- Email: [email protected]
The chemometrics approach applied to FTIR spectral data for the analysis of rice bran oil in extra virgin olive oil
Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Contents lists available at SciVerse ScienceDirect
Chemometrics and Intelligent Laboratory Systems journal homepage: www.elsevier.com/locate/chemolab
The chemometrics approach applied to FTIR spectral data for the analysis of rice bran oil in extra virgin olive oil Abdul Rohman a, b,⁎, Yaakob B. Che Man c a b c
Laboratory of Analytical Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Gadjah Mada University, Yogyakarta, Indonesia Research Center of Halal Products, Gadjah Mada University, Yogyakarta, Indonesia Laboratory Analysis and Authentication, Halal Products Research Institute, Universiti Putra Malaysia 43400 Selangor, Malaysia
a r t i c l e
i n f o
Article history: Received 12 April 2011 Received in revised form 12 October 2011 Accepted 18 October 2011 Available online 25 October 2011 Keywords: Chemometrics FTIR spectroscopy Rice bran oil Extra virgin olive oil
a b s t r a c t Among eleven studied vegetable oils, rice bran oil (RBO) has the close similarity to extra virgin olive oil (EVOO) in terms of FTIR spectra, as shown in the score plot of first and second principal components. The peak intensities at 18 frequency regions were used as matrix variables in principal component analysis (PCA). Consequently, the presence of RBO in EVOO is difficult to detect. This study aimed to use the chemometrics approach, namely discriminant analysis (DA) and multivariate calibrations of partial least square and principle component regression to analyze RBO in EVOO. DA was used for the classification of EVOO and EVOO mixed with RBO. Multivariate calibrations were exploited for the quantification of RBO in EVOO. The combined frequency regions of 1200–900 and 3020–3000 cm − 1 were used for such analysis. The results showed that no misclassification was reported for the classification of EVOO and EVOO mixed with RBO. Partial least square regression either using normal or first derivative FTIR spectra can be successfully used for the quantification of RBO in EVOO. In addition, analysis of fatty acid composition can complement the results obtained from FTIR spectral data. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Today, the chemometrics techniques have played a very important role in the study of edible fats and oils, especially for the authentication study [1]. One of the chemometrics techniques widely used is multivariate calibrations in order to elaborate the relationship between the concentration of analyte(s) and the response of instrumental assay like FTIR spectra [2]. The chemical analysis by infrared spectrophotometry rests on the fast acquisition of a great number (several hundred and even several thousands) of spectral data [3]. Fourier transform infrared (FTIR) spectroscopy has emerged an attractive alternative technique for some reasons. The development of attenuated total reflectance (ATR) as sampling handling technique has revitalized the use of FTIR spectroscopy. Using ATR, there is no excessive sample preparation; consequently, the use of hazardous solvents and reagents can be avoided [4]. This fact is very attractive for scientists who are take care about the human and environmental health issues. For this reason, FTIR spectroscopy and other vibrational spectroscopic techniques can be taken into consideration as “green analytical technique” for the analysis of edible fats and oils [5]. In recent years, olive oil (OO) has received great attention owing to its biological activities and sensory qualities. It has social and
⁎ Corresponding author: Tel.: + 62 274 543120; fax: + 62 274 543120. E-mail address: [email protected] (A. Rohman). 0169-7439/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2011.10.010
economical importance for the Mediterranean regions [6]. OO is one of the strictly regulated oil products; consequently, it can be target for adulteration. Among OO classes, extra virgin olive oil (EVOO) is the highest quality of OO. Due to the therapeutic value and high price of EVOO, some market players intentionally or unintentionally try to blend EVOO with much cheaper plant oils like palm, soya, and sunflower oils [7]. The adulteration of food products is of primary importance for consumers, food processors, regulatory bodies, and industries [8]. The adulteration practice frequently involves the replacement or dilution of high-cost ingredients with cheaper substitutes. Although the adulteration is done for economic reasons, the action can cause severe health and safety problems such as the Spanish toxic syndrome that killed some people. In addition, the adulteration of EVOO can be a potential risk for patients having the allergic history to EVOO's adulterants [9, 10]. Several publications have reported the application of chemometrics techniques applied to FTIR spectral data for quantitative analysis of certain plant oils. The presence of hazelnut oil [11], sunflower and corn oils [12], sunflower, corn, soybean and hazelnut oils [13], sunflower, soybean, sesame, and corn oils [14], and palm oil [15] has been analyzed using FTIR spectroscopy combined with chemometrics techniques. However, there is no reported work in relation to the use of FTIR spectroscopy for the analysis of RBO which has the similar FTIR spectra to EVOO. The objective of this research was to use the chemometrics techniques of discriminant analysis and
130
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 1. The score plot of principal component analysis (PCA) for the differentiation of EVOO and other plant oils.
multivariate calibrations (partial least square and principle component regressions) for the analysis of RBO in EVOO. Furthermore, the change of fatty acid profiles in EVOO due to the addition of RBO was also reported in order to complement the FTIR spectroscopy results. 2. Experimental
Malaysia. The oil samples were packaged in polyethylene terephthalate (PET) bottles and the dates of manufacturing were not known. Virgin coconut oil (VCO) was obtained from Jogjakarta, Indonesia with the brand name of POVCO®. The used VCO was made using cold extraction under the supervision of Prof. Bambang Setiadji from the Department of Chemistry, Gadjah Mada University, Yogyakarta, Indonesia.
2.1. Materials 2.2. Classification Extra virgin olive oil (EVOO), rice bran (RBO), canola, corn, grape seed, palm, pumpkin seed, soybean, sesame, sunflower, and walnut oils (WO) were purchased from the local market in Serdang, Selangor,
Classification of pure EVOO and EVOO adulterated with RBO was carried out using discriminant analysis (DA) by computing the Mahalanobis
Fig. 2. The loading plot for the projection of PC1 and PC2 using peak absorbancies as matrix variables.
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
131
Fig. 3. FTIR spectra of extra virgin olive oil and other vegetable oils as oil adulterants at frequency regions of 4000–650 cm− 1.
distance from each class center of analyte(s) being classified. DA can be used to determine the class of RBO having the FTIR spectral similarity to EVOO [16]. To carry out DA, EVOO and RBO were mixed to obtain a series of standard or trained sets of 20 pure and 20 adulterated samples containing 1–50% of RBO in chloroform. The use of chloroform in this study is to facilitate the ease homogenization between RBO and EVOO. The samples containing RBO were assigned as adulterated, while a series of pure EVOO in chloroform (5–100%) was marked with EVOO and classified using FTIR spectra at selected frequency regions. All samples were measured using FTIR spectrometer. 2.3. Quantification Quantification of RBO in EVOO was performed using two multivariate calibrations of partial least square regression (PLSR) and principle component regression (PCR). Both calibrations transform the original variables (FTIR spectra absorbancies) into the new ones, which are linear combination of original variables, known as factors [17]. PLSR and PCR are sometimes called with factor analysis. Both techniques relied on two steps, namely calibration and validation/ prediction steps. In the calibration step, a mathematical model was built to correlate between the matrix of FTIR spectra (predictor) and the concentration of analyte(s) of interest (response) from the reference values. In the prediction step, the developed calibration model was used to calculate the concentration of unknown samples [18]. For calibration, a set of 18 samples containing EVOO and RBO was mixed together in the concentration range of 1.0–50.0% (v/v) of RBO in EVOO. These samples were shaken vigorously to ensure the
total homogenization. For prediction or validation data set, another 18 independent samples were built. The pure EVOO, pure RBO, and their binary mixtures were further analyzed using FTIR spectrometer. 2.4. FTIR spectra measurement and analysis The spectra measurement of all samples was performed using FTIR spectrometer (Nicolet 6700 from Thermo Nicolet Corp., Madison, WI) equipped with a deuterated triglycine sulphate (DTGS) as a detector and a KBr/Germanium as beam splitter. The instrument was interfaced to computer operating under Windows-based, and connected to software of the OMNIC operating system (Version 7.0 Thermo Nicolet). The rest of the procedure and condition were as previously reported [19]. 2.5. Chemometrics analysis Principal component analysis of FTIR spectra using absorbencies at 18 wavenumber regions was carried out using the Unscrambler software (Camo, Oslo, Norway, USA). DA and multivariate calibrations (PLSR and PCR) were performed using the software TQ AnalystTM version 6 (Thermo electron Corporation, Madison, WI). The spectral regions where the variations were observed were chosen for developing PLSR and PCR as well as for DA. The optimum number of PLSR and PCR factors was determined using cross validation by plotting the number of factors against the root mean square error of cross validation (RMSECV) and determining the minimum factors. The predictability
Fig. 4. Details of the FTIR spectra of EVOO and RBO at 1200–900 cm− 1.
132
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 5. The Cooman plot for the classification of extra virgin olive oil (EVOO) and adulterated EVOO with rice bran oil or RBO (F).
of the models was tested by computing root mean square error of prediction (RMSEP) as used by [21].
terms of the number of peak and the peak intensities at fingerprint region [22].
2.6. Fatty acid analysis
3.1. FTIR spectra analysis
In order to determine the FA changes during EVOO adulteration with RBO, EVOO was mixed with RBO in the range of 5–60% (v/v). These mixtures were kept in controlled room temperature (20 °C) before being used for analysis. Determination of FA compositions in all samples was carried out using GC-FID as reported in [20]. Standard FAMEs of 37 compounds (C4 to C24) (Sigma Chemicals, St. Louis, MO, USA) were used to identify the retention times. Quantitative analysis of FA was performed using internal normalization technique.
Fig. 3 exhibits FTIR spectra of EVOO, RBO, and other plant oils in mid infrared region (4000–650 cm − 1). All spectra look very similar because all plant oils are mainly composed from triacylglycerols (90–95%) along with the di- and monoacylglycerols with the minor concentration (about 5%) and other trace levels of some components. Focusing on EVOO and RBO, there are minor differences between both oils at a more detailed analysis in terms of small band shifts and of small changes in the relative peak intensity (absorbancies), especially at frequency region of about 3007 cm − 1 and 1117 cm − 1 (Fig. 4). The band at 3007 cm − 1 was attributed from the stretching vibration of cis-double bonds, meanwhile peak at 1117 cm −1 corresponds to the C–O group vibration [13]. These minor differences served as frequency region selection for the classification and quantification of RBO in EVOO, as discussed in the following.
Percentage ð%Þ fatty acid x ¼
Peak area of fatty acid x 100: Total peak area of all fatty acids
Meanwhile, FA changes during adulteration were subjected to oneway ANOVA (analysis of variance) followed by Duncan multiple comparison using SPSS version 17.0 software (SPSS Inc., Chicago, IL, USA). The significance value (p) less than 0.05 was statistically different. 3. Results and discussion In order to know the plant oils having the close similarity with extra virgin olive oil (EVOO) in terms of FTIR spectra, the chemometrics of principal component analysis (PCA) was used. Fig. 1 exhibited the PCA score plot obtained from the correlation matrix of peak absorbancies at 18 frequency regions, namely 3007, 2953, 2922, 2853, 1743, 1654, 1463, 1417, 1402, 1377, 1236, 1160, 1117, 1098, 1030, 962, 850, and 721 cm − 1 (Fig. 3). In PCA, the first principal component (PC1) and the second principal component (PC2) account the largest and the next largest of variable variation. PC 1 explained 72% variance, meanwhile PC 2 accounted 16%; therefore, an approximate of 88% of variance can be described by the first two PCs. Fig. 1 described that among the studied plant oils, rice bran oil (RBO) has the closer distance to EVOO than others. EVOO and RBO were separated on positive side, either in PC1 or in PC2. From the loading plot, the selected frequencies at fingerprint regions (1500– 721) were more contributed than others, namely at 1117 and 1236 cm − 1 (Fig. 2). This fact supported that FTIR spectroscopy is a fingerprint technique in which the samples can be differentiated in
3.2. Classification EVOO and EVOO mixed with RBO was classified using discriminant analysis at the frequency regions of 1200–900 and 3020– 3000 cm − 1. The selection of the frequency regions is based on its ability to provide the classification power with no misclassification results between two classes (EVOO and EVOO adulterated with RBO). In addition, using detailed investigation, it is known that these frequency regions reveal the peak intensity differences between EVOO and RBO. Fig. 5 shows the Coomans plot for the classification of both classes. The x-axis shows the Mahalanobis distance to EVOO, while the y-axis shows the distance to EVOO adulterated with RBO. DA can classify pure EVOO and EVOO adulterated with RBO with accuracy level of 100%. This means that no samples were mistakenly classified into the wrong class. 3.3. Quantification of RBO in EVOO Quantification of RBO in EVOO was carried out using multivariate calibrations of PLSR and PCR. PLSR and PCR are the most used regression techniques in chemometrics [21]. In the optimization of frequency
Table 1 The multivariate calibrations (PLSR and PCR) along with FTIR spectral treatments for quantification of rice bran oil (RBO) in extra virgin olive oil (EVOO). Regression
PLS
PCR
Treatment
Normal 1st der 2nd der Normal 1st der 2nd der
Factor
2 5 4 10 10 10
R2
Equation Calibration
Prediction
Calibration
Prediction
y = 0.977x + 0.663 y = 0.998x + 0.040 y = 0.982x + 0.360 y = 0.999x + 0.013 y = 0.994x + 0.111 y = 0.942x + 1.159
y = 0.990x − 0.111 y = 0.880x + 1.560 y = 0.045x + 13.58 y = 0.946x + 0.490 y = 0.803x + 3.088 y = − 0.079x + 17.47
0.993 0.998 0.982 0.999 0.995 0.942
0.981 0.931 0.001 0.962 0.908 0.003
RMSEC (% v/v)
RMSEP (% v/v)
1.34 0.620 1.90 0.380 1.05 3.40
2.15 2.45 15.0 1.80 2.99 16.3
0.36 ± 0.01h 19.08 ± 0.17f 0.19 ± 0.01g 1.93 ± 0.06e 41.49 ± 0.11g 32.38 ± 0.38g 1.32 ± 0.11g 0.76 ± 0.02d 0.46 ± 0.01g 0.24 ± 0.01f
133 FA = fatty acid; †Each value in the table represents the means of triplicate analysis; SD is given after ±. Means within each row with different letters are significantly different at P b 0.05. C14:0, myristic acid; C16:0, palmitic acid; C16:1, palmitoleic acid; C18:0, stearic acid; C18:1, oleic acid; C18:2, linoleic acid; C18:3, linolenic acid; C20:0, decanoic acid; C20:1, decenoic acid; and C22:0, dodecanoic acid.
(50%:50%)
0.29 ± 0.01g 16.94 ± 0.12e 0.33 ± 0.01f 2.29 ± 0.01d 49.67 ± 0.09f 26.04 ± 1.02f 1.16 ± 0.05f 0.672 ± 0.01c 0.41 ± 0.00f 0.21 ± 0.00e 0.28 ± 0.00g 14.34 ± 0.22d 0.47 ± 0.01e 2.37 ± 0.04cd 59.20 ± 0.08e 19.76 ± 0.30e 0.99 ± 0.02e 0.62 ± 0.01c 0.36 ± 0.00e 0.17 ± 0.01d
(40%:60%) (30%:70%)
0.19 ± 0.01f 13.74 ± 0.07c 0.52 ± 0.01bd 2.42 ± 0.04cd 62.70 ± 0.44bc 17.65 ± 0.23cd 0.94 ± 0.01bcde 0.55 ± 0.00b 0.34 ± 0.01de 0.16 ± 0.00cd 0.17 ± 0.01e 13.99 ± 0.10cd 0.55 ± 0.01cd 2.42 ± 0.03cd 62.07 ± 1.05d 17.11 ± 0.02cd 0.88 ± 0.01bcd 0.54 ± 0.01b 0.34 ± 0.00de 0.16 ± 0.00cd
(25%:75%) (20%:80%)
0.15 ± 0.00d 13.81 ± 0.09c 0.53 ± 0.02d 2.54 ± 0.05c 62.42 ± 2.06d 16.58 ± 0.21cd 0.91 ± 0.03cde 0.54 ± 0.00b 0.33 ± 0.00cd 0.16 ± 0.00cd 0.14 ± 0.00d 13.71 ± 0.02c 0.56 ± 0.01cd 2.53 ± 0.10c 65.07 ± 0.12bc 16.15 ± 0.16c 0.89 ± 0.01cde 0.53 ± 0.05db 0.33 ± 0.02bcd 0.16 ± 0.01c
(15%:85%)
0.09 ± 0.00b 12.72 ± 0.20b 0.61 ± 0.01b 3.01 ± 0.04b 66.29 ± 1.04b 13.28 ± 0.09b 0.79 ± 0.02b 0.50 ± 0.00b 0.31 ± 0.01ab 0.14 ± 0.01ab 0.01 ± 0.00a 10.83 ± 0.41a 0.76 ± 0.05a 3.24 ± 0.14a 73.27 ± 0.76a 7.06 ± 0.02a 0.60 ± 0.00a 0.33 ± 0.03a 0.29 ± 0.01a 0.12 ± 0.01a
(10%:90%) (5%:95%) (0%:100%)
Ratio (RBO: EVOO, v/v)
FA composition (% w/w)†
Table 2 The fatty acid composition of extra virgin olive oil (EVOO) adulterated with rice bran oil (RBO).
regions used, the regions containing the significant information were selected and all the useless signals coming from the interferences or instrumental drifts were ignored [18]. The frequency regions used for such quantification were based on its capability to provide the high correlation between actual and FTIR-predicted levels of RBO in EVOO. Based on this optimization, the frequency regions used for classification (the frequency regions of 1200–900 and 3020–3000 cm− 1) were used for the quantification of RBO in EVOO. Table 1 lists the performance of multivariate calibrations (PLSR and PCR) along with the FTIR spectral treatments (normal and Savitzy–Golay first and second derivatives) in terms of coefficient of determination (R 2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). In general, PLSR offers the better results than PCR for quantitative analysis of RBO in EVOO. Furthermore, both normal and first derivative FTIR spectra offer good model for such quantification. From Table 1, it is known that normal FTIR spectra give the better prediction model than first derivative spectra, as indicated by RMSEP values (2.15% for normal spectra and 2.45% for first derivative). Inversely, the first derivative spectra give the better calibration model than normal spectra, as indicated by RMSEC values (0.620% for first derivative and 1.34% for normal spectra). However, the differences of RMSEC and RMSEP values for both normal and first derivative FTIR spectra are relatively low. Using PLSR and normal spectra, the R 2 values obtained are 0.993 (in calibration) and 0.981 (in prediction). Two and five latent variables (factors) were selected for building PLSR in normal and first derivative FTIR spectra, respectively. The high value of R 2 and the low value of errors in calibration and prediction indicated that FTIR spectral data combined with PLSR can be effective tools in terms of its accuracy and precision to measure the levels of RBO in EVOO. Fig. 6 exhibits the scatter plot for the relationship between actual value (x-axis) and FTIR-predicted value (y-axis) of RBO in EVOO using PLSR with normal FTIR spectra, showing the close relationship between two variables assessed. This result indicated that FTIR spectroscopy combined with PLSR can be reliable technique for the quantification of RBO in EVOO.
C14:0 C16:0 C16:1 C18:0 C18:1 C18:2 C20:0 C18:3 C20:1 C22:0
Fig. 6. PLS model for the relationship between actual value and FTIR-predicted value of rice bran oil using FTIR normal spectra at 1200–900 and 3020–3000 cm− 1. A = calibration; B = validation.
0.11 ± 0.00c 12.97 ± 0.25b 0.59 ± 0.01bc 2.94 ± 0.02b 65.01 ± 0.06bc 14.13 ± 0.10b 0.81 ± 0.01bc 0.51 ± 0.01db 0.31 ± 0.00abc 0.14 ± 0.01bc
(100%:0%)
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
134
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 7. The relationship between the increasing level of rice bran oil (RBO) and the fatty acid changes.
3.4. Fatty acid analysis Analysis of fatty acid composition of edible oils seems to be an important tool to detect the presence of specific oil (RBO) in another (EVOO). Table 2 shows the major fatty acids present in RBO, EVOO, and their mixtures. Palmitic (C16:0), stearic (C18:0), oleic (C18:1), and linoleic acids (C18:2) are the main fatty acids composing EVOO and RBO. Therefore, these fatty acids are used for the detection of RBO in EVOO. During the addition of RBO into EVOO, the levels of stearic and oleic acids were decreased linearly with increasing levels of RBO with R 2 values of 0.793 and 859, respectively. In addition, the levels of stearic and linoleic acids were increased with the increasing concentrations of RBO with R 2 values of 0.823 and 0.884, respectively (Fig. 7). 4. Conclusion The chemometrics approach of discriminant analysis (DA) and multivariate calibrations of PLSR can facilitate the classification and quantification of RBO in EVOO at the combined frequency regions of 1200–900 and 3020–3000 cm − 1. DA can discriminate EVOO and EVOO adulterated with RBO, with accuracy level of 100%. PLSR, either using normal or first derivative FTIR spectra, offers reliable technique for the quantification of RBO in EVOO, as indicated by the high levels of R 2 and the low level of errors in calibration and validation. In addition, analysis of fatty acid compositions can be used as complementary data for detecting the presence of RBO in EVOO. Acknowledgement Abdul Rohman thanks The Directorate for Higher Education (Dikti), The Ministry of National Education, Republic of Indonesia, for the scholarship during his Ph.D. program in Halal Products Research Institute, UPM, Malaysia. References [1] I.S. Arvanitoyannis, A. Vlachos, Implementation of physicochemical and sensory analysis in conjunction with multivariate analysis towards assessing olive oil authentication/adulteration, Crit. Rev. Food Sci. Nutr. 47 (2007) 441–498. [2] R.G. Brereton, Introduction to multivariate calibration in analytical chemistry, Analyst 125 (2000) 2125–2154.
[3] N. Benoudjit, E. Cools, M. Meurens, M. Verleysen, Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models, Chem. Intel. Lab. Systems 70 (2004) 47–53. [4] L.E. Rodriguez-Saona, M.E. Allendorf, Use of FTIR for rapid authentication and detection of adulteration of food, Annu. Rev. Food Sci. Technol. 2 (2011) 17.1–17.17. [5] J. Moros, S. Garrigues, M. de la Guardia, Vibrational spectroscopy provides a green tool for multi-component analysis, Trends Anal. Chem. 29 (2009) 578–590. [6] M. De Luca, W. Terouzi, G. Loele, F. Kzaiber, A. Oussama, F. Oliverio, R. Tauler, G. Ragno, Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils, Food Chem. 124 (2011) 1113–1118. [7] D.L. Garcia-Gonzalez, R. Aparacio, Research in olive oil: challenges for the near future, J. Agric. Food Chem. 58 (2010) 12569–12577. [8] T. Gallardo-Velázquez, G. Osorio-Revilla, M. Zuñiga-de Loa, Y. Rivera-Espinoza, Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys, Food Res. Int. 42 (2009) 313–318. [9] A.A. Christy, S. Kasemsumran, Y. Du, K. Ozaki, The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics, Anal. Sci. 20 (2004) 935–940. [10] M. Arlorio, D.J. Coisson, M. Bordiga, F. Travaglia, C. Garino, L. Zuidmeer, R. Van Ree, M.G. Giuffrida, A. Conti, A. Martelli, Olive oil adulterated with hazelnut oils: simulation to identify possible risks to allergic consumers, Food Addit. Contam. 27 (2010) 11–18. [11] N. Groselj, M. Marjan Vracko, J.A. Fernández Pierna, V. Baeten, M. Novic, The use of FT-MIR spectroscopy and counter-propagation artificial neural networks for tracing the adulteration of olive oil, Acta Chim. Slovenia 55 (2008) 935–941. [12] D. Özdemir, B. Öztürk, Near infrared spectroscopic determination of olive oil adulteration with sunflower and corn oil, J. Food Drug Anal. 15 (2007) 40–47. [13] M.J. Lerma-García, G. Ramis-Ramos, J.M. Herrero-Martínez, E.F. Simó-Alfonso, Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy, Food Chem. 118 (2010) 78–83. [14] N. Vlachos, Y. Skopelitis, M. Psaroudaki, V. Konstantinidou, A. Chatzilazarou, E. Tegou, Applications of Fourier transform-infrared spectroscopy to edible oils, Anal. Chim. Acta 573–574 (2006) 459–465. [15] A. Rohman, Y.B. Che Man, Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil, Food Res. Int. 43 (2010) 886–892. [16] D. Ballabio, R. Todeschini, Multivariate classification for qualitative analysis, in: D.-W. Sun (Ed.), Infrared Spectroscopy for Food Quality: Analysis and Control, Elsevier, New York, 2009, pp. 83–104. [17] J.N. Miller, J.C. Miller, Statistics and Chemometrics for Analytical chemistry, 5th Ed. Pearson Education Limited, Edinburgh Gate Harlow, 2005. [18] M. De Luca, F. Oliverio, G. loele, G. Ragno, Multivariate calibration techniques applied to derivative spectroscopy data for analysis of pharmaceutical mixtures, Chem. Intel. Lab. Systems 96 (2009) 14–21. [19] Y.B. Che Man, A. Rohman, T.S.T. Mansor, Differentiation of lard from other edible oils by means of Fourier transform infrared spectroscopy and chemometrics, J. Am. Oil Chem. Soc. 88 (2011) 187–192. [20] A. Rohman, Y.B. Che Man, Monitoring of virgin coconut oil (VCO) adulteration with palm oil using Fourier transform infrared (FTIR) spectroscopy, J. Food Lipids 16 (2009) 618–628. [21] F. Marini, R. Bucci, I. Ginevro, A.L. Magrì, Coupling of IR measurements and multivariate calibration techniques for the determination of enantiomeric excess in pharmaceutical preparations, Chem. Intel. Lab. Systems 97 (2009) 52–63. [22] D.L. Pavia, G.M. Lampman, G.S. Kriz-jr, J.R. Vyvyan, Introduction to Spectroscopy, 4th Ed. Cencage Learning Inc., Australia, 2009.
Contents lists available at SciVerse ScienceDirect
Chemometrics and Intelligent Laboratory Systems journal homepage: www.elsevier.com/locate/chemolab
The chemometrics approach applied to FTIR spectral data for the analysis of rice bran oil in extra virgin olive oil Abdul Rohman a, b,⁎, Yaakob B. Che Man c a b c
Laboratory of Analytical Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Gadjah Mada University, Yogyakarta, Indonesia Research Center of Halal Products, Gadjah Mada University, Yogyakarta, Indonesia Laboratory Analysis and Authentication, Halal Products Research Institute, Universiti Putra Malaysia 43400 Selangor, Malaysia
a r t i c l e
i n f o
Article history: Received 12 April 2011 Received in revised form 12 October 2011 Accepted 18 October 2011 Available online 25 October 2011 Keywords: Chemometrics FTIR spectroscopy Rice bran oil Extra virgin olive oil
a b s t r a c t Among eleven studied vegetable oils, rice bran oil (RBO) has the close similarity to extra virgin olive oil (EVOO) in terms of FTIR spectra, as shown in the score plot of first and second principal components. The peak intensities at 18 frequency regions were used as matrix variables in principal component analysis (PCA). Consequently, the presence of RBO in EVOO is difficult to detect. This study aimed to use the chemometrics approach, namely discriminant analysis (DA) and multivariate calibrations of partial least square and principle component regression to analyze RBO in EVOO. DA was used for the classification of EVOO and EVOO mixed with RBO. Multivariate calibrations were exploited for the quantification of RBO in EVOO. The combined frequency regions of 1200–900 and 3020–3000 cm − 1 were used for such analysis. The results showed that no misclassification was reported for the classification of EVOO and EVOO mixed with RBO. Partial least square regression either using normal or first derivative FTIR spectra can be successfully used for the quantification of RBO in EVOO. In addition, analysis of fatty acid composition can complement the results obtained from FTIR spectral data. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Today, the chemometrics techniques have played a very important role in the study of edible fats and oils, especially for the authentication study [1]. One of the chemometrics techniques widely used is multivariate calibrations in order to elaborate the relationship between the concentration of analyte(s) and the response of instrumental assay like FTIR spectra [2]. The chemical analysis by infrared spectrophotometry rests on the fast acquisition of a great number (several hundred and even several thousands) of spectral data [3]. Fourier transform infrared (FTIR) spectroscopy has emerged an attractive alternative technique for some reasons. The development of attenuated total reflectance (ATR) as sampling handling technique has revitalized the use of FTIR spectroscopy. Using ATR, there is no excessive sample preparation; consequently, the use of hazardous solvents and reagents can be avoided [4]. This fact is very attractive for scientists who are take care about the human and environmental health issues. For this reason, FTIR spectroscopy and other vibrational spectroscopic techniques can be taken into consideration as “green analytical technique” for the analysis of edible fats and oils [5]. In recent years, olive oil (OO) has received great attention owing to its biological activities and sensory qualities. It has social and
⁎ Corresponding author: Tel.: + 62 274 543120; fax: + 62 274 543120. E-mail address: [email protected] (A. Rohman). 0169-7439/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2011.10.010
economical importance for the Mediterranean regions [6]. OO is one of the strictly regulated oil products; consequently, it can be target for adulteration. Among OO classes, extra virgin olive oil (EVOO) is the highest quality of OO. Due to the therapeutic value and high price of EVOO, some market players intentionally or unintentionally try to blend EVOO with much cheaper plant oils like palm, soya, and sunflower oils [7]. The adulteration of food products is of primary importance for consumers, food processors, regulatory bodies, and industries [8]. The adulteration practice frequently involves the replacement or dilution of high-cost ingredients with cheaper substitutes. Although the adulteration is done for economic reasons, the action can cause severe health and safety problems such as the Spanish toxic syndrome that killed some people. In addition, the adulteration of EVOO can be a potential risk for patients having the allergic history to EVOO's adulterants [9, 10]. Several publications have reported the application of chemometrics techniques applied to FTIR spectral data for quantitative analysis of certain plant oils. The presence of hazelnut oil [11], sunflower and corn oils [12], sunflower, corn, soybean and hazelnut oils [13], sunflower, soybean, sesame, and corn oils [14], and palm oil [15] has been analyzed using FTIR spectroscopy combined with chemometrics techniques. However, there is no reported work in relation to the use of FTIR spectroscopy for the analysis of RBO which has the similar FTIR spectra to EVOO. The objective of this research was to use the chemometrics techniques of discriminant analysis and
130
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 1. The score plot of principal component analysis (PCA) for the differentiation of EVOO and other plant oils.
multivariate calibrations (partial least square and principle component regressions) for the analysis of RBO in EVOO. Furthermore, the change of fatty acid profiles in EVOO due to the addition of RBO was also reported in order to complement the FTIR spectroscopy results. 2. Experimental
Malaysia. The oil samples were packaged in polyethylene terephthalate (PET) bottles and the dates of manufacturing were not known. Virgin coconut oil (VCO) was obtained from Jogjakarta, Indonesia with the brand name of POVCO®. The used VCO was made using cold extraction under the supervision of Prof. Bambang Setiadji from the Department of Chemistry, Gadjah Mada University, Yogyakarta, Indonesia.
2.1. Materials 2.2. Classification Extra virgin olive oil (EVOO), rice bran (RBO), canola, corn, grape seed, palm, pumpkin seed, soybean, sesame, sunflower, and walnut oils (WO) were purchased from the local market in Serdang, Selangor,
Classification of pure EVOO and EVOO adulterated with RBO was carried out using discriminant analysis (DA) by computing the Mahalanobis
Fig. 2. The loading plot for the projection of PC1 and PC2 using peak absorbancies as matrix variables.
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
131
Fig. 3. FTIR spectra of extra virgin olive oil and other vegetable oils as oil adulterants at frequency regions of 4000–650 cm− 1.
distance from each class center of analyte(s) being classified. DA can be used to determine the class of RBO having the FTIR spectral similarity to EVOO [16]. To carry out DA, EVOO and RBO were mixed to obtain a series of standard or trained sets of 20 pure and 20 adulterated samples containing 1–50% of RBO in chloroform. The use of chloroform in this study is to facilitate the ease homogenization between RBO and EVOO. The samples containing RBO were assigned as adulterated, while a series of pure EVOO in chloroform (5–100%) was marked with EVOO and classified using FTIR spectra at selected frequency regions. All samples were measured using FTIR spectrometer. 2.3. Quantification Quantification of RBO in EVOO was performed using two multivariate calibrations of partial least square regression (PLSR) and principle component regression (PCR). Both calibrations transform the original variables (FTIR spectra absorbancies) into the new ones, which are linear combination of original variables, known as factors [17]. PLSR and PCR are sometimes called with factor analysis. Both techniques relied on two steps, namely calibration and validation/ prediction steps. In the calibration step, a mathematical model was built to correlate between the matrix of FTIR spectra (predictor) and the concentration of analyte(s) of interest (response) from the reference values. In the prediction step, the developed calibration model was used to calculate the concentration of unknown samples [18]. For calibration, a set of 18 samples containing EVOO and RBO was mixed together in the concentration range of 1.0–50.0% (v/v) of RBO in EVOO. These samples were shaken vigorously to ensure the
total homogenization. For prediction or validation data set, another 18 independent samples were built. The pure EVOO, pure RBO, and their binary mixtures were further analyzed using FTIR spectrometer. 2.4. FTIR spectra measurement and analysis The spectra measurement of all samples was performed using FTIR spectrometer (Nicolet 6700 from Thermo Nicolet Corp., Madison, WI) equipped with a deuterated triglycine sulphate (DTGS) as a detector and a KBr/Germanium as beam splitter. The instrument was interfaced to computer operating under Windows-based, and connected to software of the OMNIC operating system (Version 7.0 Thermo Nicolet). The rest of the procedure and condition were as previously reported [19]. 2.5. Chemometrics analysis Principal component analysis of FTIR spectra using absorbencies at 18 wavenumber regions was carried out using the Unscrambler software (Camo, Oslo, Norway, USA). DA and multivariate calibrations (PLSR and PCR) were performed using the software TQ AnalystTM version 6 (Thermo electron Corporation, Madison, WI). The spectral regions where the variations were observed were chosen for developing PLSR and PCR as well as for DA. The optimum number of PLSR and PCR factors was determined using cross validation by plotting the number of factors against the root mean square error of cross validation (RMSECV) and determining the minimum factors. The predictability
Fig. 4. Details of the FTIR spectra of EVOO and RBO at 1200–900 cm− 1.
132
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 5. The Cooman plot for the classification of extra virgin olive oil (EVOO) and adulterated EVOO with rice bran oil or RBO (F).
of the models was tested by computing root mean square error of prediction (RMSEP) as used by [21].
terms of the number of peak and the peak intensities at fingerprint region [22].
2.6. Fatty acid analysis
3.1. FTIR spectra analysis
In order to determine the FA changes during EVOO adulteration with RBO, EVOO was mixed with RBO in the range of 5–60% (v/v). These mixtures were kept in controlled room temperature (20 °C) before being used for analysis. Determination of FA compositions in all samples was carried out using GC-FID as reported in [20]. Standard FAMEs of 37 compounds (C4 to C24) (Sigma Chemicals, St. Louis, MO, USA) were used to identify the retention times. Quantitative analysis of FA was performed using internal normalization technique.
Fig. 3 exhibits FTIR spectra of EVOO, RBO, and other plant oils in mid infrared region (4000–650 cm − 1). All spectra look very similar because all plant oils are mainly composed from triacylglycerols (90–95%) along with the di- and monoacylglycerols with the minor concentration (about 5%) and other trace levels of some components. Focusing on EVOO and RBO, there are minor differences between both oils at a more detailed analysis in terms of small band shifts and of small changes in the relative peak intensity (absorbancies), especially at frequency region of about 3007 cm − 1 and 1117 cm − 1 (Fig. 4). The band at 3007 cm − 1 was attributed from the stretching vibration of cis-double bonds, meanwhile peak at 1117 cm −1 corresponds to the C–O group vibration [13]. These minor differences served as frequency region selection for the classification and quantification of RBO in EVOO, as discussed in the following.
Percentage ð%Þ fatty acid x ¼
Peak area of fatty acid x 100: Total peak area of all fatty acids
Meanwhile, FA changes during adulteration were subjected to oneway ANOVA (analysis of variance) followed by Duncan multiple comparison using SPSS version 17.0 software (SPSS Inc., Chicago, IL, USA). The significance value (p) less than 0.05 was statistically different. 3. Results and discussion In order to know the plant oils having the close similarity with extra virgin olive oil (EVOO) in terms of FTIR spectra, the chemometrics of principal component analysis (PCA) was used. Fig. 1 exhibited the PCA score plot obtained from the correlation matrix of peak absorbancies at 18 frequency regions, namely 3007, 2953, 2922, 2853, 1743, 1654, 1463, 1417, 1402, 1377, 1236, 1160, 1117, 1098, 1030, 962, 850, and 721 cm − 1 (Fig. 3). In PCA, the first principal component (PC1) and the second principal component (PC2) account the largest and the next largest of variable variation. PC 1 explained 72% variance, meanwhile PC 2 accounted 16%; therefore, an approximate of 88% of variance can be described by the first two PCs. Fig. 1 described that among the studied plant oils, rice bran oil (RBO) has the closer distance to EVOO than others. EVOO and RBO were separated on positive side, either in PC1 or in PC2. From the loading plot, the selected frequencies at fingerprint regions (1500– 721) were more contributed than others, namely at 1117 and 1236 cm − 1 (Fig. 2). This fact supported that FTIR spectroscopy is a fingerprint technique in which the samples can be differentiated in
3.2. Classification EVOO and EVOO mixed with RBO was classified using discriminant analysis at the frequency regions of 1200–900 and 3020– 3000 cm − 1. The selection of the frequency regions is based on its ability to provide the classification power with no misclassification results between two classes (EVOO and EVOO adulterated with RBO). In addition, using detailed investigation, it is known that these frequency regions reveal the peak intensity differences between EVOO and RBO. Fig. 5 shows the Coomans plot for the classification of both classes. The x-axis shows the Mahalanobis distance to EVOO, while the y-axis shows the distance to EVOO adulterated with RBO. DA can classify pure EVOO and EVOO adulterated with RBO with accuracy level of 100%. This means that no samples were mistakenly classified into the wrong class. 3.3. Quantification of RBO in EVOO Quantification of RBO in EVOO was carried out using multivariate calibrations of PLSR and PCR. PLSR and PCR are the most used regression techniques in chemometrics [21]. In the optimization of frequency
Table 1 The multivariate calibrations (PLSR and PCR) along with FTIR spectral treatments for quantification of rice bran oil (RBO) in extra virgin olive oil (EVOO). Regression
PLS
PCR
Treatment
Normal 1st der 2nd der Normal 1st der 2nd der
Factor
2 5 4 10 10 10
R2
Equation Calibration
Prediction
Calibration
Prediction
y = 0.977x + 0.663 y = 0.998x + 0.040 y = 0.982x + 0.360 y = 0.999x + 0.013 y = 0.994x + 0.111 y = 0.942x + 1.159
y = 0.990x − 0.111 y = 0.880x + 1.560 y = 0.045x + 13.58 y = 0.946x + 0.490 y = 0.803x + 3.088 y = − 0.079x + 17.47
0.993 0.998 0.982 0.999 0.995 0.942
0.981 0.931 0.001 0.962 0.908 0.003
RMSEC (% v/v)
RMSEP (% v/v)
1.34 0.620 1.90 0.380 1.05 3.40
2.15 2.45 15.0 1.80 2.99 16.3
0.36 ± 0.01h 19.08 ± 0.17f 0.19 ± 0.01g 1.93 ± 0.06e 41.49 ± 0.11g 32.38 ± 0.38g 1.32 ± 0.11g 0.76 ± 0.02d 0.46 ± 0.01g 0.24 ± 0.01f
133 FA = fatty acid; †Each value in the table represents the means of triplicate analysis; SD is given after ±. Means within each row with different letters are significantly different at P b 0.05. C14:0, myristic acid; C16:0, palmitic acid; C16:1, palmitoleic acid; C18:0, stearic acid; C18:1, oleic acid; C18:2, linoleic acid; C18:3, linolenic acid; C20:0, decanoic acid; C20:1, decenoic acid; and C22:0, dodecanoic acid.
(50%:50%)
0.29 ± 0.01g 16.94 ± 0.12e 0.33 ± 0.01f 2.29 ± 0.01d 49.67 ± 0.09f 26.04 ± 1.02f 1.16 ± 0.05f 0.672 ± 0.01c 0.41 ± 0.00f 0.21 ± 0.00e 0.28 ± 0.00g 14.34 ± 0.22d 0.47 ± 0.01e 2.37 ± 0.04cd 59.20 ± 0.08e 19.76 ± 0.30e 0.99 ± 0.02e 0.62 ± 0.01c 0.36 ± 0.00e 0.17 ± 0.01d
(40%:60%) (30%:70%)
0.19 ± 0.01f 13.74 ± 0.07c 0.52 ± 0.01bd 2.42 ± 0.04cd 62.70 ± 0.44bc 17.65 ± 0.23cd 0.94 ± 0.01bcde 0.55 ± 0.00b 0.34 ± 0.01de 0.16 ± 0.00cd 0.17 ± 0.01e 13.99 ± 0.10cd 0.55 ± 0.01cd 2.42 ± 0.03cd 62.07 ± 1.05d 17.11 ± 0.02cd 0.88 ± 0.01bcd 0.54 ± 0.01b 0.34 ± 0.00de 0.16 ± 0.00cd
(25%:75%) (20%:80%)
0.15 ± 0.00d 13.81 ± 0.09c 0.53 ± 0.02d 2.54 ± 0.05c 62.42 ± 2.06d 16.58 ± 0.21cd 0.91 ± 0.03cde 0.54 ± 0.00b 0.33 ± 0.00cd 0.16 ± 0.00cd 0.14 ± 0.00d 13.71 ± 0.02c 0.56 ± 0.01cd 2.53 ± 0.10c 65.07 ± 0.12bc 16.15 ± 0.16c 0.89 ± 0.01cde 0.53 ± 0.05db 0.33 ± 0.02bcd 0.16 ± 0.01c
(15%:85%)
0.09 ± 0.00b 12.72 ± 0.20b 0.61 ± 0.01b 3.01 ± 0.04b 66.29 ± 1.04b 13.28 ± 0.09b 0.79 ± 0.02b 0.50 ± 0.00b 0.31 ± 0.01ab 0.14 ± 0.01ab 0.01 ± 0.00a 10.83 ± 0.41a 0.76 ± 0.05a 3.24 ± 0.14a 73.27 ± 0.76a 7.06 ± 0.02a 0.60 ± 0.00a 0.33 ± 0.03a 0.29 ± 0.01a 0.12 ± 0.01a
(10%:90%) (5%:95%) (0%:100%)
Ratio (RBO: EVOO, v/v)
FA composition (% w/w)†
Table 2 The fatty acid composition of extra virgin olive oil (EVOO) adulterated with rice bran oil (RBO).
regions used, the regions containing the significant information were selected and all the useless signals coming from the interferences or instrumental drifts were ignored [18]. The frequency regions used for such quantification were based on its capability to provide the high correlation between actual and FTIR-predicted levels of RBO in EVOO. Based on this optimization, the frequency regions used for classification (the frequency regions of 1200–900 and 3020–3000 cm− 1) were used for the quantification of RBO in EVOO. Table 1 lists the performance of multivariate calibrations (PLSR and PCR) along with the FTIR spectral treatments (normal and Savitzy–Golay first and second derivatives) in terms of coefficient of determination (R 2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). In general, PLSR offers the better results than PCR for quantitative analysis of RBO in EVOO. Furthermore, both normal and first derivative FTIR spectra offer good model for such quantification. From Table 1, it is known that normal FTIR spectra give the better prediction model than first derivative spectra, as indicated by RMSEP values (2.15% for normal spectra and 2.45% for first derivative). Inversely, the first derivative spectra give the better calibration model than normal spectra, as indicated by RMSEC values (0.620% for first derivative and 1.34% for normal spectra). However, the differences of RMSEC and RMSEP values for both normal and first derivative FTIR spectra are relatively low. Using PLSR and normal spectra, the R 2 values obtained are 0.993 (in calibration) and 0.981 (in prediction). Two and five latent variables (factors) were selected for building PLSR in normal and first derivative FTIR spectra, respectively. The high value of R 2 and the low value of errors in calibration and prediction indicated that FTIR spectral data combined with PLSR can be effective tools in terms of its accuracy and precision to measure the levels of RBO in EVOO. Fig. 6 exhibits the scatter plot for the relationship between actual value (x-axis) and FTIR-predicted value (y-axis) of RBO in EVOO using PLSR with normal FTIR spectra, showing the close relationship between two variables assessed. This result indicated that FTIR spectroscopy combined with PLSR can be reliable technique for the quantification of RBO in EVOO.
C14:0 C16:0 C16:1 C18:0 C18:1 C18:2 C20:0 C18:3 C20:1 C22:0
Fig. 6. PLS model for the relationship between actual value and FTIR-predicted value of rice bran oil using FTIR normal spectra at 1200–900 and 3020–3000 cm− 1. A = calibration; B = validation.
0.11 ± 0.00c 12.97 ± 0.25b 0.59 ± 0.01bc 2.94 ± 0.02b 65.01 ± 0.06bc 14.13 ± 0.10b 0.81 ± 0.01bc 0.51 ± 0.01db 0.31 ± 0.00abc 0.14 ± 0.01bc
(100%:0%)
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
134
A. Rohman, Y.B.C. Man / Chemometrics and Intelligent Laboratory Systems 110 (2012) 129–134
Fig. 7. The relationship between the increasing level of rice bran oil (RBO) and the fatty acid changes.
3.4. Fatty acid analysis Analysis of fatty acid composition of edible oils seems to be an important tool to detect the presence of specific oil (RBO) in another (EVOO). Table 2 shows the major fatty acids present in RBO, EVOO, and their mixtures. Palmitic (C16:0), stearic (C18:0), oleic (C18:1), and linoleic acids (C18:2) are the main fatty acids composing EVOO and RBO. Therefore, these fatty acids are used for the detection of RBO in EVOO. During the addition of RBO into EVOO, the levels of stearic and oleic acids were decreased linearly with increasing levels of RBO with R 2 values of 0.793 and 859, respectively. In addition, the levels of stearic and linoleic acids were increased with the increasing concentrations of RBO with R 2 values of 0.823 and 0.884, respectively (Fig. 7). 4. Conclusion The chemometrics approach of discriminant analysis (DA) and multivariate calibrations of PLSR can facilitate the classification and quantification of RBO in EVOO at the combined frequency regions of 1200–900 and 3020–3000 cm − 1. DA can discriminate EVOO and EVOO adulterated with RBO, with accuracy level of 100%. PLSR, either using normal or first derivative FTIR spectra, offers reliable technique for the quantification of RBO in EVOO, as indicated by the high levels of R 2 and the low level of errors in calibration and validation. In addition, analysis of fatty acid compositions can be used as complementary data for detecting the presence of RBO in EVOO. Acknowledgement Abdul Rohman thanks The Directorate for Higher Education (Dikti), The Ministry of National Education, Republic of Indonesia, for the scholarship during his Ph.D. program in Halal Products Research Institute, UPM, Malaysia. References [1] I.S. Arvanitoyannis, A. Vlachos, Implementation of physicochemical and sensory analysis in conjunction with multivariate analysis towards assessing olive oil authentication/adulteration, Crit. Rev. Food Sci. Nutr. 47 (2007) 441–498. [2] R.G. Brereton, Introduction to multivariate calibration in analytical chemistry, Analyst 125 (2000) 2125–2154.
[3] N. Benoudjit, E. Cools, M. Meurens, M. Verleysen, Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models, Chem. Intel. Lab. Systems 70 (2004) 47–53. [4] L.E. Rodriguez-Saona, M.E. Allendorf, Use of FTIR for rapid authentication and detection of adulteration of food, Annu. Rev. Food Sci. Technol. 2 (2011) 17.1–17.17. [5] J. Moros, S. Garrigues, M. de la Guardia, Vibrational spectroscopy provides a green tool for multi-component analysis, Trends Anal. Chem. 29 (2009) 578–590. [6] M. De Luca, W. Terouzi, G. Loele, F. Kzaiber, A. Oussama, F. Oliverio, R. Tauler, G. Ragno, Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils, Food Chem. 124 (2011) 1113–1118. [7] D.L. Garcia-Gonzalez, R. Aparacio, Research in olive oil: challenges for the near future, J. Agric. Food Chem. 58 (2010) 12569–12577. [8] T. Gallardo-Velázquez, G. Osorio-Revilla, M. Zuñiga-de Loa, Y. Rivera-Espinoza, Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys, Food Res. Int. 42 (2009) 313–318. [9] A.A. Christy, S. Kasemsumran, Y. Du, K. Ozaki, The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics, Anal. Sci. 20 (2004) 935–940. [10] M. Arlorio, D.J. Coisson, M. Bordiga, F. Travaglia, C. Garino, L. Zuidmeer, R. Van Ree, M.G. Giuffrida, A. Conti, A. Martelli, Olive oil adulterated with hazelnut oils: simulation to identify possible risks to allergic consumers, Food Addit. Contam. 27 (2010) 11–18. [11] N. Groselj, M. Marjan Vracko, J.A. Fernández Pierna, V. Baeten, M. Novic, The use of FT-MIR spectroscopy and counter-propagation artificial neural networks for tracing the adulteration of olive oil, Acta Chim. Slovenia 55 (2008) 935–941. [12] D. Özdemir, B. Öztürk, Near infrared spectroscopic determination of olive oil adulteration with sunflower and corn oil, J. Food Drug Anal. 15 (2007) 40–47. [13] M.J. Lerma-García, G. Ramis-Ramos, J.M. Herrero-Martínez, E.F. Simó-Alfonso, Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy, Food Chem. 118 (2010) 78–83. [14] N. Vlachos, Y. Skopelitis, M. Psaroudaki, V. Konstantinidou, A. Chatzilazarou, E. Tegou, Applications of Fourier transform-infrared spectroscopy to edible oils, Anal. Chim. Acta 573–574 (2006) 459–465. [15] A. Rohman, Y.B. Che Man, Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil, Food Res. Int. 43 (2010) 886–892. [16] D. Ballabio, R. Todeschini, Multivariate classification for qualitative analysis, in: D.-W. Sun (Ed.), Infrared Spectroscopy for Food Quality: Analysis and Control, Elsevier, New York, 2009, pp. 83–104. [17] J.N. Miller, J.C. Miller, Statistics and Chemometrics for Analytical chemistry, 5th Ed. Pearson Education Limited, Edinburgh Gate Harlow, 2005. [18] M. De Luca, F. Oliverio, G. loele, G. Ragno, Multivariate calibration techniques applied to derivative spectroscopy data for analysis of pharmaceutical mixtures, Chem. Intel. Lab. Systems 96 (2009) 14–21. [19] Y.B. Che Man, A. Rohman, T.S.T. Mansor, Differentiation of lard from other edible oils by means of Fourier transform infrared spectroscopy and chemometrics, J. Am. Oil Chem. Soc. 88 (2011) 187–192. [20] A. Rohman, Y.B. Che Man, Monitoring of virgin coconut oil (VCO) adulteration with palm oil using Fourier transform infrared (FTIR) spectroscopy, J. Food Lipids 16 (2009) 618–628. [21] F. Marini, R. Bucci, I. Ginevro, A.L. Magrì, Coupling of IR measurements and multivariate calibration techniques for the determination of enantiomeric excess in pharmaceutical preparations, Chem. Intel. Lab. Systems 97 (2009) 52–63. [22] D.L. Pavia, G.M. Lampman, G.S. Kriz-jr, J.R. Vyvyan, Introduction to Spectroscopy, 4th Ed. Cencage Learning Inc., Australia, 2009.