**3. Interpreting and using the infrared spectra**

Spectroscopic techniques are neither invasive nor sample destructive and may contribute to rapid quality and authenticity evaluation, with low operating cost. From a physicochemical point of view, infrared spectroscopy is based on the vibrational transitions occurring in the ground electronic state of the molecules. The infrared absorption requires a change of the intrinsic dipole moment with the molecular vibration. The regions of the infrared spectrum which are used for applications in food analysis are: mid-infrared (MID-IR) and nearinfrared (NIR).

Mid-infrared spectra present well resolved bands showing absorbances of varying intensity in the range of 4000 to 400 cm−1 originating from the fundamental vibrations.

Figure 1 shows a representative olive oil spectrum in the 4000 – 900 cm-1 region, where several characteristic bands related to lipid functional groups can be observed. In the 3100 - 2800 cm-1 spectral region appear the signals, assigned to C-H stretching mode from methylene and methyl groups of fatty acid and triacylglycerols. The low intensity peak near 3100 cm-1 may be explained by the CH=CH elongation and the signals of weak absorption around 2800 cm-1 are the result of the presence of secondary oxidation products, such as aldehydes and ketones. At 1800-1700 cm-1 the C=O stretching mode is found. The very strong band located at 1743 cm-1 can be ascribed to the triacylglycerol n-C=O ester group and a shoulder found around 1710 cm-1 is characteristic of the presence of free fatty acids (carboxylic n-C=O). The C-H deformation is detected between 1400 and 900 cm-1, spectroscopic region which is also known as fingerprint region (Tay et al. 2002).

Fig. 1. Typical ATR-Mid-IR spectrum of olive oil.

As mentioned previously the most relevant parameters for olives and olive pomace are fat and water content. Another important parameter is the free fatty acids (FFA) content of the

Olive oil, after its extraction, classification and quality evaluation should be labeled and priced. Quality evaluation and authenticity are based on organoleptic assessment and chemical characterization according to the European Commission Regulation (EC Reg No 2568/1991 and its later amendments EC Reg No 1989/2003), the Codex Alimentarius Norm (Codex Alimentarius Commission Draft, 2009) and International Olive Oil Council (IOOC) Trade standards (IOC/T.15/NC nº 3/Rev.4, 2009). Usual adulterations of olive oil are the addition of olive residue oil, refined olive oil, low-price vegetable oils, and even mineral oil

IOOC standards for olive oil and olive pomace oils contain a set of values and limits for the following parameters: fatty acid composition, *trans* fatty acid content, sterol composition and total sterol content, content of erythrodiol + uvaol, wax content, aliphatic alcohol content, stigmastadiene content, 2-glyceryl monopalmitate, unsaponifiable matter, free acidity, peroxide value, specific absorbance in ultra-violet, moisture and volatile matter, insoluble impurities in light petroleum, flash point, trace metals, -tocopherol, traces of heavy metals and traces of halogenated solvents (IOC/T.15/NC nº 3/Rev.4, 2009). Olive oil chemical characterization involves complex, time consuming and expensive analytical

Spectroscopic techniques are neither invasive nor sample destructive and may contribute to rapid quality and authenticity evaluation, with low operating cost. From a physicochemical point of view, infrared spectroscopy is based on the vibrational transitions occurring in the ground electronic state of the molecules. The infrared absorption requires a change of the intrinsic dipole moment with the molecular vibration. The regions of the infrared spectrum which are used for applications in food analysis are: mid-infrared (MID-IR) and near-

Mid-infrared spectra present well resolved bands showing absorbances of varying intensity

Figure 1 shows a representative olive oil spectrum in the 4000 – 900 cm-1 region, where several characteristic bands related to lipid functional groups can be observed. In the 3100 - 2800 cm-1 spectral region appear the signals, assigned to C-H stretching mode from methylene and methyl groups of fatty acid and triacylglycerols. The low intensity peak near 3100 cm-1 may be explained by the CH=CH elongation and the signals of weak absorption around 2800 cm-1 are the result of the presence of secondary oxidation products, such as aldehydes and ketones. At 1800-1700 cm-1 the C=O stretching mode is found. The very strong band located at 1743 cm-1 can be ascribed to the triacylglycerol n-C=O ester group and a shoulder found around 1710 cm-1 is characteristic of the presence of free fatty acids (carboxylic n-C=O). The C-H deformation is detected between 1400 and 900 cm-1,

in the range of 4000 to 400 cm−1 originating from the fundamental vibrations.

spectroscopic region which is also known as fingerprint region (Tay et al. 2002).

oil of the fruit, which will determine the acid value of the produced olive oil .

**2. Overview of the olive oil quality parameters** 

(Wahrburg et al. 2002; Dobarganes & Marquez-Ruiz 2003).

methodologies, which also destroy the sample.

infrared (NIR).

**3. Interpreting and using the infrared spectra** 

Near-infrared spectra present less well resolved bands in the range of 14000 to 4000 cm−1 corresponding to overtones and combinations of fundamental vibrations.

Figure 2 a) and b) show NIR spectra of olive oil, hammer milled olive and olive pomace. The following main spectroscopic regions can be observed: the region between 9000 – 8000 cm-1, can be ascribed to the second overtone of the C–H stretching vibration of modes of methyl, methylene and ethylene groups of fatty acids and triacylglycerols; the region between 7500 and 6150 cm-1 can be attributed to the first overtone of the O-H stretching vibrations; whereas the absorptions located around 6000 – 5700 cm-1 correspond to the first overtone of the C-H stretching vibration modes of methyl, methylene and ethylene groups; in next region bands between 5350 and 4550 cm-1 result from combinations of fundamentals of the C-H stretching vibration and of bands of water molecules (specially in olives and olive pomace); finally, the 4370 – 4260 cm-1 region can be ascribed to the C–H stretching combination of methyl and methylene groups (Galtier et al. 2007; Muick et al. 2004).

Several aspects must be considered when spectroscopic data are used in order to achieve multiple parameter determination, by direct analysis of spectra. A careful calibration framework should be devised, comprising: 1) an adequate sampling strategy, taking in account sampling variability and a suitable physicochemical range set; 2) a robust spectroscopic equipment in order to detect and quantify olive oil parameters in lower amounts, which is particularly important in the industrial in-line process; 3) a proper validation of the results given by infrared spectra and multivariate models; 4) a careful control of the outcome from the instrumental results and chemometric models, by employing control charts to evaluate the performance of the methodology and 5) a plan to address models sustainability through a periodic assessment of models performance, e.g. by performing traditional analysis and comparing to the outcome of the infrared spectra, in order to correct possible deviations. This last aspect is very important due to the nature of the samples (e.g. different harvest periods and samples origin, etc.) and equipment efficacy.

Quality Evaluation of Olives, Olive Pomace and Olive Oil by Infrared Spectroscopy 135

addition of other vegetable oils and olive pomace oil, to virgin olive oil (Wesley et al. 1995; Yang & Irudayaraj 2001; Doweny et al. 2002; Vlachos et al. 2006); and for olive oil authentication (Bertran et al. 2000; Downey et al. 2003; Woodcock et al. 2008). The chemical characterization of fatty acids and sterols (Ollivier et al. 2006; Ollivier et al. 2003; Aranda et al.

The authors attempted to apply NIR for the quantification of fatty acids, sterols and wax in an industrial scenario (for in-line analysis). 40 chemically characterized olive oil samples, from different origins, were used for this study. Partial Least Squares regression (PLS1) was applied in combination with NIR spectra in the 9000 - 4500 cm-1 range. Model validations were carried out using Monte-Carlo Cross-Validation (MCCV) (500 runs to evaluate models robustness); the predictive power of each one of the models were assessed through the computation of 1) Root Mean Square Error of Cross Validation (RMSECV); 2) Root Mean Square Error of Prediction (RMSEP); 3) Coefficient of Determination (R2) and 4) the crossvalidated coefficient of determination (Q2). At the same time several data pre-treatments

A summary of the results obtained is described in Table 1. Two data pre-treatments were selected: 1) Standard Normal Deviate (SNV) and 2) 2nd derivative computed with the Savistzky-Golay procedure, using a 2nd degree polynomial with 11 points (5+5+1). As it can be seen from the table the obtained models have from reasonable to good predictive power.

16:0 9.0 - 12.1 % 4 0.88 0.82 0.270 0.570 2nd derivative 16:1 0.7 - 1.3 % 5 0.88 0.82 0.050 0.101 2nd derivative 18:0 2.8 - 3.8 % 9 0.81 0.98 0.123 0.216 SNV 18:1 73.3 - 78.9 % 9 0.86 0.98 0.521 1.47 SNV 18:2 5.3 - 9.0 % 8 0.91 0.99 0.263 0.32 SNV 18:3 0.7 - 0.8 % 4 0.64 0.81 0.027 0.038 2nd derivative 24:0 0.0 - 0.1 % 7 0.76 0.80 0.021 0.087 SNV

Campesterol 2.97 - 3.64 % 7 0.82 0.98 0.070 0.131 2nd derivative Cholesterol 0.07 - 0.27 % 6 0.85 0.96 0.020 0.052 2nd derivative Stigmastenol 0.23 - 0.33 % 9 0.80 0.96 0.012 0.027 SNV Stigmasterol 0.52 - 1.21 % 8 0.85 0.96 0.088 0.106 SNV Sitosterol 94.6 - 98.3 % 7 0.63 0.99 0.111 0.175 2nd derivative Total Sterols 1434 - 1636 (mg/kg) 9 0.70 0.96 23.90 57.0 SNV

Wax 59.42 - 240.67 (mg/kg) 5 0.71 0.95 29.20 37.7 2nd derivative

Nomenclature: palmitic acid (16:0), palmitoleic acid (16:1), stearic acid (18:0), oleic acid (18:1), linoleic

Table 1. Fatty acids, sterols and wax determination for olive oil by NIR and PLS1 regression

**(%)**

**RMSEP**

**(%)** Pre-treatment

2004; Leardi and Paganuzzi 1987), is useful for assessing quality and authenticity.

were tested in order to find the most suitable ones (predictive ability).

**Parameter Range LV Q2 R2 RMSECV**

**Fatty acids** 

**Sterols** 

**Others** 

acid (18:2), linolenic acid (18:3), lignoceric acid (24:0).

in the spectral range of 9000 – 4500 cm-1.

Fig. 2. Typical NIR spectra of (a) olive oil and (b) hammer milled olive and olive pomace.

#### **4. Quantification of chemical parameters in olive oil**

To ensure a more reliable control of every step in the extraction process, a good sampling system is necessary. For industrial in-process analysis of olive oil, spectroscopic techniques (mostly, NIR and MID-IR spectroscopy) in tandem with chemometric methods are the cornerstone for quality control. According to Marquez et al. (2005) these techniques have shown a high potential as an alternative to time-consuming and expensive chromatographic or wet chemistry analysis. In fact the application of optical on-line NIR sensor for olive oil characterization is an appealing approach for real-time chemical evaluation, allowing the estimation of acid value, bitter taste (K255) and fatty acids (Marquez et al. 2005). Near infrared spectroscopy has been valuable for the assessment of physicochemical parameters of vegetable oils (Sato 1994; Hourant et al. 2000; Takamura et al. 1995; Franco et al. 2006). Infrared spectroscopy (NIR and MID), has also been applied successfully for olive oil evaluation and geographical origin determination using chemometric approaches (Tapp et al. 2003; Iñón et al. 2003; Galtier et al. 2007). In addition, NIR technique was employed to detect fraudulent

(b) Fig. 2. Typical NIR spectra of (a) olive oil and (b) hammer milled olive and olive pomace.

To ensure a more reliable control of every step in the extraction process, a good sampling system is necessary. For industrial in-process analysis of olive oil, spectroscopic techniques (mostly, NIR and MID-IR spectroscopy) in tandem with chemometric methods are the cornerstone for quality control. According to Marquez et al. (2005) these techniques have shown a high potential as an alternative to time-consuming and expensive chromatographic or wet chemistry analysis. In fact the application of optical on-line NIR sensor for olive oil characterization is an appealing approach for real-time chemical evaluation, allowing the estimation of acid value, bitter taste (K255) and fatty acids (Marquez et al. 2005). Near infrared spectroscopy has been valuable for the assessment of physicochemical parameters of vegetable oils (Sato 1994; Hourant et al. 2000; Takamura et al. 1995; Franco et al. 2006). Infrared spectroscopy (NIR and MID), has also been applied successfully for olive oil evaluation and geographical origin determination using chemometric approaches (Tapp et al. 2003; Iñón et al. 2003; Galtier et al. 2007). In addition, NIR technique was employed to detect fraudulent

**4. Quantification of chemical parameters in olive oil** 

(a)

addition of other vegetable oils and olive pomace oil, to virgin olive oil (Wesley et al. 1995; Yang & Irudayaraj 2001; Doweny et al. 2002; Vlachos et al. 2006); and for olive oil authentication (Bertran et al. 2000; Downey et al. 2003; Woodcock et al. 2008). The chemical characterization of fatty acids and sterols (Ollivier et al. 2006; Ollivier et al. 2003; Aranda et al. 2004; Leardi and Paganuzzi 1987), is useful for assessing quality and authenticity.

The authors attempted to apply NIR for the quantification of fatty acids, sterols and wax in an industrial scenario (for in-line analysis). 40 chemically characterized olive oil samples, from different origins, were used for this study. Partial Least Squares regression (PLS1) was applied in combination with NIR spectra in the 9000 - 4500 cm-1 range. Model validations were carried out using Monte-Carlo Cross-Validation (MCCV) (500 runs to evaluate models robustness); the predictive power of each one of the models were assessed through the computation of 1) Root Mean Square Error of Cross Validation (RMSECV); 2) Root Mean Square Error of Prediction (RMSEP); 3) Coefficient of Determination (R2) and 4) the crossvalidated coefficient of determination (Q2). At the same time several data pre-treatments were tested in order to find the most suitable ones (predictive ability).

A summary of the results obtained is described in Table 1. Two data pre-treatments were selected: 1) Standard Normal Deviate (SNV) and 2) 2nd derivative computed with the Savistzky-Golay procedure, using a 2nd degree polynomial with 11 points (5+5+1). As it can be seen from the table the obtained models have from reasonable to good predictive power.


Nomenclature: palmitic acid (16:0), palmitoleic acid (16:1), stearic acid (18:0), oleic acid (18:1), linoleic acid (18:2), linolenic acid (18:3), lignoceric acid (24:0).

Table 1. Fatty acids, sterols and wax determination for olive oil by NIR and PLS1 regression in the spectral range of 9000 – 4500 cm-1.

Quality Evaluation of Olives, Olive Pomace and Olive Oil by Infrared Spectroscopy 137

0 100 200 300 400 500 600

(a)

sample ID voo & refined soya voo refined oil voo & soya voo & maize maize voo & sunflower sunflower

(b) Fig. 3. (a) Scores plot of the first principal component (PC1), obtained for the set of virgin olive oil (voo) adulterated with refined oil, sunflower oil, maize oil and soya oil. (b) PC1

**Spectral region (cm-1) Pre-treatment LV RMSEP(%) R2 RMSEC(%)** 

4536-6108 SNV 6 0.20 0.99 0.29

4536-6108 SNV 5 0.23 0.99 0.34

4536-6108 SNV 2 0.38 0.99 0.67

4536-6108 1st derivative 7 2.81 0.99 3.74

Table 2. Statistical parameters obtained for the calibration models built for each adulterant.

Model 1: quantification of sunflower oil in virgin olive oil

Model 2: quantification of maize oil in virgin olive oil

Model 3: quantification of soya oil in virgin olive oil

Model 4: quantification of refined olive in virgin olive oil

loading profile.


PC1(95.8%)

Several factors may reduce the predictive ability of such modeling techniques: 1) low amount of some parameters present in the olive oil; 2) NIR instrument characteristics (selectivity, specificity, signal to noise ratio, etc.) and 3) sampling distribution. At this stage seven fatty acids can be estimated using NIR, but many more could be quantified. It will be necessary to add more samples with wider ranges of parameters in order to enhance the robustness of the models. Galtier et al. (2007) have managed to quantify 14 fatty acids, squalene and triacilglycerols.

The spectral profiles extracted from infrared spectra using chemometric methods could be in many cases a substitute for the traditional analysis, for olive oil overall characterization.
