filled silicone and a range silicone-nanoclay composite systems. Solid, dashed, dotted, squares & circles represent 0, 2, 4, 6, & 8% clay filled composites respectively. Degradation products elute as a function of their boiling point as the trap is heated at a rate of 10 °C/min. The peaks labeled 1-7 were identified by a combination of gas-phase MS, gasphase FTIR and GC-MS and are listed in Table 1. Reprinted with permission from. [51] Copyright Elsevier (2009).

product fractions into separate traps and gas cells.

61] One such example is given in Figure 7.

**Figure 6.** A general schematic representation of a TVA vacuum pyrolysis system

The apparatus consists of a sample chamber (heated by a programmable tube furnace) connected in series to a primary liquid nitrogen cooled cryo-trap and a set of four secondary cold traps. The system is continuously pumped to a vacuum of ~1x10-4 torr. Volatile conden‐ sable products can be initially trapped at two stages: The water jacket cooled 'cold-ring' (T ~ 12 °C) immediately above the heated area of sample tube which condenses high boiling point materials and the main cryo trap (T ~-196 °C) which collects all the lower boiling point condensable species. Two linear response pressure gauges are positioned at the entrance and exit of the main cryo-trap to monitor the evolution of both condensable and non-condensable volatiles as a function of pressure vs. temperature/time from the sample. The primary cryotrap captures all of the condensable lower boiling point species evolved during the pyrolysis of a sample. This trapped condensate can then be distilled into separate secondary liquid nitrogen cooled cold traps by controlled heating of the primary trap to ambient or elevated temperatures. A mass spectrometer samples the gas stream continuously at the exit of the primary cryo-trap, providing a means of identifying non-condensable species such as methane or hydrogen evolved from a sample in addition to mass spectral identification of products in as they are separated during the differential distillation stage. Data from the mass spectrometer can be correlated with the sub-ambient distillation pressure peak plots and greatly aids in the identification of volatile species. Distilled product fractions can be subsequently removed into gas-phase cells for offline FTIR and GC/MS analysis. A series of secondary pressure gauges are placed at the entrance and exits of all secondary traps to monitor the distillation of specific product fractions into separate traps and gas cells.

TVA is essentially an evolved gas analysis technique which is based upon the principle of accurate measurement of the pressure of volatile species evolved from a material undergoing a heating regime. TVA effectively monitors the evolution of volatile degradation products of a sample as a function of pressure vs. temperature as the sample is subjected to a linear heating ramp under vacuum. The technique was originally developed in the 1960s by Ian McNeill and co-workers [55] as a tool for studying polymer degradation. Being related to other vacuum based thermal analysis techniques used at this period to study polymer degradation, [56, 57] it rapidly became popular amongst polymer degradation groups and was utilized in many of the seminal studies of polymer degradation. [28] There were several incarnations of the basic technique and it saw its most advanced development in sub-ambient thermal volatilization analysis (SATVA) - which combined the differential monitoring of degradation behavior as a function of pressure, with the collection and separation of condensable volatile products by cryo-trapping and sub-ambient differential distillation. A general schematic diagram of a TVA

system is shown in Figure 6.

162 Advances in Gas Chromatography

**Figure 6.** A general schematic representation of a TVA vacuum pyrolysis system

The apparatus consists of a sample chamber (heated by a programmable tube furnace) connected in series to a primary liquid nitrogen cooled cryo-trap and a set of four secondary cold traps. The system is continuously pumped to a vacuum of ~1x10-4 torr. Volatile conden‐ sable products can be initially trapped at two stages: The water jacket cooled 'cold-ring' (T ~ 12 °C) immediately above the heated area of sample tube which condenses high boiling point materials and the main cryo trap (T ~-196 °C) which collects all the lower boiling point condensable species. Two linear response pressure gauges are positioned at the entrance and exit of the main cryo-trap to monitor the evolution of both condensable and non-condensable volatiles as a function of pressure vs. temperature/time from the sample. The primary cryotrap captures all of the condensable lower boiling point species evolved during the pyrolysis of a sample. This trapped condensate can then be distilled into separate secondary liquid

The TVA technique is a vacuum pyrolysis method, coupled with a thermal trap/desorption system for volatile product separation and in many ways it is a predecessor to the modern pyrolysis-gas chromatography systems commonly in use today. The relative complexity both in design and operation of the method, along with the molecular weight range and resolution limitations of vacuum distillation separation prevented TVA and related vacuum pyrolysis 'line' systems from ever becoming more than a niche technique. And with the introduction of commercial gas chromatography systems directly coupled to fast quadrupolar mass filters throughout the 1970-1980s – the technique has been largely superseded by simpler and more versatile in-line pyrolysis GC/MS systems. That being said, a limited number of academics still utilize TVA as a means of studying polymer degradation [58-60] and indeed, it has even been used in recent years for the study of complex silicone nanocomposite elastomer systems. [51, 61] One such example is given in Figure 7.

**Figure 7.** Sub-ambient differential distillation traces of the captured volatiles from the thermal degradation of an un‐ filled silicone and a range silicone-nanoclay composite systems. Solid, dashed, dotted, squares & circles represent 0, 2, 4, 6, & 8% clay filled composites respectively. Degradation products elute as a function of their boiling point as the trap is heated at a rate of 10 °C/min. The peaks labeled 1-7 were identified by a combination of gas-phase MS, gasphase FTIR and GC-MS and are listed in Table 1. Reprinted with permission from. [51] Copyright Elsevier (2009).


**Table 1.** Identified products from the TVA analysis of a range of silicone nano-composite elastomers.

From the TVA data given in Figure 7 and Table 1 it is clear that while the quality of the chromatography from differential distillation is poor compared with GC/MS, there is still much data that can be obtained from such analyses. The speciation and distribution of products observed in this particular study allowed the authors to determine that a clay filler additive in a particular silicone network significantly changes the speciation of minor products formed via catalyzing secondary racial scission reactions at high temperatures.

and a high maximum temperature are therefore accessible (0.01 to 999°C/min with a maximum

**Figure 8.** Schematic representation of an inline pyrolysis GC/MS system. Reprinted with permission from. [62] Copy‐

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**Figure 9.** On the left is a modern pyrolysis probe showing the platinum coil heater and quartz sample tube. Right is an example of the typical mass of a silicone elastomer (highlighted by the blue circle) required for an analytical degrada‐

The ability to analyze such small samples of material; importantly allows degradation and offgassing processes to be studied in a more ideal non-diffusion limited regime, negating the limitations of bulk analysis techniques such as TGA. One of the other great advantages of analytical Py-GC/MS is that it can provide both hi-fidelity, in-depth chemical speciation data in addition to rate/loss thermogram type data simply by means of changing the elution

Using pyrolysis-GC/MS we can monitor the degradation of a silicone system in real time (as in the left plot of Figure 10) to yield assessments of thermal stability, overall degradation profile and level of volatiles released. For example, in this study it was observed that the level of volatiles released from a silicone elastomer decrease markedly with increasing carbon nanotube content. Importantly, however, we can also obtain full product separation and identifi‐

of 1200 °C are typical).

right Elsevier (2006).

tive analysis.

conditions of the GC column (see Figure 10).

#### **3.3. Modern micro-pyrolysis-GC/MS**

A more versatile approach to the analytical degradative analysis of the complex silicone based materials is modern Micro-Pyrolysis-coupled Gas Chromatography / Mass Spectrometry. More commonly referred to as Pyrolysis GC/MS, this technique was developed by and grew popular in the materials research and the oil industries. Py-GC/MS paralleled the development of commercial GC systems and found extensive application in the study of materials degra‐ dation and destructive analysis of intractable organic materials. The technique today remains disarmingly simple: a low thermal mass oven which is directly in-line with a detector (GC/MS etc.) heats a very small sample of analyte rapidly under inert or reactive carrier gas to de‐ struction and the volatile products of degradation are passed by means of the carrier gas for analysis and optional separation by the detection instrument (see Figure 8).

Owing to the fact that the effluent stream of the furnace can be directly fed into the inlet of a sensitive GC/MS system, modern pyrolysis requires only extremely small analyte masses; from 1x10-3 to 1 mg is typical for an analytical pyrolysis of an organic containing material. Added to that, the thermal mass of a pyrolysis furnace is typically low and often in the form of a platinum coil/quartz tube 'probe' type geometry (See Figure 9). A broad range of heating rates Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex… http://dx.doi.org/10.5772/57509 165

**Peak number Product ID Relative Abundance Detector Source**

1 Propene & CO2 trace MS/FTIR

the enolate form of

**3.3. Modern micro-pyrolysis-GC/MS**

3

8 (not shown due to

164 Advances in Gas Chromatography

scale)

N/A non-condensable Methane minor MS high temp radical reactions

2 butene minor MS/FTIR catalyst residue elimination

5 Propanal trace MS/FTIR scission of chain end modifier 6 Benzene minor MS/FTIR catalyst residue elimination 7 Ethyl hexanoic acid minor FTIR/GCMS Catalyst residue (intact)

N/A semi-volatile Higher oligomeric siloxanes major GCMS thermal backbiting reactions &

From the TVA data given in Figure 7 and Table 1 it is clear that while the quality of the chromatography from differential distillation is poor compared with GC/MS, there is still much data that can be obtained from such analyses. The speciation and distribution of products observed in this particular study allowed the authors to determine that a clay filler additive in a particular silicone network significantly changes the speciation of minor products formed

A more versatile approach to the analytical degradative analysis of the complex silicone based materials is modern Micro-Pyrolysis-coupled Gas Chromatography / Mass Spectrometry. More commonly referred to as Pyrolysis GC/MS, this technique was developed by and grew popular in the materials research and the oil industries. Py-GC/MS paralleled the development of commercial GC systems and found extensive application in the study of materials degra‐ dation and destructive analysis of intractable organic materials. The technique today remains disarmingly simple: a low thermal mass oven which is directly in-line with a detector (GC/MS etc.) heats a very small sample of analyte rapidly under inert or reactive carrier gas to de‐ struction and the volatile products of degradation are passed by means of the carrier gas for

Owing to the fact that the effluent stream of the furnace can be directly fed into the inlet of a sensitive GC/MS system, modern pyrolysis requires only extremely small analyte masses; from 1x10-3 to 1 mg is typical for an analytical pyrolysis of an organic containing material. Added to that, the thermal mass of a pyrolysis furnace is typically low and often in the form of a platinum coil/quartz tube 'probe' type geometry (See Figure 9). A broad range of heating rates

4 Linear silicone – minor MS x-linker residues

**Table 1.** Identified products from the TVA analysis of a range of silicone nano-composite elastomers.

via catalyzing secondary racial scission reactions at high temperatures.

analysis and optional separation by the detection instrument (see Figure 8).

dimethylsilanone trace MS/FTIR main chain radical scission

D3-D5 cyclic oligomers major GCMS thermal backbiting reactions

elimination reaction of x-linker

residues

equilibration

and a high maximum temperature are therefore accessible (0.01 to 999°C/min with a maximum of 1200 °C are typical).

**Figure 9.** On the left is a modern pyrolysis probe showing the platinum coil heater and quartz sample tube. Right is an example of the typical mass of a silicone elastomer (highlighted by the blue circle) required for an analytical degrada‐ tive analysis.

The ability to analyze such small samples of material; importantly allows degradation and offgassing processes to be studied in a more ideal non-diffusion limited regime, negating the limitations of bulk analysis techniques such as TGA. One of the other great advantages of analytical Py-GC/MS is that it can provide both hi-fidelity, in-depth chemical speciation data in addition to rate/loss thermogram type data simply by means of changing the elution conditions of the GC column (see Figure 10).

Using pyrolysis-GC/MS we can monitor the degradation of a silicone system in real time (as in the left plot of Figure 10) to yield assessments of thermal stability, overall degradation profile and level of volatiles released. For example, in this study it was observed that the level of volatiles released from a silicone elastomer decrease markedly with increasing carbon nanotube content. Importantly, however, we can also obtain full product separation and identifi‐

**Figure 10.** Degradative analysis of a series of silicone-carbon nanotube composites using Py-GC/MS. Left: pyrolysis thermogram data obtained by pyrolysing a series of silicone composites at a ramp rate of 100°C/min while using the GC/MS in a non-separating direct detection mode. A-G corresponds to silicone formations with increasing levels of carbon nanotubes. Right: full product speciation obtained from the ballistic pyrolysis of sample 'A' at 999 °C/min with the subsequent separation and speciation of the degradation products by GC/MS.

Indeed, these data have been used in the following example to quantify the diffusion of CO2 through a silicone membrane directly and as a function of temperature using Py-GC/MS. Shown in Figure 12 are the results of quantitative Py-GC/MS analysis of the diffusion of a sealed, finite CO2 source through a 250 µm thick silicone membrane at a temperature of 120°C

**Figure 11.** Py-GC/MS calibration curves obtained from the pyrolysis of known quantities of NaHCO3. Both ions 44 and 18 were monitored during chromatographic separation to yield a detector response as a function of moles of CO2 and

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**Figure 12.** Evolution of CO2 from a finite source, as measured by Py-GC/MS through a thin silicone membrane at ele‐ vated temperatures (quantitative on CO2). Note that the initial rate of diffusion through the membrane is rapid but as

the CO2 source is depleted, a sharp decrease rate is observed.

as a function of time.

H2O.

cation for a given degradation process using the same apparatus, now operating in the more conventional flash or 'ballistic' heating mode with full GC/MS separation and detection of analytes (right hand Figure 10). In this particular study it was shown from an analysis of the degradation products provided by Py-GC/MS that while the carbon nanotube additive did not significantly change the chemistry of the silicone degradation, it did physically retard the release of volatile species and increase the yield of calcined 'char' – thus leading to improved thermal stability.

### **3.4. Quantitative Py-GC/MS methods**

As with the majority of GC based techniques, Py-GC/MS can through calibration, be fully quantitative with respect to the outgassing or degradation species being monitored. Through the use of an external calibrant standard (in the following example, analytical grade NaHCO3) the instrument response to various gaseous species as a function of the total number of moles evolved from the pyrolysis event can be used to build a calibration curve. See Figure 11.

In the relatively simple example shown in Figure 11 varying masses of NaHCO3 were pyro‐ lysed under He in-line to 400 °C. This rapidly decomposed the bicarbonate to carbonate, CO2 and H2O in a fixed stoichiometric ratio. The pyrolysates were trapped on a cryo-cooled column to collimate them and then allowed to elute through the detector separately. Both ions 44 and 18 were monitored during chromatographic separation to yield a detector response as a function of moles of CO2 and H2O. With enough data points over a suitable mass range, instrument/method dependent calibration curves can be constructed which allow quantifica‐ tion of analyte gasses from unknown samples.

Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex… http://dx.doi.org/10.5772/57509 167

**Figure 11.** Py-GC/MS calibration curves obtained from the pyrolysis of known quantities of NaHCO3. Both ions 44 and 18 were monitored during chromatographic separation to yield a detector response as a function of moles of CO2 and H2O.

Indeed, these data have been used in the following example to quantify the diffusion of CO2 through a silicone membrane directly and as a function of temperature using Py-GC/MS. Shown in Figure 12 are the results of quantitative Py-GC/MS analysis of the diffusion of a sealed, finite CO2 source through a 250 µm thick silicone membrane at a temperature of 120°C as a function of time.

cation for a given degradation process using the same apparatus, now operating in the more conventional flash or 'ballistic' heating mode with full GC/MS separation and detection of analytes (right hand Figure 10). In this particular study it was shown from an analysis of the degradation products provided by Py-GC/MS that while the carbon nanotube additive did not significantly change the chemistry of the silicone degradation, it did physically retard the release of volatile species and increase the yield of calcined 'char' – thus leading to improved

the subsequent separation and speciation of the degradation products by GC/MS.

**Figure 10.** Degradative analysis of a series of silicone-carbon nanotube composites using Py-GC/MS. Left: pyrolysis thermogram data obtained by pyrolysing a series of silicone composites at a ramp rate of 100°C/min while using the GC/MS in a non-separating direct detection mode. A-G corresponds to silicone formations with increasing levels of carbon nanotubes. Right: full product speciation obtained from the ballistic pyrolysis of sample 'A' at 999 °C/min with

As with the majority of GC based techniques, Py-GC/MS can through calibration, be fully quantitative with respect to the outgassing or degradation species being monitored. Through the use of an external calibrant standard (in the following example, analytical grade NaHCO3) the instrument response to various gaseous species as a function of the total number of moles evolved from the pyrolysis event can be used to build a calibration curve. See Figure 11.

In the relatively simple example shown in Figure 11 varying masses of NaHCO3 were pyro‐ lysed under He in-line to 400 °C. This rapidly decomposed the bicarbonate to carbonate, CO2 and H2O in a fixed stoichiometric ratio. The pyrolysates were trapped on a cryo-cooled column to collimate them and then allowed to elute through the detector separately. Both ions 44 and 18 were monitored during chromatographic separation to yield a detector response as a function of moles of CO2 and H2O. With enough data points over a suitable mass range, instrument/method dependent calibration curves can be constructed which allow quantifica‐

thermal stability.

166 Advances in Gas Chromatography

**3.4. Quantitative Py-GC/MS methods**

tion of analyte gasses from unknown samples.

**Figure 12.** Evolution of CO2 from a finite source, as measured by Py-GC/MS through a thin silicone membrane at ele‐ vated temperatures (quantitative on CO2). Note that the initial rate of diffusion through the membrane is rapid but as the CO2 source is depleted, a sharp decrease rate is observed.

### **4. The application of chemometric techniques to large Py-GC/MS datasets**

Pyrolysis GC/MS data from silicone systems can be complex, with ion chromatograms from a single degradation or off-gassing process often having many hundred eluted products and a single study may yield many such individual chromatograms which must be interpreted. Often, therefore we must rely on more advanced data processing and mining techniques to both analyze these data in a timely manner and ensure that significant trends or processes are not lost in the sheer volume of data produced. A comparatively simple, yet highly effective chemometric technique that lends itself well to the analysis of such large chromatographic datasets is multivariate statistical analysis – specifically, Principle Components Analysis (PCA).

In PCA as with many other multivariate statistical techniques, sample-to-sample or object-toobject variation can be represented by a series of principal components (PCs), which preserve the structure of the underlying variance between two or more variables. The general aim of PCA is the reduction of the dimensionality of a data set by the computation of a small number of these components (typically much less than the number of variables) that are parameterized by so-called scores and loadings. See Figure 13.

and accounts for the second largest percentage of the overall variance. Additional PCs can be defined similarly. For the pyrolysis data considered presently, the loading vectors profile the extent of retention time, with peaks representing regions of significant variance among samples. Scores provide information about the degree to which a given loading is important for a particular sample/chromatogram. In other terms, scores can be thought of as weights or

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PCA analysis has recently been applied to a number of studies of silicone degradation. [5, 52, 53] And in the first of two such studies we will discuss, the thermal degradation behavior of a series of well-defined model poly (dimethylsiloxane) model networks were investigated with Py-GC/MS and PCA in order to probe the influence of controlled network architectures on degradation chemistries and product profiles. A matrix of model silicone networks were formulated to incorporate a range of well-defined network architectures. Specifically; mono‐ modal over a range of chain molar masses above and below the critical entanglement molar mass, Mc (~12 KDa); bimodal with varying mol. percentages of short (8 KDa) and long (133 KDa) chains; free chain end containing (a monomodal network with 1-20 mol. percent free chain ends). Samples were pyrolysed at a ballistic heating rate to 1000 °C under helium. The products of degradation were analyzed using in-line GC/MS to yield total ion chromatograms. Shown in Figure 14 is an example of a typical GC trace obtained from the pyrolysis of one of

**Figure 14.** GC total ion chromatogram (TIC) of the pyrolysis products from a 0.04 mg sample of a 54.4 KDa monomo‐ dal crosslinked PDMS elastomer. The products of pyrolytic degradation are labeled D3 to D15 (cyclic siloxanes) and i-vii (misc small molecule, branched and linear species). Due the large size of the datasets obtained from such a study (>100 high resolution chromatorgrams each with 20-40 identified products) PCA was applied to the datasets. Shown in Figure 15 is a 'samples and 'scores' plot for principal components 1 & 2 of a PCA model capturing 90% of the total

as "concentrations" of loadings for the latent variables. [63]

the model silicone networks.

variance in the complete matrix of silicones studied.

**Figure 13.** An illustration of the dimensional reduction that allows PCA to de-convolute large datasets. In this example there is elliptically distributed x, y, & z data (blue). Through an orthogonal coordinate rotation the largest variances in the data can be separated into principle components each with their own dimensionality. At the same time we reduce the overall dimensionality of the dataset by separating and discarding unwanted variables such as spectral noise. Fi‐ nally, outliers are clearly identified outside of confidence limits in each PC.

Each component derived from PCA contains within it some proportion of the overall variance (generally expressed as a percentage of the total), with the first principal component (PC1) being the latent variable which describes the maximum amount of variance over a given dataset. PC2, the second principal component, is uncorrelated with (orthogonal to) the first PC and accounts for the second largest percentage of the overall variance. Additional PCs can be defined similarly. For the pyrolysis data considered presently, the loading vectors profile the extent of retention time, with peaks representing regions of significant variance among samples. Scores provide information about the degree to which a given loading is important for a particular sample/chromatogram. In other terms, scores can be thought of as weights or as "concentrations" of loadings for the latent variables. [63]

**4. The application of chemometric techniques to large Py-GC/MS datasets**

Pyrolysis GC/MS data from silicone systems can be complex, with ion chromatograms from a single degradation or off-gassing process often having many hundred eluted products and a single study may yield many such individual chromatograms which must be interpreted. Often, therefore we must rely on more advanced data processing and mining techniques to both analyze these data in a timely manner and ensure that significant trends or processes are not lost in the sheer volume of data produced. A comparatively simple, yet highly effective chemometric technique that lends itself well to the analysis of such large chromatographic datasets is multivariate statistical analysis – specifically, Principle Components Analysis

In PCA as with many other multivariate statistical techniques, sample-to-sample or object-toobject variation can be represented by a series of principal components (PCs), which preserve the structure of the underlying variance between two or more variables. The general aim of PCA is the reduction of the dimensionality of a data set by the computation of a small number of these components (typically much less than the number of variables) that are parameterized

**Figure 13.** An illustration of the dimensional reduction that allows PCA to de-convolute large datasets. In this example there is elliptically distributed x, y, & z data (blue). Through an orthogonal coordinate rotation the largest variances in the data can be separated into principle components each with their own dimensionality. At the same time we reduce the overall dimensionality of the dataset by separating and discarding unwanted variables such as spectral noise. Fi‐

Each component derived from PCA contains within it some proportion of the overall variance (generally expressed as a percentage of the total), with the first principal component (PC1) being the latent variable which describes the maximum amount of variance over a given dataset. PC2, the second principal component, is uncorrelated with (orthogonal to) the first PC

(PCA).

168 Advances in Gas Chromatography

by so-called scores and loadings. See Figure 13.

nally, outliers are clearly identified outside of confidence limits in each PC.

PCA analysis has recently been applied to a number of studies of silicone degradation. [5, 52, 53] And in the first of two such studies we will discuss, the thermal degradation behavior of a series of well-defined model poly (dimethylsiloxane) model networks were investigated with Py-GC/MS and PCA in order to probe the influence of controlled network architectures on degradation chemistries and product profiles. A matrix of model silicone networks were formulated to incorporate a range of well-defined network architectures. Specifically; mono‐ modal over a range of chain molar masses above and below the critical entanglement molar mass, Mc (~12 KDa); bimodal with varying mol. percentages of short (8 KDa) and long (133 KDa) chains; free chain end containing (a monomodal network with 1-20 mol. percent free chain ends). Samples were pyrolysed at a ballistic heating rate to 1000 °C under helium. The products of degradation were analyzed using in-line GC/MS to yield total ion chromatograms. Shown in Figure 14 is an example of a typical GC trace obtained from the pyrolysis of one of the model silicone networks.

**Figure 14.** GC total ion chromatogram (TIC) of the pyrolysis products from a 0.04 mg sample of a 54.4 KDa monomo‐ dal crosslinked PDMS elastomer. The products of pyrolytic degradation are labeled D3 to D15 (cyclic siloxanes) and i-vii (misc small molecule, branched and linear species). Due the large size of the datasets obtained from such a study (>100 high resolution chromatorgrams each with 20-40 identified products) PCA was applied to the datasets. Shown in Figure 15 is a 'samples and 'scores' plot for principal components 1 & 2 of a PCA model capturing 90% of the total variance in the complete matrix of silicones studied.

**Figure 15.** Samples-scores plot for PC's 1&2, complete sample matrix. The matrix had been grouped into four classes of data, corresponding to the linear PDMS, the monomodal, bimodal and free chain end subsets respectively. Group‐ ings of samples are circled and labeled A-D.

From Figure 15 it is apparent that there are a number of significant groupings of samples (labeled A-D) suggesting that samples within these groupings degrade in a similar manner. Positive scores on PC1 (x-axis) have been correlated with increased relative yields of D6, D7 & D10-25 cyclics. Positive scores on PC2 have been correlated with increased relative yields of D4 & D5 cyclics. These correlations are obtained from the analysis of variable 'loadings' plots as shown in Figure 16.

'A' in Figure 15 includes both 8 and 132 KDa linear PDMS and a series of 8, 10, 33 and 54 KDa monomodal networks. The strong positive score on PC1 for this group indicates that they yield increased levels of larger cyclic siloxanes relative the rest of the matrix. The samples in group 'B' include 1 and 10% free chain end samples, a 68 KDa monomodal material and a series 90, 80, 70 & 50% short chain bimodal networks. Group 'B' is representative of the mean degrada‐ tion behavior of the model networks. Group 'C' includes a 5% free chain end, 80% and 95% short chain bimodal systems. Group 'C' displays broadly similar behavior to the mean group (B) however there are a somewhat reduced level of D4-5 cyclics produced in this group of samples. The final group 'D' includes the 132 KDa monomodal sample and the 20% free chain end sample. Both of these samples evolve significantly reduced quantities of larger cyclics and

Loadings data such as the example shown in **Figure 16** allow the variables responsible for a component to be readily examined. Here positive loadings correlate with increases in D4 & D5 cyclics and negative loadings relate to decreases in methane, propene levels and D6

 From an examination of the scores and loadings of a valid PCA model we can make confident assertions as to both global trends and specific changes in degradation chemistry. From an examination of our PCA model in this example it can be observed that global degradation behavior of a large series of model silicones can be readily mapped using PCA. Trends made apparent through scores analysis can be related back to the underlying chemical information by means of the variable loadings data. These relations can in turn be used to correlate a specific chemical degradation behavior to the underlying network architecture. For example, Group 'A' in **Figure 15** includes both 8 and 132 KDa linear PDMS and a series of 8, 10, 33 and 54 KDa monomodal networks. The strong positive score on

 **Figure 16**. A loadings plot generated from a PCA model of silicone degradation pyrolysis data. Shown for example here is are the variables responsible for the 4th principle

here is are the variables responsible for the 4th principle component of the model.

**Figure 16.** A loadings plot generated from a PCA model of silicone degradation pyrolysis data. Shown for example

Retention Time (min)

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In the first example we demonstrated that Py-GC/MS methodologies can effectively discrim‐ inate between specific network architectures in simple model silicone systems as a function of

increased levels of smaller D4-5 cyclic siloxanes.

component of the model.

cyclics

Loadings data such as the example shown in Figure 16 allow the variables responsible for a component to be readily examined. Here positive loadings correlate with increases in D4 & D5 cyclics and negative loadings relate to decreases in methane, propene levels and D6 cyclics

From an examination of the scores and loadings of a valid PCA model we can make confident assertions as to both global trends and specific changes in degradation chemistry. From an examination of our PCA model in this example it can be observed that global degradation behavior of a large series of model silicones can be readily mapped using PCA. Trends made apparent through scores analysis can be related back to the underlying chemical information by means of the variable loadings data. These relations can in turn be used to correlate a specific chemical degradation behavior to the underlying network architecture. For example, Group

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 **Figure 16**. A loadings plot generated from a PCA model of silicone degradation pyrolysis data. Shown for example here is are the variables responsible for the 4th principle **Figure 16.** A loadings plot generated from a PCA model of silicone degradation pyrolysis data. Shown for example here is are the variables responsible for the 4th principle component of the model.

component of the model.

**Figure 15.** Samples-scores plot for PC's 1&2, complete sample matrix. The matrix had been grouped into four classes of data, corresponding to the linear PDMS, the monomodal, bimodal and free chain end subsets respectively. Group‐

From Figure 15 it is apparent that there are a number of significant groupings of samples (labeled A-D) suggesting that samples within these groupings degrade in a similar manner. Positive scores on PC1 (x-axis) have been correlated with increased relative yields of D6, D7 & D10-25 cyclics. Positive scores on PC2 have been correlated with increased relative yields of D4 & D5 cyclics. These correlations are obtained from the analysis of variable 'loadings' plots as

Loadings data such as the example shown in Figure 16 allow the variables responsible for a component to be readily examined. Here positive loadings correlate with increases in D4 & D5 cyclics and negative loadings relate to decreases in methane, propene levels and D6 cyclics From an examination of the scores and loadings of a valid PCA model we can make confident assertions as to both global trends and specific changes in degradation chemistry. From an examination of our PCA model in this example it can be observed that global degradation behavior of a large series of model silicones can be readily mapped using PCA. Trends made apparent through scores analysis can be related back to the underlying chemical information by means of the variable loadings data. These relations can in turn be used to correlate a specific chemical degradation behavior to the underlying network architecture. For example, Group

ings of samples are circled and labeled A-D.

shown in Figure 16.

170 Advances in Gas Chromatography

'A' in Figure 15 includes both 8 and 132 KDa linear PDMS and a series of 8, 10, 33 and 54 KDa monomodal networks. The strong positive score on PC1 for this group indicates that they yield increased levels of larger cyclic siloxanes relative the rest of the matrix. The samples in group 'B' include 1 and 10% free chain end samples, a 68 KDa monomodal material and a series 90, 80, 70 & 50% short chain bimodal networks. Group 'B' is representative of the mean degrada‐ tion behavior of the model networks. Group 'C' includes a 5% free chain end, 80% and 95% short chain bimodal systems. Group 'C' displays broadly similar behavior to the mean group (B) however there are a somewhat reduced level of D4-5 cyclics produced in this group of samples. The final group 'D' includes the 132 KDa monomodal sample and the 20% free chain end sample. Both of these samples evolve significantly reduced quantities of larger cyclics and increased levels of smaller D4-5 cyclic siloxanes. Loadings data such as the example shown in **Figure 16** allow the variables responsible for a component to be readily examined. Here positive loadings correlate with increases in D4 & D5 cyclics and negative loadings relate to decreases in methane, propene levels and D6 cyclics From an examination of the scores and loadings of a valid PCA model we can make confident assertions as to both global trends and specific changes in degradation chemistry. From an examination of our PCA model in this example it can be observed that global degradation behavior of a large series of model silicones can be readily mapped using PCA. Trends made apparent through scores analysis can be related back to the underlying chemical information by means of the variable loadings data. These relations can in turn be used to correlate a specific chemical degradation behavior to the underlying network architecture. For example, Group 'A' in **Figure 15** includes both 8 and 132 KDa linear PDMS

In the first example we demonstrated that Py-GC/MS methodologies can effectively discrim‐ inate between specific network architectures in simple model silicone systems as a function of and a series of 8, 10, 33 and 54 KDa monomodal networks. The strong positive score on

their degradation chemistry. Significantly, it has also recently been demonstrated [53] that valid structure property correlations can be drawn from the pyrolytic analysis of complex, real-world silicone elastomers. In this second example the thermal degradation behavior of a series of commercial and specialty silicones are compared, using a combination of py-GC/MS and PCA. A summary of the formulations analyzed are given in Table 2.


only system that is condensation cured and therefore significantly different in formulation to the other systems studied. In grouping (B) the M97 systems are observed to broadly group together on one axis *irrespective of their thermal or irradiative history*. This group of elastomers is in whole positive in PC1, however scattering is observed in the more extreme aged/irradiated samples; suggesting that the starting network structure is a more significant factor in deter‐ mining the degradation behavior than any subsequent environmental factor. Moreover, the M97 series despite being peroxy cured addition networks and thus similar in chemistry to the majority of the other systems, remain distinct from the related commercial materials (TR55, DC745 and Sylgard 184). This distinction demonstrates that chemically similar silicone elastomers can be differentiated by means of pyrolysis GC/MS and multivariate analysis. Grouping (C) encompasses the behavior of TR55, which is neutral with respect to PC1 and negative on PC2. Finally grouping (D) highlights a correlation between The DC745 and Sylgard

line represents the 95% confidence interval. Reprinted with permission from. [53] Copyright Elsevier (2013).

**Figure 17.** Global PC1 vs. PC2 Scores plots from the PCA of pyrolysis data of a series of commercial silicone elastomer formulations. The center of the plot at 0,0 can be considered represent the 'mean' of the degradation profiles aver‐ aged across the dataset as a whole. Solid lines are drawn for emphasis of groupings and labeled A-D and the dotted

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It can therefore be shown that it is possible to link these distinct degradation 'fingerprints' to underlying chemical features of the networks themselves. The elastomers showing negative PC1 scores include those systems that are peroxy-cured: DC 745 and TR-55. DC 745 and an uncured DC 745 gumstock also group significantly in the lower left quadrant and are correlated

184 degradation profiles.

**Table 2.** Formulation reference data for each engineering silicone elastomer system studied categorized by base polymer, final network modality, filler type/loading and cure chemistry. Reprinted with permission from. [53] Copyright Elsevier (2013).

Using standard micro-pyrolysis methodologies, these silicone elastomers were analyzed and with the application of PCA, the following data were obtained; shown in Figure 17 is the global PC1&2 scores plot encapsulating >95% of the total variance in the complete dataset under a single four component model.

PCA of the complete pyrolysis dataset show distinct groupings (labeled A-D in Figure 17) of various elastomer types. These groupings can be correlated with aspects of their underlying network chemistries: The S5370 samples (A) are clear outliers due to the fact that they are the

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their degradation chemistry. Significantly, it has also recently been demonstrated [53] that valid structure property correlations can be drawn from the pyrolytic analysis of complex, real-world silicone elastomers. In this second example the thermal degradation behavior of a series of commercial and specialty silicones are compared, using a combination of py-GC/MS

> **Network Modality**

Bimodal

Bimodal

Bimodal

**Table 2.** Formulation reference data for each engineering silicone elastomer system studied categorized by base polymer, final network modality, filler type/loading and cure chemistry. Reprinted with permission from. [53]

Using standard micro-pyrolysis methodologies, these silicone elastomers were analyzed and with the application of PCA, the following data were obtained; shown in Figure 17 is the global PC1&2 scores plot encapsulating >95% of the total variance in the complete dataset under a

PCA of the complete pyrolysis dataset show distinct groupings (labeled A-D in Figure 17) of various elastomer types. These groupings can be correlated with aspects of their underlying network chemistries: The S5370 samples (A) are clear outliers due to the fact that they are the

**Filler type and**

30 Wt. % SiO2 (Mixture of high surface area fumed silica and low surface area quartz)

21.6% Cab-o-Sil M7-D

4% Hi Sil 233 silica

functionalized Silica

30% diatomaceous earth silicate filler (Celite 30 B)

Tri-modal 15-40% trimethylated silica

silica

Bimodal 30% Vinyl

**loading level Cure Chemistry**

Free radical vinyl addition cure Organic peroxide

Free radical vinyl addition cure Organic peroxide

Free radical vinyl addition cure Organic peroxide

Platinum mediated vinyl addition cure Platinum (0) complex

catalyst.

catalyst.

catalyst.

catalyst

Catalyst

Silanol-silane condensation cure Stannous Octanoate

and PCA. A summary of the formulations analyzed are given in Table 2.

**Elastomer Base polymer(s)**

172 Advances in Gas Chromatography

PDMS

PDMS PDPS PMVS

PDMS PMHS

PDMS PMHS

single four component model.

Copyright Elsevier (2013).

Polydimethylsiloxane Polydiphenylsiloxane

Polydimethylhydrosilane Polymethylvinylsiloxane

DC 745 (Dow Corning)

TR-55 (Dow Corning)

M97 (LLNL)

Sylgard ® 184 (Dow Corning)

S5370 (Dow Corning)

**Figure 17.** Global PC1 vs. PC2 Scores plots from the PCA of pyrolysis data of a series of commercial silicone elastomer formulations. The center of the plot at 0,0 can be considered represent the 'mean' of the degradation profiles aver‐ aged across the dataset as a whole. Solid lines are drawn for emphasis of groupings and labeled A-D and the dotted line represents the 95% confidence interval. Reprinted with permission from. [53] Copyright Elsevier (2013).

only system that is condensation cured and therefore significantly different in formulation to the other systems studied. In grouping (B) the M97 systems are observed to broadly group together on one axis *irrespective of their thermal or irradiative history*. This group of elastomers is in whole positive in PC1, however scattering is observed in the more extreme aged/irradiated samples; suggesting that the starting network structure is a more significant factor in deter‐ mining the degradation behavior than any subsequent environmental factor. Moreover, the M97 series despite being peroxy cured addition networks and thus similar in chemistry to the majority of the other systems, remain distinct from the related commercial materials (TR55, DC745 and Sylgard 184). This distinction demonstrates that chemically similar silicone elastomers can be differentiated by means of pyrolysis GC/MS and multivariate analysis. Grouping (C) encompasses the behavior of TR55, which is neutral with respect to PC1 and negative on PC2. Finally grouping (D) highlights a correlation between The DC745 and Sylgard 184 degradation profiles.

It can therefore be shown that it is possible to link these distinct degradation 'fingerprints' to underlying chemical features of the networks themselves. The elastomers showing negative PC1 scores include those systems that are peroxy-cured: DC 745 and TR-55. DC 745 and an uncured DC 745 gumstock also group significantly in the lower left quadrant and are correlated with Sylgard 184, a Pt catalyzed addition cured system (C-D). The most notable grouping on the PC1 vs. PC2 scores is that of S5370 (group A). Notably, S5370 is the only system which employs tin catalyzed condensation crosslinking chemistry [27] rather than radical or Pt mediated addition crosslinking. As such, the network structure of S5370 has no alkyl linkages at crosslinks and is therefore significantly different to that of all the other materials. S5370 is also only one of two materials in this study that are foams (the other being M97). There is however no correlation between M97 and S5370 on the PCA map, suggesting that the physical foam structure of these materials is not the predominant factor in determining their degrada‐ tion behavior. The distinct S5370 grouping therefore appears to reflect a significantly different degradation behavior as a direct result of its differing network chemistry (a fully [Si-O] based network which retains an active tin catalyst residue, capable of promoting degradation reactions). [46, 64, 65]

has been shown to be a rapid and effective investigative and predictive tool for analysis of

Applications of Modern Pyrolysis Gas Chromatography for the Study of Degradation and Aging in Complex…

http://dx.doi.org/10.5772/57509

175

Portions of this work were performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. The authors also gratefully acknowledge Mark E. Pearson and Cynthia T. Alviso (LLNL) for their assistant

[1] Arkles B. Look what you can make out of silicones. Chem Tech. 1983;13 (9):542-55.

[2] Yoda R. Elastomers for biomedical applications. Journal of Biomaterials Science-Poly‐

[3] Allen KJ. Reel to real: Prospects for flexible displays. Proceedings of the Ieee. 2005

[4] Yoon J, Baca AJ, Park SI, Elvikis P, Geddes JB, Li LF, et al. Ultrathin silicon solar mi‐ crocells for semitransparent, mechanically flexible and microconcentrator module de‐ signs. Nature Materials. 2008 Nov;7 (11):907-15. PubMed PMID: WOS:

[5] Chinn SC, Alviso CT, Berman ESF, Harvey CA, Maxwell RS, Wilson TS, et al. MQ NMR and SPME Analysis of Nonlinearity in the Degradation of a Filled Silicone Elastomer. J Phys Chem B. 2010 Aug;114 (30):9729-36. PubMed PMID: WOS:

[6] Maxwell RS, Chinn SC, Alviso CT, Harvey CA, Giuliani JR, Wilson TS, et al. Quanti‐ fication of radiation induced crosslinking in a commercial, toughened silicone rub‐ ber, TR55 by H-1 MQ-NMR. Polym Degrad Stab. 2009 Mar;94 (3):456-64. PubMed

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complex silicone elastomers.

**Acknowledgements**

with TGA and photography.

James P. Lewicki and Robert S. Maxwell

000260472800025.

000280361100002.

PMID: WOS:000264010500023.

Lawrence Livermore National Laboratory, Livermore, CA, USA

PubMed PMID: WOS:A1983RF44300007.

**Author details**

**References**

The data clearly show that the degradation product profiles of the studied elastomers fall into distinct observable groupings which appear to correlate directly to major features of these systems formulation chemistry, e.g. the network chemistry and catalyst type. The physical structure of the materials (fully dense vs. cellular) does not appear to be a major factor in the degradation behavior; an observation that is consistent with the fact that microgram scale ballistic pyrolytic degradation occurs in a non-diffusion limited regime and should remain broadly independent of bulk sample effects. All of these observations are both notable and consistent with what is known about silicone network thermal degradation: Addition cured networks contain alkyl linkages and are as such not purely [Si-O] networks. Both the peroxy and Pt cured systems retain no active catalyst residues (unlike the tin cured systems) therefore backbiting thermolysis and catalytic de-polymerization reactions may be expected to occur at differing rates and with differing favorability to those of a purely [Si-O] based network which retains an active catalyst residue.

### **5. Conclusion**

The aim of this chapter has been to demonstrate that analytical degradative analysis, when supported by modern gas-chromatography techniques is an effective and versatile tool for the in-depth study of highly complex, yet important silicone elastomers systems that are chal‐ lenging to analyze with other established techniques. The case studies and reviews discussed in this chapter, demonstrate that silicone materials formulated via differing cure chemistries have distinct degradation fingerprints observable by means of analytical pyrolysis. The application of PCA statistical methodologies to Py-GC/MS data allows these unique signatures to be rapidly and reliably identified. Furthermore, PCA allows the chemical origins of the degradation fingerprints to be assessed with comparative ease. The structural architecture of a network elastomer and crosslinking chemistries employed in its formation can be related to its degradative behavior. It has also been demonstrated that the analytical pyrolysis method‐ ologies currently employed are *insensitive* to those comparatively subtle chemical and physical alterations to a network as a result of thermal or irradiative 'aging' of a particular silicone elastomer system. Despite these limitations, degradative pyrolysis-GC/MS coupled with PCA has been shown to be a rapid and effective investigative and predictive tool for analysis of complex silicone elastomers.

### **Acknowledgements**

with Sylgard 184, a Pt catalyzed addition cured system (C-D). The most notable grouping on the PC1 vs. PC2 scores is that of S5370 (group A). Notably, S5370 is the only system which employs tin catalyzed condensation crosslinking chemistry [27] rather than radical or Pt mediated addition crosslinking. As such, the network structure of S5370 has no alkyl linkages at crosslinks and is therefore significantly different to that of all the other materials. S5370 is also only one of two materials in this study that are foams (the other being M97). There is however no correlation between M97 and S5370 on the PCA map, suggesting that the physical foam structure of these materials is not the predominant factor in determining their degrada‐ tion behavior. The distinct S5370 grouping therefore appears to reflect a significantly different degradation behavior as a direct result of its differing network chemistry (a fully [Si-O] based network which retains an active tin catalyst residue, capable of promoting degradation

The data clearly show that the degradation product profiles of the studied elastomers fall into distinct observable groupings which appear to correlate directly to major features of these systems formulation chemistry, e.g. the network chemistry and catalyst type. The physical structure of the materials (fully dense vs. cellular) does not appear to be a major factor in the degradation behavior; an observation that is consistent with the fact that microgram scale ballistic pyrolytic degradation occurs in a non-diffusion limited regime and should remain broadly independent of bulk sample effects. All of these observations are both notable and consistent with what is known about silicone network thermal degradation: Addition cured networks contain alkyl linkages and are as such not purely [Si-O] networks. Both the peroxy and Pt cured systems retain no active catalyst residues (unlike the tin cured systems) therefore backbiting thermolysis and catalytic de-polymerization reactions may be expected to occur at differing rates and with differing favorability to those of a purely [Si-O] based network which

The aim of this chapter has been to demonstrate that analytical degradative analysis, when supported by modern gas-chromatography techniques is an effective and versatile tool for the in-depth study of highly complex, yet important silicone elastomers systems that are chal‐ lenging to analyze with other established techniques. The case studies and reviews discussed in this chapter, demonstrate that silicone materials formulated via differing cure chemistries have distinct degradation fingerprints observable by means of analytical pyrolysis. The application of PCA statistical methodologies to Py-GC/MS data allows these unique signatures to be rapidly and reliably identified. Furthermore, PCA allows the chemical origins of the degradation fingerprints to be assessed with comparative ease. The structural architecture of a network elastomer and crosslinking chemistries employed in its formation can be related to its degradative behavior. It has also been demonstrated that the analytical pyrolysis method‐ ologies currently employed are *insensitive* to those comparatively subtle chemical and physical alterations to a network as a result of thermal or irradiative 'aging' of a particular silicone elastomer system. Despite these limitations, degradative pyrolysis-GC/MS coupled with PCA

reactions). [46, 64, 65]

174 Advances in Gas Chromatography

retains an active catalyst residue.

**5. Conclusion**

Portions of this work were performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. The authors also gratefully acknowledge Mark E. Pearson and Cynthia T. Alviso (LLNL) for their assistant with TGA and photography.

### **Author details**

James P. Lewicki and Robert S. Maxwell

Lawrence Livermore National Laboratory, Livermore, CA, USA

### **References**


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**Chapter 8**

**Measurement of Off-Gases**

Fahimeh Yazdanpanah, Shahab Sokhansanj, Hamid Rezaei, C. Jim Lim, Anthony K. Lau,

Additional information is available at the end of the chapter

to cause irritation to the respiratory system [4].

X. Tony Bi, S. Melin, Jaya Shankar Tumuluru and

Gas emissions (CO2, CO, CH4 and volatile organic compounds) from woody biomass storage systems have been studied extensively in recent years. The decomposition is due to biological, chemical oxidative and slow pyrolytic processes depending on the availability of oxygen [1-3]. The major VOCs emitted from stored wood pellets were aldehydes, some of which are known

Previous measurements have shown that freshly made wood pellets continue to emit flam‐ mable gases CO, H2, and CH4 during storage and handling. A large portion of this emission is CO as opposed to emission of volatile organic compounds (VOC) like formic acid, methanol and aldehydes [5, 6]. More volatile organic compounds emit from stored sawdust and wood chips compared to stored wood pellets [7-9]. This is because of the fact that most of the VOCs easily evaporate during heating and drying processes [10, 11] and as wood pellets pass through high temperature during drying and pelletization, decomposition of extractives would result in less VOCs emission. During pelletization, sawdust is dried at temperatures from 100 to 400 °C so the moisture content goes below 10%. Roffael [12], Rupar and Sanati [13] also reported the emission of VOCs during biomass storage. Large amount of monoterpenes and other volatile organic compounds are emitted during drying [10, 11, 14, 15]. High levels of CO were first measured in compartments of a ship in 2002 when an ocean vessel was discharging wood pellets in Rotterdam, The Netherlands. The wood pellets were shipped from Vancouver, British Columbia and the duration of the voyage was 38 days. One person was killed and

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**in Wood Pellet Storage**

Chang Soo Kim

**1. Introduction**

http://dx.doi.org/10.5772/58301


**Chapter 8**

### **Measurement of Off-Gases in Wood Pellet Storage**

Fahimeh Yazdanpanah, Shahab Sokhansanj, Hamid Rezaei, C. Jim Lim, Anthony K. Lau, X. Tony Bi, S. Melin, Jaya Shankar Tumuluru and Chang Soo Kim

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58301

**1. Introduction**

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PMID: WOS:000239536200035.

WOS:A1977DW34100008.

180 Advances in Gas Chromatography

Gas emissions (CO2, CO, CH4 and volatile organic compounds) from woody biomass storage systems have been studied extensively in recent years. The decomposition is due to biological, chemical oxidative and slow pyrolytic processes depending on the availability of oxygen [1-3]. The major VOCs emitted from stored wood pellets were aldehydes, some of which are known to cause irritation to the respiratory system [4].

Previous measurements have shown that freshly made wood pellets continue to emit flam‐ mable gases CO, H2, and CH4 during storage and handling. A large portion of this emission is CO as opposed to emission of volatile organic compounds (VOC) like formic acid, methanol and aldehydes [5, 6]. More volatile organic compounds emit from stored sawdust and wood chips compared to stored wood pellets [7-9]. This is because of the fact that most of the VOCs easily evaporate during heating and drying processes [10, 11] and as wood pellets pass through high temperature during drying and pelletization, decomposition of extractives would result in less VOCs emission. During pelletization, sawdust is dried at temperatures from 100 to 400 °C so the moisture content goes below 10%. Roffael [12], Rupar and Sanati [13] also reported the emission of VOCs during biomass storage. Large amount of monoterpenes and other volatile organic compounds are emitted during drying [10, 11, 14, 15]. High levels of CO were first measured in compartments of a ship in 2002 when an ocean vessel was discharging wood pellets in Rotterdam, The Netherlands. The wood pellets were shipped from Vancouver, British Columbia and the duration of the voyage was 38 days. One person was killed and

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

several people were severely injured as a result of exposure to high concentration of CO when entering a stairway adjacent to the cargo space [16]. Svedberg identified that the storage of wood pellets led to the emission of high levels of hexanal as a result of the general degradation processes of wood.

LABTECH software using a data acquisition board (PCI-DAS08, Techmatron Instruments Inc, Canada) consisting of pressure transducers and thermocouples. However the pilot size of the set-up made it challenging to have it equipped with different monitoring systems and make it sealed. Fourteen 12mm diameter gas sampling ports with 3mm ball valves are provided at 2 sides of the pilot silo along the wall at different levels for gas sampling. The positions of the gas sampling ports in the large silo are indicated in Table 1. Six of the gas sampling ports are

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**Gas sampling ports on side A Gas sampling ports on side B Gas Port Name Elevation (m) Gas Port Name Elevation (m)** G0 4.16 G0 4.16 G2 3.55 G3 3.35 G4 2.94 G5 2.74 G6 2.33 G7 2.13 G8 1.65 G9 1.52 G10 1.21 G11 0.91 G12 0.50 G13 0.30

In small scale tests, initially and at intervals, approximately 25 mL of gas was drawn from each container by an air-tight GC syringe (25mL SGE Gas-Tight Syringe, Luer-Lock and TOGAS Luer Lock Adapter, Mandel Scientific Company) and analyzed by GC/FID (Flame Ionization Detector) and GC/TCD (Thermal Conductivity Detector) methods for the composition of the sampled gases (CO, CO2, CH4, N2, O2 and H2). In pilot scale tests, Gas samples were collected using an airtight syringe (SG-009770-100mL SGE Gas-Tight Syringe, Luer-Lock, Mandel Scientific, Canada). The syringe had a Luer lock device to help in collecting a known quantity of gas sample. The Shimadzu GC needle used with the 100mL SGE syringe was Togas Loop Fill Interface N711 Needle (Model number: 220-90615-00, Mandel Scientific Inc.).The GC has three columns in series: Porapak-N (80/100 mesh, 3 m), Porapak-Q (80/100 mesh, 3 m) and a MS-5A (60/80, 2.25 m). The FID detector was used for CO, CO2 and CH4 and the TCD was used for N2, O2 and H2, with Argon as the carrier gas. Argon was the reference gas for the TCD.

located on one side of the silo and another 7 on the other side.

**Table 1.** Location of the gas sampling ports on the experimental silo

**3. Materials and methods**

**3.1. Small and pilot scale experiments**

Compressed air was the reference gas for the FID.

In terms of CO2, CO, and CH4 emissions, higher emission factors are associated with higher temperatures, whereas increased relative humidity in the enclosed container increased the rate of gas emission and a corresponding depletion of oxygen [17, 18]. Wihersaari [8] concluded that the CO2 emissions from fresh forest residues were almost three times higher than the dried materials, and suggested that mixing the heaps during the storage period would probably cause increased emissions rates. Emissions of CO, CO2 and CH4 are likely due to biodegrada‐ tion and auto-oxidative reactions of organic constituents naturally present in wood [1].

### **2. Experimental set-up**

### **2.1. Small scale experiments**

The off-gassing tests were conducted at three nominal temperatures of 25 °C, 40 °C and 60 °C. Room temperature provided the 25 °C storage environment. Two identical ovens were used to provide 40 °C and 60 °C storage environments. Twenty four (24) glass containers, 2L each, were divided into three equal groups for testing at the three temperatures. For each set of 8 containers, 6 containers were used for gas sampling and 2 containers were used for tempera‐ ture measurement. The temperature inside the containers was measured with thermocouples and data was logged onto a PC. The following procedure was followed for each test container. The empty weight of the container without the lid was recorded (M1). The container was filled to 75% volume with wood pellets (M2). Finally, the weight of the wood pellets was calculated (M3). The lid with sampling port was then applied, thereby sealing the container. Filled containers along two empty containers (for each storage temperature) were arranged in upright position. The two empty jars were included to have consistent condition as the filled jars for temperature reading. Temperature was also directly recorded from a thermocouple inside the oven.

### **2.2. Pilot scale experiments**

All measurements of the gases (CO, CO2 and CH4) were made in an experimental silo designed and installed at the Clean Energy Research Centre (CERC), the Department of Chemical & Biological Engineering, University of British Columbia [19, 20]. The experimental silo has a volume of 5.65m3 (1.2m diameter, 4.6m height) and is made of mild steel. The silo is designed to represent the storage of substantial quantities of wood pellets. A two level structure was designed and built to support the pilot scale silo. The set-up was further equipped with sensors for temperature, gas pressure and relative humidity measurements. Thermocouples and pressure transducers were installed at various levels along the height of the silo. Two EXP-32 cards were used to log the temperature and pressure data during the experiments. The experimental data of pressure and temperature were logged into a computer running on the LABTECH software using a data acquisition board (PCI-DAS08, Techmatron Instruments Inc, Canada) consisting of pressure transducers and thermocouples. However the pilot size of the set-up made it challenging to have it equipped with different monitoring systems and make it sealed. Fourteen 12mm diameter gas sampling ports with 3mm ball valves are provided at 2 sides of the pilot silo along the wall at different levels for gas sampling. The positions of the gas sampling ports in the large silo are indicated in Table 1. Six of the gas sampling ports are located on one side of the silo and another 7 on the other side.


**Table 1.** Location of the gas sampling ports on the experimental silo

### **3. Materials and methods**

several people were severely injured as a result of exposure to high concentration of CO when entering a stairway adjacent to the cargo space [16]. Svedberg identified that the storage of wood pellets led to the emission of high levels of hexanal as a result of the general degradation

In terms of CO2, CO, and CH4 emissions, higher emission factors are associated with higher temperatures, whereas increased relative humidity in the enclosed container increased the rate of gas emission and a corresponding depletion of oxygen [17, 18]. Wihersaari [8] concluded that the CO2 emissions from fresh forest residues were almost three times higher than the dried materials, and suggested that mixing the heaps during the storage period would probably cause increased emissions rates. Emissions of CO, CO2 and CH4 are likely due to biodegrada‐ tion and auto-oxidative reactions of organic constituents naturally present in wood [1].

The off-gassing tests were conducted at three nominal temperatures of 25 °C, 40 °C and 60 °C. Room temperature provided the 25 °C storage environment. Two identical ovens were used to provide 40 °C and 60 °C storage environments. Twenty four (24) glass containers, 2L each, were divided into three equal groups for testing at the three temperatures. For each set of 8 containers, 6 containers were used for gas sampling and 2 containers were used for tempera‐ ture measurement. The temperature inside the containers was measured with thermocouples and data was logged onto a PC. The following procedure was followed for each test container. The empty weight of the container without the lid was recorded (M1). The container was filled to 75% volume with wood pellets (M2). Finally, the weight of the wood pellets was calculated (M3). The lid with sampling port was then applied, thereby sealing the container. Filled containers along two empty containers (for each storage temperature) were arranged in upright position. The two empty jars were included to have consistent condition as the filled jars for temperature reading. Temperature was also directly recorded from a thermocouple

All measurements of the gases (CO, CO2 and CH4) were made in an experimental silo designed and installed at the Clean Energy Research Centre (CERC), the Department of Chemical & Biological Engineering, University of British Columbia [19, 20]. The experimental silo has a

to represent the storage of substantial quantities of wood pellets. A two level structure was designed and built to support the pilot scale silo. The set-up was further equipped with sensors for temperature, gas pressure and relative humidity measurements. Thermocouples and pressure transducers were installed at various levels along the height of the silo. Two EXP-32 cards were used to log the temperature and pressure data during the experiments. The experimental data of pressure and temperature were logged into a computer running on the

(1.2m diameter, 4.6m height) and is made of mild steel. The silo is designed

processes of wood.

182 Advances in Gas Chromatography

**2. Experimental set-up**

**2.1. Small scale experiments**

inside the oven.

volume of 5.65m3

**2.2. Pilot scale experiments**

### **3.1. Small and pilot scale experiments**

In small scale tests, initially and at intervals, approximately 25 mL of gas was drawn from each container by an air-tight GC syringe (25mL SGE Gas-Tight Syringe, Luer-Lock and TOGAS Luer Lock Adapter, Mandel Scientific Company) and analyzed by GC/FID (Flame Ionization Detector) and GC/TCD (Thermal Conductivity Detector) methods for the composition of the sampled gases (CO, CO2, CH4, N2, O2 and H2). In pilot scale tests, Gas samples were collected using an airtight syringe (SG-009770-100mL SGE Gas-Tight Syringe, Luer-Lock, Mandel Scientific, Canada). The syringe had a Luer lock device to help in collecting a known quantity of gas sample. The Shimadzu GC needle used with the 100mL SGE syringe was Togas Loop Fill Interface N711 Needle (Model number: 220-90615-00, Mandel Scientific Inc.).The GC has three columns in series: Porapak-N (80/100 mesh, 3 m), Porapak-Q (80/100 mesh, 3 m) and a MS-5A (60/80, 2.25 m). The FID detector was used for CO, CO2 and CH4 and the TCD was used for N2, O2 and H2, with Argon as the carrier gas. Argon was the reference gas for the TCD. Compressed air was the reference gas for the FID.

Gas sampling started one day after loading of the containers. Prior to gas concentration measurements, the GC was calibrated with three different standard gases, which contained known concentrations of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), oxygen (O2), nitrogen (N2), hydrogen (H2) and helium (He) (Table 2). To ensure steady and accurate readings by the GC, 25 mL of a standard gas with known concentration of gases was injected to the GC for calibration before and after gas analysis every day.

Eight glass containers with wood pellets were put on the lab shelf for 25°C for testing. The 8 containers were named A-4-X, A-4-Y, A-9-X, A-9-Y, A-15-X and A-15-Y [e.g. A-4-X indicates the first replicate of wood pellets sample at ambient temperature (25°C) and 4% moisture content]. The same format was used for labeling the samples at higher temperature. The letter A was replaced by 40 for the tests at 40°C and with 60 for the tests at 60°C. Each set of 8 containers (6 containers for gas sample measurement and 2 for temperature measurement) were placed in 40°C and 60°C ovens. The temperature inside the containers was measured with thermocouple sensor connected to a computer for temperature logging. All containers were checked before conducting the experiments to ensure proper sealing. The exact same

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To create the baseline for gas concentrations, two empty containers were placed in room temperature (25°C), another two in the 40°C oven and another two in the 60°C oven. 100 mL of gas was drawn from each container and analyzed by the GC for gas composition. Figure 1 to Figure 10 show plots of gas concentrations vs. storage time for the three temperatures and 2 moisture concentrations [4 and 50%] for each gas. The concentration of each gas is presented on the same graph for one moisture content and 3 different storage temperatures. The concentrations are given in percent on volumetric scale in ppmv (parts per million volume).

0 10 20 30 40 50 60 70

Storage day

**Figure 1.** Concentration of CO2 for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C.

labeling and procedure were used for tests at 15, 35 and 50% moisture content.

CO2

CO2

CO2

Concetration 4 % MC and 25°C

Concetration 4 % MC and 40°C

Concetration 4 % MC and 60°C

A concentration of 0.6% means 6000 ppmv.

0

2

4

6

CO2 Concentration (%)

8

10

12


**Table 2.** Compositions (volumetric) of standard gases for GC calibration

### **4. Results and discussion**

### **4.1. Small scale tests**

#### *4.1.1. Off-gas concentration at high temperature and high moisture*

The moisture content of the reference wood pellets was 4% as received. They were subse‐ quently conditioned to 9, 15, 35 and 50% moisture content respectively by spraying distilled water on the wood pellets in a container during rotary motion. At 15% moisture or higher the integrity and wood pellet shape were compromised.

The off-gassing tests were conducted at three temperatures 25°C, 40°C and 60°C. For the tests at 25°C, the containers were placed on the lab shelf. To provide the higher storage temperatures two identical ovens were used. The Biomass and Bioenergy Research Group (BBRG) at UBC has developed the method of using multiple smaller containers and sampling a very limited amount of gas from each container at lower frequency rather than frequent sampling from a larger container in order to minimize the distortion in gas concentration due to extraction of gas. This method allows a much smaller sample size to be used for testing.

Eight glass containers with wood pellets were put on the lab shelf for 25°C for testing. The 8 containers were named A-4-X, A-4-Y, A-9-X, A-9-Y, A-15-X and A-15-Y [e.g. A-4-X indicates the first replicate of wood pellets sample at ambient temperature (25°C) and 4% moisture content]. The same format was used for labeling the samples at higher temperature. The letter A was replaced by 40 for the tests at 40°C and with 60 for the tests at 60°C. Each set of 8 containers (6 containers for gas sample measurement and 2 for temperature measurement) were placed in 40°C and 60°C ovens. The temperature inside the containers was measured with thermocouple sensor connected to a computer for temperature logging. All containers were checked before conducting the experiments to ensure proper sealing. The exact same labeling and procedure were used for tests at 15, 35 and 50% moisture content.

Gas sampling started one day after loading of the containers. Prior to gas concentration measurements, the GC was calibrated with three different standard gases, which contained known concentrations of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), oxygen (O2), nitrogen (N2), hydrogen (H2) and helium (He) (Table 2). To ensure steady and accurate readings by the GC, 25 mL of a standard gas with known concentration of gases was injected

**Standard gas 1 Standard gas 2 Standard gas 3** Components Concentration (%) Components Concentration (%) Components Concentration (%) CO 2.5 CO 0.05 CO 0.50 CO2 6.0 CO2 0.10 CO2 0.50 CH4 1.5 CH4 0.50 CH4 0.10 N2 3.0 H2 0.50 H2 1.00 O2 10.0 Air Balance Air balance

The moisture content of the reference wood pellets was 4% as received. They were subse‐ quently conditioned to 9, 15, 35 and 50% moisture content respectively by spraying distilled water on the wood pellets in a container during rotary motion. At 15% moisture or higher the

The off-gassing tests were conducted at three temperatures 25°C, 40°C and 60°C. For the tests at 25°C, the containers were placed on the lab shelf. To provide the higher storage temperatures two identical ovens were used. The Biomass and Bioenergy Research Group (BBRG) at UBC has developed the method of using multiple smaller containers and sampling a very limited amount of gas from each container at lower frequency rather than frequent sampling from a larger container in order to minimize the distortion in gas concentration due to extraction of

gas. This method allows a much smaller sample size to be used for testing.

to the GC for calibration before and after gas analysis every day.

**Table 2.** Compositions (volumetric) of standard gases for GC calibration

*4.1.1. Off-gas concentration at high temperature and high moisture*

integrity and wood pellet shape were compromised.

He 2.0 Ar balance

184 Advances in Gas Chromatography

**4. Results and discussion**

**4.1. Small scale tests**

To create the baseline for gas concentrations, two empty containers were placed in room temperature (25°C), another two in the 40°C oven and another two in the 60°C oven. 100 mL of gas was drawn from each container and analyzed by the GC for gas composition. Figure 1 to Figure 10 show plots of gas concentrations vs. storage time for the three temperatures and 2 moisture concentrations [4 and 50%] for each gas. The concentration of each gas is presented on the same graph for one moisture content and 3 different storage temperatures. The concentrations are given in percent on volumetric scale in ppmv (parts per million volume). A concentration of 0.6% means 6000 ppmv.

**Figure 1.** Concentration of CO2 for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C.

**Figure 2.** Concentration of CO2 for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

Concetration 4 % MC and 25°C

Concetration 4 % MC and 40°C

Concetration 4 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure 5.** Concentration of CH4 for emissions from wood pellet with 4% moisture content over 62 days at 25°C, 40°C

Storage day

**Figure 4.** Concentration of CO for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

CH4

CH4

CH4

 CO Concetration 50 % MC and 25°C CO Concetration 50 % MC and 40°C CO Concetration 50 % MC and 60°C

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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

0.00

and 60°C

0.05

0.10

0.15

CH4 Concentration (%)

0.20

0.25

0.30

CO Concentration (%)

**Figure 3.** Concentration of CO for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure 4.** Concentration of CO for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

 CO Concetration 4 % MC and 25°C CO Concetration 4 % MC and 40°C CO Concetration 4 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure 3.** Concentration of CO for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure 2.** Concentration of CO2 for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

CO Concentration (%)

2

4

6

CO2 Concentration (%)

8

10

12

186 Advances in Gas Chromatography

CO2

CO2

CO2

Concetration 50 % MC and 25°C

Concetration 50 % MC and 40°C

Concetration 50 % MC and 60°C

**Figure 5.** Concentration of CH4 for emissions from wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure 6.** Concentration of CH4 for emissions from wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

Concetration 4 % MC and 25°C

Concetration 4 % MC and 40°C

Concetration 4 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure 9.** Concentration of hydrogen for emissions from wood pellet with 4% moisture content over 62 days at 25°C,

Storage day

**Figure 8.** Oxygen depletion for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

H2

H2

H2

Concetration 50 % MC and 25°C

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Concetration 50 % MC and 40°C

Concetration 50 % MC and 60°C

O2

O2

O2

> 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

40°C and 60°C

H2 Concentration (%)

O2 Concentration (%)

**Figure 7.** Oxygen depletion for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure 8.** Oxygen depletion for wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure 6.** Concentration of CH4 for emissions from wood pellet with 50% moisture content over 62 days at 25°C, 40°C

Concetration 4 % MC and 25°C

Concetration 4 % MC and 40°C

Concetration 4 % MC and 60°C

O2

O2

O2

0 10 20 30 40 50 60 70

Storage day

**Figure 7.** Oxygen depletion for wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

0.00

O2 Concentration (%)

and 60°C

0.05

0.10

0.15

CH4 Concentration (%)

0.20

0.25

0.30

188 Advances in Gas Chromatography

CH4

CH4

CH4

Concetration 50 % MC and 25°C

Concetration 50 % MC and 40°C

Concetration 50 % MC and 60°C

**Figure 9.** Concentration of hydrogen for emissions from wood pellet with 4% moisture content over 62 days at 25°C, 40°C and 60°C

wood pellets with 35%, the peak concentration stayed at 1.0% and increased slightly when the moisture content of wood pellets increased to 50%. The same CO concentration change was observed at 60°C for wood pellets with 4 and 9 and 15%. When the moisture content increased to 35%, CO peak concentration increased to 1.52% but stayed the same for higher moisture

Similar to carbon dioxide concentration, methane concentration over time increased with storage temperature for all levels of moisture contents except for 35% where the concentration

the peak concentration of CO to increase from 0.07% to 0.21%. Further increase in temperature to 60°C, resulted in peak concentration of CO to 0.28%. Except for the gas concentration at 25°C, concentration of CO decreased over time as the moisture content increased to 40°C and

Plots in Figure 7 to 8 as well as Figure A.10 to A.12 show the concentration of oxygen for wood pellets with six different moisture content at 25°C followed by the concentration at higher temperatures (40°C and 60°C) as a function of time. Depletion of oxygen was least significant for samples at 25°C and moisture content higher than 15%. For wood pellets with 4%, 15%, 35% and 50% moisture content the oxygen content decreased as a function of time. However, for wood pellets with 9% moisture content the oxygen depletion was more rapid during the first few days, at 25°C to 40°C. After 20 days the, oxygen concentration was leveling out at

Figure 9 to 10 shows the concentration of hydrogen for wood pellets with 4 and 50% moisture content over time at 25°C followed by the concentration at higher temperatures (40°C and 60°C). The release of hydrogen was minimal for samples with 50% moisture at 25°C. It was concluded that for the same moisture content, the hydrogen concentration increased as the temperature increased. However for wood pellets at the same temperature, the hydrogen

This research conducted to provide a scientific basis to determine whether or not the concen‐ tration of the composite mix of gases emitted from wood pellets could reach flammable concentrations and if so, under what conditions. Table 3 summarizes the results of the

*BkKk* factor in Table 4 represents the equivalent concentration of the flammable gases

in a mixture of nitrogen as calculated, which if mixed with full complement of air (oxygen), may sustain a flame if ignited with an external source. Since the oxygen content is extremely low in an enclosed cargo hold there is even lower risk of fires and explosions in the oxygen

Table 4 represents the equivalent concentration of flammable gases as measured.

C to 40°C caused

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was quite the same at 40°C and 60°C. An increase in temperature from 25o

C and reached 0% very quickly at 40°C.

concentration decreased as the moisture content increased.

*4.1.2. Estimation of flammability of emissions from wood pellets*

calculations using the ISO 10156:2010 Standard [21].

−1 factor in

content like 50%.

60°C respectively.

5-6% at 25o

The ∑ *i*=1 *n Ai* 100 *TCi*

The ∑ *k*=1 *p*

**Figure 10.** Concentration of hydrogen for emissions from wood pellet with 50% moisture content over 62 days at 25°C, 40°C and 60°C

Plots in Figure 1 to 2 show the concentration of carbon dioxide over time at 25°C, 40°C and 60°C for wood pellets with 4 and 50% moisture content respectively. Figures of carbon dioxide concentration for wood pellets with 9, 15 and 35% moisture content are presented in Appendix A. As expected, the higher the temperature, the higher the concentration of the CO2 gas. However, for wood pellets with 4, 9 and 15% moisture content at 25°C, the CO2 concentration increased from ~0.9% to ~1.7% instead showing a relation between time and moisture content. At 25°C when the moisture content increased higher to 35 and 50%, the maximum concentra‐ tion of CO2 did not go beyond what was seen for the wood pellets with 15% moisture content. At 40°C and 60°C, the increase was from 2.84% to 5.55% and 9.58% to 10.94% respectively (for wood pellets with 4% to 50%). The maximum concentration of CO2 was observed at 15% moisture content. Above 15% moisture content, the maximum concentration of CO2 increased only marginally at 40°C and stayed almost the same at 60°C.

Figure 3 to 4 show CO concentration at 4 and 50% moisture content at 25°C, 40°C and 60°C [Rest of the Figures for CO can be found in Appendix A]. The concentration of CO increased over time for wood pellets with 4 and 9% moisture content as the storage temperature increased. CO concentration did not increase at 25°C, 40°C and 60°C for wood pellets with 15% moisture content. The CO concentrations at room temperature and the six moisture contents the peak concentration of CO was the same for 4% and 9% moisture content and decreased to ~0.8% for wood pellets with 15% moisture content. The concentration decreased even further for wood pellets with 35 and 50% moisture content. At 40°C, for wood pellets with 4 and 9 and 15%, carbon monoxide concentration decreased from 1.5% to 1.4% and 1.1% respectively. For wood pellets with 35%, the peak concentration stayed at 1.0% and increased slightly when the moisture content of wood pellets increased to 50%. The same CO concentration change was observed at 60°C for wood pellets with 4 and 9 and 15%. When the moisture content increased to 35%, CO peak concentration increased to 1.52% but stayed the same for higher moisture content like 50%.

Similar to carbon dioxide concentration, methane concentration over time increased with storage temperature for all levels of moisture contents except for 35% where the concentration was quite the same at 40°C and 60°C. An increase in temperature from 25o C to 40°C caused the peak concentration of CO to increase from 0.07% to 0.21%. Further increase in temperature to 60°C, resulted in peak concentration of CO to 0.28%. Except for the gas concentration at 25°C, concentration of CO decreased over time as the moisture content increased to 40°C and 60°C respectively.

Plots in Figure 7 to 8 as well as Figure A.10 to A.12 show the concentration of oxygen for wood pellets with six different moisture content at 25°C followed by the concentration at higher temperatures (40°C and 60°C) as a function of time. Depletion of oxygen was least significant for samples at 25°C and moisture content higher than 15%. For wood pellets with 4%, 15%, 35% and 50% moisture content the oxygen content decreased as a function of time. However, for wood pellets with 9% moisture content the oxygen depletion was more rapid during the first few days, at 25°C to 40°C. After 20 days the, oxygen concentration was leveling out at 5-6% at 25o C and reached 0% very quickly at 40°C.

Figure 9 to 10 shows the concentration of hydrogen for wood pellets with 4 and 50% moisture content over time at 25°C followed by the concentration at higher temperatures (40°C and 60°C). The release of hydrogen was minimal for samples with 50% moisture at 25°C. It was concluded that for the same moisture content, the hydrogen concentration increased as the temperature increased. However for wood pellets at the same temperature, the hydrogen concentration decreased as the moisture content increased.

### *4.1.2. Estimation of flammability of emissions from wood pellets*

This research conducted to provide a scientific basis to determine whether or not the concen‐ tration of the composite mix of gases emitted from wood pellets could reach flammable concentrations and if so, under what conditions. Table 3 summarizes the results of the calculations using the ISO 10156:2010 Standard [21].

$$\text{The } \sum\_{i=1}^{n} A\_i \mathbf{I} \frac{100}{T\_{Ci}} - \mathbf{1} \mathbf{j} \text{ factor in } \mathbf{I}$$

0 10 20 30 40 50 60 70

Storage day

**Figure 10.** Concentration of hydrogen for emissions from wood pellet with 50% moisture content over 62 days at

Plots in Figure 1 to 2 show the concentration of carbon dioxide over time at 25°C, 40°C and 60°C for wood pellets with 4 and 50% moisture content respectively. Figures of carbon dioxide concentration for wood pellets with 9, 15 and 35% moisture content are presented in Appendix A. As expected, the higher the temperature, the higher the concentration of the CO2 gas. However, for wood pellets with 4, 9 and 15% moisture content at 25°C, the CO2 concentration increased from ~0.9% to ~1.7% instead showing a relation between time and moisture content. At 25°C when the moisture content increased higher to 35 and 50%, the maximum concentra‐ tion of CO2 did not go beyond what was seen for the wood pellets with 15% moisture content. At 40°C and 60°C, the increase was from 2.84% to 5.55% and 9.58% to 10.94% respectively (for wood pellets with 4% to 50%). The maximum concentration of CO2 was observed at 15% moisture content. Above 15% moisture content, the maximum concentration of CO2 increased

Figure 3 to 4 show CO concentration at 4 and 50% moisture content at 25°C, 40°C and 60°C [Rest of the Figures for CO can be found in Appendix A]. The concentration of CO increased over time for wood pellets with 4 and 9% moisture content as the storage temperature increased. CO concentration did not increase at 25°C, 40°C and 60°C for wood pellets with 15% moisture content. The CO concentrations at room temperature and the six moisture contents the peak concentration of CO was the same for 4% and 9% moisture content and decreased to ~0.8% for wood pellets with 15% moisture content. The concentration decreased even further for wood pellets with 35 and 50% moisture content. At 40°C, for wood pellets with 4 and 9 and 15%, carbon monoxide concentration decreased from 1.5% to 1.4% and 1.1% respectively. For

Concetration 50 % MC and 25°C

Concetration 50 % MC and 40°C

Concetration 50 % MC and 60°C

H2

H2

H2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

only marginally at 40°C and stayed almost the same at 60°C.

25°C, 40°C and 60°C

H2 Concentration (%)

190 Advances in Gas Chromatography

Table 4 represents the equivalent concentration of flammable gases as measured.

The ∑ *k*=1 *p BkKk* factor in Table 4 represents the equivalent concentration of the flammable gases in a mixture of nitrogen as calculated, which if mixed with full complement of air (oxygen), may sustain a flame if ignited with an external source. Since the oxygen content is extremely low in an enclosed cargo hold there is even lower risk of fires and explosions in the oxygen deprived conditions in an enclosed cargo hold than in open atmospheric conditions saturated with oxygen.

**25°C 40°C 60°C**

**9% Moisture**

1.835 1.869 1.379 3.244 2.840 2.297 3.920 3.283 2.521

**15% Moisture**

**4% Moisture**

Measurement of Off-Gases in Wood Pellet Storage

**9% Moisture**

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**15% Moisture** 193

**Gas Species**

CO/CH4/H2 mixture (sum)

∑ *i*=1 *n Ai* 100 *TCi*

> ∑ *k*=1 *p*

**5. Pilot scale tests**

**4% Moisture**

**9% Moisture**

**15% Moisture**

**4% Moisture**

CO2 0.805 1.643 1.593 2.840 4.680 4.820 9.571 10.925 11.890

CO 1.010 1.103 0.758 1.561 1.412 1.112 1.775 1.560 1.128

CH4 0.072 0.086 0.062 0.211 0.177 0.114 0.287 0.241 0.132

H2 0.753 0.680 0.559 1.472 1.251 1.071 1.858 1.482 1.261

Sum 2.640 3.512 2.972 6.084 7.520 7.117 13.491 14.208 14.411

O2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

N2 97.360 96.488 97.028 93.916 92.480 92.883 86.509 85.792 85.589

−1 19.328 18.740 14.484 36.215 31.229 25.802 44.838 36.702 29.345

*BkKk* 98.568 98.953 99.418 98.176 99.500 100.113 100.866 102.179 103.424

Flammable No No No No No No No No No

Gas composition analysis was performed for the first 9 weeks after loading the pilot silo. Results (Figure 11 to Figure 13) showed a rapid increase in the concentration of CO2, CO and CH4 in the head-space of the silo. Off-gas concentrations in the first 7 days were 1.0-1.6% CO2, 0.8-1.0% CO and 0.73- 0.85% CH4. All gas concentrations increased with storage time. Figure 14 shows the increase in the CO/CO2 ratio with time, which reached a constant value after 50

**Table 4.** Concentration of gases from wood pellets after 62 days of storage

**5.1. Off-gas concentration in silo head-space**

**Flammability determination in accordance with ISO 10156 Standard**

Wood pellets exported to Europe have typically between 4 and 6% moisture. The 15% and higher represented wood pellets which have been accidentally exposed to moist conditions for a prolonged period of time. Testing of wood pellets with abnormal moisture content is important to make sure there is good safety margin for long duration shipments such as a 30-34 days voyage from for example British Columbia to Europe. The emission of gases from wood pellets reached a peak (plateau) as summarized in Table 3 within the first 20-30 days. At the same time the oxygen content decreased rapidly to a very low level as the emission of CO2 increased resulting in a near inert condition in the space. The flammability of the mixed gases was calculated when exposed to air with full complement of oxygen as per the ISO Stand‐ ard.The research was carried out over a period of 62 days, just to ensure the gas evolution continued to be stable. The same calculations are done for 62 days as well and presented in Table 4.


**Table 3.** Concentration of gases from wood pellets after 34 days of storage


**Table 4.** Concentration of gases from wood pellets after 62 days of storage

### **5. Pilot scale tests**

deprived conditions in an enclosed cargo hold than in open atmospheric conditions saturated

Wood pellets exported to Europe have typically between 4 and 6% moisture. The 15% and higher represented wood pellets which have been accidentally exposed to moist conditions for a prolonged period of time. Testing of wood pellets with abnormal moisture content is important to make sure there is good safety margin for long duration shipments such as a 30-34 days voyage from for example British Columbia to Europe. The emission of gases from wood pellets reached a peak (plateau) as summarized in Table 3 within the first 20-30 days. At the same time the oxygen content decreased rapidly to a very low level as the emission of CO2 increased resulting in a near inert condition in the space. The flammability of the mixed gases was calculated when exposed to air with full complement of oxygen as per the ISO Stand‐ ard.The research was carried out over a period of 62 days, just to ensure the gas evolution continued to be stable. The same calculations are done for 62 days as well and presented in

**25°C 40°C 60°C**

**9% Moisture**

1.394 1.581 1.062 3.194 2.712 2.162 3.817 3.248 2.470

**15% Moisture**

**4% Moisture**

**9% Moisture**

**15% Moisture**

with oxygen.

192 Advances in Gas Chromatography

Table 4.

**Gas Species**

CO/CH4/H2 mixture (sum)

∑ *i*=1 *n Ai* 100 *TCi*

> ∑ *k*=1 *p*

**4% Moisture**

**9% Moisture**

**Table 3.** Concentration of gases from wood pellets after 34 days of storage

**15% Moisture**

**4% Moisture**

CO2 0.617 1.334 1.110 1.741 3.572 3.620 7.980 8.490 10.290 CO 0.975 1.119 0.678 1.551 1.329 1.120 1.752 1.551 1.120 CH4 0.036 0.041 0.019 0.183 0.174 0.096 0.254 0.225 0.120 H2 0.383 0.421 0.365 1.460 1.209 0.946 1.811 1.473 1.230

Sum 2.011 2.915 2.172 4.935 6.284 5.782 11.797 11.738 12.760 O2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 N2 97.989 97.085 97.828 95.065 93.716 94.218 88.203 88.262 87.240

**Flammability determination in accordance with ISO 10156 Standard**

Flammable No No No No No No No No No

−1 12.398 13.907 10.253 35.659 30.013 23.510 43.556 36.311 28.641

*BkKk* 98.915 99.086 99.493 97.677 99.074 99.648 100.173 100.997 102.675

### **5.1. Off-gas concentration in silo head-space**

Gas composition analysis was performed for the first 9 weeks after loading the pilot silo. Results (Figure 11 to Figure 13) showed a rapid increase in the concentration of CO2, CO and CH4 in the head-space of the silo. Off-gas concentrations in the first 7 days were 1.0-1.6% CO2, 0.8-1.0% CO and 0.73- 0.85% CH4. All gas concentrations increased with storage time. Figure 14 shows the increase in the CO/CO2 ratio with time, which reached a constant value after 50

days of storage. Some scattering of data is seen between days 10 to 15 with a peak CO/CO2 ratio of about 0.59 on day 10. Minimum oxygen concentration in the bed was also seen on day 10 (Figure 15). It could be explained by slightly (~2%) lower local relative humidity recorded by the cable and higher H2 and thus the shift of water-gas shift reaction towards reactants. Oxygen concentrations were also measured according to the same gas sampling schedule. As shown in Figure 15 the O2 level dropped dramatically from 21% to 7-8% after 3 days and reached close to 0% after 1 week and again after 3 weeks of storage. The concentration of carbon dioxide in the head-space increased to about 2.7% (27,000 ppm), and this is higher compared to the CO2 concentrations in the pellet bed which could be attributed to the exposure of pellet surface to head-space oxygen and thus higher local emission. Lab-scale experiments had been previously performed to determine the effect of oxygen availability on the emission rates of off-gasses. Highest emission of CO2 was seen in containers with higher percentage of headspace or where oxygen was pumped in regularly. When wood pellets were stored in helium, minimum emission of CO2 was detected. For carbon monoxide, its concentration increased from the very first days of storage and reached a maximum value of about 1.7% after 9 weeks of storage. This accumulated concentration was well above the threshold [22] limit value (TLV) for human health, and it can cause injuries and immediate death.

**0 10 20 30 40 50 60**

**Figure 12.** CH4 concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space

**0 10 20 30 40 50 60**

**T=25 C Head-space =25%**

**Time (Day)**

**Figure 13.** CO concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space

**Time (Day)**

**T=25<sup>o</sup> C Head-space =25%**

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195

**0.03**

gas sampling port) [Concentrations are volume percentage]

**0.4**

gas sampling port) [Concentrations are volume percentage]

**0.6**

**0.8**

**1.0**

**CO Concentration (%)**

**1.2**

**1.4**

**1.6**

**1.8**

**0.06**

**0.09**

**CH4 Concentration (%)**

**0.12**

**0.15**

**Figure 11.** CO2 concentration in head-space of the silo as a function of storage time for wood pellets. (G0: head-space gas sampling port) [Concentrations are volume percentage]

days of storage. Some scattering of data is seen between days 10 to 15 with a peak CO/CO2 ratio of about 0.59 on day 10. Minimum oxygen concentration in the bed was also seen on day 10 (Figure 15). It could be explained by slightly (~2%) lower local relative humidity recorded by the cable and higher H2 and thus the shift of water-gas shift reaction towards reactants. Oxygen concentrations were also measured according to the same gas sampling schedule. As shown in Figure 15 the O2 level dropped dramatically from 21% to 7-8% after 3 days and reached close to 0% after 1 week and again after 3 weeks of storage. The concentration of carbon dioxide in the head-space increased to about 2.7% (27,000 ppm), and this is higher compared to the CO2 concentrations in the pellet bed which could be attributed to the exposure of pellet surface to head-space oxygen and thus higher local emission. Lab-scale experiments had been previously performed to determine the effect of oxygen availability on the emission rates of off-gasses. Highest emission of CO2 was seen in containers with higher percentage of headspace or where oxygen was pumped in regularly. When wood pellets were stored in helium, minimum emission of CO2 was detected. For carbon monoxide, its concentration increased from the very first days of storage and reached a maximum value of about 1.7% after 9 weeks of storage. This accumulated concentration was well above the threshold [22] limit value (TLV)

**0 10 20 30 40 50 60**

**Figure 11.** CO2 concentration in head-space of the silo as a function of storage time for wood pellets. (G0: head-space

**Time (Day)**

**T=25 C Head-space = 25%**

for human health, and it can cause injuries and immediate death.

**0.5**

gas sampling port) [Concentrations are volume percentage]

**1.0**

**1.5**

**CO2 Concentration**

 **(%)**

194 Advances in Gas Chromatography

**2.0**

**2.5**

**3.0**

**Figure 12.** CH4 concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space gas sampling port) [Concentrations are volume percentage]

**Figure 13.** CO concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space gas sampling port) [Concentrations are volume percentage]

The gas emissions are converted to emission factors using Eqn (1) in order to compare the

**Vg**)**Mwt CN**<sup>0</sup>

(1)

197

**RTMPCNt**

is the pressure (Pa), MP is the mass of pellets (kg). Assuming that the amount of N2 remains constant during off-gassing process, CN0/CNt is introduced as a correction factor to balance the change in gas volume where CNt is the concentration of nitrogen measured at any time and CN0 is the concentration of nitrogen at the beginning of test. The gas emission results for 25% head-space are compared to those obtained by Kuang et al. [1] for the storage of wood pellets. As shown in Table 5, the peak emission factors of all three gases derived from this study (using a large pilot silo) are higher than their findings (using a small lab-scale reactor); though both

**(Storage volume=5200 L)**

CO 0.0191 0.0124 CO2 0.0485 0.0200 CH4 0.0009 0.0002

Methane emission is due to the activities of anaerobic microorganisms. For instance, in a typical anaerobic digestion process for biogas production from readily biodegradable feedstock, the methane content can range from 50-70%. In this study, the CH4 concentration was observed to increase from 0.03% to 0.14% after 9 weeks, while an oxygen deficient environment was created in the silo just a few days after the start of the test. The relatively low CH4 contents are in line with the results of total bacteria counts, which were less than 5 cfu/g of pellets as compared to

The measured concentrations of the gases derived from all 13 gas sampling ports over time are plotted in 3D graphs and contours. The contour plots would provide a supplemental view of how the various gases are stratified in the bed. Figure 16 show the 3D and contour plots of the concentration of carbon dioxide in all locations. For the first several days, the gas concen‐ tration was quite the same for all locations. After 10 days, CO2 concentrations were different; yet, after about 40 days, the concentrations became uniform again everywhere in the silo. The contour plot clearly illustrates that the CO2 concentration was always higher in the head-space

is the gas concentration, Mwt is the molecular weight

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**Kuang et al. [1] (Storage volume=45 L)**

), R is universal gas constant (8.314 J/mol K), T is temperature (K),P

**fi**<sup>=</sup> **<sup>P</sup>**(**Ci**

results with other researchers' work on the same basis:

is emission factor of species "i", Ci

sets of values are in the same order of magnitude.

**Peak emission factor (g/kg) This study**

**Table 5.** Comparison of peak emission factor for CO, CO2 and CH4with previous research

orders of magnitude higher microbial counts in a typical anaerobic digester.

**5.2. 3D analysis of longitudinal distribution of emitted gases**

where fi

(g),Vg is the gas volume (m3

**Figure 14.** CO/CO2 ratio in head-space of the silo as a function of storage time for wood pellets. (G0 head-space gas sampling port) [Concentrations are volume percentage]

**Figure 15.** O2 concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space gas sampling port) [Concentrations are volume percentage]

The gas emissions are converted to emission factors using Eqn (1) in order to compare the results with other researchers' work on the same basis:

$$\mathbf{f}\_{\mathbf{i}} = \frac{\mathbf{P}(\mathbf{C}\_{\mathbf{i}}\mathbf{V}\_{\mathbf{y}})\mathbf{M}\_{\text{wt}}\mathbf{C}\_{\mathbf{N}0}}{\mathbf{R}\mathbf{T}\mathbf{M}\_{\text{p}}\mathbf{C}\_{\mathbf{N}t}} \tag{1}$$

where fi is emission factor of species "i", Ci is the gas concentration, Mwt is the molecular weight (g),Vg is the gas volume (m3 ), R is universal gas constant (8.314 J/mol K), T is temperature (K),P is the pressure (Pa), MP is the mass of pellets (kg). Assuming that the amount of N2 remains constant during off-gassing process, CN0/CNt is introduced as a correction factor to balance the change in gas volume where CNt is the concentration of nitrogen measured at any time and CN0 is the concentration of nitrogen at the beginning of test. The gas emission results for 25% head-space are compared to those obtained by Kuang et al. [1] for the storage of wood pellets. As shown in Table 5, the peak emission factors of all three gases derived from this study (using a large pilot silo) are higher than their findings (using a small lab-scale reactor); though both sets of values are in the same order of magnitude.


**Table 5.** Comparison of peak emission factor for CO, CO2 and CH4with previous research

**0 10 20 30 40 50 60**

**Figure 14.** CO/CO2 ratio in head-space of the silo as a function of storage time for wood pellets. (G0 head-space gas

**0 10 20 30 40 50 60**

**Storage days**

**Figure 15.** O2 concentration in head-space of the silo as a function of storage time for wood pellets. (G0 head-space

**Time (Day)**

**T=25 C Head-space = 25%**

**T=25 C Head-space =25%**

**0.48**

sampling port) [Concentrations are volume percentage]

**0**

gas sampling port) [Concentrations are volume percentage]

**4**

**O2**

 **Concentration (%)**

**8**

**12**

**16**

**20**

**0.52**

**0.56**

**CO/CO2**

196 Advances in Gas Chromatography

**0.60**

**0.64**

Methane emission is due to the activities of anaerobic microorganisms. For instance, in a typical anaerobic digestion process for biogas production from readily biodegradable feedstock, the methane content can range from 50-70%. In this study, the CH4 concentration was observed to increase from 0.03% to 0.14% after 9 weeks, while an oxygen deficient environment was created in the silo just a few days after the start of the test. The relatively low CH4 contents are in line with the results of total bacteria counts, which were less than 5 cfu/g of pellets as compared to orders of magnitude higher microbial counts in a typical anaerobic digester.

### **5.2. 3D analysis of longitudinal distribution of emitted gases**

The measured concentrations of the gases derived from all 13 gas sampling ports over time are plotted in 3D graphs and contours. The contour plots would provide a supplemental view of how the various gases are stratified in the bed. Figure 16 show the 3D and contour plots of the concentration of carbon dioxide in all locations. For the first several days, the gas concen‐ tration was quite the same for all locations. After 10 days, CO2 concentrations were different; yet, after about 40 days, the concentrations became uniform again everywhere in the silo. The contour plot clearly illustrates that the CO2 concentration was always higher in the head-space compared to the other areas within the pellet bulk. CO2 emission is more sensitive to temper‐ ature versus CO; this could explain why higher CO2 concentration was observed in the silo head-space. While the peak CO2 concentration of 2.7% was reached just after 2 weeks in the head-space of silo, such a high concentration was not seen in the pellet bulk section until after 45 days. Carbon dioxide concentration was above the threshold limit value for worker safety and this limit was reached sooner in the silo head-space. In terms of worker safety, both the Occupational Safety and Health Administration (OSHA) and the American Conference of Governmental Industrial Hygienists (ACGIH) have set the permissible exposure limit (PEL) and the threshold limit value (TLV), respectively for CO2 of 5,000 ppm (0.5%) by volume over an 8-hour average exposure.

**10**

**0.2**

in the other areas within the pellet bed.

are volume percentage]

mixing.

**0.4**

**0.6**

**0.8**

**1.0**

**Concentration (%)**

**1.2**

**1.4**

**1.6**

**1.8**

**20**

**Time (days)**

**30**

**40**

**50**

**60**

**0.5**

**Figure 17.** map of CO concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations

Figure 18 illustrate the distribution of emitted methane (CH4) during 63 days of sealed storage of wood pellets. After 4 days, the concentration of methane was the highest close to the bottom of the silo. Over time the emitted gas started to stratify; but after 48 days almost the same concentration was observed in all locations, with slightly higher CH4 concentration at the upper sections of the silo near the interface of wood pellets and head-space. The same stratification was noted until the end of the test. Methane emission has a similar behavior as carbon dioxide; both gases reached higher concentrations more quickly in the head-space than

Diffusion of gases in air and its effect on oxygen deficiency could also partly account for the created environment. Studies [23, 24] have been conducted to understand how easily the released gases (carbon dioxide, helium and sulfurhexafluoride) would mix with air and whether they would remain fully mixed. The gases were found to diffuse in air more readily than expected and modest gas velocities due to natural convection would fully mix the released gases with fresh air. In the wood pellet storage, gases were emitted over time from the first day till the gas concentrations reached a plateau. Some gas stratification was detected in the early weeks of storage especially for carbon monoxide which is due to chemical and physical adsorption between the material and the gases as well as differences in temperature and relative humidity at various levels in the silo. However after a few weeks, gases started to mix and remained mixed. In this regard, natural convection within the silo can bring about gas

**1.0**

**1.5**

**2.0**

**2.5**

**3.0**

**Height (m)**

**3.5**

**4.0**

**0.000 0.2000 0.4000 0.6000 0.8000 1.000 1.200 1.400 1.600 1.800 2.000**

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199

**Figure 16.** map of CO2 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations are volume percentage]

Figure 17 shows the overview of carbon monoxide emission, accumulation and dispersion over 63 days of storage. As distinguished from the concentration profile for carbon dioxide, a higher CO concentration was observed in the head-space of the silo at the beginning of the test, and the CO concentrations were similar in most other locations within the pellet bulk. This difference may be due to the gas dispersion phenomenon. Nevertheless, after 18 days of storage, stratification was observed with the highest concentration of CO at the G5, G6 and G7 locations (2.03-2.54 m from the bottom of the silo). Due to close value of density for air and carbon monoxide, not much gravitational stratification of CO was expected. Although carbon monoxide emissions are attributed to oxidation of unsaturated acids in wood pellets, the exact pathway through which CO is emitted hasn't been identified yet. More distinct stratification of gas was seen in this study for carbon monoxide compared to other off-gases, which could be attributed to the high uptake of CO by pellets during storage.

compared to the other areas within the pellet bulk. CO2 emission is more sensitive to temper‐ ature versus CO; this could explain why higher CO2 concentration was observed in the silo head-space. While the peak CO2 concentration of 2.7% was reached just after 2 weeks in the head-space of silo, such a high concentration was not seen in the pellet bulk section until after 45 days. Carbon dioxide concentration was above the threshold limit value for worker safety and this limit was reached sooner in the silo head-space. In terms of worker safety, both the Occupational Safety and Health Administration (OSHA) and the American Conference of Governmental Industrial Hygienists (ACGIH) have set the permissible exposure limit (PEL) and the threshold limit value (TLV), respectively for CO2 of 5,000 ppm (0.5%) by volume over

an 8-hour average exposure.

198 Advances in Gas Chromatography

are volume percentage]

**10**

**1.0**

**1.5**

**2.0**

**Concentration (%)**

**2.5**

**3.0**

**20**

**Time (day)**

**30**

be attributed to the high uptake of CO by pellets during storage.

**40**

**50**

**60**

**0.5**

**Figure 16.** map of CO2 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations

Figure 17 shows the overview of carbon monoxide emission, accumulation and dispersion over 63 days of storage. As distinguished from the concentration profile for carbon dioxide, a higher CO concentration was observed in the head-space of the silo at the beginning of the test, and the CO concentrations were similar in most other locations within the pellet bulk. This difference may be due to the gas dispersion phenomenon. Nevertheless, after 18 days of storage, stratification was observed with the highest concentration of CO at the G5, G6 and G7 locations (2.03-2.54 m from the bottom of the silo). Due to close value of density for air and carbon monoxide, not much gravitational stratification of CO was expected. Although carbon monoxide emissions are attributed to oxidation of unsaturated acids in wood pellets, the exact pathway through which CO is emitted hasn't been identified yet. More distinct stratification of gas was seen in this study for carbon monoxide compared to other off-gases, which could

**1.0**

**1.5**

**2.0**

**2.5**

**3.0**

**Height (m)**

**3.5**

**4.0**

**0.000 0.4000 0.8000 1.200 1.600 2.000 2.400 2.800 3.200**

**Figure 17.** map of CO concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations are volume percentage]

Figure 18 illustrate the distribution of emitted methane (CH4) during 63 days of sealed storage of wood pellets. After 4 days, the concentration of methane was the highest close to the bottom of the silo. Over time the emitted gas started to stratify; but after 48 days almost the same concentration was observed in all locations, with slightly higher CH4 concentration at the upper sections of the silo near the interface of wood pellets and head-space. The same stratification was noted until the end of the test. Methane emission has a similar behavior as carbon dioxide; both gases reached higher concentrations more quickly in the head-space than in the other areas within the pellet bed.

Diffusion of gases in air and its effect on oxygen deficiency could also partly account for the created environment. Studies [23, 24] have been conducted to understand how easily the released gases (carbon dioxide, helium and sulfurhexafluoride) would mix with air and whether they would remain fully mixed. The gases were found to diffuse in air more readily than expected and modest gas velocities due to natural convection would fully mix the released gases with fresh air. In the wood pellet storage, gases were emitted over time from the first day till the gas concentrations reached a plateau. Some gas stratification was detected in the early weeks of storage especially for carbon monoxide which is due to chemical and physical adsorption between the material and the gases as well as differences in temperature and relative humidity at various levels in the silo. However after a few weeks, gases started to mix and remained mixed. In this regard, natural convection within the silo can bring about gas mixing.

non–condensable gases, the O2 content of the pellet material could be high enough to induce high amount of CO emission compared to pellets stored in air. Emission of carbon monoxide for pellets stored in CO2 showed 50% less emission compared to pellets stored in air. Results obtained from stored pellets exposed to different head-space (HD) percentage showed that an increase in head-space is related to the peak emission factor for carbon monoxide and carbon dioxide with the following linear relationships (average values of two replicates each):

Oxygen concentration was also measured in all 13 locations including the head-space. Ever since the first gas sample was taken on day 2 of storage, the O2 concentration decreased to 15% in the reactor. As seen in Figure 19, O2 concentration was depleted rapidly to less than 5% within the first 7 days thus generating an oxygen-deficient atmosphere in a confined space. More oxygen was available within the bed of pellets compared to the head-space as a result of oxygen being trapped within the pellets until 40 days of storage when oxygen was consumed everywhere in the reactor. When oxygen levels fall below 19.5% by volume, air cannot support metabolism for an unlimited period of time. At 17% oxygen, the symptoms might simply be worse as reflected by hyper-ventilation. The oxygen-deficient atmosphere becomes more dangerous when oxygen content is further lowered to 15%; people can quickly progress to dizziness and rapid heartbeat. Finally, oxygen levels below 13% can lead to unconsciousness

**fCO** =3×10-4 HD% <sup>+</sup> 0.0065

=1.9×10-3 HD% + 0.0041

and eventually to death at around 6% oxygen [25].

**0**

**-15**

are volume percentage]

**-10**

**-5**

**0**

**5**

**Concentration (%)**

**10**

**15**

**20**

**10**

**20**

**Time (days)**

**30**

**40**

**50**

**Figure 19.** map of O2 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations

**60**

**0.5**

**1.0**

**1.5**

**2.0**

**2.5**

**3.0**

**3.5**

**Height (m)**

**4.0**

**0.000 2.000 4.000 6.000 8.000 10.00 12.00 14.00 16.00 18.00 20.00**

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201

**fCO**<sup>2</sup>

**Figure 18.** map of CH4 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations are volume percentage]

#### **5.3. Oxygen depletion**

Figure 19 demonstrates the oxygen depletion in the silo head-space during storage of wood pellet, which is much faster than the oxygen within the bed of wood pellets. Oxygen concen‐ tration dropped very quickly in less than 10 days of storage and this can be partially explained by the emission of carbon monoxide. Depletion of oxygen is partly related to CO formation but a greater amount could be due to the radical-induced oxidative degradation of natural lipids, particularly the polyunsaturated linoleic acid [1]. A high energy of desorption required to overcome the bond indicates chemical adsorption of oxygen to the pellets. The autooxidation of fatty acids starts with formation of free radicals. In the presence of oxygen, hydroxyperoxide radicals are formed and in interaction with an unsaturated fatty acid produce two hydroxyperoxides and a new free radical. When pellets stored in air, hydroxyperoxides are formed from oxidation of fatty acids. Depending on whether oxygen bond or carbon bond break in them alcoxi radical, aldehydes, acids, hydrocarbons or ketones will be formed.

In order to examine the role of oxygen availability in the storage space on the rate of off-gassing, a set of experiments was conducted under controlled environment conditions. The same reactors described in small scale tests section were used in these experiments. Pellets were placed in 6 sealed reactors (2L by volume) that were purged with different gases. In two of the reactors pellets were stored in oxygen-free environments. When pellets were stored in an environment dominated by N2 after purging, CO emission was as high as when pellets were stored under regular conditions. However when the reactors were purged with He (helium), the peak CO emission decreased to 25% of the values when pellets were stored under regular conditions. A hypothesis is that, although oxygen availability can accelerate the emission of non–condensable gases, the O2 content of the pellet material could be high enough to induce high amount of CO emission compared to pellets stored in air. Emission of carbon monoxide for pellets stored in CO2 showed 50% less emission compared to pellets stored in air. Results obtained from stored pellets exposed to different head-space (HD) percentage showed that an increase in head-space is related to the peak emission factor for carbon monoxide and carbon dioxide with the following linear relationships (average values of two replicates each):

**fCO** =3×10-4 HD% <sup>+</sup> 0.0065

**fCO**<sup>2</sup> =1.9×10-3 HD% + 0.0041

**5.3. Oxygen depletion**

are volume percentage]

**10**

**0.02**

**0.04**

**0.06**

**0.08**

**0.10**

**Concentration (%)**

**0.12**

**0.14**

**0.16**

200 Advances in Gas Chromatography

**20**

**30**

**Time (day)**

**40**

**50**

**60**

**0.5**

**1.0**

**1.5**

**2.0**

**2.5**

**Height (m)**

**3.0**

**3.5**

**4.0**

**0.000 0.02000 0.04000 0.06000 0.08000 0.1000 0.1200 0.1400**

Figure 19 demonstrates the oxygen depletion in the silo head-space during storage of wood pellet, which is much faster than the oxygen within the bed of wood pellets. Oxygen concen‐ tration dropped very quickly in less than 10 days of storage and this can be partially explained by the emission of carbon monoxide. Depletion of oxygen is partly related to CO formation but a greater amount could be due to the radical-induced oxidative degradation of natural lipids, particularly the polyunsaturated linoleic acid [1]. A high energy of desorption required to overcome the bond indicates chemical adsorption of oxygen to the pellets. The autooxidation of fatty acids starts with formation of free radicals. In the presence of oxygen, hydroxyperoxide radicals are formed and in interaction with an unsaturated fatty acid produce two hydroxyperoxides and a new free radical. When pellets stored in air, hydroxyperoxides are formed from oxidation of fatty acids. Depending on whether oxygen bond or carbon bond break in them alcoxi radical, aldehydes, acids, hydrocarbons or ketones will be formed.

**Figure 18.** map of CH4 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations

In order to examine the role of oxygen availability in the storage space on the rate of off-gassing, a set of experiments was conducted under controlled environment conditions. The same reactors described in small scale tests section were used in these experiments. Pellets were placed in 6 sealed reactors (2L by volume) that were purged with different gases. In two of the reactors pellets were stored in oxygen-free environments. When pellets were stored in an environment dominated by N2 after purging, CO emission was as high as when pellets were stored under regular conditions. However when the reactors were purged with He (helium), the peak CO emission decreased to 25% of the values when pellets were stored under regular conditions. A hypothesis is that, although oxygen availability can accelerate the emission of

Oxygen concentration was also measured in all 13 locations including the head-space. Ever since the first gas sample was taken on day 2 of storage, the O2 concentration decreased to 15% in the reactor. As seen in Figure 19, O2 concentration was depleted rapidly to less than 5% within the first 7 days thus generating an oxygen-deficient atmosphere in a confined space. More oxygen was available within the bed of pellets compared to the head-space as a result of oxygen being trapped within the pellets until 40 days of storage when oxygen was consumed everywhere in the reactor. When oxygen levels fall below 19.5% by volume, air cannot support metabolism for an unlimited period of time. At 17% oxygen, the symptoms might simply be worse as reflected by hyper-ventilation. The oxygen-deficient atmosphere becomes more dangerous when oxygen content is further lowered to 15%; people can quickly progress to dizziness and rapid heartbeat. Finally, oxygen levels below 13% can lead to unconsciousness and eventually to death at around 6% oxygen [25].

**Figure 19.** map of O2 concentration in the silo at all locations (G0 to G13) during 63 days of storage [Concentrations are volume percentage]

#### **5.4. 3D Analysis of radial distribution of emitted gases**

Figure 20 shows CO2 concentrations in the head-space and the bottom of the silo respectively. The gas concentration was measured at the same elevation but at 3 different radial positions (0.0, 0.6 and 1.2 m from the center) in order to investigate any possible radial concentration gradient. The results showed no significant variations in the CO2 concentrations in different radial positions, due to the uniform radial temperature profile. The same measurements were made for all three gases (CO2, CO and CH4); again no significant differences in any of the gas concentrations were observed at different radial positions. However, Figure 20 clearly shows the difference in CO2 concentrations along the axial locations, with the head-space concentra‐ tion higher than that at the silo bottom which could be due to lower rate of mixing in the silo compared to the rate of reaction. The same observations were obtained for radial concentra‐ tions of carbon monoxide and methane over time.

for flammability of gases emitted from wood pellets samples with moisture contents ranging from 4 to 50% stored in environment of 25 °C to 60 °C was investigated. The gas emission for CO2, CO, CH4 followed exponential profiles and the gas concentration was proportional to storage temperature, as expected. For samples with less than 15% MC at room temperature, CO2 concentration increased up to ~1.7% which was the highest peak emission compared to even pellets with 35% and 50% MC. At higher temperatures, the maximum CO2 concentration increased from 2.8% (40 °C, 4% MC) to 5.6% (40 °C, 50% MC) and from 9.6% (60 °C, 4% MC) to 10.9% (60 °C, 50% MC). For pellets with 4 and 15% MC, the concentration of CO increased as the temperature increased from 25 °C to 60 °C. However at a constant temperature of 25 °C, the peak concentration of CO was the same for wood pellets with 4% and 9% moisture contents and lower for pellets with 15%, 35% and 50% MC. Oxygen depletion was observed among all experimental conditions but showed to decrease more rapid for pellets with 9% MC (25 °C and 40 °C ) compared to wood pellets with 4%, 15%, 35% and 50% MC. Least depletion of oxygen was observed for samples at 25 °C and 50% MC and accelerated as the temperature increased. In terms of the gas mixture (CO/CH4/H2), at higher temperatures (40 °C and 60 °C), the gas concentration decreased as the moisture content increased. Using ISO method for estimating the potential flammability of the gases emitted from the wood pellets, it can be concluded that gas concentrations does not reach flammable concentrations within the 5-week or even the 9-

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Emission and stratification of off-gasses from storage of wood pellets was also studied in pilot scale. The focus of the study was to investigate the spatial and temporal concentration of offgases and purging effectiveness. Emission and stratification of off-gases was studied in pilot scale storage for over one year. Non-condensable gases that emitted from storage of material were carbon monoxide, carbon dioxide, methane and hydrogen. To study the stratification of gases, analysis of gas composition was done over time for different axial and radial positions. The emitted gases showed to have higher emission factor compared to work done with white wood pellets in small scale. It could be explained by the fact that off-gassing is a surface phenomenon and thus much active surface is available for reaction when larger amount of wood pellets are stored. Concentration of gases at plateau after 9 weeks of storage was 2.7 % CO2, 0.14% CH4 and 1.7% CO in the rector head-space. After a few days of storage some stratification were observed for carbon monoxide and methane. The clear stratification of carbon monoxide could be due to high uptake of CO by wood pellets over time. The unstable environment in the storage of wood pellets in the first few weeks of storage was due to very rapid consumption of oxygen in the space, reaction of emitted CO and CO2 with pellets, development of high temperature spots within the bulk due to higher activity of pellets and thus higher local temperatures, and moisture migration within the bed. Oxygen plays an important role in accelerating the emission of carbon monoxide. Results from experiment where material was kept in oxygen-free environment (N2-rich) confirmed the same lethal

concentration of CO possibly through onsuming the oxygen within the material.

week testing period.

**Figure 20.** CO2 concentration in the silo head-space and the bottom of the silo at different radial positions

#### **6. Concluding remarks**

The chapter has discussed various measurements of gases and volatiles, resulting data on emission concentrations and emission factors of gases in storages of wood pellets. The potential for flammability of gases emitted from wood pellets samples with moisture contents ranging from 4 to 50% stored in environment of 25 °C to 60 °C was investigated. The gas emission for CO2, CO, CH4 followed exponential profiles and the gas concentration was proportional to storage temperature, as expected. For samples with less than 15% MC at room temperature, CO2 concentration increased up to ~1.7% which was the highest peak emission compared to even pellets with 35% and 50% MC. At higher temperatures, the maximum CO2 concentration increased from 2.8% (40 °C, 4% MC) to 5.6% (40 °C, 50% MC) and from 9.6% (60 °C, 4% MC) to 10.9% (60 °C, 50% MC). For pellets with 4 and 15% MC, the concentration of CO increased as the temperature increased from 25 °C to 60 °C. However at a constant temperature of 25 °C, the peak concentration of CO was the same for wood pellets with 4% and 9% moisture contents and lower for pellets with 15%, 35% and 50% MC. Oxygen depletion was observed among all experimental conditions but showed to decrease more rapid for pellets with 9% MC (25 °C and 40 °C ) compared to wood pellets with 4%, 15%, 35% and 50% MC. Least depletion of oxygen was observed for samples at 25 °C and 50% MC and accelerated as the temperature increased. In terms of the gas mixture (CO/CH4/H2), at higher temperatures (40 °C and 60 °C), the gas concentration decreased as the moisture content increased. Using ISO method for estimating the potential flammability of the gases emitted from the wood pellets, it can be concluded that gas concentrations does not reach flammable concentrations within the 5-week or even the 9 week testing period.

**5.4. 3D Analysis of radial distribution of emitted gases**

202 Advances in Gas Chromatography

tions of carbon monoxide and methane over time.

**0.5**

**1.0**

**1.5**

**CO2**

**6. Concluding remarks**

 **Concentration (%)**

**2.0**

**2.5**

**3.0**

Figure 20 shows CO2 concentrations in the head-space and the bottom of the silo respectively. The gas concentration was measured at the same elevation but at 3 different radial positions (0.0, 0.6 and 1.2 m from the center) in order to investigate any possible radial concentration gradient. The results showed no significant variations in the CO2 concentrations in different radial positions, due to the uniform radial temperature profile. The same measurements were made for all three gases (CO2, CO and CH4); again no significant differences in any of the gas concentrations were observed at different radial positions. However, Figure 20 clearly shows the difference in CO2 concentrations along the axial locations, with the head-space concentra‐ tion higher than that at the silo bottom which could be due to lower rate of mixing in the silo compared to the rate of reaction. The same observations were obtained for radial concentra‐

**0 10 20 30 40 50 60**

The chapter has discussed various measurements of gases and volatiles, resulting data on emission concentrations and emission factors of gases in storages of wood pellets. The potential

**Figure 20.** CO2 concentration in the silo head-space and the bottom of the silo at different radial positions

**Time (day)**

 **Concentration at head-space radius=0m Concentration at the bottom radius=0m Concentration at head-space radius=0.60m Concentration at the bottom radius=0.6m Concentration at head-space radius=1.2m Concentration at the bottom radius=1.2m**

> Emission and stratification of off-gasses from storage of wood pellets was also studied in pilot scale. The focus of the study was to investigate the spatial and temporal concentration of offgases and purging effectiveness. Emission and stratification of off-gases was studied in pilot scale storage for over one year. Non-condensable gases that emitted from storage of material were carbon monoxide, carbon dioxide, methane and hydrogen. To study the stratification of gases, analysis of gas composition was done over time for different axial and radial positions. The emitted gases showed to have higher emission factor compared to work done with white wood pellets in small scale. It could be explained by the fact that off-gassing is a surface phenomenon and thus much active surface is available for reaction when larger amount of wood pellets are stored. Concentration of gases at plateau after 9 weeks of storage was 2.7 % CO2, 0.14% CH4 and 1.7% CO in the rector head-space. After a few days of storage some stratification were observed for carbon monoxide and methane. The clear stratification of carbon monoxide could be due to high uptake of CO by wood pellets over time. The unstable environment in the storage of wood pellets in the first few weeks of storage was due to very rapid consumption of oxygen in the space, reaction of emitted CO and CO2 with pellets, development of high temperature spots within the bulk due to higher activity of pellets and thus higher local temperatures, and moisture migration within the bed. Oxygen plays an important role in accelerating the emission of carbon monoxide. Results from experiment where material was kept in oxygen-free environment (N2-rich) confirmed the same lethal concentration of CO possibly through onsuming the oxygen within the material.

### **Apendix A**

0 10 20 30 40 50 60 70

Storage day

 CO Concetration 9 % MC and 25°C CO Concetration 9 % MC and 40°C CO Concetration 9 % MC and 60°C

**Figure A 3.** Concentration of CO2 for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure A 4.** Concentration of CO for wood pellet with 9% moisture concentration over 62 days at 25°C, 40°C and

CO2

CO2

CO2

Concetration 35 % MC and 25°C

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Concetration 35 % MC and 40°C

Concetration 35 % MC and 60°C

0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

60°C

CO Concentration (%)

2

4

CO2

Concentration (%)

6

8

10

12

**Figure A 1.** Concentration of CO2 for wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 2.** Concentration of CO2 for wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

**Apendix A**

204 Advances in Gas Chromatography

0 10 20 30 40 50 60 70

Concetration 15 % MC and 25°C

Concetration 15 % MC and 40°C

Concetration 15 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure A 2.** Concentration of CO2 for wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure A 1.** Concentration of CO2 for wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

CO2

CO2

CO2

0

0

2

4

CO2

Concentration (%)

6

8

10

12

2

4

CO2

Concentration (%)

6

8

10

12

CO2

CO2

CO2

Concetration 9 % MC and 25°C

Concetration 9 % MC and 40°C

Concetration 9 % MC and 60°C

**Figure A 3.** Concentration of CO2 for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 4.** Concentration of CO for wood pellet with 9% moisture concentration over 62 days at 25°C, 40°C and 60°C

**Figure A 5.** Concentration of CO for wood pellet with 15% moisture concentration over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure A 7.** Concentration of CH4 for emissions from wood pellet with 9% moisture content over 62 days at 25°C,

Concetration 15 % MC and 25°C

Concetration 15 % MC and 40°C

Concetration 15 % MC and 60°C

CH4

CH4

CH4

0 10 20 30 40 50 60 70

Storage day

**Figure A 8.** Concentration of CH4 for emissions from wood pellet with 15% moisture content over 62 days at 25°C,

0.00

0.00

0.05

0.10

CH4

Concentration (%)

0.15

0.20

0.25

0.30

0.05

0.10

CH4

40°C and 60°C

40°C and 60°C

Concentration (%)

0.15

0.20

0.25

0.30

CH4

CH4

CH4

Concetration 9 % MC and 25°C

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Concetration 9 % MC and 40°C

Concetration 9 % MC and 60°C

**Figure A 6.** Concentration of CO for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure A 5.** Concentration of CO for wood pellet with 15% moisture concentration over 62 days at 25°C, 40°C and

 CO Concetration 35 % MC and 25°C CO Concetration 35 % MC and 40°C CO Concetration 35 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure A 6.** Concentration of CO for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

 CO Concetration 15 % MC and 25°C CO Concetration 15 % MC and 40°C CO Concetration 15 % MC and 60°C

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

> 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

CO Concentration (%)

60°C

CO Concentration (%)

206 Advances in Gas Chromatography

**Figure A 7.** Concentration of CH4 for emissions from wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 8.** Concentration of CH4 for emissions from wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 9.** Concentration of CH4 for emissions from wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

Concetration 35 % MC and 25°C

Concetration 35 % MC and 40°C

Concetration 35 % MC and 60°C

0 10 20 30 40 50 60 70

Storage day

**Figure A 12.** Oxygen depletion for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 11.** Oxygen depletion for wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

O2

O2

O2

Concetration 15 % MC and 25°C

Measurement of Off-Gases in Wood Pellet Storage

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209

Concetration 15 % MC and 40°C

Concetration 15 % MC and 60°C

O2

O2

O2

O2

Concentration (%)

O2

Concentration (%)

**Figure A 10.** Oxygen depletion for wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 11.** Oxygen depletion for wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

Concetration 9 % MC and 25°C

Concetration 9 % MC and 40°C

Concetration 9 % MC and 60°C

**Figure A 9.** Concentration of CH4 for emissions from wood pellet with 35% moisture content over 62 days at 25°C,

O2

O2

O2

0 10 20 30 40 50 60 70

Storage day

**Figure A 10.** Oxygen depletion for wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

CH4

CH4

CH4

Concetration 35 % MC and 25°C

Concetration 35 % MC and 40°C

Concetration 35 % MC and 60°C

0.00

O2

Concentration (%)

0.05

0.10

CH4

40°C and 60°C

Concentration (%)

0.15

0.20

0.25

0.30

208 Advances in Gas Chromatography

**Figure A 12.** Oxygen depletion for wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 13.** Concentration of hydrogen for emissions from wood pellet with 9% moisture content over 62 days at 25°C, 40°C and 60°C

Storage day

**Figure A 15.** Concentration of hydrogen for emissions from wood pellet with 35% moisture content over 62 days at

This research was funded in parts by the Wood pellet Association of Canada, Natural Sciences and Engineering Research Council of Canada and Oak Ridge National Laboratory. Thanks are also extended to Premium Pellet Ltd. and Fibreco Export Inc. and Pinnacle Renewable Energy

1 Chemical & Biological Engineering, University of British Columbia, Vancouver, Canada

, Hamid Rezaei1

and Chang Soo Kim4

, C. Jim Lim1

, Anthony K. Lau1

,

H2

H2

H2

Concetration 35 % MC and 25°C

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211

Concetration 35 % MC and 40°C

Concetration 35 % MC and 60°C

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Group, Inc. for donating wood pellets for this study.

, Jaya Shankar Tumuluru3

\*Address all correspondence to: yazdanpanah@gmail.com

2 Wood pellet Association of Canada, Vancouver, Canada

3 Bioenergy Group, Idaho National Laboratory, Idaho Falls, USA

4 Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Fahimeh Yazdanpanah1\*, Shahab Sokhansanj1

, S. Melin2

H2

25°C, 40°C and 60°C

**Author details**

X. Tony Bi1

**Acknowledgements**

Concentration (%)

**Figure A 14.** Concentration of hydrogen for emissions from wood pellet with 15% moisture content over 62 days at 25°C, 40°C and 60°C

**Figure A 15.** Concentration of hydrogen for emissions from wood pellet with 35% moisture content over 62 days at 25°C, 40°C and 60°C

### **Acknowledgements**

0 10 20 30 40 50 60 70

Storage day

**Figure A 13.** Concentration of hydrogen for emissions from wood pellet with 9% moisture content over 62 days at

Concetration 15 % MC and 25°C

Concetration 15 % MC and 40°C

Concetration 15 % MC and 60°C

H2

H2

H2

0 10 20 30 40 50 60 70

Storage day

**Figure A 14.** Concentration of hydrogen for emissions from wood pellet with 15% moisture content over 62 days at

Concetration 9 % MC and 25°C

Concetration 9 % MC and 40°C

Concetration 9 % MC and 60°C

H2

H2

H2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

> 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

H2

Concentration (%)

H2

25°C, 40°C and 60°C

25°C, 40°C and 60°C

Concentration (%)

210 Advances in Gas Chromatography

This research was funded in parts by the Wood pellet Association of Canada, Natural Sciences and Engineering Research Council of Canada and Oak Ridge National Laboratory. Thanks are also extended to Premium Pellet Ltd. and Fibreco Export Inc. and Pinnacle Renewable Energy Group, Inc. for donating wood pellets for this study.

### **Author details**

Fahimeh Yazdanpanah1\*, Shahab Sokhansanj1 , Hamid Rezaei1 , C. Jim Lim1 , Anthony K. Lau1 , X. Tony Bi1 , S. Melin2 , Jaya Shankar Tumuluru3 and Chang Soo Kim4

\*Address all correspondence to: yazdanpanah@gmail.com


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[12] Roffael E. Volatile organic compounds and formaldehyde in nature, wood and wood

[13] Rupar K, Sanati M. The release of terpenes during storage of biomass. Biomass Bio‐

[14] McGraw GW, Hemingway RW, Ingram LL, Canady CS, McGraw WB. Thermal deg‐ radation of terpene: camphene, delta-3-carenic, limonene and alfa-terpinene. Envi‐

PhD Dissertation, University of British Columbia 2013.


### *Edited by Xinghua Guo*

For decades gas chromatography has been and will remain an irreplaceable analytical technique in many research areas for both quantitative analysis and qualitative characterization/identification, which is still supplementary with HPLC. This book highlights a few areas where significant advances have been reported recently and/ or a revisit of basic concepts is deserved. It provides an overview of instrumental developments, frontline and modern research as well as practical industrial applications. The topics include GC-based metabolomics in biomedical, plant and microbial research, natural products as well as characterization of aging of synthetic materials and industrial monitoring, which are contributions of several experts from different disciplines. It also contains best hand-on practices of sample preparation (derivatization) and data processing in daily research. This book is recommended to both basic and experienced researchers in gas chromatography.

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Advances in Gas Chromatography

Advances in

Gas Chromatography

*Edited by Xinghua Guo*