**Microprobing Structural Architecture Using Mid-Infrared Vibrational Molecular Spectroscopy Provisional chapterMicroprobing Structural Architecture Using Mid-Infrared Vibrational Molecular Spectroscopy**

Yuguang Ying and Peiqiang Yu Yuguang Ying and Peiqiang Yu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64927

#### **Abstract**

[34] Arranz I, Mischke C, Stroka J, Sizoo E, van Egmond H, Neugebauer M. Liquid chromatographic method for the quantification of zearalenone in baby food and animal

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feed: interlaboratory study. J AOAC Int. 2007;90(6):1598–609.

326 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Available from: http://dx.doi.org/10.1016/j.foodcont.2012.04.035

etry. Anal Chem. 1979;51(1):67–9.

The biofunctions of biopolymers are closely related to their microstructures in the complex plant-based tissue in biological systems. In this chapter, molecular spectroscopy is introduced as an approach to microprobe the structural architecture of plant-based seed tissues. Some recent progresses are made using molecular spectroscopy techniques. The working principles of the techniques, along with the methods of molecular spectral analyses and applications in feed architecture research are described.

**Keywords:** molecular spectroscopy, chemical functional groups, biopolymers, plantbased feed and food, bio-functions

## **1. Molecular spectroscopy techniques**

According to wavelengths from short to long, the electromagnetic spectrum includes gamma rays, hard X-rays, soft X-rays, ultraviolet, visible light, near infrared, mid-infrared, far infrared, microwaves, and radio waves. Gamma rays have the highest frequency and strongest penetration ability. They are produced by the most energetic objects in the universe, or by nuclear explosions, lightning, and radioactive decay on the earth. X-rays are widely used in medical and science areas, while soft X-rays are also used to analyze the characterization of different layers of plant tissues [1, 2]. Near infrared (NIR), as well as mid-infrared, are effective tools in feed analysis and quality assessment. The mid-IR spectral region (ca. 4000–400 cm−1) is a domain of interest to many scientific areas because many molecules have strong characteristic vibrational transitions, especially in the wave number range of ca. 1800– 800 cm−1, which is also called the "fingerprint region" [3–5].

© 2016 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. © 2016 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.

As we know, plants are made up of molecules and the internal molecular energy consists of the electronic, translational, rotational, and vibrational energies. Under normal conditions, the functional groups in organic molecules vibrate independently and only interact weakly with each other. However, the interference from outside, such as electromagnetic radiation, could trigger the nonequilibrium phase and cause energy transitions between the rotational and vibrational energies, which can induce the net change in the electric dipole moment and the absorption of the IR. As the ratio of absorption and transmission IR differs between molecules, nearly every molecular species gives a unique IR absorption spectrum. Hence, IR spectrometry could be used to identify molecular functional groups [5].

## **2. ATR-FTIR molecular spectroscopy techniques**

## **2.1. Working principles**

Attenuated total reflectance Fourier transform infrared (ATR-FTIR) is a globar-sourced FTIR spectroscopy, which could be used to identify the molecular constituents in a wide range of samples in areas such as physics, chemistry, and biology. ATR-FTIR is mainly the combination of the globar light source and a microscope and based on the attenuation effect of light [5, 6].

The core part of a FTIR spectrometer is the Michelson interferometer (**Figure 1**, adapted from [9]).

**Figure 1.** Schematic diagram of a FTIR spectrometer (adapted with permission from McCluskey [9]).

The collimated light from the broadband source travels through the beamsplitter and is split into two beams. One beam travels through the splitter and is reflected by a movable mirror, and the other beam travels to a fixed mirror and is reflected back. A portion of the light finally reaches the sample that may be placed in a liquid-helium cryostat with IR-transparent windows made of ZnSe, KBr, or polypropylene. In an ATR-FTIR instrument, a crystal made of material such as zinc selenide (ZnSe), germanium (Ge), and thallium-iodide is placed under the samples and the incident beam entering at an angle larger than the critical angle, the total reflection could be achieved and only the part of energy that is absorbed by the sample is lost during the process [7, 8]. The part of light that passes through the sample is sensed by the detector, which could be a photoconducting detector such as Ge:Cu placed right behind the sample or a mercury-cadmium-telluride (MCT) mounted in the outside [9]. In this way, all spectral elements are measured simultaneously on the detector and the time consumption (Fellgett advantage or multiplex advantage) depends primarily on the movement of the movable mirror, which could be very short [8, 10].

This technology has a high spectral resolution, a broad measure range and short measure time [9]. At the same time, with no slits to attenuate the infrared light, FTIR has a higher throughput of radiation compared to conventional IR methods (Jacquinot advantage) [10]. Another advantage of ATR-FTIR is it only requires simple sample preparation by finely grinding them and depositing a thin layer on the infrared transparent windows [6].

Nevertheless, the technique also has its shortcomings. Due to the limited brightness, when ATR-FTIR is used to analyze a small region of interest, the decreased aperture would result in the diffraction effects and reduce the signal-to-noise ratio [5]. It is reported that as the plant cell size is normally between 5 and 30 μm, the globar sourced FTIR is not able to obtain a good signal-to-noise ratio within this dimension [5].

## **2.2. Application of ATR-FTIR techniques in feed research**

As we know, plants are made up of molecules and the internal molecular energy consists of the electronic, translational, rotational, and vibrational energies. Under normal conditions, the functional groups in organic molecules vibrate independently and only interact weakly with each other. However, the interference from outside, such as electromagnetic radiation, could trigger the nonequilibrium phase and cause energy transitions between the rotational and vibrational energies, which can induce the net change in the electric dipole moment and the absorption of the IR. As the ratio of absorption and transmission IR differs between molecules, nearly every molecular species gives a unique IR absorption spectrum. Hence, IR spectrometry

328 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Attenuated total reflectance Fourier transform infrared (ATR-FTIR) is a globar-sourced FTIR spectroscopy, which could be used to identify the molecular constituents in a wide range of samples in areas such as physics, chemistry, and biology. ATR-FTIR is mainly the combination of the globar light source and a microscope and based on the attenuation effect of light [5, 6].

The core part of a FTIR spectrometer is the Michelson interferometer (**Figure 1**, adapted from

**Figure 1.** Schematic diagram of a FTIR spectrometer (adapted with permission from McCluskey [9]).

could be used to identify molecular functional groups [5].

**2. ATR-FTIR molecular spectroscopy techniques**

**2.1. Working principles**

[9]).

The ATR-FTIR technique has been proven to be effective in mineral samples in oil shale [11] and biological samples such as cytology and tissue sections, live cells or biofluids [12], and plant samples including transgenic alfalfa [13], hulless barley [14, 15], corn forage [16], DDGS [17, 18], coproducts from bio-oil processing [19–22], heat processing impact [23, 24].

The lipid region, protein region, and carbohydrate region are the three main regions in a spectrum of feed material being analyzed (**Figure 2**). Taking cereal grains as an example, the assessed items included infrared intensity of protein amide I (ca. 1725–1578 cm−1), amide II (ca. 1578–1482 cm−1), amide I peak height (ca. ~1647 cm−1), amide II peak height (ca. ~1537 cm−1), α-helix height (ca. ~1653 cm−1), β-sheet (ca. ~1632 cm−1), lipid (ca. 1798–1709 cm−1) and its peak height (ca. ~1744 cm−1), cellulosic compounds (ca. 1291–1184 cm−1) and its peak height (ca. ~1238 cm−1), total carbohydrates (CHO; ca. 1191–944 cm−1), three major CHO peaks: first peak (ca. 1191–1132 cm−1) and its peak height (ca. ~1150 cm−1), second peak (ca. 1132–1066 cm−1) and its peak height (ca. 67–1078 cm−1), third peak (ca. 1066–944 cm−1) and its peak height (ca. ~1012 cm−1) [25].

**Figure 2.** Spectra and fingerprint region and chemical functional groups in plant-based feeds and food.

## **3. Synchrotron-based molecular spectroscopy techniques (SR-IMS)**

#### **3.1. Working principles and advantages**

The biggest advantage of SR-IMS is it could preserve the information about the spatial distribution of the objects when detecting the inner structures. This is achieved by the use of synchrotron infrared light source. The nondivergent, intense and extremely fine beamline is created by a giant particle accelerator that turns electrons into light, which is 100–1000 times brighter than the globar source [5]. Therefore, high spatial resolution and signal to noise spectra can be collected at a faster speed [5].

#### **3.2. Novel applications of SR-IMS techniques in feed research**

The SR-IMS technology was first applied to animal feed research in 1999 [26]. Since then it has been utilized on several feeds, including transgenic alfalfa [27, 28], hulless barley [5, 15, 29], canola seeds [5, 30], corn seed [28], flaxseeds [28, 30], sorghum seeds [31], wheat [30, 32], wheat DDGS [28], and corn DDGS [28]. In spite of all these applications, this research is still in its infancy.

## **4. Spectra analysis**

Functional groups such as amide I and amide II bonds have certain percentages of C═O, C─N, and N─H stretching vibrations, the wave numbers (cm−1) at which they are absorbed and generally fixed, but they also slightly shift depending on the samples [5]. Some typical IR absorption bands include: amide I (centered at about 1650 cm−1, includes about 80% C═O stretching, 10% C─N stretching and 10% N─H bending), amide II (centered at about 1550 cm −1, includes about 60% N─H bending and 40% C─N stretching), lipid carbonyl C═O (peaks at about 1738 cm−1), and cellulose (at about 1100 cm−1) [33]. Among them, amides I and II are the most dominant vibrational bands of the protein backbone and amide I, due to its high C═O stretching composition, is the most sensitive and highly related to secondary structural elements of proteins [34].

## **4.1. Univariate analysis**

Using univariate analysis, it is possible to discover quantitative differences in the spectra information, such as the component areas, peak heights, and ratios among different components. Univariate analysis gives very straightforward results in terms of what changes occurred on the mathematical parameters characterizing the spectrum, such as the band intensities, integrated intensities, band frequencies and the band intensity ratios. In addition, this method makes it possible to connect the spectra information to the biological meaning on a mathematical basis [35].

## **4.2. Multivariate analysis**

**Figure 2.** Spectra and fingerprint region and chemical functional groups in plant-based feeds and food.

330 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

**3. Synchrotron-based molecular spectroscopy techniques (SR-IMS)**

The biggest advantage of SR-IMS is it could preserve the information about the spatial distribution of the objects when detecting the inner structures. This is achieved by the use of synchrotron infrared light source. The nondivergent, intense and extremely fine beamline is created by a giant particle accelerator that turns electrons into light, which is 100–1000 times brighter than the globar source [5]. Therefore, high spatial resolution and signal to noise spectra

The SR-IMS technology was first applied to animal feed research in 1999 [26]. Since then it has been utilized on several feeds, including transgenic alfalfa [27, 28], hulless barley [5, 15, 29], canola seeds [5, 30], corn seed [28], flaxseeds [28, 30], sorghum seeds [31], wheat [30, 32], wheat DDGS [28], and corn DDGS [28]. In spite of all these applications, this research is still in its

Functional groups such as amide I and amide II bonds have certain percentages of C═O, C─N, and N─H stretching vibrations, the wave numbers (cm−1) at which they are absorbed and generally fixed, but they also slightly shift depending on the samples [5]. Some typical IR absorption bands include: amide I (centered at about 1650 cm−1, includes about 80% C═O stretching, 10% C─N stretching and 10% N─H bending), amide II (centered at about 1550 cm −1, includes about 60% N─H bending and 40% C─N stretching), lipid carbonyl C═O (peaks at about 1738 cm−1), and cellulose (at about 1100 cm−1) [33]. Among them, amides I and II are the most dominant vibrational bands of the protein backbone and amide I, due to its high

**3.1. Working principles and advantages**

can be collected at a faster speed [5].

infancy.

**4. Spectra analysis**

**3.2. Novel applications of SR-IMS techniques in feed research**

Multivariate analysis is capable of analyzing multiple variables at same time. Principal component analysis (PCA) and hierarchical cluster analysis (CLA or HCHA) are two of the commonly used methods.

The PCA transforms the original set of variables based on the correlations among them, into a set of independent linear combinations called principal components (PCs), which contain most of the information in the original variables and empirically summarizes their correlations [32, 36]. The first few PCs usually account for more than 95% of the total variation among the variables [27].

The CLA is another data reduction method that calculates a distance matrix, searches for the two most similar objects, and displays the results as dendrograms [32, 37]. In the hierarchical approach, the object or objects are gathered as a group step by step, being nested to the previous groups. Thus, the number of clusters reduces sequentially as the clusters' sizes grow and end up with only one [37].

## **5. Application**

## **5.1. Application 1: structural responses of functional groups in cereal grains to heat processing methods**

The research [25] in processing-induced molecular structure study showed that the sensitivity and responses of functional groups can be detected by both ATR-FTIR and SR-IMS techniques, and different functional groups in cereal grain tissues respond differently to the heating methods, although not all heat-induced structural changes detected by the two mid-IR techniques are highly related to the nutrient availability of cereal grains in ruminants.

Due to the difference in sample-preparation and sampling areas, the results found by the two mid-IR techniques were also different. Similar to the conventional studies, the grains were ground and well-mixed before using the ATR-FTIR technique. The results found in the conventional studies indicated that moist heating had greater impact on nutrient availabilities compared with dry heating. In accordance with such results, ATR-FTIR method also detected stronger influence on spectral peak areas, heights, and ratios (**Figure 3**). These alterations, especially the changes on protein secondary structure, were highly related with the nutrient availability in cereal grains.

**Figure 3.** Ratios of modeled α-helix to modeled β-sheet height affected by processing methods detected by ATR-FTIR technique.

The grain seeds were cross-sectioned into thin (6 μm) sections and spectra were collected from the endosperm area. The results discovered by the SR-IMS technique indicated that dry heating also played a big role in changing the secondary structures and functional groups of the grains. As the peak areas and peak heights represent combined information of nutrient amount and molecular structure, with the nutrient contents affected by moist heating, it is very likely that the molecular structures in the endosperm were also changed. Unfortunately, such change was not specified in this part of study. In comparison with results found by the ATR-FTIR technique, less and weaker correlation was discovered between the heat-induced structural changes and the nutrient availability in the endosperm area of cereal grains in ruminants by the SR-IMS technique.

#### **5.2. Application 2: microwave irradiation-induced changes in protein molecular structures of barley grains: relationship with changes in protein chemical profile, protein subfractions, and digestion in dairy cows**

These studies [38, 39] aimed to evaluate microwave irradiation (MIR)-induced changes in crude protein (CP) subfraction profiles; ruminal CP degradation characteristics and intestinal digestibility of rumen undegraded protein (RUP); and protein molecular structures in barley (*Hordeum vulgare*) grains. Samples from hulled and hulless cultivars of barley, harvested in two consecutive years from four replicate plots, were evaluated. The samples were either kept as raw or irradiated in a microwave for 3 min (MIR3) or 5 min (MIR5). Compared with raw grains, MIR5 decreased the contents rapidly degradable CP subfraction (45.2–6.4% CP) and the ruminal degradation rate (8.16–3.53%/h) of potentially degradable subfraction. As a consequence the effective ruminal degradability of CP decreased (55.7–34.1% CP), and RUP supply (43.3–65.9% CP) to the postruminal tract increased. The MIR decreased the spectral intensities of amide 1, amide II, α-helix and β-sheet, and increased their ratios. The changes in protein spectral intensities were strongly correlated with the changes in CP subfractions and digestive kinetics. These results show that MIR for a short period (5 min) with a lower energy input can improve the nutritive value and utilization of CP in barely grains.

## **6. Summary, implications, and future research areas**

## **6.1. Summary**

conventional studies indicated that moist heating had greater impact on nutrient availabilities compared with dry heating. In accordance with such results, ATR-FTIR method also detected stronger influence on spectral peak areas, heights, and ratios (**Figure 3**). These alterations, especially the changes on protein secondary structure, were highly related with the nutrient

332 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

**Figure 3.** Ratios of modeled α-helix to modeled β-sheet height affected by processing methods detected by ATR-FTIR

The grain seeds were cross-sectioned into thin (6 μm) sections and spectra were collected from the endosperm area. The results discovered by the SR-IMS technique indicated that dry heating also played a big role in changing the secondary structures and functional groups of the grains. As the peak areas and peak heights represent combined information of nutrient amount and molecular structure, with the nutrient contents affected by moist heating, it is very likely that the molecular structures in the endosperm were also changed. Unfortunately, such change was not specified in this part of study. In comparison with results found by the ATR-FTIR technique, less and weaker correlation was discovered between the heat-induced structural changes and the nutrient availability in the endosperm area of cereal grains in ruminants by the SR-IMS

**5.2. Application 2: microwave irradiation-induced changes in protein molecular structures of barley grains: relationship with changes in protein chemical profile, protein subfractions,**

These studies [38, 39] aimed to evaluate microwave irradiation (MIR)-induced changes in crude protein (CP) subfraction profiles; ruminal CP degradation characteristics and intestinal digestibility of rumen undegraded protein (RUP); and protein molecular structures in barley

availability in cereal grains.

technique.

technique.

**and digestion in dairy cows**

As different ratios of IR could be absorbed in different molecules when mid-IR is applied, the functional groups have their unique spectra, especially in the "fingerprint region." There have been many applications of ATR-FTIR and SR-IMS on animal feed researches in recent years. The results show that these two advanced mid-IR approaches can effectively detect the microstructural changes in some plant tissues. With the help of the statistical analysis, quantitative differences lie between different spectra could be discovered.

## **6.2. Implications**

Functional groups in different type of plants could have different sensibility and react differently to external changes. Feed processing methods could change the inner structure of the plant tissues, such change can probably be detected by mid-IR techniques such as SR-IMS and ATR-FTIR. Combined with conventional animal nutrition studies, the link between structural changes in spectral areas such as amide, CHO, and cellulosic compounds and nutrient availability of the plant could be found.

#### **6.3. Further research areas**

Our further research plans include using ATR-FTIR technique to detect the sensitivity and responses of various chemical functional groups in different types of feed materials to different types of feed processing methods, and building up models using the spectral parameters to estimate the nutrient utilization and availability in ruminants. We would also expand the sampling areas when using the SR-IMS technique and combine methods such as Mid-IR microspectroscopic mapping to better understand the inner structural changes in the plant tissues.

## **Acknowledgements**

The Ministry of Agriculture Strategic Research Chair (PY) research programs have been supported by grants from Natural Sciences and Engineering Research Council of Canada (NSERC—Individual Discovery Grants and CRD grants), Saskatchewan Agricultural Development Fund (ADF), Ministry of Agriculture Strategic Feed Research Chair Program, Western Grain Research Foundation (WGRF), Saskatchewan Forage Network, SaskPulse, SaskCanola, SaskMilk, etc. Parts of this chapter are reproduced with permission from [25].

## **Author details**

Yuguang Ying and Peiqiang Yu\*

\*Address all correspondence to: peiqiang.yu@usask.ca

Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Canada

## **References**


[7] Ochiai, S. 2015. Attenuated total reflection measurements. In: Tasumi, M., ed. Introduction to experimental infrared spectroscopy: fundamentals and practical methods. John Wiley & Sons, Ltd., West Sussex, UK, pp. 179–198.

**Acknowledgements**

**Author details**

**References**

Yuguang Ying and Peiqiang Yu\*

\*Address all correspondence to: peiqiang.yu@usask.ca

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University of Saskatchewan, Saskatoon, Canada

The Ministry of Agriculture Strategic Research Chair (PY) research programs have been supported by grants from Natural Sciences and Engineering Research Council of Canada (NSERC—Individual Discovery Grants and CRD grants), Saskatchewan Agricultural Development Fund (ADF), Ministry of Agriculture Strategic Feed Research Chair Program, Western Grain Research Foundation (WGRF), Saskatchewan Forage Network, SaskPulse, SaskCanola,

SaskMilk, etc. Parts of this chapter are reproduced with permission from [25].

334 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

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Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

#### **Fluorescence Spectroscopy for the Analysis of Spirit Drinks Fluorescence Spectroscopy for the Analysis of Spirit Drinks**

Jana Sádecká, Veronika Uríčková and Michaela Jakubíková Jana Sádecká, Veronika Uríčková and Michaela Jakubíková

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64002

#### **Abstract**

338 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

There are many prescribed methods for the analysis of important components and parameters of spirit drinks. Nevertheless, there is a continuous search for new rapid and simple alternative methods that can be used together with recommended methods. The aim of the chapter is to make a review about themes such as quantification of individual components in the spirit drinks, classification of spirit drinks, and determi‐ nation of adulterants. The chapter shows that fluorescence spectroscopy has a significant potential for being used in spirit drink research because many alcoholic beverage products contain intrinsic fluorophores. Fluorescence spectroscopy allows the determination of some compounds at concentration as low as 0.1–1 μg/L often without sample preparation, there is no use of chemicals and the time of analysis can be very short. The combination of fluorescence data with chemometric tools is a promising approach for the classification of spirit drinks and for the detection of spirit drink adulteration.

**Keywords:** fluorescence spectroscopy, chemometrics, beverage, spirit drink, classifica‐ tion

## **1. Introduction**

Fluorescence, like the other molecular spectroscopies, represents an attractive option for food and beverage analysis because it is rapid, sensitive and non‐destructive. The reviews on this matter have been reported [1–3]. According to Regulation (EC) No 110/2008 [4], 'spirit drink' means an alcoholic beverage possessing particular organoleptic qualities, having a minimum

© 2016 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. © 2016 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.

alcoholic strength of 15% vol., having been produced: (i) either directly (by the distillation, with or without added flavorings, and/or by the maceration of plant materials in ethyl alcohol of agricultural origin, and/or by the addition of flavorings to ethyl alcohol of agricultural origin), (ii) or by the mixture of a spirit drink with one or more other spirit drinks. The Regulation (EC) No 110/2008 defines 46 different categories of spirit drinks. For the purposes of this review, spirit drinks are divided into two general classes: (1) unaged (vodka, gin, juniper‐flavoured spirit drink and fruit spirit) and (2) aged in wooden casks (brandy, whisky, mezcal, tequila, cachaça and calvados). The term age refers to the actual duration of storage, while maturity expresses the degree to which chemical changes occur during storage. Most governments specify storage time for various products.

The major constituents of each spirit drink consist of ethanol and water. The minor or trace constituents are higher alcohols, carbonyl compounds, esters, aldehydes, lactones, organic acids, etc. [5]. However, there are almost the same fluorophores in the different spirits, among others, volatile phenols and anisols in unaged spirits, and phenolic compounds and coumarins in spirits aged in wooden casks.

Fluorescence spectra of distilled spirits are typically composed of broad overlapping fluores‐ cence bands containing chemical, physical and structural information of all sample compo‐ nents. Therefore, conventional fluorescence technique based on recording of single emission or excitation spectra is often insufficient for analysing spirit drinks. In some cases, total luminescence or synchronous scanning fluorescence techniques may improve the analytic potential of fluorescence measurements. The analytical information should be extracted from fluorescence spectra using multivariate and multiway methods, which allow to group samples with similar characteristics, to establish classification methods for unknown samples (quali‐ tative analysis) or to perform methods determining some property of unknown samples (quantitative analysis) [6].

There are many prescribed methods for the analysis of important components and parameters of spirit drinks. The most widely used methods are sensory evaluation, gas chromatography, liquid chromatography, mass spectrometry, ultraviolet–visible (UV/VIS) spectrophotometry and infrared spectrometry [5]. Nevertheless, there is a continuous search for new alternative methods that can be used together with recommended methods.

The aim of the chapter is to make a review about themes such as quantification of individual components in the spirit drinks, classification of spirit drinks and adulteration detection in order to highlight the potential of fluorescence spectroscopy in the beverage analysis.

## **2. Fluorescence spectra of spirit drinks**

Conventional fluorescence spectroscopy uses either a fixed excitation wavelength (λex) to record an emission spectrum or a fixed emission wavelength (λem) to record an excitation spectrum. The broad shape of both the excitation and emission fluorescence bands limits the possibility of finding a unique λex and λem for each potential analyte [7]. Selectivity is often improved through fluorimetric strategies such as total luminescence, synchronous scanning fluorescence or total synchronous scanning fluorescence.

alcoholic strength of 15% vol., having been produced: (i) either directly (by the distillation, with or without added flavorings, and/or by the maceration of plant materials in ethyl alcohol of agricultural origin, and/or by the addition of flavorings to ethyl alcohol of agricultural origin), (ii) or by the mixture of a spirit drink with one or more other spirit drinks. The Regulation (EC) No 110/2008 defines 46 different categories of spirit drinks. For the purposes of this review, spirit drinks are divided into two general classes: (1) unaged (vodka, gin, juniper‐flavoured spirit drink and fruit spirit) and (2) aged in wooden casks (brandy, whisky, mezcal, tequila, cachaça and calvados). The term age refers to the actual duration of storage, while maturity expresses the degree to which chemical changes occur during storage. Most

340 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

The major constituents of each spirit drink consist of ethanol and water. The minor or trace constituents are higher alcohols, carbonyl compounds, esters, aldehydes, lactones, organic acids, etc. [5]. However, there are almost the same fluorophores in the different spirits, among others, volatile phenols and anisols in unaged spirits, and phenolic compounds and coumarins

Fluorescence spectra of distilled spirits are typically composed of broad overlapping fluores‐ cence bands containing chemical, physical and structural information of all sample compo‐ nents. Therefore, conventional fluorescence technique based on recording of single emission or excitation spectra is often insufficient for analysing spirit drinks. In some cases, total luminescence or synchronous scanning fluorescence techniques may improve the analytic potential of fluorescence measurements. The analytical information should be extracted from fluorescence spectra using multivariate and multiway methods, which allow to group samples with similar characteristics, to establish classification methods for unknown samples (quali‐ tative analysis) or to perform methods determining some property of unknown samples

There are many prescribed methods for the analysis of important components and parameters of spirit drinks. The most widely used methods are sensory evaluation, gas chromatography, liquid chromatography, mass spectrometry, ultraviolet–visible (UV/VIS) spectrophotometry and infrared spectrometry [5]. Nevertheless, there is a continuous search for new alternative

The aim of the chapter is to make a review about themes such as quantification of individual components in the spirit drinks, classification of spirit drinks and adulteration detection in

Conventional fluorescence spectroscopy uses either a fixed excitation wavelength (λex) to record an emission spectrum or a fixed emission wavelength (λem) to record an excitation spectrum. The broad shape of both the excitation and emission fluorescence bands limits the possibility of finding a unique λex and λem for each potential analyte [7]. Selectivity is often

order to highlight the potential of fluorescence spectroscopy in the beverage analysis.

methods that can be used together with recommended methods.

**2. Fluorescence spectra of spirit drinks**

governments specify storage time for various products.

in spirits aged in wooden casks.

(quantitative analysis) [6].

Total luminescence spectrum (TLS) presents simultaneously all the excitation and emission spectra over the range of wavelengths scanned [8] and can be shown as a contour map with λem and λex as *x*‐ and *y*‐axes, respectively, and contours linking points of equal fluorescence intensity (**Figure 1a**). In TLS, two types of scattering peaks can be found: Rayleigh scattering at the λex = λem, and Raman scattering at a distance from the Rayleigh peak that is charac‐ teristic for the solvent. Because TLS spectra represent the total fluorescence profiles of the samples, they are particularly useful in pattern recognition of samples characterised by small differences in their composition [9].

**Figure 1.** Total luminescence spectra (a,c,d) and TSFS (b) of undiluted (a,b,d) and diluted (c) brandy obtained using right‐angled (a,b,c) and front‐face geometry (d). \*\*TLS and TSFS were recorded using the Perkin–Elmer LS 50 Lumi‐ nescence Spectrometer equipped with the Xenon lamp. Samples were placed in 10 × 10 × 45 mm quartz cell. Excitation and emission slits were both set at 5.0 nm. Scan speed was 200 nm.min–1.

In synchronous fluorescence spectroscopy [10], the λex and λem are scanned simultaneously in such a way that a constant wavelength interval Δλ = λem – λex is kept between them. When a value of Δλ is chosen properly, the resulting synchronous fluorescence spectrum (SFS) shows one or a few features that are much more resolvable than those in the conventional fluorescence spectrum because synchronous fluorescence reduces spectral overlaps by narrowing spectral bands and simplifies spectra by amplifying strong fluorescence bands. A choice of Δλ could be either the difference between the wavelength of emission maximum (λem, max) and the corresponding wavelength of excitation maximum (λex, max) to provide the highest sensitiv‐ ity, or the particular difference to give a compromise between sensitivity and selectivity [11, 12].

Total synchronous fluorescence spectrum (TSFS) is obtained by plotting fluorescence intensity as a function of the wavelength and Δλ value (**Figure 1b**) and combine the advantages of TLS and SFS. Because λem is always higher than λex, Rayleigh scattering is not found in TSFS.

Independent of the type of spectrum, the apparent fluorescence intensity and spectral distri‐ bution is affected by both the optical density of the sample (**Figure 1a** and **c**) and the geometry of sample illumination (**Figure 1a** and **d**). The most common geometry is right‐angle obser‐ vation of the center of a centrally illuminated cuvette. It is typically used to analyse dilute solutions and other transparent samples (absorbance < 0.1). At high optical densities, signal reaching detector will be significantly disturbed due to the inner filtering effects. In the front‐ face geometry, the excitation light is focused to the front surface of the samples and then fluorescence emission is collected from the same region at an angle that minimizes reflected and scattered light. Front‐face illumination is generally used to decrease the inner filtering effects [7].

### **2.1. Spirit drinks unaged in wooden casks**

The major constituents of each spirit drink, ethanol and water molecules do not exhibit fluorescence. However, when ethanol mixes with water, ethanol and water molecules form molecular clusters by hydrogen bonding and emit different fluorescence photons [13]. When excited by λex = 236 nm, there were eight kinds of luminescence structures in the ethanol– water mixtures, giving the emission bands at 292, 304, 314, 330, 345, 355, 365 and 377 nm, respectively. The fluorescence bands at 355 and 377 nm have maximum intensity when the percent of ethanol is 20%. The other six kinds have maximum intensity for 60% ethanol content [14].

Different flavour exhibits different effect on the fluorescence of 60% ethanol–water mixture characterised by the main band centred at λex/λem = 225/335 nm. The simultaneous addition of eight major flavours (acetaldehyde, ethyl acetate, methanol, propyl alcohol, isobutyl alcohol, isoamyl alcohol, ethyl lactate and acetic acid) make the band at 225/335 nm in excitation/ emission disappear and cause the appearance of bands at λex/λem of 285/325 nm as well as at 375/425 nm. The 225/335 nm fluorescence band initially increases and then decreases with increased ethyl acetate or acetate concentration in the 60% ethanol–water mixture. For the Fenjiu samples aged in ceramic containers, the effect of total ester concentration is consistent with the result of ethyl acetate in the 60% ethanol–water mixture, however, the effect of acetic acid differs [15].

Vodka is the simplest distilled spirit, the character of which comes from the ethanol, normally distilled from grain fermentation. Vodka Finlandia (40%) is amongst the purest in the world, its typical TLS and TSFS are shown in **Figure 2**. The short‐wavelength band in TLS, which has maximum at λex/λem = 230/335 nm, corresponds to the band at 220–230 nm (Δλ = 90 – 100 nm) in TSFS and can be assigned to luminescence structures in the ethanol–water mixture. It should be noticed that there is no available information or data on the origin of fluorescence of vodka. However, some of the volatile compounds (1,3,5‐trimethylbenzene and p‐cymene) identified by GC‐MS in vodka [16, 17] are known fluorophores. The micro array based on fluorescence dye solutions and their binary mixtures shows vodka pattern with a certain similarity but slightly different from the aqueous ethanol pattern [18].

spectrum because synchronous fluorescence reduces spectral overlaps by narrowing spectral bands and simplifies spectra by amplifying strong fluorescence bands. A choice of Δλ could be either the difference between the wavelength of emission maximum (λem, max) and the corresponding wavelength of excitation maximum (λex, max) to provide the highest sensitiv‐ ity, or the particular difference to give a compromise between sensitivity and selectivity [11, 12].

342 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Total synchronous fluorescence spectrum (TSFS) is obtained by plotting fluorescence intensity as a function of the wavelength and Δλ value (**Figure 1b**) and combine the advantages of TLS and SFS. Because λem is always higher than λex, Rayleigh scattering is not found in TSFS.

Independent of the type of spectrum, the apparent fluorescence intensity and spectral distri‐ bution is affected by both the optical density of the sample (**Figure 1a** and **c**) and the geometry of sample illumination (**Figure 1a** and **d**). The most common geometry is right‐angle obser‐ vation of the center of a centrally illuminated cuvette. It is typically used to analyse dilute solutions and other transparent samples (absorbance < 0.1). At high optical densities, signal reaching detector will be significantly disturbed due to the inner filtering effects. In the front‐ face geometry, the excitation light is focused to the front surface of the samples and then fluorescence emission is collected from the same region at an angle that minimizes reflected and scattered light. Front‐face illumination is generally used to decrease the inner filtering

The major constituents of each spirit drink, ethanol and water molecules do not exhibit fluorescence. However, when ethanol mixes with water, ethanol and water molecules form molecular clusters by hydrogen bonding and emit different fluorescence photons [13]. When excited by λex = 236 nm, there were eight kinds of luminescence structures in the ethanol– water mixtures, giving the emission bands at 292, 304, 314, 330, 345, 355, 365 and 377 nm, respectively. The fluorescence bands at 355 and 377 nm have maximum intensity when the percent of ethanol is 20%. The other six kinds have maximum intensity for 60% ethanol

Different flavour exhibits different effect on the fluorescence of 60% ethanol–water mixture characterised by the main band centred at λex/λem = 225/335 nm. The simultaneous addition of eight major flavours (acetaldehyde, ethyl acetate, methanol, propyl alcohol, isobutyl alcohol, isoamyl alcohol, ethyl lactate and acetic acid) make the band at 225/335 nm in excitation/ emission disappear and cause the appearance of bands at λex/λem of 285/325 nm as well as at 375/425 nm. The 225/335 nm fluorescence band initially increases and then decreases with increased ethyl acetate or acetate concentration in the 60% ethanol–water mixture. For the Fenjiu samples aged in ceramic containers, the effect of total ester concentration is consistent with the result of ethyl acetate in the 60% ethanol–water mixture, however, the effect of acetic

Vodka is the simplest distilled spirit, the character of which comes from the ethanol, normally distilled from grain fermentation. Vodka Finlandia (40%) is amongst the purest in the world, its typical TLS and TSFS are shown in **Figure 2**. The short‐wavelength band in TLS, which has

effects [7].

content [14].

acid differs [15].

**2.1. Spirit drinks unaged in wooden casks**

**Figure 2.** Total luminescence spectrum (TLS) (a) and total synchronous fluorescence spectrum (TSFS) (b) of vodka Fin‐ landia \*\*TLS and TSFS were recorded using the Perkin–Elmer LS 50 Luminescence Spectrometer equipped with the Xenon lamp. Samples were placed in 10 × 10 × 45 mm quartz cell. Excitation and emission slits were both set at 5.0 nm. Scan speed was 200 nm.min–1.

Juniper‐flavoured spirit drinks (JFSDs) are produced by flavoring ethyl alcohol of agricultural origin and/or grain spirit with juniper (*Juniperus communis L*. and/or *Juniperus oxicedrus L*.) berries. Eugenol, totarol, *o*‐cymene, *p*‐cymene, *p*‐cymene‐8‐ol, calamenene, calacorene, phenolic acids, flavonoids, biflavonoids, coumarins, tyrosol and chlorophyll are the best known fluorescent molecules in juniper berries (see references in [19]). Volatile compounds that survived distillation (*o*‐cymene, *p*‐cymene, calamenene, calacorene [20, 21] and totarol [22], were found in gin, and more than 30 possible fluorophores were detected in JFSDs (mainly substituted benzenes, phenols and anisols) [19, 20]. Many substituted phenols or anisols and diterpenoids show similar fluorescence properties, e.g., λex/λem = 288/315 nm for eugenol [23], λex/λem = 275/315 nm for totarol [24].

The most popular JFSD is gin, which TLS is characterised by the main fluorophores centred at λex = 220 and 304 nm and λem = 337 nm. The first pair of wavelengths (λex/λem = 220/337 nm) is similar to that observed for vodka, the second one (λex/λem = 304/337 nm) is characteristic for London gin. Other JFSDs show band with excitation at about 250–290 nm and emission at about 330–340 nm. Moreover, Belgian and Czech JFSDs show additional band at longer wavelength (**Table 1**). Modelling of TLS allowed relating the fluorescence bands of drinks to 2‐phenylethanol, eugenol, carvacrol, 4‐allylanisole, *p*‐cymene and coumarin derivatives [19]. JFSDs are sometimes marketed in the glass bottles containing dried berries or twig inside. Such JFSDs had abnormally high fluorescence intensity at about 260/335 nm in excitation/emission, which could be attributed to compounds extracted from berries or twig [25].


λex,max wavelength of excitation maximum, λem,max wavelength of emission maximum.

**Table 1.** Fluorescent properties of bulk spirit drinks obtained using right‐angled geometry.


**Table 2.** Fluorescent properties of diluted spirit drinks.

JFSDs had abnormally high fluorescence intensity at about 260/335 nm in excitation/emission,

317–350 426–447

300–304 400–402

300 419

302 420

304 417

350 443

330 423

330 460 [39] 365 460 [39] 405 510 [39]

**Spirit drink λex,max (nm) λem,max (nm) Reference**

Vodka Finlandia 230, 260 335 This work London Gin 220, 302–306 335–340 [19] Juniper‐flavoured (Slovak) 277–290 330–343 [19] Juniper‐flavoured (Belgian) 280–290 330–340 [19]

Juniper‐flavoured (German) 250–260 331–333 [19] Juniper‐flavoured (Czech) 260–265 335–337 [19]

Apple 250 327 [26]

Apricot 290 317 [26] Pear 235 349 [26]

Plum 266 330 [26]

Apricot spirit with fruit 270 360 [26]

Pear spirit with fruit 260 366 [26]

Mixed wine 390–400 480–500 [27]

Brandy 450–460 520–540 [27] Mezcal 514 580 [35] Tequila 337 430 [38] Tequila 255 470 [39]

Cachaça (amendoim) 330 400 [44] Cachaça (balsam) 340 480 [44] Cachaça (oak) 280 320 [44] Cachaça (jequitibá) 260 370 [44] Cachaça (umburana) 380 450 [44] Calvados 410 506 [26]

λex,max wavelength of excitation maximum, λem,max wavelength of emission maximum.

**Table 1.** Fluorescent properties of bulk spirit drinks obtained using right‐angled geometry.

which could be attributed to compounds extracted from berries or twig [25].

344 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Unaged in wooden casks

Aged in wooden casks

Fruit spirits are made of different varieties of fruits by the alcoholic fermentation and distillation. They are usually aged in glass containers, marketed as 'pure' beverages or in the bottles containing a whole dried fruit. **Table 1** shows the characteristic λex,max and λem,max corresponding to the four types of fruit spirits. Bulk apple, pear and plum spirits exhibit two fluorescent bands, one with fluorescent maximum between 250 and 290 nm in the λex and between 330 and 350 nm in the λem range, whose exact position depended on the fruit type, and the second with excitation maximum at about 300 nm and emission at about 420 nm. In contrast, bulk apricot spirit exhibits only the short‐wavelength band. Bulk spirits containing fruit show two fluorescent maxima at longer wavelengths (**Table 1**). The UV absorption of bulk fruit spirits is from 2 up to 4 absorbance units when scanning from 225 to 300 nm, and therefore the inner filter phenomena affect the right‐angle spectra considerably. One way to reduce the inner filter effects is to dilute the sample with an appropriate solvent. On the other hand, dilution can reduce concentration of some components bellow limit of detection. As an example, **Table 2** shows the λex,max and λem,max of fruit sprits upon dilution. Apple spirits exhibited no reasonable fluorescence upon 40‐fold dilution. Both diluted apricot and pear spirits exhibit a band with a maximum fluorescence at λex = 280 nm. The different position of emission band for apricot and pear spirits enables us to distinguish between them. In addition, λex,max and λem,max of diluted plum spirits are different from the other fruit spirits. The compounds such as 1‐phenylethanol, 2‐phenylethanol, eugenol, 4‐allylanisole, 4‐vinylanisole, 4‐ethylphenol, 4‐ethylguaiacol and p‐cymene can be detected using λex /λem of 280/320 nm after separation by HPLC. In the case of spirits containing fruit, there is a wider variety of fluorescent compounds, including not only those found in pure spirits but also benzoic and cinnamic acids and their aldehydes [26].

Mixed wine spirits are wine distillates diluted with ethanol from other sources, frequently blended with sugar, brandy aroma and caramel. Some mixed wine spirits contain honey or colourants. TLS contours of bulk mixed wine spirits are concentrated in the λem region from 460 to 530 nm and the λex between 380 and 420 nm [27]. The spectra recorded in right‐ angled geometry are distorted due to inner‐filter effect. Diluted wine distillates exhibit two fluorescence bands centered at the λex /λem pairs of 280/350 nm and 330/430 nm, respec‐ tively (**Table 2**). The short‐wavelength band is similar to the one observed in the fluorescence spectra of other distilled spirits and it may partly originate from compounds of the grape distillate. The long‐wavelength band originates mainly from caramel [28].

## **2.2. Spirit drinks aged in wooden casks**

Freshly distilled spirits are colourless and possess only the flavour and aroma of the grain and the alcohol. Many producers use "ageing wooden barrels" to mature distilled spirits like brandy, Calvados, whisky, mezcal, cachaça and tequila. Barrels are typically made of French or American oak, but chestnut and redwood are also used. The ageing involves several processes: lignins decompose with formation of phenolic compounds (vanillin, syringalde‐ hyde, coniferaldehyde, sinapaldehyde, cinnamic and benzoic acids), hydrolysable tannins and their products (gallic and ellagic acids) and coumarins (particularly scopoletin) are extracted from wood, and reactions may occur between components of wood and spirit. These processes and their products are very important for the quality of the matured spirits (taste, flavour and colour) [29]. In addition, phenolic compounds and coumarins are well‐known fluorophores.

Brandy is a spirit drink produced from wine spirit, whether or not blended with a wine distillate. Types of brandies, originally at least, tended to be location‐specific. Brandy has to be aged for a certain period in oak casks. Using right‐angled geometry, the TLS contours for bulk brandy are concentrated in the λem region from 510 to 570 nm and λex region from 430 to 480 nm [27]. Using front‐face geometry, the total luminescence contours for bulk brandy are concentrated in the λem region from 470 to 520 nm and λex region from 390 to 430 nm [30]. Undiluted brandy exhibits a high UV/VIS absorption, thus the fluorescence recorded on the bulk brandy is severely distorted due to the inner filter effects. The short‐wavelength fluores‐ cence, with λex,max = 280 nm and λem,max =370 and 450 nm, is clearly observed for diluted brandy samples, along with the longer‐wavelength fluorescence, with excitation at 340 nm and emission at 450 nm (**Table 2**). The former band is preliminary attributed to the tryptophol, tyrosol and phenolic acids, the latter band to cinnamic acids, coumarins, tannins and other unknown fluorescent compounds [28].

Whisky (whiskey) is spirit‐based drink made from malted or saccharified grains, which should mature for at least 3 years in wooden barrels. Plain spirited caramel of a specific grade is added simply in order to adjust the consistency of the colour [31]. Regarding bulk whisky, front‐face fluorescence spectrum recorded at λex = 404 nm exhibit a wide emission band in the 450–700 nm range with maximum at 520 nm. The fluorescent band arises from the caramel, coumarins, tannins and other fluorescent compounds originating from wooden casks [32]. Tequila and mezcal are two traditional Mexican distilled beverages with similar production phases. Tequila must be made exclusively from Agave tequilana Weber blue variety, whereas mezcal is made from different agave species, among them A. salmiana, A. angustifolia and A. potatorum [33]. Maturation of mezcal and tequila is optional, contributing flavour in a similar way to all the other wood‐matured spirits. Using liquid chromatography with ion trap mass spectrometry detection, ten phenolic acids were quantified in tequilas [34]. Fluorescence spectra of bulk mezcal obtained using right‐angled geometry have emission maximum at about 580 nm (λex = 517 nm). White/young mezcal exhibit spectra similar to ethanol. On the other hand, aged mezcal, and the other types of mezcal differ in the intensity of the emission spectra due to the higher concentration of organic molecules extracted from the wood cask [35, 36]. Using the fluorescent background of Raman spectra, it has been possible to distinguish tequila blanco (unaged) from aged tequila [37]. Later fluorescence between 370 and 510 nm of bulk tequila excited at 337 nm has been observed [38]. Recently reference [39] reported the right‐angled fluorescence spectra recorded at four λex (255, 330, 365 and 405 nm) by original tequilas and counterfeit tequilas.

4‐ethylphenol, 4‐ethylguaiacol and p‐cymene can be detected using λex /λem of 280/320 nm after separation by HPLC. In the case of spirits containing fruit, there is a wider variety of fluorescent compounds, including not only those found in pure spirits but also benzoic and

Mixed wine spirits are wine distillates diluted with ethanol from other sources, frequently blended with sugar, brandy aroma and caramel. Some mixed wine spirits contain honey or colourants. TLS contours of bulk mixed wine spirits are concentrated in the λem region from 460 to 530 nm and the λex between 380 and 420 nm [27]. The spectra recorded in right‐ angled geometry are distorted due to inner‐filter effect. Diluted wine distillates exhibit two fluorescence bands centered at the λex /λem pairs of 280/350 nm and 330/430 nm, respec‐ tively (**Table 2**). The short‐wavelength band is similar to the one observed in the fluorescence spectra of other distilled spirits and it may partly originate from compounds of the grape

Freshly distilled spirits are colourless and possess only the flavour and aroma of the grain and the alcohol. Many producers use "ageing wooden barrels" to mature distilled spirits like brandy, Calvados, whisky, mezcal, cachaça and tequila. Barrels are typically made of French or American oak, but chestnut and redwood are also used. The ageing involves several processes: lignins decompose with formation of phenolic compounds (vanillin, syringalde‐ hyde, coniferaldehyde, sinapaldehyde, cinnamic and benzoic acids), hydrolysable tannins and their products (gallic and ellagic acids) and coumarins (particularly scopoletin) are extracted from wood, and reactions may occur between components of wood and spirit. These processes and their products are very important for the quality of the matured spirits (taste, flavour and colour) [29]. In addition, phenolic compounds and coumarins are well‐known fluorophores. Brandy is a spirit drink produced from wine spirit, whether or not blended with a wine distillate. Types of brandies, originally at least, tended to be location‐specific. Brandy has to be aged for a certain period in oak casks. Using right‐angled geometry, the TLS contours for bulk brandy are concentrated in the λem region from 510 to 570 nm and λex region from 430 to 480 nm [27]. Using front‐face geometry, the total luminescence contours for bulk brandy are concentrated in the λem region from 470 to 520 nm and λex region from 390 to 430 nm [30]. Undiluted brandy exhibits a high UV/VIS absorption, thus the fluorescence recorded on the bulk brandy is severely distorted due to the inner filter effects. The short‐wavelength fluores‐ cence, with λex,max = 280 nm and λem,max =370 and 450 nm, is clearly observed for diluted brandy samples, along with the longer‐wavelength fluorescence, with excitation at 340 nm and emission at 450 nm (**Table 2**). The former band is preliminary attributed to the tryptophol, tyrosol and phenolic acids, the latter band to cinnamic acids, coumarins, tannins and other

Whisky (whiskey) is spirit‐based drink made from malted or saccharified grains, which should mature for at least 3 years in wooden barrels. Plain spirited caramel of a specific grade is added simply in order to adjust the consistency of the colour [31]. Regarding bulk whisky, front‐face fluorescence spectrum recorded at λex = 404 nm exhibit a wide emission band in the 450–700

distillate. The long‐wavelength band originates mainly from caramel [28].

346 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

cinnamic acids and their aldehydes [26].

**2.2. Spirit drinks aged in wooden casks**

unknown fluorescent compounds [28].

Cachaça, the most popular distilled alcoholic beverage in Brazil, is a distilled spirit made from sugarcane juice. It can be aged in barrels of amendoim (*Pterogyne nitens*), balsam (*Myroxylon peruiferum*), jequitibá (*Cariniana estrellensis*), umburana (*Amburana cearensis*) and oak (*Quercus sp*.). Using HPLC‐ESI‐MS(n), 14 phenolic compounds and two coumarins were detected in sugarcane spirit extracts of six different Brazilian woods and oak, commonly used by cooper‐ age industries for ageing cachaça [40]. TLSs of bulk cachaça exhibit excitation and emission maxima in the range 260–380 nm and 320–480 nm (**Table 1**), respectively, corresponding to phenolic acids (gallic, syringic, vanillic and ellagic), phenolic aldehydes (sinapaldehyde, coniferaldehyde, syringaldehyde and vanillin) and coumarin [41].

Calvados is an apple‐brandy of France. Fluorescent compounds such as 4‐vinylanisole, 4‐ methylguaiacol, methyleugenol, 4‐ethylguaiacol, eugenol, 4‐ethylphenol and 4‐vinylguaiacol are found in freshly distilled Calvados [42], while 2‐phenylethanol, 4‐methylguaiacol, meth‐ yleugenol, 4‐ethylguaiacol, eugenol and 4‐ethylphenol [43] in matured Calvados. Bulk Calvados is easily distinguishable from the other fruit drinks because its λex,max and λem,max are considerably higher. Diluted Calvados revealed the same fluorescence band as that observed for diluted grape brandies—wine spirits aged in oak barrels. The band could be due to the presence of phenolic compounds extracted from wood [26].

The absorption of undiluted aged spirit samples is from 1 up to 5 absorbance units, thus, brandies, cachaças and mezcals have by far the highest absorbances, regardless of wavelength [33, 44, 45]. Therefore, the analysis of spectra recorded using right‐angled geometry, which are affected by inner filter effects, may lead to spectral misinterpretation and invalid assignments of origin of some fluorescent bands. So far, fluorescence spectra unaffected by inner filter effects are available only for diluted brandy, mixed wine spirit and Calvados.

## **3. Applications of fluorescence spectroscopy**

#### **3.1. Quantification of individual components**

#### *3.1.1. Naturally occurring components*

To determine alcohol, several fluorescence biosensors have been produced by integrating alcohol oxidase or alcohol dehydrogenase enzymes with optical fibers. The utility of enzyme biosensors is restricted due to their low stability and short lifetime determined mainly by enzyme kinetics, the necessity to add the coenzyme to the solution and the temperature [46–48].

Chemosensors are another big group of devices for the determination of alcohol. The appli‐ cation of a fluorescent reagent, fluorescein octadecyl ester, in a fiber optic sensor for the determination of aliphatic alcohols in a range of 10–60 v/v % has been reported [49]. Fluores‐ cence intensity was enhanced due to the formation of hydrogen bonds between alcohol and the hydroxyl group of fluorescein octadecyl ester [49]. The fluorescence quenching of the 5,10,15,20‐tetraphenyl porphyrin doped on polyvinyl chloride film by ethanol showed a linear response over the ethanol concentration in the range of 1–75 v/v % with a detection limit of 0.05 v/v % [47]. Using admixture of terphenyl‐ol and sodium carbonate, which exhibited bright sky‐blue fluorescence in the solid state upon addition of small quantities of ethanol, detection limit at about 5 v/v % of ethanol was demonstrated [50]. A simple visual test has been devel‐ oped to check the ethanol content of drinks and to detect counterfeit beverages containing methanol. When imidazolium‐based dication C10(mim)2 and dianionic 2,2′‐azino‐bis (3‐ ethylbenzothiazoline‐6‐sulfonic acid) are mixed together, they self‐assemble into a supramo‐ lecular ionic material (SIM). The product is capable of encapsulating the fluorescent dye Rhodamine 6G (R6G) to form SIM‐R6G. The addition of ethanol destructs the R6G‐SIM structure, resulting in the release of R6G. Alcohol content can be determined by measuring the fluorescence line of R6G on a thin‐layer chromatography (TLC) plate within a concentration range from 15 to 40%. The addition of a trace amount of methanol leads to a large increase of the length of R6G on TLC plates [51]. Another supramolecular material has been prepared with 1,4‐bis(imidazol‐1‐ylmethyl)benzene (bix) as the ligand, Zn2+ as the central metal ion and encapsulated fluorescent dye Rhodamine B (RhB). The formed RhB/Zn(bix) is stable in ethanol, however, the addition of water results in the release of RhB, allowing the determination of alcohol content within a linear range from 20 to 100 v/v % [52].

The appropriateness of both spectrofluorimetry and HPLC to determine the level of individual coumarins (umbelliferone, scopoletin and 4‐methylumbelliferone) in commercial white rum samples has been demonstrated [53]. Recently a simple multivariate calibration spectrofluori‐ metric method has been developed for the simultaneous determination of gallic, vanillic, syringic and ferulic acids and scopoletin in brandy samples, providing comparable results with those obtained by HPLC method [12].

Ellagic acid is the most explored phenolic acid compound, probably due to direct extraction of free ellagic acid and hydrolysis of wood ellagitannins [54]. Two spectrofluorimetric methods have been developed for the rapid determination of ellagic acid in brandy samples. The first method was based on the complex formation between ellagic acid and borax in methanol solution (λex/λem = 383/456 nm). In the second method, the complex was formed between ellagic acid and boric acid in ethanol solution (λex/λem = 387/447 nm). The limit of determi‐ nation was at about 0.3 μg/L. The results were found to be in good agreement with those obtained by HPLC method [55]. The potential of SFS (Δλ = 40 nm) has been demonstrated to differentiate caramel from oak wood extract. The method was selective for the determination of caramel in the presence of common components of brandies (gallic acid, syringic acid, vanillic acid, caffeic acid, ferulic acid, p‐coumaric acid, vanillin, syringaldehyde, coniferalde‐ hyde, sinapaldehyde, furfural, 5‐hydroxymethylfurfural and scopoletine). The limit of determination was 5 mg/L for caramel [56].

## *3.1.2. Contaminants*

**3. Applications of fluorescence spectroscopy**

348 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

alcohol content within a linear range from 20 to 100 v/v % [52].

those obtained by HPLC method [12].

To determine alcohol, several fluorescence biosensors have been produced by integrating alcohol oxidase or alcohol dehydrogenase enzymes with optical fibers. The utility of enzyme biosensors is restricted due to their low stability and short lifetime determined mainly by enzyme kinetics, the necessity to add the coenzyme to the solution and the temperature [46–48].

Chemosensors are another big group of devices for the determination of alcohol. The appli‐ cation of a fluorescent reagent, fluorescein octadecyl ester, in a fiber optic sensor for the determination of aliphatic alcohols in a range of 10–60 v/v % has been reported [49]. Fluores‐ cence intensity was enhanced due to the formation of hydrogen bonds between alcohol and the hydroxyl group of fluorescein octadecyl ester [49]. The fluorescence quenching of the 5,10,15,20‐tetraphenyl porphyrin doped on polyvinyl chloride film by ethanol showed a linear response over the ethanol concentration in the range of 1–75 v/v % with a detection limit of 0.05 v/v % [47]. Using admixture of terphenyl‐ol and sodium carbonate, which exhibited bright sky‐blue fluorescence in the solid state upon addition of small quantities of ethanol, detection limit at about 5 v/v % of ethanol was demonstrated [50]. A simple visual test has been devel‐ oped to check the ethanol content of drinks and to detect counterfeit beverages containing methanol. When imidazolium‐based dication C10(mim)2 and dianionic 2,2′‐azino‐bis (3‐ ethylbenzothiazoline‐6‐sulfonic acid) are mixed together, they self‐assemble into a supramo‐ lecular ionic material (SIM). The product is capable of encapsulating the fluorescent dye Rhodamine 6G (R6G) to form SIM‐R6G. The addition of ethanol destructs the R6G‐SIM structure, resulting in the release of R6G. Alcohol content can be determined by measuring the fluorescence line of R6G on a thin‐layer chromatography (TLC) plate within a concentration range from 15 to 40%. The addition of a trace amount of methanol leads to a large increase of the length of R6G on TLC plates [51]. Another supramolecular material has been prepared with 1,4‐bis(imidazol‐1‐ylmethyl)benzene (bix) as the ligand, Zn2+ as the central metal ion and encapsulated fluorescent dye Rhodamine B (RhB). The formed RhB/Zn(bix) is stable in ethanol, however, the addition of water results in the release of RhB, allowing the determination of

The appropriateness of both spectrofluorimetry and HPLC to determine the level of individual coumarins (umbelliferone, scopoletin and 4‐methylumbelliferone) in commercial white rum samples has been demonstrated [53]. Recently a simple multivariate calibration spectrofluori‐ metric method has been developed for the simultaneous determination of gallic, vanillic, syringic and ferulic acids and scopoletin in brandy samples, providing comparable results with

Ellagic acid is the most explored phenolic acid compound, probably due to direct extraction of free ellagic acid and hydrolysis of wood ellagitannins [54]. Two spectrofluorimetric methods have been developed for the rapid determination of ellagic acid in brandy samples. The first

**3.1. Quantification of individual components**

*3.1.1. Naturally occurring components*

AMPHORA project, which assessed the quality of illegally and informally produced alcohol in the European Region, reports that compared to the health effects of ethanol, the contami‐ nation problems may be of minor importance as exposure will only in worst‐case scenarios reach tolerable daily intakes of the substances as ethyl carbamate, copper manganese, acetal‐ dehyde, methanol, higher alcohols and phthalates [57]. The incidence of the aldehydes, especially of formaldehyde, in the Asian samples was considerably higher than that found in European alcoholic beverages [58].

Fluorimetry with Hantzsch reaction is commonly used for the determination of formaldehyde. Cyclohexane‐1,3‐dione (CHD) [59] and 4‐amino‐3‐penten‐2‐one (Fluoral‐P) [60, 61] have been used as Hantzsch reaction reagents. The Fluoral‐P method is based on the reaction of 4‐ amino‐3‐penten‐2‐one with formaldehyde, producing 3,5‐diacetyl‐1,4‐dihydrolutidine, which fluoresces at 510 nm when excited at 410 nm. The method is specific for formaldehyde, allowing for the determination of this analyte even in the presence of acetaldehyde concentrations 1000 times higher than formaldehyde [60]. Limit of detection was 3 μg/L for formaldehyde in cachaça, rum and vodka [61]. Aldehydes, such as formaldehyde, acetaldehyde, propionalde‐ hyde and *n*‐butyraldehyde, can completely react with CHD to form fluorescence derivatives (9‐substituted decahydroacridine‐l,8‐diones) in the presence of ammonium acetate in 1 h at 60°C. The application of microwave irradiation accelerates considerably the derivatisation reaction of formaldehyde with CHD and allows attaining a limit of detection 0.02 μg/L for formaldehyde in a shorter derivatisation reaction time. Tolerated ratio of acetaldehyde was 100 in the determination of 0.500 mg/L formaldehyde [59].

Based on the fluorescence properties of 2,4‐(1H,3H)‐quinazolinedione (λex/λem = 310/410 nm), a product of the reaction between cyanate and 2‐aminobenzoic acid, a method for the deter‐ mination of cyanate was developed with a limit of detection 4 μg/L. A correlation between the cyanate and ethyl carbamate concentrations in the sugar cane spirit was observed [62].

Fluorescent molecularly imprinted polymer (fluorescein 5(6)‐isothiocyanate‐3‐aminopropyl‐ triethoxysilane /SiO2 particles) has been used for the selective recognition and the determina‐ tion of λ‐cyhalothrin (pesticide) in Chinese spirits. Based on fluorescence quenching, the limit of detection 4 μg/L was obtained [63].

Recently, a rapid methodology has been proposed for simultaneous quantification of five PAHs (acenaphten, anthracene, benzo[*a*]pyrene, fluoranthene and pyrene) in three types of spirits (rum, cachaça and vodka) [64].

#### *3.1.3. Drugs*

Three most commonly used drugs in drink spiking are ketamine, benzodiazepines, including diazepam and flunitrazepam, and gamma‐hydroxybutyric acid (GHB).

The determination of diazepam in commercial beverages, previously spiked with drug, has been implemented through photo degradation of diazepam and detection of degradation products at λem= 463 nm (λem = 262 nm). The limit of detection was 2 mg/L [65]. A screening method for flunitrazepam in colourless alcoholic beverages is based on emission at 472 nm of protonated drug given the limit of detection 1 mg/L [66].

Zhai group has recently reported the first fluorescent sensor for gamma‐butyrolactone (GBL), the pro‐drug of GHB. GBL sensor was named Green Date and required an extraction to eliminate alcohol effects for GBL detection in real drinks [67]. The team also found that an orange fluorescent compound named GHB Orange is capable of detecting GHB in different beverages with explicit intensity change under the irradiation of a hand‐held 365 nm lamp [68].

## **3.2. Classification of spirit drinks**

Visual inspection of fluorescence spectra seldom shows that they fall naturally into a number of groups [25, 39]. Thus, pattern recognition methods are usually required to gain significant meaningful information from the spectrometric data (**Table 3**). Non‐supervised pattern recognition methods as hierarchical cluster analysis (HCA) or principal component analysis (PCA) discover, previously unknown, the group structure in the data. With supervised pattern recognition methods, the number of groups is known in advance and representative samples of each group are available. This information is used to develop a suitable discriminating rule or discriminate function with which new, unknown samples can be assigned to one of the groups. Supervised pattern recognition methods as linear discriminant analysis (LDA), general discriminant analysis (GDA), k‐nearest neighbour (kNN), support vector machine (SVM) and partial least squares discriminant analysis (PLS‐DA) can be used. The choice of the chemo‐ metric method often depends on preference of the analyst and the complexity of the data. LDA requires the number of variables (wavelengths) smaller than the number of samples in each group. Consequently, large spectral datasets with few samples cannot be analysed using LDA. As PCA is a dimensionality reduction method, combining LDA with a PCA overcomes this problem. On the other hand, PLS‐DA is well suited to deal with a much larger number of variables than samples [6]. Parallel factor analysis (PARAFAC) is commonly used for modeling fluorescence excitation‐emission data. PARAFAC decomposition gives the loading and the score profiles of the components. The comparison of loading profiles of component with the fluorescence spectra for a standard of the analyte often leads to the identification of the fluorophore. Calibration model can be obtained by PLS regression between the scores related to the fluorophore and the reference concentrations of the fluorophore in the calibration samples [69].

Recently, a rapid methodology has been proposed for simultaneous quantification of five PAHs (acenaphten, anthracene, benzo[*a*]pyrene, fluoranthene and pyrene) in three types of spirits

Three most commonly used drugs in drink spiking are ketamine, benzodiazepines, including

The determination of diazepam in commercial beverages, previously spiked with drug, has been implemented through photo degradation of diazepam and detection of degradation products at λem= 463 nm (λem = 262 nm). The limit of detection was 2 mg/L [65]. A screening method for flunitrazepam in colourless alcoholic beverages is based on emission at 472 nm of

Zhai group has recently reported the first fluorescent sensor for gamma‐butyrolactone (GBL), the pro‐drug of GHB. GBL sensor was named Green Date and required an extraction to eliminate alcohol effects for GBL detection in real drinks [67]. The team also found that an orange fluorescent compound named GHB Orange is capable of detecting GHB in different beverages with explicit intensity change under the irradiation of a hand‐held 365 nm lamp

Visual inspection of fluorescence spectra seldom shows that they fall naturally into a number of groups [25, 39]. Thus, pattern recognition methods are usually required to gain significant meaningful information from the spectrometric data (**Table 3**). Non‐supervised pattern recognition methods as hierarchical cluster analysis (HCA) or principal component analysis (PCA) discover, previously unknown, the group structure in the data. With supervised pattern recognition methods, the number of groups is known in advance and representative samples of each group are available. This information is used to develop a suitable discriminating rule or discriminate function with which new, unknown samples can be assigned to one of the groups. Supervised pattern recognition methods as linear discriminant analysis (LDA), general discriminant analysis (GDA), k‐nearest neighbour (kNN), support vector machine (SVM) and partial least squares discriminant analysis (PLS‐DA) can be used. The choice of the chemo‐ metric method often depends on preference of the analyst and the complexity of the data. LDA requires the number of variables (wavelengths) smaller than the number of samples in each group. Consequently, large spectral datasets with few samples cannot be analysed using LDA. As PCA is a dimensionality reduction method, combining LDA with a PCA overcomes this problem. On the other hand, PLS‐DA is well suited to deal with a much larger number of variables than samples [6]. Parallel factor analysis (PARAFAC) is commonly used for modeling fluorescence excitation‐emission data. PARAFAC decomposition gives the loading and the score profiles of the components. The comparison of loading profiles of component with the fluorescence spectra for a standard of the analyte often leads to the identification of the fluorophore. Calibration model can be obtained by PLS regression between the scores related

diazepam and flunitrazepam, and gamma‐hydroxybutyric acid (GHB).

350 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

protonated drug given the limit of detection 1 mg/L [66].

(rum, cachaça and vodka) [64].

**3.2. Classification of spirit drinks**

*3.1.3. Drugs*

[68].



a EX excitation wavelength, EM emission wavelength, SFS synchronous fluorescence spectrum, ∆λ wavelength interval, UV/VIS ultraviolet/visible, NIR near infrared, TLS total luminescence spectrum.

b PCA principal component analysis, HCA hierarchical cluster analysis, LDA linear discriminant analysis, GDA general discriminant analysis, PLS‐DA partial least squares regression discriminant analysis, NPLS‐DA multi‐way partial least squares discriminant analysis, PARAFAC‐LDA parallel factor analysis‐linear discriminant analysis, *k*NN k‐nearest neighbour, SVM support vector machine.

c RMSEP root mean square error of prediction, R2 Pred coefficient of determination of prediction.

**Table 3.** Application of fluorescence spectroscopy and pattern recognition methods.

#### *3.2.1. Classification of spirit drinks according to the quality*

Spirit drinks can be sometimes adulterated in the flavour and colour to imitate the sensorial and visual characteristics of the authentic matured beverages. Thus, one way of classifying spirit drinks is as aged or unaged—for example, brandy or less expensive mixed wine spirit. The λex/λem values of the major peaks of the bulk brandies are generally longer than those recorded for bulk mixed wine spirits. Thus, both PCA and HCA carried out on the front‐face emission spectra recorded at λex = 350 nm and SFSs collected at Δλ = 90 nm provided very good differentiation between brandies and mixed wine spirits. Less good classification was obtained using excitation spectra recorded at λem = 440 nm [30]. Right‐angle fluorescence spectroscopy can be used as an alternative method to front‐face fluorescence technique, exigent of special front surface accessory, as both the techniques provide similar classification [27]. Regardless of fluorescence technique used, scattering is much more intense and/or heteroge‐ neous for mixed wine spirits than for brandies and can result from the presence of the colloids in mixed wine spirits. Although the phenomenon was not studied in detail, the differences between brandy and mixed wine spirit are also due to scatter bands [27, 30]. Regarding classification of diluted samples, again better results were obtained from excitation and synchronous fluorescence spectra [28, 70].

**Sample Spectral regiona**

Whisky UV‐Vis (290‐600 nm);

JFSD UV (250–325 nm); SFS (250–450 nm), Δλ = 10 nm; TLS (λem = cx275–490 nm, λex = 250–400 nm)

Plum SFS (230–550 nm), Δλ = 60 nm

JFSD SFS (250–350 nm), Δλ = 10 nm

Brandy TLS (λem = 485–580 nm, λex = 363–475 nm)

Brandy TLS (λem = 510–600 nm, λex = 393–497 nm)

Fruit TLS (λem = 315–450 nm, λex = 240–305 nm)

k‐nearest neighbour, SVM support vector machine.

RMSEP root mean square error of prediction, R2

NIR (1200‐1880 nm); EM (450‐700 nm), λex= 404 nm

Region

Producer

Adulteration

a

b

c

**Multivariate analysisb**

352 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

PCA‐LDA, PARAFAC‐ LDA

PCA‐LDA, GDA, *k*NN, SVM

PARAFAC‐ PLS

PARAFAC‐ PLS

PARAFAC‐ PLS

UV/VIS ultraviolet/visible, NIR near infrared, TLS total luminescence spectrum.

**Table 3.** Application of fluorescence spectroscopy and pattern recognition methods.

*3.2.1. Classification of spirit drinks according to the quality*

 **Purpose of analysis and quality of method (the percentage of correct classification in the prediction step)a, b, c**

PCA‐LDA Classification of single‐malt whiskies come from two

PCA‐LDA Differentiation of Czech, Hungarian and Slovak plum

Distinguishing between (1) drinks from different producers and (2) distillates of different geographical indications and others; GDA: 100 % classification

Determination of the mixed wine spirit in adulterated

Determination of the adulterants (water, ethanol, methanol) in adulterant‐brandy blends; RMSEP:

0.995 for water, ethanol and methanol, respectively.

Determination of water or ethanol in adulterant‐fruit spirit blends; apple spirit: RMSEP: 1,8% and 1.9%, R2

Pred 0.92 and 0.90, for ethanol and water,

plum spirit: RMSEP: 3.5% and 0.7%, R2

0.99, for ethanol and water, respectively.

Pred coefficient of determination of prediction.

Pred: 0.995.

Pred 0.993, 0.997 and

Pred 0.66 and

respectively: 89% classification

spirits; 100 % classification

brandy; RMSEP: 1.9%, R2

0.24%, 0.20% and 0.22%, R2

respectively;

EX excitation wavelength, EM emission wavelength, SFS synchronous fluorescence spectrum, ∆λ wavelength interval,

Spirit drinks can be sometimes adulterated in the flavour and colour to imitate the sensorial and visual characteristics of the authentic matured beverages. Thus, one way of classifying spirit drinks is as aged or unaged—for example, brandy or less expensive mixed wine spirit. The λex/λem values of the major peaks of the bulk brandies are generally longer than those

 PCA principal component analysis, HCA hierarchical cluster analysis, LDA linear discriminant analysis, GDA general discriminant analysis, PLS‐DA partial least squares regression discriminant analysis, NPLS‐DA multi‐way partial least squares discriminant analysis, PARAFAC‐LDA parallel factor analysis‐linear discriminant analysis, *k*NN

main production areas, the islands and the highlands,

Distinguishing between Slovak, Belgian, German, Czech and British JFSDs; UV (PCA‐LDA) 88 %, SFS (PCA‐LDA) 97 %, TLS (PARAFAC‐LDA) 88 %

**Reference**

[32]

[19]

[72]

[25]

[73]

[74]

[75]

UV‐absorption and fluorescence spectroscopy have been compared for the evaluation of the authenticity of matured mezcal. The results showed that PCA conducted over a set of UV absorption spectra allows a reliable discrimination between artificially and naturally matu‐ rated mezcals. On the other hand, PCA conducted over fluorescence spectra allowed the identification of two main groups, not necessarily correlated with maturation in the wood casks (**Table 3**) [35].

Raman spectroscopy has been able to distinguish unaged (silver) tequila from aged tequilas by the application of a PCA to the fluorescence background of the Raman spectra [37]. The same authors observed that the lower and highest fluorescence background of the Raman spectra corresponds to the Herradura tequila and Rancho Escondido distillated of the given samples, respectively. It is supposed that this fluorescence background behaviour is related with the production processes of the samples [37]. PCA performed on the combination of Raman spectra and the fluorescent background information has been used to classify various brands of whiskies based on flavour, age and type of cask. The fluorescence decay constant can be also used as another parameter to distinguish whisky types which are otherwise non‐ distinguishable [71].

The character and potential nutritional value of spirits is reliant, among others, on the type of wood used for the barrel in which spirits may be aged. UV‐Vis spectrophotometry and fluorescence spectrometry have been compared for the discrimination of the cachaças accord‐ ing to the wood used in their ageing. It was observed that the PLS‐DA based on UV‐Vis spectrophotometry provided better results for two classes of aged cachaça, amendoim and jequitibá, whereas NPLS‐DA of emission spectra recorded at λex = 250, 280, 330, 360, and 450 nm provided better results for the other two classes of aged cachaças, balsam and oak. For the class of cachaça aged in umburana, both models provided similar and good results. Conse‐ quently, a fused PLS‐DA model based on the UV‐Vis and emission spectra was developed, providing highest classification for four out of the five analysed classes. The only exception was the class of samples aged in oak, better classified using emission spectra and NPLS‐DA [44].

Using the combination of absorption (UV/VIS, NIR) and fluorescence spectroscopic data, it has been possible to distinguish the single‐malt whiskies from the commercial‐grade blended whiskies. First, PCA was applied to each data‐block. Next a joint‐data matrix containing PC1 and PC2 scores from UV/VIS data, PC1, PC2 and PC3 scores from NIR data and PC1 scores from fluorescence data was created. Then, LDA was applied to this matrix, and 100% classifi‐ cation was obtained [32].

#### *3.2.2. Classification of spirit drinks according to the region of production*

A few papers have been published on the use of fluorescence to classify spirit drinks according to the region of production. UV absorption spectra, TLS and SFS combined with PCA, PARAFAC and LDA were applied to distinguish between Slovak, Belgian, German, Czech and British JFSDs. PCA‐LDA performed on the UV spectral data showed a good discrimination of Slovak, British, German and Czech drinks; however, the UV spectra failed to discriminate Belgian samples. LDA applied to the PARAFAC components calculated on TLS showed correct classification for German, Czech and Belgian drinks, whereas British samples were classified as belonging to Slovak group. PCA‐LDA performed on the SFS data lead to the best discrimination as only one Slovak sample was classified as Belgian in the prediction step [19].

SFS combined with PCA‐LDA have been used for the differentiation of plum spirits according to their geographical origin. The samples were divided in two categories: colourless and coloured. All colourless and Czech and Hungarian coloured samples were properly classified in both calibration and prediction sets. A group of Slovak coloured was classified as belonging to the Hungarian group in the calibration set; however, it was correctly classified in the prediction step [72].

SFS and pattern‐recognition methods have been used for searching the natural grouping among Slovak JFSDs. LDA was applied to the first PCs; however, GDA, *k*NN and SVM were performed on the whole SFS. Regarding different producers, both GDA and SVM resulted in 100% correct classification. Regarding geographical indication, 100% correct classification was obtained using GDA [25].

## **3.3. Determination of adulterants**

TLS and PARAFAC‐PLS have been used for the determination of the adulterants (mixed wine spirits, water, ethanol and methanol) in adulterant‐brandy blends [73, 74]; the best results were obtained for ethanol (RMSEP = 0.20% and *R*<sup>2</sup> Pred = 0.997). A comparison with UV/VIS absorption and NIR spectroscopy showed that the fluorescence method is slightly less sensitive than UV/VIS absorption, but more sensitive than the NIR technique in the process of deter‐ mining the percentage of adulterant (water, ethanol and methanol) in the adulterant–brandy blend. NIR technique showed the best discrimination of the adulterant type [74]. Regarding determination of water or ethanol in adulterant‐fruit spirit blends, PARAFAC‐PLS provided a model with very limited predictive ability for ethanol‐plum spirit blends (RMSEP = 3.5% and R2 Pred = 0.66) [75].

## **4. Conclusions**

whiskies. First, PCA was applied to each data‐block. Next a joint‐data matrix containing PC1 and PC2 scores from UV/VIS data, PC1, PC2 and PC3 scores from NIR data and PC1 scores from fluorescence data was created. Then, LDA was applied to this matrix, and 100% classifi‐

A few papers have been published on the use of fluorescence to classify spirit drinks according to the region of production. UV absorption spectra, TLS and SFS combined with PCA, PARAFAC and LDA were applied to distinguish between Slovak, Belgian, German, Czech and British JFSDs. PCA‐LDA performed on the UV spectral data showed a good discrimination of Slovak, British, German and Czech drinks; however, the UV spectra failed to discriminate Belgian samples. LDA applied to the PARAFAC components calculated on TLS showed correct classification for German, Czech and Belgian drinks, whereas British samples were classified as belonging to Slovak group. PCA‐LDA performed on the SFS data lead to the best discrimination as only one Slovak sample was classified as Belgian in the

SFS combined with PCA‐LDA have been used for the differentiation of plum spirits according to their geographical origin. The samples were divided in two categories: colourless and coloured. All colourless and Czech and Hungarian coloured samples were properly classified in both calibration and prediction sets. A group of Slovak coloured was classified as belonging to the Hungarian group in the calibration set; however, it was correctly classified in the

SFS and pattern‐recognition methods have been used for searching the natural grouping among Slovak JFSDs. LDA was applied to the first PCs; however, GDA, *k*NN and SVM were performed on the whole SFS. Regarding different producers, both GDA and SVM resulted in 100% correct classification. Regarding geographical indication, 100% correct classification was

TLS and PARAFAC‐PLS have been used for the determination of the adulterants (mixed wine spirits, water, ethanol and methanol) in adulterant‐brandy blends [73, 74]; the best results were

absorption and NIR spectroscopy showed that the fluorescence method is slightly less sensitive than UV/VIS absorption, but more sensitive than the NIR technique in the process of deter‐ mining the percentage of adulterant (water, ethanol and methanol) in the adulterant–brandy blend. NIR technique showed the best discrimination of the adulterant type [74]. Regarding determination of water or ethanol in adulterant‐fruit spirit blends, PARAFAC‐PLS provided a model with very limited predictive ability for ethanol‐plum spirit blends (RMSEP = 3.5%

Pred = 0.997). A comparison with UV/VIS

*3.2.2. Classification of spirit drinks according to the region of production*

354 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

cation was obtained [32].

prediction step [19].

prediction step [72].

obtained using GDA [25].

and R2

**3.3. Determination of adulterants**

Pred = 0.66) [75].

obtained for ethanol (RMSEP = 0.20% and *R*<sup>2</sup>

Our literature survey revealed that the intrinsic fluorescence from spirit drinks contains valuable information on the quality and origin of such products. Many of the reported studies examining the potential of fluorescence spectroscopy to classify spirit drinks and/or quantify adulterants in spirit drinks until now have been preliminary or feasibility studies, performed on a limited number of samples. This was mainly due to the price and complexity of collecting an adequate number of samples with sufficient variation within the sample set. Therefore, appropriate verification should always be performed before implementation of any such method. The results presented were usually achieved using a conventional spectrophotometer, which can be replaced by diode lasers or bright light‐emitting diodes as good alternative light sources. This reduces hardware complexity and can lead to a compact portable device to be used for authentication or fraud detection. The increasing research work is needed to better explore the connection between chemical composition and fluorescence spectra, which in most cases is not fully described. Instead, the tentative assignments of fluorophores are suggested in the application studies. Thus, fluorescence spectroscopy presents several opportunities for future research with potential application in spirit drink analysis.

## **Acknowledgements**

This research was supported by the Scientific Grant Agency of the Ministry of Education of Slovak Republic and the Slovak Academy of Sciences VEGA No 1/0499/14.

## **Author details**

Jana Sádecká\* , Veronika Uríčková and Michaela Jakubíková

\*Address all correspondence to: jana.sadecka@stuba.sk

Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic

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#### **Laser Spectroscopy in Hollow‐Core Fibers: Principles and Applications Laser Spectroscopy in Hollow**‐**Core Fibers: Principles and Applications**

Philip G. Westergaard Philip G. Westergaard

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/63536

#### **Abstract**

The development of hollow‐core photonic crystal fiber (HC‐PCF) technology over the past decade has opened up a vast array of possibilities for new applications. When the hollow core is filled with gas, the HC‐PCF is ideal for molecular spectroscopy applications that require long path length interaction. When light is coupled into the HC‐PCF, the overlap between light and the molecules inside the hollow core is excellent all along the length of the fiber, which can be hundreds of meters long. Coiling the fiber up provides a compact, low‐weight gas cell at the same time featuring a high level of interaction between laser light coupled through the fiber and the molecules inside.

This chapter presents some theoretical background, different applications, and selected results for molecular spectroscopy using gas‐filled hollow‐core fibers. The applications include frequency stabilization of a laser to a molecular transition and stimulated Raman scattering in a hollow‐core fiber.

**Keywords:** photonic crystal fiber, hollow‐core, HC‐PCF, CO2, laser stabilization, stimulated Raman scattering

## **1. Introduction**

Spectroscopy of weak molecular lines or weak scattering processes (such Raman scattering) is typically troubled by a small signal strength caused by insufficient interaction length or optical power, depending on the type of spectroscopy. One possible solution is to use multipass cells for an effective interaction length of up to a few hundred meters. Another, more compact solution is to fill gas into the hollow core of specially designed optical fibers —the so‐called hollow‐core photonic crystal fibers (HC‐PCFs). Here, an interaction length of

© 2016 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. © 2016 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.

more than 100 m can be coiled up to a diameter of less than 10 cm. The hollow core ranges from around 10 to 100 microns in diameter, thus providing high intensity of the probe light propagating through the fiber and interacting with the molecules confined to the hollow core inside the fiber.

Applications of gas‐filled HC‐PCFs include high‐accuracy laser stabilization, which can be useful in determining, e.g., the CO2 content in the atmosphere using remote sensing techniques. These sensing techniques require stabilized laser systems for optimum sensitivity, and using a gas‐filled HC‐PCF for spectroscopy to stabilize the laser frequency is well suited for this purpose.

Hollow‐core fibers with their high optical intensity inside the hollow core and long interaction length are also well suited for observing weak processes such as Raman scattering. This can be carried out by either observing the spontaneously generated stokes light at the output of the fiber, or by stimulating the Raman transition coherently using two input laser beams. Stimulating the Raman transition gives orders of magnitude higher signal and a possibility for higher spectral resolution.

## **2. Hollow‐core fibers**

#### **2.1. Guiding light through a hollow core**

Conventional optical fibers developed in the beginning of the 1970s [1] guide light inside a core with higher index of refraction than the surrounding layers (the cladding). These fibers are known as step index fibers and guide the light via classical refraction mechanisms such as total internal reflection.

In the beginning of the 1990s, a new technique for guiding light was envisioned [2]. Instead of relying on the mechanism that results from Maxwell's equations for the interface between two infinite mediums, this new technique exploits the constraints on the propagation of the electromagnetic field that arises from a periodic lattice structure of varying index of refraction. This lattice—or crystal—structure gives rise to a band gap in the energy spectrum in which light cannot propagate. This effect is also known from solid‐state physics where it is observed for electrons in various periodic structures such as metals and semiconductors. By introducing a regular pattern of tiny holes in the silica structure that makes up a regular fiber (see **Figure 1**, left), an effective lattice pattern emerges and the light will be guided inside the lattice structure according to the band gap equations.

Most notably, it became possible to guide light in a central large hole inside the fiber instead of in a high index medium. This type of fiber is called hollow‐core photonic crystal fiber (HC‐ PCF) and a first realization of this type was produced in 1999 [3]. Confining light in free space (as opposed to a solid core) with surrounding silica structure meant that now it was possible to fill molecules into this central empty part of the fiber and have the light interact with these molecules as it traverses the length of the fiber. This has several advantages. Since the central hollow core has a diameter of typically 10–20 microns1 depending on the type (**Figure 1**), the mode of the guided light (which has a shape close to Gaussian) will have a waist diameter around the same size as the core diameter. Such small waists give high intensity of the light, and not just in a small focused spot limited to the corresponding Rayleigh length for Gaussian beams (0 2/, where 0 is the waist radius and λ the wavelength of the light), but over the entire length of the fiber. One can imagine that this potentially allows for a very high degree of interaction between the molecules and the light.

**Figure 1.** Different examples of photonic crystal fibers. The figure shows microscope images of the fiber tips. Left: A solid‐core fiber with a periodic lattice structure of small holes. Middle: The seven‐cell HC‐PCF has a large central hole where seven rods that comprise the surrounding small holes have been removed. Similarly for the 19 cell HC‐PCF (right).

For applications that require compact and lightweight components, such as operation in space, the HC‐PCF is an excellent alternative to a bulky glass cell.

#### **2.2. Filling and coupling**

more than 100 m can be coiled up to a diameter of less than 10 cm. The hollow core ranges from around 10 to 100 microns in diameter, thus providing high intensity of the probe light propagating through the fiber and interacting with the molecules confined to the hollow core

366 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Applications of gas‐filled HC‐PCFs include high‐accuracy laser stabilization, which can be useful in determining, e.g., the CO2 content in the atmosphere using remote sensing techniques. These sensing techniques require stabilized laser systems for optimum sensitivity, and using a gas‐filled HC‐PCF for spectroscopy to stabilize the laser frequency is well suited for this

Hollow‐core fibers with their high optical intensity inside the hollow core and long interaction length are also well suited for observing weak processes such as Raman scattering. This can be carried out by either observing the spontaneously generated stokes light at the output of the fiber, or by stimulating the Raman transition coherently using two input laser beams. Stimulating the Raman transition gives orders of magnitude higher signal and a possibility for

Conventional optical fibers developed in the beginning of the 1970s [1] guide light inside a core with higher index of refraction than the surrounding layers (the cladding). These fibers are known as step index fibers and guide the light via classical refraction mechanisms such as

In the beginning of the 1990s, a new technique for guiding light was envisioned [2]. Instead of relying on the mechanism that results from Maxwell's equations for the interface between two infinite mediums, this new technique exploits the constraints on the propagation of the electromagnetic field that arises from a periodic lattice structure of varying index of refraction. This lattice—or crystal—structure gives rise to a band gap in the energy spectrum in which light cannot propagate. This effect is also known from solid‐state physics where it is observed for electrons in various periodic structures such as metals and semiconductors. By introducing a regular pattern of tiny holes in the silica structure that makes up a regular fiber (see **Figure 1**, left), an effective lattice pattern emerges and the light will be guided inside the lattice

Most notably, it became possible to guide light in a central large hole inside the fiber instead of in a high index medium. This type of fiber is called hollow‐core photonic crystal fiber (HC‐ PCF) and a first realization of this type was produced in 1999 [3]. Confining light in free space (as opposed to a solid core) with surrounding silica structure meant that now it was possible to fill molecules into this central empty part of the fiber and have the light interact with these molecules as it traverses the length of the fiber. This has several advantages. Since the central

depending on the type (**Figure 1**), the

inside the fiber.

purpose.

higher spectral resolution.

**2. Hollow‐core fibers**

total internal reflection.

**2.1. Guiding light through a hollow core**

structure according to the band gap equations.

hollow core has a diameter of typically 10–20 microns1

When light is transmitted through the HC‐PCF, it interacts with the molecules that happen to be situated inside the hollow core. If interaction with molecules other than air is desired, the core of the HC‐PCF should first be evacuated and then filled with the molecules of choice. This is done by mounting the fiber ends (or the entire fiber) inside a vacuum system. The vacuum system should also allow optical access for coupling light into the fiber. This is not easily achieved but various techniques exist. If the fiber ends are not enclosed in a vacuum system at all times, the HC‐PCF must be sealed in some way to keep the desired molecules inside the fiber.

Basically, two techniques have been used in the literature to seal the gas‐filled HC‐PCFs. The first technique is based on splicing the ends to standard single‐mode fibers. This is relatively easy to do for one end of the HC‐PCF before the gas filling, but the single‐ended filling time will be rather large for long fibers. Furthermore, Fresnel reflections at the splice interface between the two fibers often introduce interference effects that can affect the transmission properties of the fiber in an unpredictable way [4]. Losses at the splice interface due to mode mismatch can be significant [5], in particular for a (nearly) single‐mode HC‐PCF. It is also

<sup>1</sup> Other types of hollow‐core fibers, such as the Kagomé type, can have several times larger core diameters. These types of fibers will not be discussed here.

possible to splice the HC‐PCF to a single‐mode fiber after gas filling as demonstrated in [6]. Here, the gas filling is followed by filling with He gas at high pressure (above 1 atm). The high He pressure prevents air from entering the HC‐PCF during the splice process. Subsequently, He escapes by diffusion through the HC‐PCF silica. With both ends of the HC‐PCF spliced to single‐mode fiber, strong interference fringes are generally seen due to Fresnel reflections at both splice interfaces [6, 7]. It has been shown that these interferences can be suppressed by angling one splice [7, 8]. However, this technique increases losses, is not yet robust or very reproducible and is susceptible to gas leaks and contamination [7].

An alternative technique uses a compact bulk cell around one or both ends of the HC‐PCF [9, 10]. **Figure 2** shows a design recently developed at Danish Fundamental Metrology (DFM) [11] that allows for a compact solution for both coupling of the light and gas filling into the fiber in the same device.

**Figure 2.** Schematic overview of filling and coupling of the HC‐PCF. The hollow core in the fiber must first be evacuat‐ ed with a vacuum pump, and then it can be filled with a gas. Inset: The ferrule design used for gas filling and coupling light into the HC‐PCF. The HC‐PCF is inserted into the ferrule from the left and a lens is glued on the right. Evacuating and filling of the hollow core is attained through the tube connected from the top.

In the DFM design, the HC‐PCF is inserted into the back of a glass ferrule and is glued in place to avoid leaks and misalignment. The ferrule has a lens glued to the front face for optical coupling, that is, focusing the input light into the core of the HC‐PCF. The cell is evacuated and filled with gas via a second smaller tube attached to the side of the outer tube. The cell is sealed off from the pump and filling system after it has been filled with the desired gas. The output of the compact glass cell can be coupled into a single‐mode fiber using standard fiber coupling optics.

When the HC‐PCF has been set up for gas filling, the system is first evacuated for an extended period, usually several days. After that the system is filled with the relevant gas, which enters the fiber by diffusion. The filling time in the hydrodynamic regime, which is the regime for the relevant gas pressures used here, can be calculated from molecular parameters and fiber geometry as [12]

$$\log\_{\vee}\left(f\_{\prime}, f\_{0}, p, T, m\_{\circ}\right) = \frac{\sqrt{\frac{\ln\left(2\right)}{\pi}} \text{Re}\left(\exp\left(-z^{2}\right) \text{erfc}\left(-iz\right)\right)}}{\Delta f\_{\text{D}}},\tag{1}$$

Considering filling with CO2 molecules as an example, typical parameters are fiber lengths of up to *L* = 100 m, *d* = 10 µm (HC‐PCF core diameter), 0 ≃ 100 hPa (gas pressure), and η = 1.50·10‐5 kg/(m·s) (CO2 gas viscosity). The filling time is then calculated to be 28 days. This assumes filling from both ends of the fiber. If the fiber is only filled from one end, the filling time will be four times longer (112 days).

The actual filling time does have some uncertainty. First, it can be reduced by starting with a somewhat higher pressure and then lowering the pressure in the vacuum system to the final pressure at the end of the filling period. It has previously been shown that in the low‐pressure regime, the filling time can be reduced to one‐sixth of in Eq. (1) by starting out with a pressure 20 times higher than the final pressure [13]. On the other hand, CO2 tends to adsorb to glass surfaces, and this may increase the filling time [14].

Since the fiber length enters as *L*<sup>2</sup> in Eq. (1), the filling time can be reduced significantly by reducing the fiber length. Typically, a trade‐off between filling time and signal strength must be considered.

## **3. Spectroscopic applications for laser stabilization**

## **3.1. Molecular absorption**

possible to splice the HC‐PCF to a single‐mode fiber after gas filling as demonstrated in [6]. Here, the gas filling is followed by filling with He gas at high pressure (above 1 atm). The high He pressure prevents air from entering the HC‐PCF during the splice process. Subsequently, He escapes by diffusion through the HC‐PCF silica. With both ends of the HC‐PCF spliced to single‐mode fiber, strong interference fringes are generally seen due to Fresnel reflections at both splice interfaces [6, 7]. It has been shown that these interferences can be suppressed by angling one splice [7, 8]. However, this technique increases losses, is not yet robust or very

An alternative technique uses a compact bulk cell around one or both ends of the HC‐PCF [9, 10]. **Figure 2** shows a design recently developed at Danish Fundamental Metrology (DFM) [11] that allows for a compact solution for both coupling of the light and gas filling into the fiber

**Figure 2.** Schematic overview of filling and coupling of the HC‐PCF. The hollow core in the fiber must first be evacuat‐ ed with a vacuum pump, and then it can be filled with a gas. Inset: The ferrule design used for gas filling and coupling light into the HC‐PCF. The HC‐PCF is inserted into the ferrule from the left and a lens is glued on the right. Evacuating

In the DFM design, the HC‐PCF is inserted into the back of a glass ferrule and is glued in place to avoid leaks and misalignment. The ferrule has a lens glued to the front face for optical coupling, that is, focusing the input light into the core of the HC‐PCF. The cell is evacuated and filled with gas via a second smaller tube attached to the side of the outer tube. The cell is sealed off from the pump and filling system after it has been filled with the desired gas. The output of the compact glass cell can be coupled into a single‐mode fiber using standard fiber

When the HC‐PCF has been set up for gas filling, the system is first evacuated for an extended period, usually several days. After that the system is filled with the relevant gas, which enters the fiber by diffusion. The filling time in the hydrodynamic regime, which is the regime for

reproducible and is susceptible to gas leaks and contamination [7].

368 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

and filling of the hollow core is attained through the tube connected from the top.

in the same device.

coupling optics.

Spectroscopy necessarily relies on the absorption of light by the molecules that are investigat‐ ed. The amount of absorption depends on molecular parameters (line strength, pressure and velocity distribution), laser parameters (frequency and in some cases optical power), and the interaction length between light and molecules. Power‐dependent absorption involves a nonlinear process, such as Raman excitation, which is discussed in Section 4.

When the molecules are held at a given temperature , their velocity distribution will be given by the Maxwell‐Boltzmann equation. Through the Doppler effect, molecules with different velocities will absorb light at different frequencies. This affects the molecular line profile, that is, the absorption (or, correspondingly, the transmission) as a function of laser frequency. Even at zero velocity, the molecular line has a natural profile—the Lorentz profile. When the molecules are moving due to finite temperature, the Lorentz profile is changed into a so‐called Voigt profile which is broader and has less absorption on resonance. The effect is illustrated in **Figure 3** where a CO2 transition at 2051 nm is plotted for different temperatures. For zero temperature, the line profile is Lorentzian.

**Figure 3.** The line profile as a function of laser frequency detuning from molecular resonance for different tempera‐ tures of the molecular sample, in this case CO2 at 50 hPa pressure and 1 m interaction length. The line is broadened by thermal motion at increasing temperatures due to the Doppler effect. The line strength of the transition shown here is 1.504 × 10−22 cm/molecule.

Considering normal excitation of a molecular transition, the amount of absorption can be quantified by the absorption coefficient given by [15]

$$\alpha\_i(f, f\_0, p, T, m\_i) = N(p) \cdot S\_i \cdot \mathbf{g}\_r(f, f\_0, p, T, m\_i),\tag{2}$$

where *i* denotes the molecular species, *p* is the pressure, *T* is the temperature, is the laser frequency, *f*0 is the center frequency of the molecular line and *mi* is the mass of the molecule. is the number of molecules per volume, given by the ideal gas law <sup>=</sup> , with being the Boltzmann constant. is the line strength of the transition given in SI units, [ ] = m2 Hz. In the literature, the line strength is often given in units of [cm/molecule]. The relation between the value i,c in units of [cm/molecule] and the SI line strength i,c is given by [15]

$$\mathcal{S}\_i = \mathcal{c}\mathcal{S}\_{i,c}$$

with *c* being the speed of light.

The factor , 0, , , in Eq. (2) accounts for the Voigt profile of the transition and is given on resonance by [15]

Laser Spectroscopy in Hollow‐Core Fibers: Principles and Applications http://dx.doi.org/10.5772/63536 371

$$\log\_{\nu}(f, f\_o, p, T, m\_i) = \frac{\sqrt{\frac{\ln(2)}{\pi}} \text{Re}\left(\exp\left(-z^z\right)\text{erfc}\left(-iz\right)\right)}{\Delta f\_o},\tag{3}$$

where = − + with = ln 2 <sup>−</sup> 0 Δ and = PBC Δ ln 2 with PBC(*p*) being the pressure broadening coefficient for a pressure *p* and Δ <sup>=</sup> 0 2ln <sup>2</sup> is the Doppler width

of the transition. The function erfc is the complex complementary error function.

Equations (2) and (3) can be used to calculate the absorption profile of an arbitrary molecular gas if the line strength, pressure, temperature, and pressure‐broadening coefficient are known. In the following section, we will see how this is applied to laser frequency stabilization to a molecular transition.

#### **3.2. Modulation spectroscopy and frequency stabilization**

**Figure 3.** The line profile as a function of laser frequency detuning from molecular resonance for different tempera‐ tures of the molecular sample, in this case CO2 at 50 hPa pressure and 1 m interaction length. The line is broadened by thermal motion at increasing temperatures due to the Doppler effect. The line strength of the transition shown here is

370 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Considering normal excitation of a molecular transition, the amount of absorption can be

where *i* denotes the molecular species, *p* is the pressure, *T* is the temperature, is the laser

Hz. In the literature, the line strength is often given in units of [cm/molecule]. The relation between the value i,c in units of [cm/molecule] and the SI line strength i,c is given by [15]

*i ic*, *S cS* =

The factor , 0, , , in Eq. (2) accounts for the Voigt profile of the transition and is given

is the number of molecules per volume, given by the ideal gas law <sup>=</sup>

given by [15]

*<sup>i</sup>* ( *f f pT m N p S g f f pT m* , ,,, 0 0 *<sup>i</sup>*) () ( × *i V* ×= , ,,, , *<sup>i</sup>*) (2)

is the line strength of the transition given in SI units, [

is the mass of the molecule.

, with

] =

1.504 × 10−22 cm/molecule.

quantified by the absorption coefficient

a

being the Boltzmann constant.

with *c* being the speed of light.

on resonance by [15]

m2

frequency, *f*0 is the center frequency of the molecular line and *mi*

Being an indispensable prerequisite for high‐performance remote sensing such as LIDAR (see Section 3.3), frequency stabilization of the laser used for the sensing measurement hinges upon a frequency discriminator that serves as reference for the "correct" value of the laser frequency. Typically, the discriminator is composed of an optical cavity, an atomic line, or—as in the case of molecular sensing—a molecular transition.

Once a suitable frequency discriminator is found, stabilization (otherwise known as locking) of the laser frequency to this reference is accomplished by generation of an electronic signal, known as the error signal, which can be used to steer the laser frequency toward the desired frequency—the laser is then locked. The error signal typically has a large slope around the reference (cavity, molecular, or other) resonance and changes sign when the laser frequency crosses the resonance. This is useful when designing simple electronic circuits (typically PID circuits) that can be used for controlling the laser frequency and locking it to the reference resonance. The standard approach to generate the error signal is by modulation and subse‐ quent demodulation of the laser frequency or phase after the laser light has interacted with the discriminator reference. There are several ways of doing this. In the following section, the frequency modulation spectroscopy (FMS) approach will be discussed for the case of laser frequency stabilization to a molecular transition.

#### *3.2.1. Frequency modulation spectroscopy error signal*

For a given set of parameters, the error signal can be simulated from the Voigt profile (Eq. (2)) of the molecular transition. Here, we consider phase modulation of the light and subsequent demodulation to obtain an error signal.

To phase modulate laser light an electro‐optic modulator is the most common choice. To see how this affects the light, we consider the electric field component of the light, which can be written in complex notation as =0 where is the (angular) frequency of the light and 0 is the amplitude. After modulation, the field becomes (to first order in the Bessel expansion)

$$E\_{mod} = E\_0 e^{i(\alpha t - \beta \sin(\Omega t))} \equiv E\_0 \left( J\_0 \left( \beta \right) e^{i\alpha t} + J\_1 \left( \beta \right) e^{i(\alpha + \Omega)t} - J\_1 \left( \beta \right) e^{i(\alpha - \Omega)t} \right), \tag{4}$$

where Ω is the modulation (angular) frequency, is the phase modulation index, and is the Bessel function of order . Spectrally, the laser light is now composed of three components; the carrier at frequency and two modulation sidebands at ± Ω. After the light has passed through the molecular gas (which is here acting as the frequency discriminator), it will have experienced absorption and a phase shift, which both depend on the frequency of the light. Introducing the amplitude coefficient of absorption , the field after passing through the molecular gas becomes2

$$E\_{\rm out} = E\_0 \left[ \left. J\_0 \left( \beta \right) e^{i \alpha - a\_\mathbb{I} \left( \alpha \right) t} + J\_\text{l} \left( \beta \right) e^{i \left( \alpha + \Omega \right) t - a\_\mathbb{I} \left( \alpha + \Omega \right) t} - J\_\text{l} \left( \beta \right) e^{i \left( \alpha - \Omega \right) t - a\_\mathbb{I} \left( \alpha - \Omega \right) t} \right], \tag{5}$$

where is the length of the interaction.

When detecting the light, the quantity measured is the intensity 2. After detection, the signal is demodulated by mixing (multiplying) the measured signal with the modulation signal and low‐pass filtering the mixed output to get a DC error signal. Thus, with the field in Eq. (5), the DC error signal becomes after some algebra

$$\operatorname{Err}\left(\boldsymbol{\alpha},\boldsymbol{\Omega},\boldsymbol{\beta}\right) = \frac{\boldsymbol{\Omega}}{2\pi} \int\_{0}^{\frac{2\pi}{\alpha}} \kappa \sin\left(\boldsymbol{\Omega}t\right) \left|\boldsymbol{E}\_{\alpha a}\right|^{2} \operatorname{d}t = \mathcal{K}e^{-a\_{\mathbb{E}}\left(\boldsymbol{\alpha}\right)\left(\boldsymbol{e}^{-a\_{\mathbb{E}}\left(\boldsymbol{\alpha}\*\boldsymbol{\Omega}\right)\boldsymbol{\ell}} - \boldsymbol{e}^{-a\_{\mathbb{E}}\left(\boldsymbol{\alpha}\*\boldsymbol{\Omega}\right)\boldsymbol{\ell}}\right)}\tag{6}$$

where χ = 0 2 <sup>0</sup> <sup>1</sup> and is the quantum efficiency of the detector. We note that is the absorption coefficient for the field amplitude and that the transmission = − through the gas is measured for the intensity of the light using from Eq. (2). The two quantities can be related to via the relation

<sup>2</sup> Here, we neglect the phase shift imparted on the light by the molecules. For typical parameters, this phase shift will be very small and have a negligible contribution to the error signal. For a thermal ensemble, molecules with oppositely directed velocities will contribute equally to the absorption but their contribution to the phase shift will cancel each other out.

Laser Spectroscopy in Hollow‐Core Fibers: Principles and Applications http://dx.doi.org/10.5772/63536 373

$$e^{-\alpha\_k(o)l} = \sqrt{T\_r\left(oo\right)} = \sqrt{e^{-a\_i\left(o\right)l}}.\tag{7}$$

Now, we can model the error signal3 by inserting Eqs. (2), (3), and (7) in Eq. (6). The transmission and the corresponding error signal is shown in **Figure 4** for a CO2 transition at 2051 nm with a pressure of 5 hPa and 10 m interaction length.

written in complex notation as =0 where is the (angular) frequency of the light and 0 is the amplitude. After modulation, the field becomes (to first order in the Bessel expansion)

> () () ( ) ( ) ( ) ( ) ( sin( )) Ω Ω <sup>0</sup> 0 0 <sup>1</sup> <sup>1</sup> , *it t i t it it E Ee E J e J e J e mod*

the Bessel function of order . Spectrally, the laser light is now composed of three components; the carrier at frequency and two modulation sidebands at ± Ω. After the light has passed through the molecular gas (which is here acting as the frequency discriminator), it will have experienced absorption and a phase shift, which both depend on the frequency of the light. Introducing the amplitude coefficient of absorption , the field after passing through the

( ) ( ) ( ) () () ( ) Ω Ω () () Ω Ω

the signal is demodulated by mixing (multiplying) the measured signal with the modulation signal and low‐pass filtering the mixed output to get a DC error signal. Thus, with the field in

( ) ( ) () ( ) ( ) ( )

<sup>Ω</sup> <sup>2</sup> Ω Ω

c

is the absorption coefficient for the field amplitude and that the transmission = −

 Here, we neglect the phase shift imparted on the light by the molecules. For typical parameters, this phase shift will be very small and have a negligible contribution to the error signal. For a thermal ensemble, molecules with oppositely directed velocities will contribute equally to the absorption but their contribution to the phase shift will cancel each other

*out tE t e e e*

aw

<sup>W</sup> - -+ -- W= W = - ò (6)

0 0 <sup>1</sup> 1 , *EE E it l it l it l E EJ e J e out J e*

When detecting the light, the quantity measured is the intensity

w aw


waw

Eq. (5), the DC error signal becomes after some algebra

2

p

0 Err , , sin d 2

p

 k

through the gas is measured for the intensity of the light using

bb

w


 w

is

2. After detection,

from Eq. (2). The two

 b

w aw

*EE E ll l*

 aw

 aw

<sup>1</sup> and is the quantum efficiency of the detector. We note that

 b

ë û (5)

w

372 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

where Ω is the modulation (angular) frequency, is the phase modulation index, and

bb

w b

molecular gas becomes2

where is the length of the interaction.

wb

2 <sup>0</sup>

quantities can be related to via the relation

where χ = 0

2

out.

**Figure 4.** Modeled transmission (red, left scale) and error signal (black, right scale) calculated from Eqs. (2), (3), (6), and (7) for a CO2 transition at 2051 nm with a pressure of 5 hPa and 10 m interaction length.

The thing to note here is the very linear behavior and zero crossing of the error signal around resonance (i.e., zero detuning). This is ideal as input to an electronic locking mechanism, such as a PID circuit. The long interaction length required to get a large error signal can be difficult to obtain and/or bulky with standard single‐ or multipass cells. Here, the HC‐PCF is ideal as a gas container, since it can have lengths of up to 100 m while being very compact and light weight.

#### **3.3. Applications in remote sensing (LIDAR, DIAL)**

The principle of remote sensing (or LIDAR, from LIght Detection And Ranging) using a laser is shown in **Figure 5**. Here, a laser beam is sent through the molecular gas under test and the

<sup>3</sup> In the model presented, here, we do not take into account power broadening because usual power levels are much smaller than the typical saturation power.

absorption from scattered light is detected. For atmospheric sensing the laser source can be satellite based. Important molecules in this respect include the greenhouse gasses CO2, CH4, and N2O.

**Figure 5.** The principle of satellite‐based differential atmospheric sensing using LIDAR.

Depending on where the laser frequency is tuned with respect to the molecular transition, the light will experience some absorption by the molecules. If the laser detuning from resonance is well known, this absorption can be directly related to the molecular concentration in the sample gas ( in Eq. (2)). Typically, the laser transmission on resonance (or close to it) is compared to an "off" frequency, where absorption is negligible, which serves as a reference background signal. This differential approach—usually known as DIAL (DIfferential Absorp‐ tion Lidar)—is one of the most accurate for determining absolute concentrations of molecules in the atmosphere.

One of the main factors determining the accuracy and reliability of the DIAL technique is the frequency stability of the laser used for the absorption measurements. If the laser frequency drifts, the absorption from the molecular transition will change and give an error in the determination of the molecular concentration. Frequency stability down to the level of ±0.3 MHz may be necessary to achieve sufficient accuracy for CO2 measurements [16]. Most free‐ running (i.e., not stabilized) laser sources show frequency fluctuations and drift on the order of tens or even hundreds of MHz over the course of minutes to hours. It is thus imperative to lock the laser frequency to a known reference when doing DIAL measurements.

Setting the "on" frequency to the exact center of the molecular resonance does not provide the best possible sensitivity, however. Detecting the transmission at the slope of the transmission curve will give a much higher sensitivity to changes in concentration (see, e.g., **Figure 4**), since pressure‐broadening effects render this position highly sensitive to pressure/concentration changes. Therefore, it is desirable to have the possibility to lock the laser "on" frequency at a fixed detuning from the molecular resonance. This can either be performed with additional slave lasers or with just a single laser [17]. For satellite‐based operation, it is advantageous to reduce the weight and size of the laser system, and a single laser locked off resonance is preferable if the laser can live up to the stability requirements.

In recent years, efforts have been carried out to obtain sufficiently stable satellite‐based laser sources for CO2 measurements. Among others, NASA has realized stabilization of a fiber‐ coupled DBF laser to the 1572.3 nm line of CO2 using a multipass cell [18, 19]. The stability obtained here was at the level of 5.7 kHz up to 1000 s integration time. In the interest of probing atmospheric CO2 with a wide tuning range about the line center, six DFB slave lasers were offset locked to the master laser at different offsets, and the output light was switched in pulses between the six different slaves. Of course, this type of setup can be bulky and heavy and the large number of components drives up the costs.

absorption from scattered light is detected. For atmospheric sensing the laser source can be satellite based. Important molecules in this respect include the greenhouse gasses CO2, CH4,

374 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Depending on where the laser frequency is tuned with respect to the molecular transition, the light will experience some absorption by the molecules. If the laser detuning from resonance is well known, this absorption can be directly related to the molecular concentration in the sample gas ( in Eq. (2)). Typically, the laser transmission on resonance (or close to it) is compared to an "off" frequency, where absorption is negligible, which serves as a reference background signal. This differential approach—usually known as DIAL (DIfferential Absorp‐ tion Lidar)—is one of the most accurate for determining absolute concentrations of molecules

One of the main factors determining the accuracy and reliability of the DIAL technique is the frequency stability of the laser used for the absorption measurements. If the laser frequency drifts, the absorption from the molecular transition will change and give an error in the determination of the molecular concentration. Frequency stability down to the level of ±0.3 MHz may be necessary to achieve sufficient accuracy for CO2 measurements [16]. Most free‐ running (i.e., not stabilized) laser sources show frequency fluctuations and drift on the order of tens or even hundreds of MHz over the course of minutes to hours. It is thus imperative to

Setting the "on" frequency to the exact center of the molecular resonance does not provide the best possible sensitivity, however. Detecting the transmission at the slope of the transmission curve will give a much higher sensitivity to changes in concentration (see, e.g., **Figure 4**), since pressure‐broadening effects render this position highly sensitive to pressure/concentration changes. Therefore, it is desirable to have the possibility to lock the laser "on" frequency at a fixed detuning from the molecular resonance. This can either be performed with additional slave lasers or with just a single laser [17]. For satellite‐based operation, it is advantageous to reduce the weight and size of the laser system, and a single laser locked off resonance is

lock the laser frequency to a known reference when doing DIAL measurements.

preferable if the laser can live up to the stability requirements.

**Figure 5.** The principle of satellite‐based differential atmospheric sensing using LIDAR.

and N2O.

in the atmosphere.

To reduce the size and weight, instead of using a multi‐pass cell, in 2010 a fiber‐coupled Tm:Ho:YLF laser was stabilized to the line center of the CO2 transition at 2051 nm using a CO2‐filled 10 m long HC‐PCF [20]. The resulting standard deviation of the locked frequency was at the level of 2.4 MHz. Also, acetylene and iodine have been used in hollow‐core fibers for frequency stabilization, mostly using saturated absorption spectroscopy which can give a more accurate and stable lock, but less opportunity for tuning of the frequency. The most recent experiments report a long‐term stability of around 800 Hz [4, 21].

Recently, [17], a compact system was demonstrated using a 10 m HC‐PCF for stabilization of a DFB laser to CO2 at 2051 nm. The setup was designed to be compact and lightweight. This included offset locking away from resonance with just a single laser. **Figure 6** shows experi‐ mental data of the transmission and error signal from a 10 m long HC‐PCF filled with CO2 to a pressure of around 20 hPa.

The HC‐PCF in [17] was coiled up to a diameter of 8 cm on a copper mount. The control software developed for this system features an automated calibration sequence, which makes it possible to achieve an accurate and stable lock of the laser away from resonance without any additional lasers.

**Figure 6.** Experimental data from a CO2‐filled hollow‐core fiber of length 10 m. The figure was adapted from [17].

To characterize the frequency stability of a system, a quantity called the Allan deviation is typically used. The Allan deviation gives information about how well the system averages out noise over time and the type of noise present at different time scales. For the system in [17], the Allan deviation of the locked laser is shown in **Figure 7**.

**Figure 7.** The Allan deviation for the locked laser system at two different offset frequencies in [17].

Here, the laser frequency stability at two different offset frequencies was measured. The figure shows that smaller offset frequency demonstrates better stability (lower Allan deviation) and that the frequency stability is dominated by flicker noise (1/*f*‐type noise). This can be seen from the fact that the Allan deviation does not decrease over time. For white noise, the Allan deviation decreases as *t* ‐1/2, where *t* is the time. Even with the presence of flicker noise, the level of stability here is better than 10 kHz for the low‐frequency offset (50 MHz) until at least 1000 s integration time. The typical duration of the LIDAR measurement is only on the order of 10 seconds, so this stability is more than sufficient to meet the requirements.

Thus, in the study of Westergaard et al. [17], a hollow‐core fiber has successfully been used for molecular spectroscopy applied to frequency stabilization of a DFB laser with the aim of satellite‐based DIAL measurements.

## **4. Raman spectroscopy**

Raman spectroscopy is a versatile technique that finds applications in physics, chemistry, biology, and in a number of different industrial areas. Raman spectroscopy typically explores transitions between motional states in molecules that have a unique energy spectrum for each molecule—a "fingerprint"—which therefore allows identification of that molecule [22].

Raman scattering is a weak process that requires high optical intensity in the excitation beam to take place. This places some limitations on the laser sources and the damage threshold of the sample under test. Traditionally, the signal from the Raman scattering process has been obtained via a spontaneous process. The principle of the process is illustrated in **Figure 8**.

noise over time and the type of noise present at different time scales. For the system in [17],

the Allan deviation of the locked laser is shown in **Figure 7**.

376 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

**Figure 7.** The Allan deviation for the locked laser system at two different offset frequencies in [17].

seconds, so this stability is more than sufficient to meet the requirements.

deviation decreases as *t*

satellite‐based DIAL measurements.

**4. Raman spectroscopy**

Here, the laser frequency stability at two different offset frequencies was measured. The figure shows that smaller offset frequency demonstrates better stability (lower Allan deviation) and that the frequency stability is dominated by flicker noise (1/*f*‐type noise). This can be seen from the fact that the Allan deviation does not decrease over time. For white noise, the Allan

of stability here is better than 10 kHz for the low‐frequency offset (50 MHz) until at least 1000 s integration time. The typical duration of the LIDAR measurement is only on the order of 10

Thus, in the study of Westergaard et al. [17], a hollow‐core fiber has successfully been used for molecular spectroscopy applied to frequency stabilization of a DFB laser with the aim of

Raman spectroscopy is a versatile technique that finds applications in physics, chemistry, biology, and in a number of different industrial areas. Raman spectroscopy typically explores transitions between motional states in molecules that have a unique energy spectrum for each molecule—a "fingerprint"—which therefore allows identification of that molecule [22].

Raman scattering is a weak process that requires high optical intensity in the excitation beam to take place. This places some limitations on the laser sources and the damage threshold of

‐1/2, where *t* is the time. Even with the presence of flicker noise, the level

**Figure 8.** Spontaneous Raman scattering. A sample of molecules is excited by a pump laser, and when the molecules decay, a signal with lower (Stokes) or higher (anti‐Stokes) energy is emitted from the sample in an arbitrary direction.

Here, a molecular sample is excited with a powerful pump laser to a virtual level; something which is allowed by quantum mechanics—but not very likely to happen for any given pump photon, which is why a powerful laser with a large number of photons is used. After the excitation, the molecules quickly decay to a real level, which typically is excited motionally (i.e., with more rotational or vibrational energy) with respect to the initial state.

In the decay process, the molecule emits a photon corresponding to the energy difference between the virtual level and the excited motional level. This photon contains information about the motional energy levels of the molecule, thus providing the spectroscopic fingerprint of the technique. When the level to which the molecules decay has a higher energy than the initial level (as illustrated in **Figure 8**), the emitted radiation is known as the Stokes signal. If the initial level has a higher energy, the radiation is called the anti‐Stokes signal.

To achieve a larger Raman signal, a different approach can be applied, where the Raman transition is stimulated coherently using two different laser sources at the same time, with the

**Figure 9.** Stimulated Raman scattering. Here, two lasers are used to stimulate the Raman transition. The two laser beams must overlap spatially for the process to take place. Here, they are displaced slightly for clarity.

frequency difference between the two lasers corresponding to the energy difference between the two molecular levels involved in the Raman transition [22]. The situation is illustrated in **Figure 9**.

Stimulating the Raman transition in this way instead of relying on a spontaneous process has several advantages. First of all, it provides orders of magnitude larger signal [23] and makes it possible to observe the Raman signal with continuous‐wave (CW) lasers with moderate (sub‐ Watt) optical power, which otherwise for spontaneous excitation would require Watt levels of CW power in the best case [24, 25]. Secondly, when the Raman process takes place, photons are effectively transferred from one laser beam to the other. Therefore, the signal is contained in a laser beam with a well‐defined direction, in contrast to the spontaneous case where the Stokes radiation is emitted in an arbitrary direction, and the collection of the stimulated signal can be much more effective.

When the frequency difference between the two lasers matches that of an allowed Raman transition, the pump laser (typically the high‐frequency laser) will be depleted, while the probe laser (typically the low‐frequency laser) will experience a gain in intensity. As opposed to spontaneous scattering, which is proportional to only the pump intensity, the stimulated Raman signal is proportional to the product of the pump and probe intensity,

$$
\delta I(\nu\_{\rho \text{robe}}) \propto I(\nu\_{\rho \text{un} \rho}) I(\nu\_{\rho \text{robe}}) .
$$

This proportionality gives the possibility of boosting the signal by many orders of magnitude.

By containing molecules inside a hollow‐core fiber, the Raman signal is increased even further. This was first achieved around 15 years ago [23, 26]. The intrafiber interaction ensures high intensity between the light and the molecules over a long distance. It has been shown that HC‐ PCFs are very efficient for enhancing the Raman signals for both spontaneous [24, 27] and stimulated Raman transitions [23, 28, 29].

**Figure 10.** The setup in [29] for differential detection of stimulated Raman scattering. DM: dichroic mirror, PD: photo diode, BS: beam splitter, OPO: optical parametric oscillator.

Most recently, in [29], measurements were performed on the ortho‐S0(1) transition of molecular hydrogen at 587 cm‐1, which is the most intense rotational line at room temperature for low wavenumbers. For a pump wavelength of 1064 nm, the Raman transition corresponds to probe (Stokes) radiation at 1135 nm. The pump light at 1064 nm was obtained from a commercial fiber laser and the light at 1135 nm was generated via a nonlinear process in a crystal—so‐ called optical parametric oscillator (OPO). The signal‐to‐noise ratio (SNR) can be further enhanced by using modulation detection [28, 29], where the pump laser is frequency modu‐ lated and this modulation is transferred to the probe laser via the Raman transition and demodulated with a lock‐in amplifier giving the Raman signal. In [29], the setup was further improved by employing differential measurement detection of the probe laser, while sup‐ pressing the pump laser (see **Figure 10**). With this technique, any classical noise in the probe beam can be rejected, thus enhancing the SNR of the Raman signals. This resulted in spectro‐ scopic data such as shown in **Figure 11**. Here, the Raman transition is probed with high sensitivity; the achieved SNR was around 1600 using only around 200 mW and 2 mW of optical power in the pump and probe laser, respectively, and a H2 pressure of 867 hPa in 4.5 m of HC‐ PCF. Using two spectrally narrow light sources (such as the two lasers used here) additionally allows a for spectral resolution of features of Raman transitions down to around 5 MHz or 1.6 × 10‐4 cm‐1, provided there are no other broadening mechanisms (which there typically are at room temperature, however, as described in Section 3.1).

frequency difference between the two lasers corresponding to the energy difference between the two molecular levels involved in the Raman transition [22]. The situation is illustrated in

378 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Stimulating the Raman transition in this way instead of relying on a spontaneous process has several advantages. First of all, it provides orders of magnitude larger signal [23] and makes it possible to observe the Raman signal with continuous‐wave (CW) lasers with moderate (sub‐ Watt) optical power, which otherwise for spontaneous excitation would require Watt levels of CW power in the best case [24, 25]. Secondly, when the Raman process takes place, photons are effectively transferred from one laser beam to the other. Therefore, the signal is contained in a laser beam with a well‐defined direction, in contrast to the spontaneous case where the Stokes radiation is emitted in an arbitrary direction, and the collection of the stimulated signal

When the frequency difference between the two lasers matches that of an allowed Raman transition, the pump laser (typically the high‐frequency laser) will be depleted, while the probe laser (typically the low‐frequency laser) will experience a gain in intensity. As opposed to spontaneous scattering, which is proportional to only the pump intensity, the stimulated

( ) ( )( ). *probe pump probe*

 n

 n

This proportionality gives the possibility of boosting the signal by many orders of magnitude.

By containing molecules inside a hollow‐core fiber, the Raman signal is increased even further. This was first achieved around 15 years ago [23, 26]. The intrafiber interaction ensures high intensity between the light and the molecules over a long distance. It has been shown that HC‐ PCFs are very efficient for enhancing the Raman signals for both spontaneous [24, 27] and

**Figure 10.** The setup in [29] for differential detection of stimulated Raman scattering. DM: dichroic mirror, PD: photo

*I II* µ

Raman signal is proportional to the product of the pump and probe intensity,

dn

**Figure 9**.

can be much more effective.

stimulated Raman transitions [23, 28, 29].

diode, BS: beam splitter, OPO: optical parametric oscillator.

**Figure 11.** The signal from H2 with around 2 mW of power in the 1135 nm (probe) laser and 200 mW in the 1064 nm (pump) laser. The Raman transition is detected using a lock‐in technique, where the phase quadrature is recorded giv‐ ing a dispersion‐shaped curve instead of the usual absorption curve when the pump laser frequency is scanned across the Raman transition.

The measurements presented in [29] demonstrate that the use of a HC‐PCF for enhancing the Raman signal makes it possible to achieve much higher SNR than without the hollow‐core fiber and in principle the system is able to measure Raman transitions with only a few mW of power in the pump and probe beams at ambient pressure with a high spectral resolution.

## **Author details**

Philip G. Westergaard

Address all correspondence to: pgw@dfm.dk

Danish Fundamental Metrology (DFM), Lyngby, Denmark

#### **References**


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fiber and in principle the system is able to measure Raman transitions with only a few mW of power in the pump and probe beams at ambient pressure with a high spectral resolution.

380 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

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#### **Enhanced Molecular Spectroscopy via Localized Surface Plasmon Resonance Enhanced Molecular Spectroscopy via Localized Surface Plasmon Resonance**

Lu Sun, Ping Chen and Lie Lin Lu Sun, Ping Chen and Lie Lin

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64380

#### **Abstract**

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382 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

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Numerous novel spectroscopy techniques have been developed to perform detection and characterization at molecular level. Nevertheless, the resolution of spectroscopy remains to be the bottleneck, and local electric field is involved to solve this issue. Localized surface plasmon resonance (LSPR) occurred at the surface of noble metal nanoparticles is a major source of enhanced local electric field which provide notable enhancement factor of spectroscopy applying fluorescence and the Raman scattering. In this chapter, we will firstly present the physics of localized surface plasmon resonance to gain a basic understanding. Several current techniques to prepare a wide variety of nanoparticles and localized surface plasmon resonance detector are subsequently introduced. We further illustrate two examples taking advantage of experiments and modeling to elaborate the effect of localized surface plasmon resonance on spectroscopy under different circumstances. The combination of experimental and theoretical approaches elucidates the influence of each factor and promotes the design of localized surface plasmon resonance detector used in spectroscopy.

**Keywords:** spectroscopy, localized surface plasmon resonance, nanoparticle, detec‐ tion, enhancement factor, finite‐difference time‐domain

## **1. Introduction**

The advance of science and technology has drawn people's attention to the molecular level, and characterization of molecular configuration is among the most significant challenges. Spectro‐ scopy takes advantage of the interaction between electromagnetic radiation and matter and records the response of interest. The resulting spectrum containing the fingerprint of the analyte sheds light on specific structural details of a single molecule.

© 2016 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. © 2016 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.

State‐of‐the‐art spectroscopy techniques employing fluorescence [1], the Raman scattering [2], X‐ray [3], NMR [4], etc. have been successfully utilized to illustrate the conformations of biomolecules such as protein, DNA, and RNA. Moreover, utilizing lasers with impulse interval at femtosecond as excitation power has accomplished ultrafast detections. For example, the instantaneous structures of mRNA‐tRNA translocation intermediates have been characterized through single‐molecule fluorescence resonance energy transfer method, the achievement of which is a huge step toward the comprehensive mechanism of in vivo protein synthesis [5].

Despite the pronounced temporal resolution achieved during the past two decades, the spatial resolution is another key issue to fulfill detection and characterization at the single‐molecule level. For instance, the cross section of non‐resonant Raman scattering is typically ranging from 10−30 to 10−25 cm2 per molecule, a value so weak that a notable amount of analyte molecules is demanded to convert the incident photon to the Raman photon [6]. Although the laser power still has the potent to be augmented, the loss during transmission is too dramatic to exert a distinct influence by simply replacing an intensified laser. Local electromagnetic field is therefore more applicable in enhancing the resolution of spectroscopy.

The progress of nanoparticles' localized surface plasmon resonance (LSPR) is becoming a major solution to enhance the intensity of engendered signals through the highly localized electromagnetic field. It has been discovered that the electrons within the conduction band can be excited collectively at noble metal surface and the consequential oscillation of the excited electrons would be localized instead of propagating on a rough surface [7].

Extensive studies have been conducted to manipulate LSPR at the surface of different kinds of nanoparticles, and LSPR has displayed distinct properties by regulated size, shape, struc‐ ture, material, and other factors [8]. For example, it has been shown that the wavelength of plasmon varies with the particle radius [9], two distinguished plasmonic radiations have been found in nano‐rod [10], and aggregates of nanoparticles show more localized optical field with hot spots and cold zones compared with isolated nanoparticles [11].

Together with the significant improvement in the fabrication of a variety of nanostructures during last decade, gold‐ and silver‐nanostructures generating LSPR are nowadays applicable and have been integrated into sensors and detectors [12, 13] Surface‐enhanced Raman spectroscopy has recently made detection of biological and chemical analytes with concentra‐ tion as low as nanogram and femtogram feasible [14]. For different nanostructures, enhance‐ ment effect of LSPR cannot be predicted instinctively but through theoretical methods such as finite‐difference time‐domain (FDTD), discrete dipole approximation (DDA), and finite element method (FEM) [15].

In this chapter, we elaborate the efficacy of researches applying experimental techniques and computation modeling to enhance spectroscopy through LSPR. We first interpret the physics that originated LSPR. Since LSPR occurs at the surface of nanoparticles, different ways to fabricate nanostructure in order to generate LSPR and how nanoparticles are used as detector are thereafter introduced. Finally, examples of studies applying LSPR to enhance molecular spectroscopy and interpretations through finite‐difference time‐domain simulations are illustrated.

## **2. Physics of localized surface plasmon resonance**

State‐of‐the‐art spectroscopy techniques employing fluorescence [1], the Raman scattering [2], X‐ray [3], NMR [4], etc. have been successfully utilized to illustrate the conformations of biomolecules such as protein, DNA, and RNA. Moreover, utilizing lasers with impulse interval at femtosecond as excitation power has accomplished ultrafast detections. For example, the instantaneous structures of mRNA‐tRNA translocation intermediates have been characterized through single‐molecule fluorescence resonance energy transfer method, the achievement of which is a huge step toward the comprehensive mechanism of in vivo protein synthesis [5].

384 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Despite the pronounced temporal resolution achieved during the past two decades, the spatial resolution is another key issue to fulfill detection and characterization at the single‐molecule level. For instance, the cross section of non‐resonant Raman scattering is typically ranging

is demanded to convert the incident photon to the Raman photon [6]. Although the laser power still has the potent to be augmented, the loss during transmission is too dramatic to exert a distinct influence by simply replacing an intensified laser. Local electromagnetic field is

The progress of nanoparticles' localized surface plasmon resonance (LSPR) is becoming a major solution to enhance the intensity of engendered signals through the highly localized electromagnetic field. It has been discovered that the electrons within the conduction band can be excited collectively at noble metal surface and the consequential oscillation of the excited

Extensive studies have been conducted to manipulate LSPR at the surface of different kinds of nanoparticles, and LSPR has displayed distinct properties by regulated size, shape, struc‐ ture, material, and other factors [8]. For example, it has been shown that the wavelength of plasmon varies with the particle radius [9], two distinguished plasmonic radiations have been found in nano‐rod [10], and aggregates of nanoparticles show more localized optical field with

Together with the significant improvement in the fabrication of a variety of nanostructures during last decade, gold‐ and silver‐nanostructures generating LSPR are nowadays applicable and have been integrated into sensors and detectors [12, 13] Surface‐enhanced Raman spectroscopy has recently made detection of biological and chemical analytes with concentra‐ tion as low as nanogram and femtogram feasible [14]. For different nanostructures, enhance‐ ment effect of LSPR cannot be predicted instinctively but through theoretical methods such as finite‐difference time‐domain (FDTD), discrete dipole approximation (DDA), and finite

In this chapter, we elaborate the efficacy of researches applying experimental techniques and computation modeling to enhance spectroscopy through LSPR. We first interpret the physics that originated LSPR. Since LSPR occurs at the surface of nanoparticles, different ways to fabricate nanostructure in order to generate LSPR and how nanoparticles are used as detector are thereafter introduced. Finally, examples of studies applying LSPR to enhance molecular spectroscopy and interpretations through finite‐difference time‐domain simulations are

therefore more applicable in enhancing the resolution of spectroscopy.

electrons would be localized instead of propagating on a rough surface [7].

hot spots and cold zones compared with isolated nanoparticles [11].

per molecule, a value so weak that a notable amount of analyte molecules

from 10−30 to 10−25 cm2

element method (FEM) [15].

illustrated.

The observation of surface plasmon could be dated back to the beginning of the last century when Wood observed the anomalous light diffraction on a metallic diffraction grating, a phenomenon later proved to be correlated with the excitation of electromagnetic waves on the surface of the diffraction grating [16]. The plasmon generated from the collective oscillation of the free electrons can be described by the classical Maxwell's equation. We can treat the plasmon as the mechanical oscillations of the electron gas of a metal resulted from an external electric field. For the bulk system with size larger than the wavelength of the incident light, the oscillations occur at the plasma frequency with the energy:

$$E\_p = \hbar \sqrt{\frac{ne^2}{m\varepsilon\_0}}\tag{1}$$

where *n* denotes the electron density, *e* is the electron charge, *m* is the electron mass, and *ε0* represents the permittivity of free space.

Under this circumstance, the oscillations of electrons are simply called surface plasmons. Surface plasmons can be excited through incident light. A light can couple with a surface plasmon at a metal‐dielectric interface only if the incidence angle meets the criteria, because the wavevector of the incident light should accord with the propagation constant of the plasmon so that the oscillating electric field of the incident light is capable of exciting surface plasmons. The application of surface plasmon is therefore limited.

**Figure 1.** Illustration of the excitation of localized surface plasmon resonance [17].

However, when a surface plasmon is excited at the surface of a metallic nanoparticle with the size comparable to the wavelength of light, the free electrons are confined and take parts in the collective oscillations. This kind of oscillation is thus termed as localized surface plasmon (LSP), with the oscillation shown in **Figure 1**. Since the oscillation is collective, the LSP has taken advantage of significant enhancement at the surface. It is worth noticing that the magnitude of the field attenuates drastically with the distance to the surface of the nanoparticle. Moreover, the size of nanoparticle makes the frequency of LSP, which also depends on the refractive index of the medium, at visible wavelengths for noble metal nanoparticles.

Since the size of nanoparticle is comparable to the wavelength of light, the Mie theory for light scattering would be considered. Through the analytical solution to Maxwell's equation in the Mie theory, the scattering, extinction, and absorption cross sections are solved as

$$\mathcal{C}\_{sca} = \frac{2\pi}{k^2} \sum\_{N=1}^{\nu} \left(2N + 1\right) \left(\left|a\_n\right|^2 + \left|b\_n\right|^2\right) \tag{2}$$

$$\mathcal{L}\_{ext} = \frac{2\pi}{k^2} \sum\_{N=1}^{\circ} \left(2N + 1\right) \text{Re}\left\{a\_n + b\_n\right\} \tag{3}$$

$$\mathbf{C}\_{\text{abs}} = \mathbf{C}\_{\text{ext}} - \mathbf{C}\_{\text{sca}} \tag{4}$$

where *k* is the incident wavevector, *N* is an integer representing the dipole, quadrupole, and higher multipoles of the scattering, *aL* and *bL* are the parameters represented by the Riccati‐ Bessel functions *ψL* and *ξL* expressed below:

$$a\_{\boldsymbol{n}} = \frac{m\boldsymbol{\nu}\_{\boldsymbol{n}}\left(m\boldsymbol{\infty}\right)\boldsymbol{\nu}\_{\boldsymbol{n}}\left(\boldsymbol{\infty}\right) - \boldsymbol{\nu}\_{\boldsymbol{n}}\left(\boldsymbol{\infty}\right)\boldsymbol{\nu}\_{\boldsymbol{n}}\left(m\boldsymbol{\infty}\right)}{m\boldsymbol{\nu}\_{\boldsymbol{n}}\left(m\boldsymbol{\infty}\right)\boldsymbol{\xi}\_{\boldsymbol{n}}\left(\boldsymbol{\infty}\right) - \boldsymbol{\xi}\_{\boldsymbol{n}}\left(\boldsymbol{\infty}\right)\boldsymbol{\nu}\_{\boldsymbol{n}}\left(m\boldsymbol{\infty}\right)}\tag{5}$$

$$b\_{\boldsymbol{u}} = \frac{\boldsymbol{\nu}\_{\boldsymbol{u}}(m\boldsymbol{\boldsymbol{x}})\boldsymbol{\nu}\_{\boldsymbol{u}}\text{'}(\boldsymbol{\boldsymbol{x}}) - m\boldsymbol{\nu}\_{\boldsymbol{u}}(\boldsymbol{\boldsymbol{x}})\boldsymbol{\nu}\_{\boldsymbol{u}}\text{'}(m\boldsymbol{\boldsymbol{x}})}{\boldsymbol{\nu}\_{\boldsymbol{u}}(m\boldsymbol{\boldsymbol{x}})\boldsymbol{\xi}\_{\boldsymbol{u}}\text{'}(\boldsymbol{\boldsymbol{x}}) - m\boldsymbol{\xi}\_{\boldsymbol{u}}(\boldsymbol{\boldsymbol{x}})\boldsymbol{\nu}\_{\boldsymbol{u}}\text{'}(m\boldsymbol{\boldsymbol{x}})} \tag{6}$$

$$\mathbf{m} = \frac{\mathbf{n}\_p}{\mathbf{n}\_m} \tag{7}$$

where np is the complex refractive index of the metal utilized and is equivalent to *nr*+*ini* , nm is the real refractive index of the medium, and *x* equals to *kmr* (r is the radius of the particle, *km*=*2π*/ *λ*m indicates the wave number in the medium).

To simplify the equation, Riccati‐Bessel functions can be approximated by power series if we assume the nanoparticle is much smaller compared to the wavelength (i.e., x≪<1). By truncating terms after the order of x3 , we have

$$\mathbf{a}\_1 \approx -\frac{i2\pi^3}{3} \frac{m^2 - 1}{m^2 + 2} \tag{8}$$

Enhanced Molecular Spectroscopy via Localized Surface Plasmon Resonance http://dx.doi.org/10.5772/64380 387

$$b\_1 \approx 0\tag{9}$$

The real part of a1 required to calculate the cross section of extinction can be found by replacing m = nr + ini nm into a1 as

$$q\_1 \approx -\frac{i2x^3}{3} \frac{m^2 - 1}{m^2 + 2} = -\frac{i2x^3}{3} \frac{\mathbf{n}\_\succ^2 - \mathbf{n}\_i^2 + i2\mathbf{n}\_\succ\mathbf{n}\_i - \mathbf{n}\_m^2}{\mathbf{n}\_r^2 - \mathbf{n}\_i^2 + i2\mathbf{n}\_r\mathbf{n}\_i + 2\mathbf{n}\_m^2} \tag{10}$$

Further substituting the dielectric function of metal with the complex form

Moreover, the size of nanoparticle makes the frequency of LSP, which also depends on the

Since the size of nanoparticle is comparable to the wavelength of light, the Mie theory for light scattering would be considered. Through the analytical solution to Maxwell's equation in the

( )( ) 2 2

<sup>2</sup> ( ){ }

where *k* is the incident wavevector, *N* is an integer representing the dipole, quadrupole, and higher multipoles of the scattering, *aL* and *bL* are the parameters represented by the Riccati‐

> ( ) () () ( ) ( ) () () ( ) ' ' ' '

 yy

 xy

( ) () () ( ) ( ) () () ( ) ' ' ' '

 yy

*mx x m x mx*

p m

the real refractive index of the medium, and *x* equals to *kmr* (r is the radius of the particle, *km*=*2π*/

To simplify the equation, Riccati‐Bessel functions can be approximated by power series if we assume the nanoparticle is much smaller compared to the wavelength (i.e., x≪<1). By

3 2

2 1

3 2 *ix m m* - » - <sup>+</sup>

n

where np is the complex refractive index of the metal utilized and is equivalent to *nr*+*ini*

, we have

1 2

a

 xy

*mx x m x mx*

*n n nn*

 y

> x

 y

> x

*n n nn m mx x x mx*

*n n nn*

*n n nn*

m

*m mx x x mx*

= ++ å (2)

= ++ å (3)

*C CC* abs *ext sca* = - (4)



<sup>n</sup> <sup>=</sup> (7)

, nm is

(8)

refractive index of the medium, at visible wavelengths for noble metal nanoparticles.

Mie theory, the scattering, extinction, and absorption cross sections are solved as

<sup>2</sup> 2 1 *sca n n*

<sup>2</sup> 2 1 Re *ext n n*

*C N ab*

*C N ab*

2 1

*k* p ¥ =

*k* p ¥ =

Bessel functions *ψL* and *ξL* expressed below:

*n*

*n*

*b*

*λ*m indicates the wave number in the medium).

truncating terms after the order of x3

y

y

y

y

*a*

*N*

386 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

1

*N*

$$
\varepsilon\_p = \varepsilon\_1 + i\varepsilon\_2 \tag{11}
$$

$$\boldsymbol{\varepsilon}\_{1} = \mathbf{n}\_{r}^{2} - \mathbf{n}\_{l}^{2} \tag{12}$$

$$
\boldsymbol{\varepsilon}\_2 = \mathbf{2n}\_r \mathbf{n}\_i \tag{13}
$$

and replacing the dielectric function of medium, *ε*m = *nm* 2 , will result in the following relation as

$$\mathbf{a}\_1 = -\frac{i\mathbf{2}\times^3}{3} \frac{\boldsymbol{\varepsilon}\_1^2 + \boldsymbol{\varepsilon}\_1 \boldsymbol{\varepsilon}\_m - 3\boldsymbol{\varepsilon}\_2 \boldsymbol{\varepsilon}\_m + \boldsymbol{\varepsilon}\_2^2 - 2\boldsymbol{\varepsilon}\_m^2}{\left(\boldsymbol{\varepsilon}\_1 + \boldsymbol{\varepsilon}\_m\right)^2 + \boldsymbol{\varepsilon}\_2^2} \tag{14}$$

Substituting the above equation into the extinction cross section and only taking the dipole term, we can get the most quoted expression for LSPR as

$$\mathbf{C}\_{\rm cat} = \frac{18\pi\varepsilon\_{\rm m}^{\frac{3}{2}}V}{\lambda} \frac{\varepsilon\_{2}\left(\lambda\right)}{\left[\varepsilon\_{1}\left(\lambda\right) + 2\varepsilon\_{\rm m}\right]^{2} + \varepsilon\_{2}\left(\lambda\right)^{2}}\tag{15}$$

In here, *V* represents the volume of the particle. Similarly, the scattering cross section can be expressed as

$$\mathbf{C}\_{\text{sca}} = \frac{34\pi^4 \boldsymbol{\varepsilon}\_{\text{m}}^2 V^2}{\lambda^4} \frac{\left(\boldsymbol{\varepsilon}\_1 - \boldsymbol{\varepsilon}\_{\text{m}}\right)^2 + \boldsymbol{\varepsilon}\_2^2}{\left(\boldsymbol{\varepsilon}\_1 + 2\boldsymbol{\varepsilon}\_{\text{m}}\right)^2 + \boldsymbol{\varepsilon}\_2^2} \tag{16}$$

Because we supposed that the nanoparticles are small enough to use the approximation, the equation above would be strictly applied to particles with diameter smaller than 10 nm. Nevertheless, it is noteworthy that the expression will give certain accuracy for larger particles as well [18].

The functional form of the LSPR peak wavelength is dependent on the dielectric function of the medium [19], and the dependence can be derived by the following access.

The frequency‐dependent dielectric constant for *ε1* according to the Drude model of the electronic structure of metal would be

$$\omega\_1 = 1 - \frac{\left. \alpha\_p\right|^2}{\alpha^2 + \gamma^2} \tag{17}$$

in which *ω*p denotes the plasma frequency and *γ* represents the damping parameter of the bulk metal. It is notable the Drude model is a classical model of electronic transport in conductors and describes the collisions between freely moving electrons and the lattice of heavy, stationary ionic cores. The model is a very good approximation for the conductivity of noble metals. For visible and near‐infrared frequencies, where *γ*≪*ω*p, the above relation would be reduced to the following form as

$$\left(\varepsilon\_1 = 1 - \frac{\alpha\_p^{\prime}}{\alpha^2}\right) \tag{18}$$

Substituting the above expression for *ε1* and setting ε1 = −2εm as the resonance condition, we can obtain the maximum peak of the LSPR frequency as

$$
\alpha\_{\text{max}} = \frac{\alpha\_{\text{p}}}{\sqrt{2\varepsilon\_{\text{m}} + 1}} \tag{19}
$$

Because the relation between frequency and wavelength is denoted as *λ*=*2πc*/*ω*, the wavelength of LSPR can be expressed after replacing the dielectric constant with the refraction *εm* = *n2* :

$$
\mathcal{A}\_{\text{max}} = \mathcal{A}\_{\text{p}} \sqrt{2n\_{m}^{2} + 1} \tag{20}
$$

in which λmax is the peak wavelength of LSPR while λp represents the corresponding wave‐ length to the plasma frequency of the bulk metal.

Because the nanoparticles generating LSPR are generally not strictly spherical, Richard Gans further complemented the Mie theory to spheroidal particles of any aspect ratio in the small particle approximation. The absorption cross section for a prolate spheroid (nanorod structure) is found analogous to that of the spherical nanoparticles, as

Because we supposed that the nanoparticles are small enough to use the approximation, the equation above would be strictly applied to particles with diameter smaller than 10 nm. Nevertheless, it is noteworthy that the expression will give certain accuracy for larger particles

The functional form of the LSPR peak wavelength is dependent on the dielectric function of

The frequency‐dependent dielectric constant for *ε1* according to the Drude model of the

2 p <sup>1</sup> 2 2 1 w

in which *ω*p denotes the plasma frequency and *γ* represents the damping parameter of the bulk metal. It is notable the Drude model is a classical model of electronic transport in conductors and describes the collisions between freely moving electrons and the lattice of heavy, stationary ionic cores. The model is a very good approximation for the conductivity of noble metals. For visible and near‐infrared frequencies, where *γ*≪*ω*p, the above relation would be reduced to

> 2 p <sup>1</sup> <sup>2</sup> 1 w

= - (18)

= + *n* (20)

w

Substituting the above expression for *ε1* and setting ε1 = −2εm as the resonance condition, we

p

2 1 *<sup>m</sup>* w

Because the relation between frequency and wavelength is denoted as *λ*=*2πc*/*ω*, the wavelength of LSPR can be expressed after replacing the dielectric constant with the refraction *εm* = *n2*

> 2 max p 2 1 *<sup>m</sup>*

in which λmax is the peak wavelength of LSPR while λp represents the corresponding wave‐

Because the nanoparticles generating LSPR are generally not strictly spherical, Richard Gans further complemented the Mie theory to spheroidal particles of any aspect ratio in the small

e<sup>=</sup> <sup>+</sup> (17)

(19)

:

w g

= - <sup>+</sup>

the medium [19], and the dependence can be derived by the following access.

388 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

e

e

max

w

l

 l

can obtain the maximum peak of the LSPR frequency as

length to the plasma frequency of the bulk metal.

as well [18].

the following form as

electronic structure of metal would be

$$\mathbf{C\_{abs}} = \frac{\alpha \sigma}{\mathfrak{Z}c} \varepsilon\_{\mathbf{m}}^{\frac{3}{2}} V \sum\_{\neq} \frac{\left(1 \, \mathbf{1} \, \mathbf{P}\_{\neq}^{2}\right) \varepsilon\_{2}}{\left[\varepsilon\_{1} + \frac{\left(1 - \mathbf{P}\_{\neq}\right) \varepsilon\_{\mathbf{m}}}{\mathbf{P}\_{\neq}}\right]^{2} + \varepsilon\_{2}} \tag{21}$$

The sum over *j* infers to the three dimensions of the nanoparticle. *Pj* denoting the depolarization factors has three components, *PA*, *PB*, and *PC*, along each axis. For a prolate spheroid with aspect ratio A > B = C, the depolarization factors alter the dielectric constant ε1 and ε2 anisotropically. Therefore, the corresponding LSPR peak frequencies are different at different directions. The depolarization factors are expressed as

$$P\_A = \frac{1 - e^2}{e^2} \left[ \frac{1}{2e} \ln \left( \frac{1 + e}{1 - e} \right) - 1 \right] \tag{22}$$

$$P\_B = P\_B = \frac{1 - P\_A}{2} \tag{23}$$

where *e* is the ellipticity factor that includes the particle aspect ratio *R*:

$$\mathbf{e} = \sqrt{\mathbf{1} - \left(\frac{\mathbf{B}}{A}\right)^2} = \sqrt{\mathbf{1} - \left(\frac{\mathbf{1}}{R}\right)^2} \tag{24}$$

The extinction spectrum resulting from nanorod has two peaks, one corresponding to the transverse plasmon mode and the other corresponding to the longitudinal plasmon mode **(Figure 2**).

For example, the absorption spectra of nanoparticle with different aspect ratios have been simulated, and it is shown that the increase of aspect ratio would dramatically increase the wavelength. From the result by EL‐Sayed et al., the maximum peak of longitude plasmon band displayed red shift from 650 to 800 nm after altering the ratio aspect from 2.6 to 3.6 [21]. For nanoparticles beyond these spheres and spheroids, particle shape plays a significant role in determining the LSPR spectrum.

The plasmonic spectrum would also rely on many other factors, such as the local medium surrounding. The LSPR wavelength shift is in accordance with the refractive index change and follows the relation as

**Figure 2.** An illustration of LSPR excitation for prolate spheroid. The discrepant oscillation of electrons at longitudinal and transverse plasmon bands results in different plasmonic spectra [20].

$$
\Delta \mathcal{X} = \text{m} \Delta n \left[ 1 - \exp\left(\frac{-2d}{l\_d}\right) \right] \tag{25}
$$

where m represents the sensitivity factor (measured in nm per refractive index unit), Δ*n* is the change of the refractive index, *d* indicates the effective thickness of the absorbed layer, and *ld* denotes the characteristic electromagnetic field decay.

The complication of LSPR contributes to its potential applications only if we have gained a thorough understanding. Currently, there are several numerical methods for simulation of LSPR occurring at the surface of nanoparticles, including finite-difference time-domain (FDTD), discrete dipole approximation (DDA), and finite element method (FEM) [15]. An example of FDTD simulation will be illustrated in Section 5.

#### **3. Fabrication of nanoparticles**

The extensive studies on the fabrication of nanoparticles have promoted the potential application of LSPR. A reliable and reproducible synthesis method of nanostructures is the basis for LSPR detector. While the spherical nanoparticle of a noble metal is most readily prepared, it takes efforts to fabricate other desired structures. Several ways to fabricate desired nanostructure are illustrated in this section [22].

## **3.1. Citrate reduction**

Citrate reduction is the most widely applied method for producing nanoparticles. The delicate addition of a calculated amount of citrate solution into the boiling metallic salt (such as HAuCl4) solution generates solutions containing nanoparticles. The size of the nanoparticle would be controlled through the ratio between the citrate and the gold salt, the reaction temperature, and the reaction time [23]. The simplicity of these reductions makes it the most popular method to form nanoclusters.

## **3.2. Electrochemical method**

**Figure 2.** An illustration of LSPR excitation for prolate spheroid. The discrepant oscillation of electrons at longitudinal

where m represents the sensitivity factor (measured in nm per refractive index unit), Δ*n* is the change of the refractive index, *d* indicates the effective thickness of the absorbed layer, and *ld*

The complication of LSPR contributes to its potential applications only if we have gained a thorough understanding. Currently, there are several numerical methods for simulation of LSPR occurring at the surface of nanoparticles, including finite-difference time-domain (FDTD), discrete dipole approximation (DDA), and finite element method (FEM) [15]. An

The extensive studies on the fabrication of nanoparticles have promoted the potential application of LSPR. A reliable and reproducible synthesis method of nanostructures is the basis for LSPR detector. While the spherical nanoparticle of a noble metal is most readily prepared, it takes efforts to fabricate other desired structures. Several ways to fabricate desired nano-

*d d*

ç ÷ ê ú ë û è ø (25)

*l*

<sup>2</sup> m 1 exp

é ù æ ö - D= D - ê ú ç ÷

*n*

390 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

and transverse plasmon bands results in different plasmonic spectra [20].

denotes the characteristic electromagnetic field decay.

example of FDTD simulation will be illustrated in Section 5.

**3. Fabrication of nanoparticles**

structure are illustrated in this section [22].

l

The preparation of high yield of nanoparticles was initially proposed through applying an electrochemical method which can produce both nanocluster and nanorod structures [24, 25]. While preparing the cluster structure is easier, the nanorod structure was first synthesized through this method by Wang et al. in the late 1990s [26]. The synthesis of nanorod was carried out using a two‐electrode‐type electrochemical cell containing a gold metal plate as the sacrificial anode and a platinum plate as cathode and the electrolytic solution containing a rod‐ inducting cationic surfactant cetyltrimethylammonium bromide (CTAB) and a cationic co‐ surfactant tetradodecylammonium bromide (TCAB). During the electrolysis, the gold metal anode was oxidized into AuBr4 and subsequently formed complexes with the cationic surfactants. The gold nanoparticle was generated through the reduction process after the complexes migrated to the cathode. In order to control the aspect ratio, a silver plate was placed in a position behind the platinum cathode. The aspect ratio was found to be dependent on the concentration and the release rate of the silver ions.

## **3.3. Electron beam lithography method**

Electron beam lithography method is another common way used to generate metallic nano‐ structures. This method takes advantage of the precise control of the size, shape, and spatial distribution of the nanoparticles synthesized [27, 28]. Nevertheless, the lithography applied in this method makes it highly time‐consuming owing to the small region processed.

## **3.4. Seed-mediated growth method**

For the synthesis of nanostructure with specific aspect ratio, the seed‐mediated growth method is the most used and has been extensively utilized [29, 30]. The method possesses merits such as the simplicity of the experiment, the high yield and high quality of produced nanorods, the convenience of size control, and the flexibility in structural modifications [31]. Lately, nano‐ structures such as 2D gold nanorings [32], composite core‐shell nanorod [33], and branched gold nanodendrites [34] have been reported applying this experimental approach.

The seed‐mediated growth method for nanorod structure was initially demonstrated by Jana et al. [35]. In their early experiment, the seed solution was prepared by the reduction of gold salt (HAuCl4) with NaBH4 in the presence of sodium citrate. The produced nanoparticles usually have a diameter of 3–4 nm and were used as the seeds by being added to the so‐called growth solution, which was composed of HAuCl4, cetyltrimethylammonium bromide (CTAB), ascorbic acid, and AgNO3. The latter three compositions acted as the template, the reducing agent, and the shape induction agent, respectively. The nanoparticle can subsequently grow into various aspect ratios by controlling the ratio of seed solution to the growth solutions.

The method was further improved later by the same group to obtain larger aspect ratio [36]. The seed and the growth solutions were prepared similarly except for adding AgNO3. After adding the seed solution to the growth solution, the generated nanostructure was again used as seed with a repeated step. Despite nanostructures with larger aspect ratio acquired, by‐ products resulted from the reaction become significant, and more difficult purification processes are required in this process [37].

Based on the prototype, a wealth of significant improvements has been thereafter accomplish‐ ed. For example, Nikoobakht et al. synthesized high‐quality and high‐yield nanorods by replacing the sodium citrate and adjusting the concentration of silver ions [38]. Ye et al. employed aromatic additives and a low CTAB concentration to achieve a broadly tunable localized plasmon band with higher purity [39].

Above all, mastering nanostructure is becoming more and more feasible, which has signifi‐ cantly advanced the applications of LSPR in molecular spectroscopy.

## **4. Nanoparticle LSPR sensor and detector**

There are a wide variety of designs of LSPR detectors because the LSPR is more readily to be excited compared to the surface plasmon on a planar surface. The LSPR detector can be typically designed as either substrate‐based or solution‐based. We will hereby introduce three most widely studied structures: chip‐based, optical‐fiber‐based, and solution‐phase‐based.

#### **4.1. Chip-based LSPR sensor and detector**

The chip‐based LSPR detector can be fabricated by immobilizing nanostructures on the surface of a substrate, such as a glass slide and cover slip. The detector chip is easily achieved when nanostructures are produced through electron beam lithography technique or are grown on the substrate.

If the nanoparticles are produced in solution, such as citrate reduction, they can be immobilized on the surface through electrostatic force [40]. For instance, a clean glass substrate can be coated with polyelectrolyte that shows opposite charge to the surface charge of the generated nanoparticles. The charged substrate is subsequently immersed into the nanoparticle solution to attract nanoparticles by electrostatic force. However, the LSPR detector prepared in this way suffers from poor stability and poor uniformity.

Another method is based on the SAM technique [41]. A clean substrate is dipped into an alkylsilane solution, such as MPTMS, to form a thiol‐terminated silane membrane on the surface. The silanized substrate surface would subsequently form covalent bonds with single layer of nanoparticles.

## **4.2. Optical fiber-based LSPR sensor and detector**

ascorbic acid, and AgNO3. The latter three compositions acted as the template, the reducing agent, and the shape induction agent, respectively. The nanoparticle can subsequently grow into various aspect ratios by controlling the ratio of seed solution to the growth solutions.

392 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

The method was further improved later by the same group to obtain larger aspect ratio [36]. The seed and the growth solutions were prepared similarly except for adding AgNO3. After adding the seed solution to the growth solution, the generated nanostructure was again used as seed with a repeated step. Despite nanostructures with larger aspect ratio acquired, by‐ products resulted from the reaction become significant, and more difficult purification

Based on the prototype, a wealth of significant improvements has been thereafter accomplish‐ ed. For example, Nikoobakht et al. synthesized high‐quality and high‐yield nanorods by replacing the sodium citrate and adjusting the concentration of silver ions [38]. Ye et al. employed aromatic additives and a low CTAB concentration to achieve a broadly tunable

Above all, mastering nanostructure is becoming more and more feasible, which has signifi‐

There are a wide variety of designs of LSPR detectors because the LSPR is more readily to be excited compared to the surface plasmon on a planar surface. The LSPR detector can be typically designed as either substrate‐based or solution‐based. We will hereby introduce three most widely studied structures: chip‐based, optical‐fiber‐based, and solution‐phase‐based.

The chip‐based LSPR detector can be fabricated by immobilizing nanostructures on the surface of a substrate, such as a glass slide and cover slip. The detector chip is easily achieved when nanostructures are produced through electron beam lithography technique or are grown on

If the nanoparticles are produced in solution, such as citrate reduction, they can be immobilized on the surface through electrostatic force [40]. For instance, a clean glass substrate can be coated with polyelectrolyte that shows opposite charge to the surface charge of the generated nanoparticles. The charged substrate is subsequently immersed into the nanoparticle solution to attract nanoparticles by electrostatic force. However, the LSPR detector prepared in this way

Another method is based on the SAM technique [41]. A clean substrate is dipped into an alkylsilane solution, such as MPTMS, to form a thiol‐terminated silane membrane on the surface. The silanized substrate surface would subsequently form covalent bonds with single

processes are required in this process [37].

localized plasmon band with higher purity [39].

**4. Nanoparticle LSPR sensor and detector**

**4.1. Chip-based LSPR sensor and detector**

suffers from poor stability and poor uniformity.

the substrate.

layer of nanoparticles.

cantly advanced the applications of LSPR in molecular spectroscopy.

Optical fiber-based LSPR detector is typically fabricated by immobilizing nanoparticles on the decladed fiber core of a multimode or single-mode optical fiber [42, 43]. Optical fiber-based LSPR detector shows advantages such as small sample volume, simple optical design, and minor electromagnetic interference [44].

#### **4.3. Solution-phase-based LSPR sensor and detector**

Solution-phase-based LSPR detectors are the nanoparticles suspended in solution rather than immobilized on a substrate, which makes the detection process of analytes inside the solution [45]. During the detection, the functional molecules should mix evenly with the nanoparticle solution and be closed enough to the surface of nanoparticles because of the decaying electric field.

## **5. LSPR-enhanced molecular spectroscopy**

The utilization of localized surface plasmon resonance to enhance molecular spectroscopy has achieved prodigious enhancement factors. Applications of surface plasmon polariton include enhanced Raman spectrum [46], enhanced fluorescence [47], enhanced optical nano-devices [48], etc. Examples of the related experimental achievements are introduced and further elucidated through theoretical modeling applying Maxwell's equation.

#### **5.1. Metal-enhanced fluorescence spectroscopy detecting polycyclic aromatic hydrocarbons**

Oil spills are the major sea pollutants originating from tankers, offshore drilling rigs, etc. [49]. Spilled oil is toxic to living organisms, and even one spill oil would cause large mortality in the marine ecosystem [50]. The monitor of the water quality is crucial and demands trace amount detection technique. Polycyclic aromatic hydrocarbons are a major component of crude oil and would be selected as the surveillance target.

The easy-to-handle fluorescence spectroscopy is enhanced by LSPR during the detections of crude oil [51]. The silver nanoparticle solution was prepared with the citrate reduction method shown previously. The glass-based detector was prepared by (3-aminopropyl)trimethoxysilane-coated quartz substrates. Through the SEM image, an average grain size of 80 nm was analyzed over 200 particles, as shown in **Figure 3(a)**. The characteristic absorption spectrum of the silver nanoparticles has its peak at 405 nm as displayed in **Figure 3(b)**, resulting in LSPR at the surface of the silver nanoparticles.

The fluorescence spectrum of artificial diesel oil polluted seawater emulsions was measured in the presence and in the absence of the glass-based nanoparticles, as shown in **Figure 3(c)**. It is distinct that the maximum peak is enhanced with the intensity rising from 6 × 104 to more than 30 × 104 , indicating an enhancement factor of 5.44.

**Figure 3.** (a) SEM image shows the structure of the applied silver nanoparticles, (b) the absorption spectrum of silver nanoparticles at room temperature, (c) fluorescence spectra of diesel oil emulsions in artificial seawater before and af‐ ter enhancement (emitted at 355 nm), and (d) the electric field enhancements of silver nanoparticles at 355 nm.

To understand the enhancement, the electric field effect could be modeled through FDTD method, as shown in **Figure 3(d)** with the near‐field plotted. During the simulation, the excitation field is incident in the positive x‐direction and polarized along the z‐axis. The dipole resonance mode is the key to the enhancement in this case, which explains the enhanced fluorescence is resulted from the increased electric fields.

#### **5.2. Enhanced Raman scattering**

The enhancement effects of LSPR on Raman spectroscopy are generally attributed to the presence of hot spots on the rough particle surface, but there are more factors involved during this process, such as the complicated intraparticle coupling [52, 53]. Combined experiments and simulations are performed to interpret this issue.

The silver nanoparticles on quartz substrates were first synthesized through citrate reduction and applied to enhance the Raman spectrum of methylene blue. From the obtained spectra shown in **Figure 4**, it is obvious that the involvement of LSPR improves the resolution, where characteristic vibrational peaks are distinctly displayed. The enhancement factors are 3.2 × 105 and 1.3 × 107 for SERS excited by a 514.5 nm Ar‐ion laser and a 785 nm diode laser, respectively. Since the nanoparticles were attracted by (3‐aminopropyl)trimethoxysilane with SAM method, it is estimated that they lay as one layer instead of taking the form of aggregation. However, it is notable that the enhancement factor was higher than theoretically estimated for spherical particles [54]. More sophisticated explanation should be accounted.

Enhanced Molecular Spectroscopy via Localized Surface Plasmon Resonance http://dx.doi.org/10.5772/64380 395

**Figure 4.** (a) The Raman spectra of methylene blue excited by 514.5 nm in the absence (black) and the presence (red) of SERS substrates and (b) the Raman spectra of methylene blue excited by 785 nm in the absence (black) and the pres‐ ence (red) of SERS substrates.

SEM image shown in **Figure 5(a)** indicates that the submicrometer silver particles are flower‐ like with distinct surface protrusions. The average diameter of the silver particles is analyzed to be about 500 nm. Three‐dimensional finite‐difference time‐domain (FDTD) method was employed to calculate both far‐ and near‐field optical properties of the submicrometer silver particles. The control structure, i.e., the smooth spherical structure, and the mimicking structure, i.e., a large particle (D = 400 nm) with 26 small spherical particles (D = 100 nm) evenly distributed on the exterior surface, were modeled.

**Figure 3.** (a) SEM image shows the structure of the applied silver nanoparticles, (b) the absorption spectrum of silver nanoparticles at room temperature, (c) fluorescence spectra of diesel oil emulsions in artificial seawater before and af‐ ter enhancement (emitted at 355 nm), and (d) the electric field enhancements of silver nanoparticles at 355 nm.

394 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

To understand the enhancement, the electric field effect could be modeled through FDTD method, as shown in **Figure 3(d)** with the near‐field plotted. During the simulation, the excitation field is incident in the positive x‐direction and polarized along the z‐axis. The dipole resonance mode is the key to the enhancement in this case, which explains the enhanced

The enhancement effects of LSPR on Raman spectroscopy are generally attributed to the presence of hot spots on the rough particle surface, but there are more factors involved during this process, such as the complicated intraparticle coupling [52, 53]. Combined experiments

The silver nanoparticles on quartz substrates were first synthesized through citrate reduction and applied to enhance the Raman spectrum of methylene blue. From the obtained spectra shown in **Figure 4**, it is obvious that the involvement of LSPR improves the resolution, where characteristic vibrational peaks are distinctly displayed. The enhancement factors are

respectively. Since the nanoparticles were attracted by (3‐aminopropyl)trimethoxysilane with SAM method, it is estimated that they lay as one layer instead of taking the form of aggregation. However, it is notable that the enhancement factor was higher than theoretically estimated for

spherical particles [54]. More sophisticated explanation should be accounted.

for SERS excited by a 514.5 nm Ar‐ion laser and a 785 nm diode laser,

fluorescence is resulted from the increased electric fields.

and simulations are performed to interpret this issue.

**5.2. Enhanced Raman scattering**

and 1.3 × 107

3.2 × 105

**Figure 5.** (a) The SEM image of rough submicrometer silver particles. Scale bar: 100 nm. (b) Schematic diagram of the rough submicrometer silver particle model. The 26 small peripheral particles were submerged into the large core parti‐ cle, effectively generating many hemispheres on the surface. (c) Calculated extinction, absorption, and scattering spec‐ tra of the smooth silver particles model. (d) Calculated extinction, absorption, and scattering spectra of the rough silver particle model.

The characteristic spectra of both models are illustrated in **Figure 5(c)** and **(d)**. It is noteworthy that the extinction spectrum of the smooth particle shows featured bands originated from dipole resonance (ca. 800 nm) and higher‐order multipole resonances, such as quadrupole resonance (ca. 620 nm), while the featured band of rough particles located at ca. 800 nm. The dipole field at the surface of rough particles is intensified through scattering. The augmented enhancement factor at 785 nm in the SERS spectrum is thus interpreted.

Understanding the wavelength dependence of SERS requires the distribution of electric fields of metal particles, as shown in **Figure 6**. The different electric distributions at different wavelengths for the smooth surface point out that the enhancement effect under the shorter wavelength (514.5 nm) originates from the multipole effect, while the longer wavelength (785 nm) is resulted from the dipole effect. The electric distribution for the rough surface denotes the prominent near‐field enhancement at the rough surface because of a more localized distribution of the particle's conduction electrons.

**Figure 6.** The distribution of electric fields around the silver particle model through FDTD calculation. The excitation field is incident in the positive z‐direction. (a) and (b) The smooth silver particle model excited by 785 and 514.5 nm, respectively. (c) and (d) The rough silver particle model excited by 785 and 514.5 nm, respectively.

Further verification of the theoretical computed electric field is accomplished through near‐ field scanning optical microscopy (NSOM), which can measure the intensity distribution of optical fields of the rough submicrometer silver particles on a quartz slide, as shown in **Figure 7**. The rough submicrometer silver particle shows a strong optical field distribution, shedding light on the stronger enhancement factor.

The characteristic spectra of both models are illustrated in **Figure 5(c)** and **(d)**. It is noteworthy that the extinction spectrum of the smooth particle shows featured bands originated from dipole resonance (ca. 800 nm) and higher‐order multipole resonances, such as quadrupole resonance (ca. 620 nm), while the featured band of rough particles located at ca. 800 nm. The dipole field at the surface of rough particles is intensified through scattering. The augmented

Understanding the wavelength dependence of SERS requires the distribution of electric fields of metal particles, as shown in **Figure 6**. The different electric distributions at different wavelengths for the smooth surface point out that the enhancement effect under the shorter wavelength (514.5 nm) originates from the multipole effect, while the longer wavelength (785 nm) is resulted from the dipole effect. The electric distribution for the rough surface denotes the prominent near‐field enhancement at the rough surface because of a more localized

**Figure 6.** The distribution of electric fields around the silver particle model through FDTD calculation. The excitation field is incident in the positive z‐direction. (a) and (b) The smooth silver particle model excited by 785 and 514.5 nm,

Further verification of the theoretical computed electric field is accomplished through near‐ field scanning optical microscopy (NSOM), which can measure the intensity distribution of optical fields of the rough submicrometer silver particles on a quartz slide, as shown in

respectively. (c) and (d) The rough silver particle model excited by 785 and 514.5 nm, respectively.

enhancement factor at 785 nm in the SERS spectrum is thus interpreted.

396 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

distribution of the particle's conduction electrons.

**Figure 7.** The measured intensity distribution of optical fields on the surface of nanoparticles by NSOM under 785 nm excitation. (a) Rough submicrometer silver particles and (b) spherical silver nanoparticles.

In order to illustrate the enhancement of electric field with a formula, the total electric field ( otal) of LSPR is accounted as the sum of radiation field produced by LSPR of the core particle ( ) and the peripheral particles ( ), as

$$
\bar{E} = \bar{E}\_o + \sum\_{l=1}^{n} \mathcal{Z}\_l \left( \mathbf{r}\_l \right) \bar{E}\_l \tag{26}
$$

where *χi* (r*i* ) is the weighing factor for the *ith* peripheral particles at r*<sup>i</sup>* .

Based on the equation of the radiation field produced by the dipole resonance, the constant phase difference could be found for the radiation waves of large core particle and small peripheral for a direction r (*R* cos *θi* ). Electric field from the core particle ( ) and the peripheral particles ( ) can be demonstrated and otal can be rewritten as

$$
\bar{E}\_{\rm o} = \frac{\mathbf{k}^2 P\_{\rm o} \sin \left( \alpha \right)}{4 \pi \varepsilon\_0 \mathbf{r}} e^{i \mathbf{k} \cdot \mathbf{r}} \tag{27}
$$

$$\bar{E}\_i = \frac{\mathbf{k}^2 P \sin(\alpha)}{4\pi\varepsilon\_0 \mathbf{r}} e^{i\mathbf{k}\left(r \ast R\cos\theta\right)}\tag{28}$$

$$\bar{E}\_{\text{Total}}^2 = E\_\text{o}^2 + \chi\_\text{l}^2 E\_\text{l}^2 + 2\chi\_\text{l} E\_\text{o} E\_\text{l} \cos \left( kR \cos \theta\_\text{l} \right) \tag{29}$$

where *ε*0 represents the permittivity of vacuum, *Po* denotes the dipole moment of the large core particle, *k* expresses the wave number of the radiation field, and α is the angle between the incident and radiation fields. The effect of the roughness of the surface would be described by different induced dipole moments, as

$$P = E\_{\alpha \alpha} a^3 \frac{\mathcal{E}\_{\text{np}} - \mathcal{E}\_0}{\mathcal{E}\_{\text{np}} + 2\mathcal{E}\_0} \tag{30}$$

In here, *a* is the radius of the particle, *E*ex represents the incident electric field, and *εnp* and *ε*<sup>o</sup> denote the relative permittivity of the particle and the surrounding medium, respectively. The dependence of dipole enhancement with the grain is thus illustrated. A more distinct demon‐ stration can be achieved by simulation through controlling the different sizes of peripheral particles, as shown in **Figure 8**, which indicates the enhanced induced dipole moment with larger peripheral particles.

**Figure 8.** Enhanced dipole mode of LSPR with the increasing radius of the peripheral particles.

The effects of a rough surface are thus thoroughly studied combining experiment and theoretical calculation. The enlightening result indicating how small particles affect the enhancement factor helps further design more advanced nanoparticle detectors.

## **6. Conclusion**

where *ε*0 represents the permittivity of vacuum, *Po* denotes the dipole moment of the large core particle, *k* expresses the wave number of the radiation field, and α is the angle between the incident and radiation fields. The effect of the roughness of the surface would be described by

3 0

In here, *a* is the radius of the particle, *E*ex represents the incident electric field, and *εnp* and *ε*<sup>o</sup> denote the relative permittivity of the particle and the surrounding medium, respectively. The dependence of dipole enhancement with the grain is thus illustrated. A more distinct demon‐ stration can be achieved by simulation through controlling the different sizes of peripheral particles, as shown in **Figure 8**, which indicates the enhanced induced dipole moment with

*np np*

e e

e

<sup>0</sup> 2


 e

ex

*P Ea*

398 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

**Figure 8.** Enhanced dipole mode of LSPR with the increasing radius of the peripheral particles.

enhancement factor helps further design more advanced nanoparticle detectors.

The effects of a rough surface are thus thoroughly studied combining experiment and theoretical calculation. The enlightening result indicating how small particles affect the

different induced dipole moments, as

larger peripheral particles.

The localized surface plasmon resonance taking advantage of easy excitation is becoming increasing popular in the application of molecular spectroscopy, which has improved the resolution of spectroscopy and makes detection limit as low as femtogram. As we show in this chapter, there are numerous techniques to synthesize the desired nanostructures nowadays, and LSPR derived from those nanoparticles has demonstrated considerable enhancement factor to improve the applicability of molecular spectroscopy involving fluorescence, the Raman scattering, etc. On the one hand, it is to design better nanoparticles that arise localized surface plasmon; on the other hand, the mechanism of resulting electric field on the surface of nanoparticle needs to be accounted for different nanostructures. The multifactor‐determined LSPR can now be currently elaborated through FDTD method. The joint experimental data with theoretical perspective are beneficial for a better understanding of the characteristic of LSPR and the resulting enhancement factor. Further intensive studies on LSPR combining experiments and modeling will broaden the application of LSPR and favor spectroscopies at molecular precision.

## **Acknowledgements**

This work was supported by the Tianjin Municipal Science and Technology Commission (No. 15ZCZDGX00250 and No. 08ZCDFGX09400), the Doctoral Fund of Ministry of Education of China (No. 20110031110035), the National Natural Science Foundation of China (No. 60508004 and No. 60778043), and the National High Technology Research and Development Program of China ("863" Program, No. 2011AA030205).

## **Author details**

Lu Sun, Ping Chen\* and Lie Lin

\*Address all correspondence to: chping@nankai.edu.cn

Key Laboratory of Optoelectronic Information Science and Technology, Ministry of Education of China, Institute of Modern Optics, Nankai University, Tianjin, China

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## **Using Raman Spectroscopy for Characterization of Aqueous Media and Quantification of Species in Aqueous Solution Using Raman Spectroscopy for Characterization of Aqueous Media and Quantification of Species in Aqueous Solution**

Ivana Durickovic Ivana Durickovic

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Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64550

#### **Abstract**

In this chapter, the use of Raman spectroscopy (RS) for studies of aqueous solutions is shown. This technique is mainly used for the characterization of solid samples, but presents numerous features permitting its use for the analysis of aqueous media. Indeed, it possesses all the advantages of optical methods (versatility, rapidity, contact-less nondestructive measurement, etc.), but also offers possibilities for *in situ* measurements. The Raman spectrum will be influenced by several parameters such as the solution concentration or its temperature-phase. Thus, the analysis of a set of aqueous solutions of different concentrations in a certain temperature range can permit the identification of the specific effect of salt and temperature. A proper analysis based on the follow-up of the specific peak areas or intensities can permit the determination of the salt concentration or the phase transition of the studied solution. The analysis can be focused on the salt direct effect on the spectrum, analysis of the salt signature itself, or on its indirect effect on the water signature. The method for the characterization of aqueous solutions of some salts is presented: elaboration of calibration curves and concentration determination. As an application example, a special attention is devoted to aqueous solutions that are used in the winter maintenance domain (solution of acetates, formates, or chlorides), which are very relevant examples of aqueous solution behavior. A specific analysis set to determine the solution solid-liquid phase transitions is presented as well as the thus-constructed phase diagram.

**Keywords:** aqueous solutions, Raman spectroscopy, characterization, quantification, phase transition

© 2016 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. © 2016 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.

## **1. Introduction**

In this chapter, the possibility of using Raman spectroscopy (RS) for studies of aqueous solutions is shown. Section 2 is devoted to the description of the general principle and characteristics of Raman spectroscopy (RS) as a technique for the investigation of molecular structure. Since Raman spectra contain information not only on intra-molecular vibrations but also on vibrations of the crystal lattice and other solid movements, RS is a well-established technique for the studies on solid materials [1].

The aim of this chapter is to show the possibility of its use to study aqueous solutions and chemical species dissolved in water. The application of RS on the study of aqueous media is more complicated than in solids, but is still very efficient if a proper signal treatment is applied. In order to show the different possibilities of RS for the characterization of water media, a special attention is devoted to one application example: aqueous solutions that are used in the winter maintenance domain (solution of acetates, formates, or chlorides). The possibility of its use for the detection of some water pollutants is also discussed.

In the case of water quality, there is a need for a device presenting a high polyvalence degree to detect and to quantify several chemical species in aqueous solutions but also to discriminate them from each other. There is an increasing need for a technique that could permit *in situ* measurements and continuous monitoring of water.

However, the techniques actually used for the detection and quantification of chemicals in water often do not respond to these criteria [2]. Some techniques, such as the widely used solution conductometry, are fully dedicated to one chemical species or are not able to make an accurate discrimination between different chemical species. In other cases, the quantification of chemicals in water media usually needs the combination of several techniques such as ionic chromatograph and electrical conductivity. This approach is then not appropriate for *in situ* measurements and are time consuming, inducing a time delay between the sample collection and analysis. The same remark concerns the inductively coupled plasma atomic emission spectroscopy, which permits to detect chemical atoms.

On the other hand, previous studies indicated the great potential of the optical and spectroscopic tools in the detection of salts in solutions and, more specifically, RS, which could then permit to avoid the inconveniences of the techniques mentioned above [3]. Indeed, as it is shown, RS can permit us to determine the nature and the quantity of chemical species present in water media, offering the possibility to discriminate the different species present. In addition, the RS is an all-optical method, so it possesses all the advantages of optical methods (versatility, rapidity, contactless non-destructive measurement, etc.), as it will be detailed further on. Moreover, RS is generally more versatile and easier to set up *in situ*.

## **2. General principle of Raman spectroscopy**

RS provides numerous information about the structure and chemical composition of the sample, information obtained by an illumination of the sample with a monochromatic radiation (laser beam) that excites the vibrational structure of molecules; RS is thus called a vibrational spectroscopy. As illustrated in **Figure 1**, the radiation coming to the sample undergoes two types of scattering: Rayleigh scattering, where the radiation is at the exact same frequency as the excitation laser line (**υ**), and Raman scattering where the scattered radiation has a different frequency (**υ'**). The difference between the two frequencies, called "Raman shift" (**Δυ**), is the consequence of the interaction of the radiation with the sample molecules by the means of the vibrations of the chemical bonds present in the sample [4–6]. Note that, because it is a difference value, the Raman shift is consequently totally independent of the frequency of the incident radiation.

**Figure 1.** Schematic representation of the Rayleigh and the Raman diffusions.

In order to properly extract the Raman shift, the radiation coming from the Rayleigh diffraction has to be filtered out with a notch or a band pass filter from the total radiation before being collected on the spectrometer sensor (generally a CCD). Thus, only the radiation with a different wavelength than the incident laser beam is analyzed.

## **2.1. Raman spectrum**

**1. Introduction**

on solid materials [1].

In this chapter, the possibility of using Raman spectroscopy (RS) for studies of aqueous solutions is shown. Section 2 is devoted to the description of the general principle and characteristics of Raman spectroscopy (RS) as a technique for the investigation of molecular structure. Since Raman spectra contain information not only on intra-molecular vibrations but also on vibrations of the crystal lattice and other solid movements, RS is a well-established technique for the studies

406 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

The aim of this chapter is to show the possibility of its use to study aqueous solutions and chemical species dissolved in water. The application of RS on the study of aqueous media is more complicated than in solids, but is still very efficient if a proper signal treatment is applied. In order to show the different possibilities of RS for the characterization of water media, a special attention is devoted to one application example: aqueous solutions that are used in the winter maintenance domain (solution of acetates, formates, or chlorides). The possibility of its

In the case of water quality, there is a need for a device presenting a high polyvalence degree to detect and to quantify several chemical species in aqueous solutions but also to discriminate them from each other. There is an increasing need for a technique that could permit *in situ*

However, the techniques actually used for the detection and quantification of chemicals in water often do not respond to these criteria [2]. Some techniques, such as the widely used solution conductometry, are fully dedicated to one chemical species or are not able to make an accurate discrimination between different chemical species. In other cases, the quantification of chemicals in water media usually needs the combination of several techniques such as ionic chromatograph and electrical conductivity. This approach is then not appropriate for *in situ* measurements and are time consuming, inducing a time delay between the sample collection and analysis. The same remark concerns the inductively coupled plasma atomic emission

On the other hand, previous studies indicated the great potential of the optical and spectroscopic tools in the detection of salts in solutions and, more specifically, RS, which could then permit to avoid the inconveniences of the techniques mentioned above [3]. Indeed, as it is shown, RS can permit us to determine the nature and the quantity of chemical species present in water media, offering the possibility to discriminate the different species present. In addition, the RS is an all-optical method, so it possesses all the advantages of optical methods (versatility, rapidity, contactless non-destructive measurement, etc.), as it will be detailed

RS provides numerous information about the structure and chemical composition of the sample, information obtained by an illumination of the sample with a monochromatic

further on. Moreover, RS is generally more versatile and easier to set up *in situ*.

use for the detection of some water pollutants is also discussed.

measurements and continuous monitoring of water.

spectroscopy, which permits to detect chemical atoms.

**2. General principle of Raman spectroscopy**

The results of the measurements are depicted graphically as Raman spectra. The intensity of the scattered light is plotted for each energy (frequency) of light. The frequency axis represents the Raman shift **Δυ**, as it is the shift in energy/frequency of the light that is of particular interest. In vibrational spectroscopy, the frequency is traditionally measured in a unit called the "wavenumber" (number of waves per cm, cm−1), which is directly proportional to energy. Wavenumbers are easily converted into the more familiar wavelength scale by calculating the reciprocal.

A thus-plotted Raman spectrum is composed of peaks, where each peak corresponds to a specific vibration of a chemical bond present in the sample. The more complex the chemical composition, the richer is its Raman spectrum.

When the specific vibration is represented by a wider peak, we are generally talking about a "band" that can be composed of several peaks. In this case, each peak represents the vibration of the same chemical bond, but its surrounding environment is slightly different, provoking a frequency shift. Thus, the partly overlapping peaks form a resulting band that is covering a more or less large range of frequencies.

## **2.2. Information accessible**

By the analysis of the different Raman peaks present, a Raman spectrum can give us numerous qualitative and quantitative information on the sample, as presented in **Figure 2**.

**Figure 2.** Information accessible from a Raman spectrum [6].

Three spectral parameters can be derived from the analysis of a Raman line.

The **position of the peak** defined by its maximum corresponds to the vibration frequency of the chemical species. Since each chemical bond has its own characteristic vibrations, the position of the peaks lead to the identification of the chemical species. The determination of the peak locations in a Raman spectrum is sensitive enough for the recognition and the specification of chemical species in heterogeneous samples [7].

The **peak intensity** is related to the corresponding chemical species concentration. In order to determine this parameter, it is necessary to use normalization of the integrated intensity of the Raman line as the peak intensity is also sensitive to the laser power. The follow-up of the relative changes in the integrated intensities of peaks is then necessary for excluding the laser influence and to determine correctly the species concentration.

Furthermore, the full width half maximum (FWHM) reflects the order character of the sample: structure with the lower the FWHM, the higher the local order.

Thereby, the peak position, the linewidth, and intensity extracted from a Raman line can be used for the determination of some physico-chemical parameters such as the sample phase or constraint [8]. The analysis of all the peaks present in the spectrum gives us an insight on the sample composition and structure. RS can thus be used to determine various quantities that can affect the vibrational state (modes/peaks) specific to a substance.

In addition, specific Raman peaks are sensitive not only to the above-mentioned composition or concentration of the substance under study but also to external parameters such as the temperature or the pressure that affect the structure of the sample. Thus, the spectroscopic follow-up of a sample can permit to identify which chemical bond is affected by a change of an external parameter and to identify the constraint through the resulting peak shift. This aspect is explained further in this chapter in Section 3.2.

Since a Raman spectrum gives details on the chemical composition, molecular structure, and molecular interactions, it is hence considered as a chemical compound fingerprint at a molecular or crystalline level.

## **2.3. Advantages**

A thus-plotted Raman spectrum is composed of peaks, where each peak corresponds to a specific vibration of a chemical bond present in the sample. The more complex the chemical

408 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

When the specific vibration is represented by a wider peak, we are generally talking about a "band" that can be composed of several peaks. In this case, each peak represents the vibration of the same chemical bond, but its surrounding environment is slightly different, provoking a frequency shift. Thus, the partly overlapping peaks form a resulting band that is covering a

By the analysis of the different Raman peaks present, a Raman spectrum can give us numerous

qualitative and quantitative information on the sample, as presented in **Figure 2**.

Three spectral parameters can be derived from the analysis of a Raman line.

specification of chemical species in heterogeneous samples [7].

influence and to determine correctly the species concentration.

structure with the lower the FWHM, the higher the local order.

The **position of the peak** defined by its maximum corresponds to the vibration frequency of the chemical species. Since each chemical bond has its own characteristic vibrations, the position of the peaks lead to the identification of the chemical species. The determination of the peak locations in a Raman spectrum is sensitive enough for the recognition and the

The **peak intensity** is related to the corresponding chemical species concentration. In order to determine this parameter, it is necessary to use normalization of the integrated intensity of the Raman line as the peak intensity is also sensitive to the laser power. The follow-up of the relative changes in the integrated intensities of peaks is then necessary for excluding the laser

Furthermore, the full width half maximum (FWHM) reflects the order character of the sample:

composition, the richer is its Raman spectrum.

**Figure 2.** Information accessible from a Raman spectrum [6].

more or less large range of frequencies.

**2.2. Information accessible**

RS is useful for chemical analysis for several reasons [9, 10]. As many optical techniques, it is non-destructive and non-intrusive, permitting to perform contactless measurements. Furthermore, in contrast with most other chemical analysis techniques, it does not require any specific sample preparation. Moreover, this technique requires a small volume of the substance, in the order of 1 μm3 , for the analysis and it is possible to use optical fibers for deported measurement. This kind of set up is particularly well adapted for *on-site* measurements (on the field or in an industrial context). Furthermore, as a Raman spectrum can be acquired in the range of time of seconds, RS permits an almost "real-time" monitoring of chemical reactions. Depending on the experimental set up and on the application aimed, it can offer different spatial resolutions (from 1 μm to 1 cm). An additional advantage of RS is the possibility to analyze samples in solid, liquid, or gaseous state.

It is to note that RS offers the possibility to use lasers in a large spectral domain, going from the ultraviolet to the near infrared (IR). The choice of the most appropriate laser to use will depend on the nature of the sample under study. Indeed, the absorption, the fluorescence, the solidity toward light exposure, and more generally the interaction between light and the sample have to be taken into account. For the specific case of aqueous solutions, the most appropriate laser is in the visible green light (514 or 532 nm), which permits to diminish the fluorescence.1 Furthermore, the excitation wavelength will have an important impact on experimental capacities; the laser wavelength λ will influence Raman intensity, proportional to λ−4, and spatial resolution, defined by a diameter equal to 1.22 λ / NA, the numerical aperture of the objective used.

<sup>1</sup> In fluorescence, the incident excitation light is completely absorbed, transferring the system to an excited state and producing a photon with a different frequency. The fluorescence photons, which are of lower energy, are emitted after a certain resonance lifetime.

RS presents also some inconveniences and limits, but the technology development offers possibilities to overcome most of them. For instance, Raman signal of some compounds can be weak comparing to the total signal, making it difficult to determine small concentrations. A stronger signal can obviously be obtained by the increase of the laser power, but the influence of the laser on the sample state could be strong enough to be problematic for the analysis or even induce a sample damage. To compensate this detrimental effect, the recent technological development improved significantly the performance of multichannel detectors, leading to a considerable increase of the sensitivity of Raman spectrometers.

Another limiting factor is the fluorescence, which can be much higher than the Raman signal, dominating in intensity the Raman effect, at times diluting it completely in the signal noise. However, as the Raman effect is independent on the excitation frequency, it is often possible to overcome this difficulty by choosing an appropriate laser.

Despite these few restrictions, RS appears well adapted for the analysis of aqueous solutions. Compared to infrared spectroscopy where the spectrum of water is so strong and complex that it interferes with the signatures of chemical species, the Raman spectrum is not sensitive to aqueous absorption bands that are weak and unobtrusive. The analysis of liquids is easier, thanks to the transparency of glass containers in the spectral domains concerned.

## **3. Spectroscopic analysis of water**

## **3.1. Specific water signature**

The structure of water has been studied for decades by both infrared (IR) and RS [11–15]. The normal modes of water are nowadays well known and detailed on **Figure 3**; The spectral band corresponding to the O─H bending (noted v2) is located at about 1600 cm−1, and to the O─H stretching band around 2900–3700 cm−1. This broad range of the Raman spectrum of liquid water is composed of symmetric (noted v1) and asymmetric (noted v3) O─H stretching vibrations [14, 16, 17].

**Figure 3.** Normal modes of water.

In the literature, a special attention is devoted to the study of the O─H stretching region [18, 19]. The particular interest for this region originates from the fact that it is generally considered RS presents also some inconveniences and limits, but the technology development offers possibilities to overcome most of them. For instance, Raman signal of some compounds can be weak comparing to the total signal, making it difficult to determine small concentrations. A stronger signal can obviously be obtained by the increase of the laser power, but the influence of the laser on the sample state could be strong enough to be problematic for the analysis or even induce a sample damage. To compensate this detrimental effect, the recent technological development improved significantly the performance of multichannel detectors, leading to a

Another limiting factor is the fluorescence, which can be much higher than the Raman signal, dominating in intensity the Raman effect, at times diluting it completely in the signal noise. However, as the Raman effect is independent on the excitation frequency, it is often possible

Despite these few restrictions, RS appears well adapted for the analysis of aqueous solutions. Compared to infrared spectroscopy where the spectrum of water is so strong and complex that it interferes with the signatures of chemical species, the Raman spectrum is not sensitive to aqueous absorption bands that are weak and unobtrusive. The analysis of liquids is easier,

The structure of water has been studied for decades by both infrared (IR) and RS [11–15]. The normal modes of water are nowadays well known and detailed on **Figure 3**; The spectral band corresponding to the O─H bending (noted v2) is located at about 1600 cm−1, and to the O─H stretching band around 2900–3700 cm−1. This broad range of the Raman spectrum of liquid water is composed of symmetric (noted v1) and asymmetric (noted v3) O─H stretching

In the literature, a special attention is devoted to the study of the O─H stretching region [18, 19]. The particular interest for this region originates from the fact that it is generally considered

thanks to the transparency of glass containers in the spectral domains concerned.

considerable increase of the sensitivity of Raman spectrometers.

410 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

to overcome this difficulty by choosing an appropriate laser.

**3. Spectroscopic analysis of water**

**3.1. Specific water signature**

vibrations [14, 16, 17].

**Figure 3.** Normal modes of water.

to be closely related to the structure of water [20, 21] because it is an indicator of the hydrogenbonding network [22, 23]. Moreover, even though it is well known that this band constitutes of the symmetric and asymmetric O─H stretching, further designations of these normal modes are still not well elucidated. It is generally admitted that all the bands of water are made up from contributions from different components from water molecules in different hydrogen bonded environments. In order to analyze this region in a more detailed way, it is necessary to proceed to a deconvolution of the band corresponding to this complex contribution of the water spectrum into various peak components, to assign each vibrational mode and to get back to their molecular origins. However, when it comes to bands that are as large as the O─H stretching band, the decomposition is rather complicated and numerous deconvolution models can be found in the literature. Depending on the authors, different number of components as well as different mechanisms involved are considered. The use of two to five components for deconvolution is suggested to correctly describe the O─H stretching band [18, 24– 27]. The most commonly proposed deconvolution contains five components [28–30]. An example of such a deconvolution of the O─H stretching band is presented in **Figure 4**.

It is to note that even when using the same number of components for the deconvolution, different results can be found and different attributions proposed. For each deconvolution, different hydrogen bond properties, such as their number, angle or length, are used to define the attributions [18, 27, 30–33]. Generally speaking, lower frequency components are attributed to water molecules with stronger hydrogen bonds and higher frequency components have weaker hydrogen bonds [34].

All these studies show that, besides the intra-molecular O─H pairs, intermolecular O─H linked by hydrogen bonds contribute to the O─H stretching. The role of the hydrogen bonds is therefore of great importance for the understanding of this spectral range. This bond being

**Figure 4.** Example of deconvolution of the O─H stretching band into five sub-bands [28].

flexible is sensitive to temperature [35] and the presence of ions. The study of this spectral region can therefore be used for the determination of the water phase or for the detection of ions dissolved in water, as it will be presented thereafter.

#### **3.2. Phase effect**

As a molecule, water can be present at three different phases—solid, liquid, and gaseous—the state depending on the environmental conditions, namely the pressure and the temperature. This chapter concentrates on the solid-liquid phase transition, with a special focus on measurements performed on atmospheric pressure, the application example being the winter maintenance domain.

As mentioned earlier, the O─H stretching band is considered to be closely related to the structure of water. It is expected that the morphology of this band will be modified by a change in the temperature or in its chemical composition, as it can be seen on **Figure 5** where normalized spectra of liquid water and ice are compared.

**Figure 5.** Raman signature of the O─H stretching region for water in its liquid (at 20°C) and solid form (at −20°C) obtained with a 532-nm laser beam at 100 mW.

In the case of liquid water, molecules are in constant movement, provoking continuous creation and break of hydrogen bonds that are rather weak. Water can be considered as a mixture of isolated water molecules and water molecules forming clusters by the hydrogen bonds [14]. Hence, all contributions, in terms of number of hydrogen bonds present, contribute to the O─H stretching band. This leads to a broad spectrum with the symmetric and asymmetric regions of the band that are almost equally represented. An average number of hydrogen bonding for each molecule is found to be 2.75 [29], which is manifested by a slightly greater intensity at 3385 cm−1 corresponding to the contribution possessing three H-bonds.

In the case of ice, on the other hand, the decrease of temperature will strengthen the hydrogen bonds, leading to weaker O─H bonds, which will thus vibrate at lower frequencies. Furthermore, besides the shift toward the lower frequencies, due to the increased importance of intermolecular hydrogen bonding at lower temperature, the lower part of the spectrum, related to fully H-bonded atoms, enhances and becomes narrower [36].

## **3.3. Phase transition determination**

flexible is sensitive to temperature [35] and the presence of ions. The study of this spectral region can therefore be used for the determination of the water phase or for the detection of

As a molecule, water can be present at three different phases—solid, liquid, and gaseous—the state depending on the environmental conditions, namely the pressure and the temperature. This chapter concentrates on the solid-liquid phase transition, with a special focus on measurements performed on atmospheric pressure, the application example being the winter

As mentioned earlier, the O─H stretching band is considered to be closely related to the structure of water. It is expected that the morphology of this band will be modified by a change in the temperature or in its chemical composition, as it can be seen on **Figure 5** where normal-

**Figure 5.** Raman signature of the O─H stretching region for water in its liquid (at 20°C) and solid form (at −20°C) ob-

In the case of liquid water, molecules are in constant movement, provoking continuous creation and break of hydrogen bonds that are rather weak. Water can be considered as a mixture of isolated water molecules and water molecules forming clusters by the hydrogen bonds [14]. Hence, all contributions, in terms of number of hydrogen bonds present, contribute to the O─H stretching band. This leads to a broad spectrum with the symmetric and asymmetric regions of the band that are almost equally represented. An average number of hydrogen bonding for

ions dissolved in water, as it will be presented thereafter.

412 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

ized spectra of liquid water and ice are compared.

tained with a 532-nm laser beam at 100 mW.

**3.2. Phase effect**

maintenance domain.

Since the spectra of liquid water and ice are significantly different, the combination of Raman spectrometry and micro-thermometry can lead to precise detection of the water phase transition. Spectra obtained in the −3 to 3°C temperature region were collected and their O─H stretching band presented in **Figure 6**. It is reminded that the study is focused on the O─H stretching band, as it is the spectral region considered to be closely related to the structure of water and which is thus expected to be the most influenced by a change of temperature.

**Figure 6.** Raman spectra of water between −3 and 3°C, obtained with a 532-nm laser beam at 100 mW.

The center of the O─H stretching bond is located around 3325 cm−1. The temperature modification induces large changes in the shape and the intensity of the O─H stretching region in both the part corresponding to symmetric and asymmetric stretching vibrations. It can be considered that the lower frequency part of the spectrum (between 2900 and 3325 cm−1) roughly corresponds to the ordered solid phase as this contribution is related to fully H-bonded atoms, whereas partly H-bonded and free O─H are expressed in the upper frequency part, characteristic of the liquid phase [30]. It is then possible to follow the phase transition by the relative evolution of these two parts of the O─H stretching region as the evolution of the order/disorder of the water structure can be reflected from the values of their intensities. Thus, a spectral marker *SD*, can be defined as the ratio of the asymmetric and the symmetric part of the O─H band, parts centered on 3385 and 3135 cm−1, respectively, in order to detect the phase transition.

A method for the determination of the phase transition based on that principled was developed and tested [37]. That study showed that, for the calculation of the spectral marker, it is possible to use intensities at the peak maximum or integrated intensities of raw spectra.2 The comparison of curves obtained by the calculation of ratios of simple intensities of the two most intense parts (c) and of integrated intensities of more (a) or less broad (b) spectral areas around the peak maximum is shown in **Figure 7**.

**Figure 7.** Plots of the spectral marker *SD* calculated with intensities (c) or integrated intensities (a, b) as a function of temperature [37].

The temperature of the phase transition is then determined by a simple calculation of the curve inflection point. The uncertainty of the phase temperature thus obtained is dependent on the speed of the temperature change during the measurements. In the case presented here, the speed was set to 0.5°C/minute, and the uncertainty obtained is about 0.5°C for each ratio tested (ratio of simple or integrated intensities).

<sup>2</sup> The use of raw spectra permits to overcome the possible controversies about the deconvolution of the O─H stretching region. In this method, only two parts of the spectra that are affected by the temperature and phase change are then analyzed.

## **4. Spectroscopic analysis of aqueous media**

marker *SD*, can be defined as the ratio of the asymmetric and the symmetric part of the O─H band, parts centered on 3385 and 3135 cm−1, respectively, in order to detect the phase transition. A method for the determination of the phase transition based on that principled was developed and tested [37]. That study showed that, for the calculation of the spectral marker, it is possible

ison of curves obtained by the calculation of ratios of simple intensities of the two most intense parts (c) and of integrated intensities of more (a) or less broad (b) spectral areas around the

**Figure 7.** Plots of the spectral marker *SD* calculated with intensities (c) or integrated intensities (a, b) as a function of

The temperature of the phase transition is then determined by a simple calculation of the curve inflection point. The uncertainty of the phase temperature thus obtained is dependent on the speed of the temperature change during the measurements. In the case presented here, the speed was set to 0.5°C/minute, and the uncertainty obtained is about 0.5°C for each ratio tested

2 The use of raw spectra permits to overcome the possible controversies about the deconvolution of the O─H stretching region. In this method, only two parts of the spectra that are affected by the temperature and phase change are then

The compar-

to use intensities at the peak maximum or integrated intensities of raw spectra.2

414 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

peak maximum is shown in **Figure 7**.

temperature [37].

analyzed.

(ratio of simple or integrated intensities).

A Raman spectrum of an aqueous solution will present specific the different O─H bond vibrations of water, as well as specific peaks of each chemical/salt diluted. Indeed, each type of salt (acetate, formate, nitrate, etc.) diluted in an aqueous solution presents specific signatures/peaks corresponding to the vibrations of the different chemical bonds constituting it. By the analysis of each peak, it is possible to identify the nature of the chemical present, as well as its concentration. Indeed, the systematic analysis of a set of aqueous solutions of different concentrations can permit the identification of the specific effect of the chemical. A proper analysis based on the follow-up of the specific peak intensities or areas can permit the determination of the chemical concentration. The analysis can be focused on the salt direct effect, on analysis of the salt signature, or on its indirect effect by the analysis of the water signature.

As presented earlier in this chapter, the peak intensity is linked to the concentration. Peak intensity measurements are thus used in most quantitative analyses. However, the absolute intensity of a Raman spectrum can vary considerably from one instrument to another as it is also sensitive to changes in instrumental resolution, calibration and signal/noise ratio, etc. Thereby, it is necessary to use integrated intensity, a measure of the total intensity of the band, which is much less sensitive to instrumental resolution [4].

## **4.1. Application to winter maintenance aqueous solutions**

The application example will concern aqueous solutions used in winter maintenance where different anti-icing chemicals are spread on runways in order to maintain a proper grip.3 This is a commonly employed technique to avoid the occurrence of ice or to generate its melting [38, 39]. The products used are mainly salts, heard as ionic compounds composed of cations and anions. Each of these salts contains an "active compound" permitting to lower the freezing temperature of the liquid present on the road surface [40]. The most commonly employed antiicing product for road winter maintenance is the NaCl (in France, up to 99% of the cases). The active compounds of the products applied on the airport surfaces are mainly acetates and formates of sodium or potassium for corrosion reasons [41].

## *4.1.1. Concentration determination*

For the quantification of the molecules of interest, the anti-icing active compounds, it is necessary to construct appropriate calibration curves. For that purpose, solutions with well known composition and concentration of each active compound are prepared and analyzed spectroscopically. The first step is to identify the spectral signature of an analyzed compound. Raman spectra of the different salts used in winter maintenance are presented on **Figure 8**. The more complex the chemical composition, the richer is its Raman spectrum;

<sup>3</sup> The quantification of the active compound of these products is necessary for both the characterization of the nature of the commercial products and the optimization of the quantities applied on airport areas.

**Figure 8.** Raman spectra of aqueous solutions of the salts used in winter maintenance: potassium acetate (a), potassium formate (b), and sodium chloride (c) obtained with a 532-nm excitation wavelength.

The Raman spectrum of potassium acetate presents peaks corresponding to the vibrations of O─C─O bending, C─C stretching, CH3 bending, C─O stretching and CH3 stretching [42–45]. For potassium formate, the peaks corresponding to O─C─O and C─H bending, as well as C─O and C─H stretching [46–48] are present. The sodium chloride spectrum, on the other hand, presents only the peaks characteristic of the water, O─H bending and O─H stretching. Indeed, chemicals that present only monoatomic ions once dissolved in water (like NaCl gives [Na+ ] and [Cl− ]), do not possess specific peaks, as they do not have bonds anymore [49, 50].

Clearly, it is not possible to use the same analysis method for the elaboration of the calibration curves for all these species. We then consider two cases: on the one hand, the case when the salt presents specific peaks, making it possible to detect its presence and concentration directly, and, on the other hand, where the salt does not present any specific peak and it is only possible to detect and quantify it indirectly.

Furthermore, for a better baseline correction, it is possible to focus the study on only one part of the spectrum. In the case of winter maintenance salts presented below, the study considered the region above 2500 cm−1.

## *4.1.1.1. Direct concentration determination: CH3COOK and CHOOK*

The Raman spectrum of potassium acetate presents peaks corresponding to the vibrations of O─C─O bending, C─C stretching, CH3 bending, C─O stretching and CH3 stretching [42–45]. For potassium formate, the peaks corresponding to O─C─O and C─H bending, as well as C─O and C─H stretching [46–48] are present. The sodium chloride spectrum, on the other hand, presents only the peaks characteristic of the water, O─H bending and O─H stretching. Indeed, chemicals that present only monoatomic ions once dissolved in water (like NaCl gives [Na+

**Figure 8.** Raman spectra of aqueous solutions of the salts used in winter maintenance: potassium acetate (a), potassium

formate (b), and sodium chloride (c) obtained with a 532-nm excitation wavelength.

416 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

]), do not possess specific peaks, as they do not have bonds anymore [49, 50].

Clearly, it is not possible to use the same analysis method for the elaboration of the calibration curves for all these species. We then consider two cases: on the one hand, the case when the salt presents specific peaks, making it possible to detect its presence and concentration directly,

and [Cl−

]

For an optimized calibration curve, the analysis of aqueous solutions covering a large scale of concentrations is necessary. For the potassium acetate and formate, solutions with a weight percent up to 65 and 60%, respectively, were analyzed (**Figure 9**). The weight percent (wt%) is defined as the ratio of the mass of the salt dissolved msalt and the mass of the solution sample msample.

**Figure 9.** Normalized Raman spectra of aqueous solutions of potassium acetate (a) and potassium formate (b) with a weight percent between 0 and 65% and 0 and 60%, respectively. Spectra obtained with a 532-nm laser at 100 mW.

In **Figure 9** are presented spectra of potassium acetate and potassium formate solutions at different concentrations/weight percent in the 2500–4000 cm−1 spectral region. This region contains, for both salts, a salt specific peak, as well as the O─H stretching band. As expected, in both cases, the intensity of specific peaks increase with the concentration. A proper signal treatment permits to extract the effect of the active compound on the Raman spectra and to elaborate appropriate calibration curves. A calibration curve defines the relationship between an analytical signal produced by the analyte and its concentration.

In the simplest case, the calibration curve is linear and a simple linear regression permits to fit the analytical signal to the concentration. Most generally, however, the analysis is more complex since the calibration curves are often affected by overlapping bands, additional interfering components. The resultant calibration curves will often be non-linear [5].

On the whole, for the elaboration of a calibration curve, the calculation of the peak intensity as a function of the concentration can be used, however, it is important to ensure that the peak characteristics that will be calculated will be affected only by the compound itself. As mentioned before, in order to avoid the bias of spectral intensity caused by the experimental setup, it is recommended to use integrated intensity. Furthermore, experimental conditions, such as ambient light, can also influence the spectral intensity and even the integrated intensity. One way of overcoming all these difficulties and to maintain only the compound concentration influence is to use a ratio of integrated intensities [28].

Hence, for the elaboration of the calibration curves of potassium acetate and formate, a spectral marker *SD* is defined as a ratio of an integrated intensity of a specific peak ΔIsalt, over the integrated intensity of the O─H asymmetric stretching vibrations ΔIOH. For the potassium acetate, the integrated intensity 2801–3001 cm−1 including the CH3 stretching band was chosen as ΔIsalt, and for potassium acetate ΔIsalt was defined as the integrated intensity of the 2725– 2809 cm−1 region including the C─H symmetric stretch. For both cases, ΔIOH was defined as the integrated intensity of the water 3324–3486 cm−1 spectral region. The thus obtained calibration curves are presented in **Figure 10**.

**Figure 10.** Calibration curves obtained for potassium acetate (a) and potassium formate (b). Both curves present an *R*<sup>2</sup> of 0.999.

In this particular case, there is a slight overlapping of the bands chosen, as there is a small contribution of the O─H band in the region of the C─H band. As a result, the resultant calibrations curves obtained for the potassium acetate and potassium formate presents an exponential evolution.

These calibration curves can then permit not only to identify the presence but also to quantify these species in an aqueous solution. The precision of the method will depend on the quality of Raman spectra obtained. In general, with the 532 nm laser excitation and a 60 second accumulation time, it is possible to obtain a good signal/noise ratio permitting the quantification of these species with an 1% uncertainty. Depending on the application aimed, it is possible to diminish even more the uncertainty by analyzing more aqueous solutions in order to have more points for the calibration curves. For the winter maintenance application, however, the uncertainties aimed are rather high, 5%, and then this method is well-fitted just as it is shown here.

## *4.1.1.2. Indirect concentration determination: NaCl*

in both cases, the intensity of specific peaks increase with the concentration. A proper signal treatment permits to extract the effect of the active compound on the Raman spectra and to elaborate appropriate calibration curves. A calibration curve defines the relationship between

In the simplest case, the calibration curve is linear and a simple linear regression permits to fit the analytical signal to the concentration. Most generally, however, the analysis is more complex since the calibration curves are often affected by overlapping bands, additional

On the whole, for the elaboration of a calibration curve, the calculation of the peak intensity as a function of the concentration can be used, however, it is important to ensure that the peak characteristics that will be calculated will be affected only by the compound itself. As mentioned before, in order to avoid the bias of spectral intensity caused by the experimental setup, it is recommended to use integrated intensity. Furthermore, experimental conditions, such as ambient light, can also influence the spectral intensity and even the integrated intensity. One way of overcoming all these difficulties and to maintain only the compound concentration

Hence, for the elaboration of the calibration curves of potassium acetate and formate, a spectral marker *SD* is defined as a ratio of an integrated intensity of a specific peak ΔIsalt, over the integrated intensity of the O─H asymmetric stretching vibrations ΔIOH. For the potassium acetate, the integrated intensity 2801–3001 cm−1 including the CH3 stretching band was chosen as ΔIsalt, and for potassium acetate ΔIsalt was defined as the integrated intensity of the 2725– 2809 cm−1 region including the C─H symmetric stretch. For both cases, ΔIOH was defined as the integrated intensity of the water 3324–3486 cm−1 spectral region. The thus obtained calibration

**Figure 10.** Calibration curves obtained for potassium acetate (a) and potassium formate (b). Both curves present an *R*<sup>2</sup>

In this particular case, there is a slight overlapping of the bands chosen, as there is a small contribution of the O─H band in the region of the C─H band. As a result, the resultant

interfering components. The resultant calibration curves will often be non-linear [5].

an analytical signal produced by the analyte and its concentration.

418 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

influence is to use a ratio of integrated intensities [28].

curves are presented in **Figure 10**.

of 0.999.

The concentration determination by a direct analysis of a chemical specific peak is immediate and will, logically, offer better results. However, chemicals that dissolve in monoatomic ions do not possess specific peaks, as they do not have bonds [49, 50]. In order to detect their presence in water and to quantify them, it is necessary to analyze the water signature and deduce information on the chemical through their influence on the water structure. A typical example of this behavior is sodium chloride, NaCl.

**Figure 11.** O─H stretching region of Raman spectra of NaCl aqueous solutions in a weight percent up to 20%.

**Figure 11** shows the influence of NaCl concentration dissolved in water on its Raman spectrum. Two main effects can be underlined: NaCl affects both the O─H symmetric and O─H asymmetric stretch. The concentration increase enhances the morphological changes of the O─H stretching band, diminishing the O─H symmetric and increasing the O─H asymmetric stretching vibrations [51], leading to a spectrum shift towards higher wavenumbers. This can be considered as a direct result of the dissolution of NaCl in water, which provokes the decrease of the number of hydrogen bonds in the intermolecular structure [50, 52, 53].

It is then possible to use the ratio of the asymmetric and symmetric O─H stretching vibrations as a spectral marker for the elaboration of the calibration curve [54]. The O─H stretching band was then divided into two parts, 3325–3650 cm−1 as representative of the O─H asymmetric stretching vibrations, and 3000–3325 cm−1 as representative of the O─H symmetric stretching vibrations.

After an elaboration of a calibration curve, a set of blind tests should be performed in order to verify the uncertainty of this indirect way of concentration determination. The plot presenting the calculated concentration via the experimental *SD* versus the theoretically calculated concentration is presented in **Figure 12**.

**Figure 12.** Comparison of calculated weight percent (represented by the experimental points in black) and weight percent (represented by the curve in red).

As shown in the **Figure 12**, even an indirect way of concentration determination can offer good precision, especially for more concentrated solutions; For this specific case, the standard deviation is of 0.25% for weight percentages over 7 and of 0.75% for weight percentages up to 5%.

To sum up, this approach can also be applied to all salts that dissolve into monoatomic ions, since in their dissolved form they interact with water, and these interactions are detectable and quantifiable by the means of the Raman spectra of water. Hence, similar studies even in applications other than the winter maintenance domain were also conducted, for example on the effect of different alkali halide on the water structure [50, 51]. These studies showed that the influence of dissolved salts on water structure depends on the size and the charge of the ions, as well as the strength they form with the O─H complex.

It is also possible to analyze the O─H stretching band for the quantification of dissolved salts in solutions where several salts are present. For instance, quantification of NaCl with a 0.14 wt % error was realized in a solution of NaCl and NaF at 2.8 wt% [55].

#### *4.1.2. Phase diagram elaboration*

stretching band, diminishing the O─H symmetric and increasing the O─H asymmetric stretching vibrations [51], leading to a spectrum shift towards higher wavenumbers. This can be considered as a direct result of the dissolution of NaCl in water, which provokes the decrease

It is then possible to use the ratio of the asymmetric and symmetric O─H stretching vibrations as a spectral marker for the elaboration of the calibration curve [54]. The O─H stretching band was then divided into two parts, 3325–3650 cm−1 as representative of the O─H asymmetric stretching vibrations, and 3000–3325 cm−1 as representative of the O─H symmetric stretching

After an elaboration of a calibration curve, a set of blind tests should be performed in order to verify the uncertainty of this indirect way of concentration determination. The plot presenting the calculated concentration via the experimental *SD* versus the theoretically calculated

**Figure 12.** Comparison of calculated weight percent (represented by the experimental points in black) and weight per-

As shown in the **Figure 12**, even an indirect way of concentration determination can offer good precision, especially for more concentrated solutions; For this specific case, the standard deviation is of 0.25% for weight percentages over 7 and of 0.75% for weight percentages up to

of the number of hydrogen bonds in the intermolecular structure [50, 52, 53].

420 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

vibrations.

concentration is presented in **Figure 12**.

cent (represented by the curve in red).

5%.

The method described in Section 3.3 can also be applied to aqueous solutions where the main morphological change appearing with the phase change concerns the O─H stretching band. It is then possible to apply exactly the same spectral marker as for water for the detection of the phase transition, that is to say a ratio of integrated intensities of the asymmetric and the symmetric part of the O─H band.

The analysis of a set of aqueous solutions at different concentrations in a large range of temperature can therefore permit the construction of the phase diagram which has a capital importance in the winter maintenance domain. With a large temperature range, it is possible to detect the different phase transitions. Indeed, in mixtures such as aqueous solutions, upon cooling down, each component will solidify at a different temperature. The first phase transition will then be a transition from a liquid to a mixture of liquid and solid phases. For binary systems, where only two components are present, the second phase transition will then transform the solid-liquid mixture into an entirely solid. For illustration purpose, the NaCl-H2O phase diagram is presented **Figure 13**.

**Figure 13.** Phase diagram of the NaCl-H2O binary system.

Upon cooling down a mixture of NaCl and water, the ice will be formed below a certain temperature of the so-called *liquidus* curve, this temperature being dependent on the brine concentration. During a certain temperature range, the mixture will be then composed of ice and liquid brine (Zone II). Upon further cooling down, at some point the entire mixture will solidify into ice and a hydrated form of NaCl (Zone III). This second phase transition will occur at temperatures below the *solidus* line, which is located at the eutectic temperature. The concentration of the eutectic point permits to determine the precise hydrated form of salt.

Such a phase diagram can be built experimentally by applying the spectroscopic method described before to any aqueous solutions [56].

## **4.2. Application to water pollution**

Another application example could be the detection of water pollutants where there is an increasing need for a technique that could permit an *on-site* detection of the pollution sources. Many pollutant families are identified as being potentially very harmful for the environment and thus it is important to survey. As an example, we can cite the pollutants coming from the agricultural activities, such as the fertilizers, nitrates, or phosphates, or some drugs, such as hormones, which can have particularly important impacts on the environmental media.

In that objective, many studies have investigated the possibility to use optical methods, and more specifically RS for the detection of pollutants in water media [57, 58]. The method described earlier in this chapter, based on the calculation of ratios of pollutant-specific peak and water peak, can then also be applied to water pollutants [59, 60].

## **Author details**

Ivana Durickovic

Address all correspondence to: ivana.durickovic@cerema.fr

Centre for Studies and Expertise on Risks, Environment, Mobility, and Urban and Country Planning Territorial Division for Eastern Regions – Laboratory of Nancy, Tomblaine, France

## **References**

[1] McMillan P. Vibrational spectroscopy in the mineral sciences. In: Kieffer SW, Navrotsky A, editors. Microscopic to Macroscopic: Atomic Environments to Mineral Thermodynamics. Volume 14. Washington: Mineralogical Society of America; 1985, p. 9-64.

[2] Bhadekar R, Pote S, Tale V, Nirichan B. Mint: Developments in analytical methods for detection of pesticides in environmental samples. American Journal of Analytical Chemistry. 2011;2:1-15. DOI: 10.4236/ajac.2011.228118.

Upon cooling down a mixture of NaCl and water, the ice will be formed below a certain temperature of the so-called *liquidus* curve, this temperature being dependent on the brine concentration. During a certain temperature range, the mixture will be then composed of ice and liquid brine (Zone II). Upon further cooling down, at some point the entire mixture will solidify into ice and a hydrated form of NaCl (Zone III). This second phase transition will occur at temperatures below the *solidus* line, which is located at the eutectic temperature. The concentration of the eutectic point permits to determine the precise hydrated form of salt.

422 Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Such a phase diagram can be built experimentally by applying the spectroscopic method

Another application example could be the detection of water pollutants where there is an increasing need for a technique that could permit an *on-site* detection of the pollution sources. Many pollutant families are identified as being potentially very harmful for the environment and thus it is important to survey. As an example, we can cite the pollutants coming from the agricultural activities, such as the fertilizers, nitrates, or phosphates, or some drugs, such as hormones, which can have particularly important impacts on the environmental media.

In that objective, many studies have investigated the possibility to use optical methods, and more specifically RS for the detection of pollutants in water media [57, 58]. The method described earlier in this chapter, based on the calculation of ratios of pollutant-specific peak

Centre for Studies and Expertise on Risks, Environment, Mobility, and Urban and Country Planning Territorial Division for Eastern Regions – Laboratory of Nancy, Tomblaine, France

[1] McMillan P. Vibrational spectroscopy in the mineral sciences. In: Kieffer SW, Navrotsky A, editors. Microscopic to Macroscopic: Atomic Environments to Mineral Thermodynamics. Volume 14. Washington: Mineralogical Society of America; 1985, p. 9-64.

and water peak, can then also be applied to water pollutants [59, 60].

Address all correspondence to: ivana.durickovic@cerema.fr

described before to any aqueous solutions [56].

**4.2. Application to water pollution**

**Author details**

Ivana Durickovic

**References**


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Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

## *Edited by Mark T. Stauffer*

The goal of this book is to present an overview of applications of molecular spectroscopy to investigations in organic and inorganic materials, foodstuffs, biosamples and biomedicine, and novel characterization and quantitation methods. This text is a compilation of selected research articles and reviews covering current efforts in various applications of molecular spectroscopy. Sections 1 and 2 deal, respectively, with spectroscopic studies of inorganic and organic materials. Section 3 provides applications of molecular spectroscopy to biosamples and biomedicine. Section 4 explores spectroscopic characterization and quantitation of foods and beverages. Lastly, Section 5 presents research on novel spectroscopic methodologies. Overall, this book should be a great source of scientific information for anyone involved in characterization, quantitation, and method development.

Photo by PhonlamaiPhoto / iStock

Applications of Molecular Spectroscopy to Current Research in the Chemical and Biological Sciences

Applications of Molecular

Spectroscopy to Current

Research in the Chemical and

Biological Sciences

*Edited by Mark T. Stauffer*