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Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis
Food Control 68 (2016) 303e309
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Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis Brianda Elzey a, David Pollard b, Sayo O. Fakayode a, * a b
Department of Chemistry, North Carolina Agricultural and Technical State University, 1601 East Market Street, Greensboro, NC, 27411, USA Department of Chemistry, Winston-Salem State University, Winston-Salem, NC, 27014, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2016 Received in revised form 5 April 2016 Accepted 7 April 2016 Available online 9 April 2016
Natural oils are increasingly being used in the food, cosmetic, and agrochemical industries in recent years. However, natural oils with high market values are often counterfeited and adulterated with cheap, poor quality oils, with serious economic implications for the food and cosmetic industries, and potential health implications to consumers. This study reports the first combined use of Fourier transform infrared spectroscopy (FTIR) and partial-least-square (PLS) multivariate regression analysis for rapid, accurate, and low cost determination of the % compositions of two natural oils (neem oil (NO) and flaxseed oil (FO)) adulterated either with edible vegetable oil (VO) or extra virgin olive oil (EVOO). The FTIR of the calibration sample sets containing adulterated NO and FO with VO and EVOO at a concentration range of 1e99% w/w were measured and subjected to PLS multivariate regression analyses. The obtained FTIR spectra profile of the adulterated samples are highly dependent on the type of natural oil as well as the type of adulterant oil. The developed PLS models were subsequently used to determine the % compositions of independently prepared validation samples of NO and FO adulterated with VO and EVOO. The figures-of-merit of the PLS regression models were excellent and with good linearity (R2 > 0.998814). The score plots of the PLS regressions revealed interesting and useful information for pattern recognition of adulterated NO and FO samples. The PLS regression models correctly determined % compositions of adulterated NO and FO with VO with low root-mean-square-relative-percent errors (RMS%RE) of determination of 3.02%, and 4.24%, with an overall RMS%RE of 3.63%. The models also correctly determined % compositions of adulterated NO and FO with EVOO with RMS%RE of 7.13% and 2.00%, with an overall RMS%RE of 4.56%. The simplicity and high accuracy of this low-cost study presents an attractive model with potential real-world applications in quality control and quality assurance for consumer products in the food, cosmetics, and agrochemical industries. Published by Elsevier Ltd.
Keywords: Food-analysis Natural oils Adulteration FTIR Multivariate regression analysis Chemometrics Quality assurance
1. Introduction The sale of adulterated natural oil products as a result of great demand of natural oils is problematic, and presents a global challenge to the production of natural oils (European Parliament, 2014; Everstine, Spink, & Kennedy, 2013; FDA, 2009; Public Health and Food Safety, 2013; Spink & Moyer, 2013; Wheatley & Spink, 2013). Unsuspecting consumers may pay higher prices to purchase substandard products of reduced nutritional values with potential negative health effects for humans (European Parliament, 2014; Everstine et al., 2013; FDA, 2009; Public Health and Food
* Corresponding author. E-mail address: [email protected] (S.O. Fakayode). http://dx.doi.org/10.1016/j.foodcont.2016.04.008 0956-7135/Published by Elsevier Ltd.
Safety, 2013; Spink & Moyer, 2013; Wheatley & Spink, 2013). For instance, the adulteration of natural oils often results in dermal irritation in cosmetics and therapeutics, low mortality rates for insecticides, and health issues in the food industry (Do, HadjiMinaglou, Antoniotti, & Fernandez, 2014). Regulatory agencies, including the United States Food and Drug Administration (FDA) and the World Health Organization (WHO), have been very active in the monitoring of the quality and purity of cosmetics, pharmaceuticals, agrochemicals, consumable food products and food supplements to abate counterfeiting and adulteration of consumer goods (European Parliament, 2014; FDA, 2009). The development of food authenticity protocols capable of determining the authenticity of adulterated consumer goods is of a considerable interest for safety and economic reasons. Towards this effort, effective analytical strategies including gas-chromatography
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(Blomberg, Schoenmakers, & Brinkman, 2002; Maree, Kamatou, Gibbons, Viljoen, & Van Vuuren, 2014), high-performance liquid chromatography (Domingues et al., 2014; Morlock, Meyer, Zimmermann & Roussell, 2014; Salghi, Armbruster, & Schwack, 2014; Wang et al., 2015), nuclear magnetic resonance (Ribeiro et al., 2014; Santos, Pereira-Filho, & Rodriguez-Saona, 2013), and electroanalytical techniques (Apetrei & Apetrei, 2014; Guo, Zhu, Liu, & Zhuang, 2010) have been developed for the evaluation of the authenticity of food products. However, some of the protocols suffer inherent drawbacks such as long analyses times, importability, high cost of instrumentation, or specialized training skills. The use of ordinary, inexpensive, and simple analytical spectroscopic methods such as Raman (de Sa Oliveira et al., 2016; Haughey, Galvin-King, Ho, Bell, & Elliott, 2015; Rodrigues Junior, de Sa Oliveira, Almeida, De Oliveira, Stephani, Pinto, Carvalho & Perrone, 2016; Uysal, Boyaci, Genis, & Tamer, 2013), fluorescence (Dankowska, Malecka, & Kowalewski, 2015; Jakubikova, Sadecka, Majek, 2015; Li, Wang, Zhao, Ouyang, & Wu, 2015; Markechova, Majek, & Sadecka, 2014; Tanjuara da Silva, Filardi, Pepe, Chaves, & Santos, 2015), near infrared (Ding, Ni, & Kokot, 2015; Guo, Ni, & €t, & Elliott, 2013; Zhu Kokot, 2016; Haughey, Graham, Cancoue et al., 2010), and Fourier transformed infrared (FTIR) (Kurniawati, Rohman, & Triyana, 2014; Quinones-Islas, Meza-Marquez, OsorioRevilla, & Gallardo-Velazquez, 2013; Rohman & Man, 2012; Rohman, Riyanto, Sasi, & Yusof, 2014; Roman & Che Man, 2010) in conjunction with chemometric multivariate regression analysis has been widely explored recently for accurate analyses of various food products. The use of analytical spectroscopy in combination with multivariate analysis is appealing because it requires a small sample size and also allows for rapid, multicomponent, and simultaneous determination of multiple analytes in complex mixtures with little to no sample preparation. In addition, Raman and IR spectrometers are portable and relatively inexpensive, allowing affordable in-situ field sample analysis. The overreaching goal of this study is therefore to develop a rapid, low cost, and accurate analytical protocol for the detection and simultaneous determination of the % composition of two natural oils (Neem oil (NO) and flaxseed oil (FO)) adulterated with vegetable oil (VO) and extra virgin olive oil (EVOO). NO and FO were selected for this study because of their high nutritional and market values and their applications in the food, cosmetic and agrochemical industries. For instance, NO is plant-based and obtained from the fruits and seeds of the Azadirachta indica evergreen tree in India and is often referred to as ‘the village pharmacy’. Neem oil is used in the preparation of cosmetic products (soaps, hair products, hand creams), for healing skin diseases and inflammations, reducing fevers, helping with rheumatic disorders and as an insecticide (Ghosh, Sugumar, Mukherjee, & Chandrasekaran, 2016). Flaxseed oil originates from the seeds of the flax plant, Linum usitatissimum, L., and contains high concentrations of both omega-3 and omega-6 fatty acids (de Souza, de Santana, Gontijo, Mazivila, & Borges Neto, 2015; Sun et al., 2015). Flaxseed oil is also used for the treatment of a variety of conditions including: high cholesterol, heart disease and constipation (de Souza et al., 2015; Sun et al., 2015).
2.1.2. Sample preparation, FTIR measurement, and chemometrics multivariate regression analysis All glassware used was thoroughly washed with acetone and rinsed with deionized water (Thermo Scientific, GenPure UV-TOC/ UF, Hungary). Calibration samples of varying compositions of NO and FO adulterated with either vegetable oil or extra virgin olive oil ranging from 1 to 90% (wt/wt) were prepared in sample vials. The samples were shaken and kept at room temperature for approximately 48 h in order to equilibrate the samples and to ensure total homogenization. The FTIR spectra of the samples were recorded in reflectance mode using the IRAffinity-1 FTIR spectrometer (Shimadzu Corporation, Kyoto, Japan). The spectrometer was equipped with a MiRacle ZnSe 3B crystal plate attenuated total reflectance device (Pike Technologies, Madison, WI, USA) mounted on the Shimadzu platform. The MiRacle ZnSe 3B crystal plate permits the rapid and accurate measurement of a small sample size with high sensitivity. The FTIR spectrum of each sample was scanned 100 times with a resolution of 4 cm1 over a range of 600 cm1 to 4000 cm1 The FTIR spectrometer was calibrated with a polystyrene standard before each use to ensure wavelength accuracy. Chemometrics and PLS multivariate regression data analysis was performed using the software The Unscrambler (CAMO Software, 9.8, Oslo, Norway).
3. Results and discussion 3.1. FTIR spectra of adulterated NO and FO samples with edible vegetable oil and extra virgin olive oil Fig. 1 is the FTIR spectra of pure unadulterated NO and FO samples showing the characteristic CeH stretch (~2900 cm1), C] O stretch (~1700 cm1), and CeO stretch (~1100 cm1) of triglyceride component of natural NO and FO. Fig. 2A and B are the FTIR spectra of the calibration samples with varying % compositions of NO and FO adulterated with vegetable oil. Fig. 2C and D shows the cross section of FTIR spectra in Fig. 2A and cross section of FTIR spectra in Fig. 2B, respectively of the calibration samples with varying % compositions of NO and FO adulterated with vegetable
2. Experimental 2.1. Materials and methods 2.1.1. Chemicals and supplies Neem oil (NO) was purchased from Dr. Adorable, Inc, Chicago, IL USA. Flaxseed oil (FO) was purchased from New Directions Aromatics Inc., Mississauga, Ontario Canada. Adulterant oils, vegetable oil (VO) and extra virgin olive oil (EVOO) were purchased from a local grocery store in Greensboro, North Carolina, USA.
Fig. 1. FTIR spectra of pure: A. FO, B. NO.
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Fig. 2. FTIR spectra of calibration samples of: A. NO adulterated with vegetable oil, B. FO adulterated with vegetable oil, C. cross section of FTIR spectra in Fig. 2A, D. cross section of FTIR spectra in Fig. 2B. (vegetable oil adulteration ranged between 1 and 90% (wt/wt)).
oil. Considerable differences were observed in the FTIR spectra of NO and FO samples adulterated with vegetable oil. For instance, a more pronounced variation of the FTIR spectra was observed in adulterated NO with vegetable oil at around 1100 cm1 (CeO stretch). In addition, the FTIR spectra profiles and finger print signature of adulterated NO and FO with vegetable oil are notably different between 1400 and 1700 cm1 region. The observed differences in the FTIR spectra of adulterated NO and FO with
vegetable oil suggest differences in interaction of NO and FO with vegetable oil adulterant. The FTIR spectra of the calibration samples of varying % compositions of NO and FO adulterated with extra virgin olive oil are shown in Fig. 3. Once again, the FTIR spectra of adulterated NO and FO with extra virgin olive oil samples is analyte dependent and varied with % composition of NO and FO in the adulterated samples with extra virgin olive oil.
Fig. 3. FTIR spectra calibration samples of: A. NO adulterated with extra virgin olive oil, B. FO adulterated with extra virgin olive oil, C. cross section of FTIR spectra in Fig. 3A, D. cross section of FTIR spectra in Fig. 3B. (extra virgin olive oil adulteration ranged between 1 and 90% (wt/wt)).
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Fig. 4. Typical Score Plot of PLS regression of: A. NO adulterated with vegetable oil; B. FO adulterated with vegetable oil.
Determination of the composition of NO and FO adulterated with VO and EVOO by multivariate PLS regression analysis: Partial-least-square regression analysis was used to correlate changes in FTIR spectra data of NO and FO adulterated with vegetable oil (in Fig. 2) or extra virgin olive oil (in Fig. 3) with w/w % composition of NO and FO of the adulterated samples. Detailed mathematical descriptions of multivariate regression calibrations in analytical spectroscopy for chemical analysis have been reported elsewhere (Adams, 1995; Fakayode, Busch, Bellert, & Busch, 2005; Fakayode, Busch & Busch, 2006; Fakayode, Hordge, McDaniel, & Jones, 2016; Fakayode, Williams, Busch, Busch & Warner, 2006; Malinowski, 1991; Martens & Naes, 1998; Williams, Fakayode, Lowry, & Warner, 2009). In brief, a multivariate regression equation can be simplified and represented by equation (1),
y ¼ b0 þ x1 b1 þ x2 b2 þ x3 b3 þ ………xn bn
(1)
where y is the dependent variable (% oil compositions in this study), x1, x2 … …xn are the independent variables (FTIR intensity at various wavenumbers in this study), b0 is the y-intercept of the regression equation, and b1, b2 … … … bn are the regression coefficients of xvariables. Equation (1) can be expressed in matrix notation as shown in Equation (2),
Y ¼ Xb
(2)
where Y contains the matrix values of the dependent variables for all samples, X is a matrix composed of values of the independent variables of all samples, and b contains the regression vector. The regression vector therefore relates the independent and dependent variables. PLS regression balances two objectives, by seeking factors that explain both response and determinant variation. Accordingly, PLS regression aims to minimize sample response determination error by seeking linear functions of the determinants that explain as much variation in each response as possible, as well as accounting for variation in the determinants. The use of PLS regression is more advantageous because it simultaneously incorporates the
dependent variable in the data compression and decomposition operations, i.e. both X and the Y data are actively involved in the construction of the new basis set made up of PLS components (Fakayode et al., 2005; Fakayode, Busch & Busch, 2006; Martens & Naes, 1998). The PLS regression therefore focuses on those aspects of the data that are most important in determining Y. Multivariate regression analysis is a two-step process. The first step of multivariate regression is known as the calibration phase. In this phase, a model is developed using a calibration set of samples and carefully optimized. The second step of multivariate regression is known as the validation phase. In this phase, the model developed in the calibration phase is tested with an independently prepared test set of validation samples to evaluate the robustness of the model developed in calibration phase. The first task of PLS regression model development in the calibration phase is the selection of the optimum spectral region where the spectral changes (X-variable) correlate most with the Y-variable (% oil compositions in the study). The second critical task of PLS regression model development involves the selection of the appropriate number of PLS required to build the model without overfitting. In theory, (n-1) number of PLS can be used for regression models, where, n is the number of samples. One of the major challenges often encountered in a multivariate regression analysis using original raw spectral data is the collinearity in the spectral data that often reduces the robustness of such regression models. To avoid this problem, the partial-least-square (PLS) component is often used to eliminate collinearity in the spectral data. This is commonly achieved by representing the original raw spectral data in a new orthogonal variance-scaled eigenvector PLS component. The representation of samples in a new orthogonal variance-scaled eigenvector or coordinate system is known as the scores plot. In addition to the elimination of collinearity in the data set, the scores plot can also be used as a data dimensional reduction strategy. Typically, only a few PLS components are required to represent the data in the new orthogonal variance-scaled eigenvector PLS component. Higher PLS can be disregarded as being due to “noise”. Furthermore, the scores plot may provide additional valuable information that may not be apparent from the original data set. Fig. 4 is a representation of the score plots of the first PLS component against the second PLS component resulting from the PLS regression analysis of the FTIR spectra of adulterated NO and FO samples with vegetable oil in Fig. 2. The first two PLS components account for 100% of the variability in the FTIR spectra (x-variable) and 100% of useful information in the y-variable (% compositions of NO) in the vegetable oil samples contributing to the PLS regression model. Similarly, the first two PLS components account for 91% of the variability in the FTIR spectra (x-variable) and 94.4% of the variability in the y-variable (% compositions of FO) in the adulterated vegetable oil samples. Careful examination of the score plots reveals an interesting pattern of the adulterated samples. For instance, adulterated NO samples with larger compositions of vegetable oil adulterant are grouped together on the left side of the
Table 1 Figure of merit of PLS regression analysis of adulterated NO and FO in vegetable oil and extra virgin olive oil. Adulterated oil
R2
Slope
Offset
Adulterated with vegetable oil(VO) FO 0.999702 0.999404 0.029988 NO 0.999373 0.998747 0.069979 Adulterated with extra virgin olive (EVOO) FO 0.999093 0.998188 0.142703 NO 0.998814 0.997630 0.162147
Wavenumber (cm1) 600e715 600e4000 830e950 600e4000
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Fig. 5. FTIR spectra of independent validation samples of: A. NO adulterated with vegetable oil, B. FO adulterated with vegetable oil, C. NO adulterated with extra virgin olive oil, D. FO adulterated with extra virgin olive oil (vegetable oil and extra virgin olive oil adulteration ranged between 1 and 90% (wt/wt)).
score plots (Fig. 4A). However, adulterated NO samples containing larger % compositions of NO in the adulterated NO samples are grouped together on the opposite right side of the score plots in Fig. 4A. Interestingly, pure unadulterated NO that contains no vegetable adulterant was by itself in the first quadrant of the score plot in Fig. 4a. The score plot of adulterated FO with extra vegetable oil adulterant is also interesting. Adulterated FO samples with larger % composition of vegetable oil adulterant are similarly grouped together on the left side of the score plot (Fig. 4B). In contrast, adulterated FO samples containing larger % composition of FO in the adulterated FO are grouped together on the right side of the score plot (Fig. 4B). The obtained score plots of adulterated NO and FO with extra virgin olive oil also show similar grouping of
samples containing larger % composition of NO and FO in the adulterated samples. The results of the score plots are informative and can potentially be used for rapid screening and pattern recognition of the purity, authenticity and possible adulteration of oil samples. Table 1 is the summary of the figure-of-merit of the PLS regression analyses of the FTIR spectral data, including the optimum wavenumber region, the slope, square correlation coefficients, and the offset for adulterated NO and FO with vegetable oil and extra virgin olive oil. The developed PLS models have good linearity (R2 > 0.998814). A perfect model would have a slope of 1, a correlation coefficient of 1, and an offset of 0. While the figure-of-merit of the PLS model was impressive, the
Table 2 The actual and determined % compositions of adulterated NO with VO.
Table 3 The actual and determined % compositions of adulterated FO with VO.
Sample
Determined % NO composition
Actual % NO composition
%RE
Sample
Determined % FO composition
Actual % FO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 RMS%RE
98.181 93.495 88.809 84.123 79.437 74.752 70.066 65.38 60.694 56.008 51.323 46.637 41.951 37.265 32.58 27.894 23.208 18.522 13.836 9.151
96.955 93.273 87.281 83.818 79.339 74.329 69.745 65.026 59.896 54.95 51.893 47.468 42.533 37.876 32.274 27.37 22.309 18.258 12.901 10.093
1.26 0.24 1.75 0.36 0.12 0.57 0.46 0.54 1.33 1.93 1.10 1.75 1.37 1.61 0.95 1.91 4.03 1.45 7.25 9.33 3.02
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 RMS%RE
97.27 93.591 88.204 83.62 79.895 71.839 71.923 64.297 55.519 51.724 45.921 45.581 42.605 37.484 34.306 27.906 23.908 20.321 15.523 11.055
96.717 92.353 87.913 82.863 79.756 74.204 69.265 65.164 56.877 54.602 51.582 47.055 43.401 36.916 33.028 26.739 22.927 18.87 14.606 10.719
0.57 1.34 0.33 0.91 0.17 3.19 3.84 1.33 2.39 5.27 10.9 3.13 1.83 1.54 3.87 4.36 4.28 7.69 6.28 3.13 4.24
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Table 4 The actual and determined % compositions of adulterated NO with EVOO. Sample
Determined % NO composition
Actual % NO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 RMS%RE
85.708 91.393 87.662 80.429 76.848 65.708 64.474 58.42
91.825 87.144 80.145 74.59 70.835 64.855 59.234 55.067
6.66 4.87 9.38 7.83 8.49 1.32 8.85 6.09 7.13
practical utility of any regression model is the ability of the model to correctly determine % compositions of independently prepared adulterated oil validation samples. Fig. 5 is the FTIR spectra of independently prepared test validation samples of NO and FO adulterated either with VO or adulterated with EVOO. It must be highlighted that the % compositions of adulterated NO and FO in the validation samples are different from the % compositions of adulterated NO and FO in the calibration samples used for the PLS regression model development. The result of the validation study of the determined and actual % composition of NO and FO in validation samples adulterated with vegetable oil is shown in Tables 2 and 3 respectively. The capability of the PLS regressions to accurately predict the % composition of NO and FO in the adulterated validation samples were evaluated using a root-mean-square-relative-percent error (RMS%RE) in equation (3).
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ð%REi Þ2 RMS%RE ¼ n
(3)
where, %REi is the percent relative error calculated from the known and determined values for the ith validation sample, and n is the number of validation samples in the set. The PLS regression models correctly determined % compositions of NO and FO adulterated with VO with RMS%RE error of 3.02% and 4.24%, respectively, with an overall RMS%RE 3.63. The summary of the corresponding validation study conducted for the determination of % compositions of NO and FO samples adulterated with virgin olive oil are shown in Tables 4 and 5. Once again, the PLS models correctly determined % compositions of NO and FO in the adulterated virgin olive oil samples with RMS%RE of 7.13% and 2.00%, respectively, with an overall all average RMS%RE of 4.56%.
4. Conclusion The result of a rapid, low cost, and accurate analytical protocol
Table 5 The actual and determined % compositions of adulterated FO with EVOO. Sample
Determined % FO composition
Actual % FO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 S9 RMS%RE
97.889 90.6 86.328 80.73 79.627 73.964 57.222 53.382 22.122
97.657 90.678 86.435 80.397 79.553 72.682 60.21 54.922 22.18
0.24 0.09 0.12 0.41 0.09 1.76 4.96 2.80 0.26 2.00
that involved a combined used of FTIR spectroscopy and multivariate PLS regression analysis for rapid and accurate determinations of % composition of two natural oils (neem oil and flaxseed oil) adulterated with edible vegetable oil and extra virgin olive oil is reported. The FTIR spectra were observed to vary with % compositions of essential oils in the adulterated samples. A PLS multivariate regression was used to correlate changes with % compositions of essential oils in the adulterated samples. The PLS regression models accurately determined the % compositions of adulterated NO and FO validation samples with low error of determination. The simplicity, low-cost, and the obtained high accuracy of the protocol are attractive, with potential real-world applications for fast screening and pattern recognition for quality control and quality assurance in the food, cosmetic, and agrochemical industries. Acknowledgements The financial support for Brianda Elzey from Research Initiative for Scientific Enhancement (RISE) Program through a NIGMS/NIH R25GM0706162 grant is acknowledged. References Adams, M. J. (1995). Chemometrics in analytical spectroscopy (2nd ed.). Cambridge: Royal Society of Chemistry. Apetrei, I. M., & Apetrei, C. (2014). Detection of virgin olive oil adulteration using a voltammetric e-tongue. Computers and Electronics in Agriculture, 108, 148e154. Blomberg, J., Schoenmakers, P. J., & Brinkman, U. A. Th (2002). Gas chromatographic methods for oil analysis. Journal of Chromatography A, 972, 137e173. Dankowska, A., Malecka, M., & Kowalewski, W. (2015). Detection of plant oil addition to cheese by synchronous fluorescence spectroscopy. Dairy Science & Technology, 95, 413e424. Ding, X., Ni, Y., & Kokot, S. (2015). NIR spectroscopy and chemometrics for the discrimination of pure, powdered, purple sweet potatoes and their samples adulterated with the white sweet potato flour. Chemometrics and Intelligent Laboratory Systems, 144, 17e23. Do, T. K. T., Hadji-Minaglou, F., Antoniotti, S., & Fernandez, X. (2014). Authenticity of essential oils. Trends in Analytical Chemistry, 66, 146e157. Domingues, D. S., Pauli, E. D., de Abreu, J. E. M., Massura, F. W., Cristiano, V., Santos, M. J., et al. (2014). Detection of roasted and ground coffee adulteration by HPLC by amperometric and by post-column derivatization UVeVis detection. Food Chemistry, 146, 353e362. Environment, Public Health and Food Safety, (2013/2091(INI), October 10, 2013. European Parliament. (2014). Report e On the food crisis, fraud in the food chain and the control there of, Rapporteur (chair). Esther de Lange, Committee on the Environment, Public Health and Food Safety. Everstine, K., Spink, J., & Kennedy, S. (2013). Analysis of food fraud and economically motivated adulteration incidents. Journal of Food Protection, 4, 560e735. Fakayode, S. O., Busch, M. A., Bellert, D. J., & Busch, K. W. (2005). .Determination of the enantiomeric composition of phenylalanine by chemometric analysis of the fluorescence spectra of cyclodextrin guest-host complexes. Analyst, 130, 233e241. Fakayode, S. O., Busch, M. A., & Busch, K. W. (2006a). Determination of the enantiomeric composition of samples by multivariate regression modeling of spectral data obtained with cyclodextrin guest-host complexes effect of an achiral surfactant and use of mixed cyclodextrin. Talanta, 68, 1574e1583. Fakayode, S. O., Hordge, L. N., McDaniel, K. L., & Jones, D. D., Jr. (2016). Multicomponent and simultaneous determination of estrogens (ethinylestradiol and Norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and partial-least square regression analysis. Talanta, 152, 401e409. Fakayode, S. O., Williams, A. A., Busch, M. A., Busch, K. W., & Warner, I. M. (2006b). The use of polymeric surfactant as a chiral selector for chiral analysis by chemometric analysis of the fluorescence spectra of guest-host complexes. Journal of Fluorescence, 16, 659e670. FDA, US Food and Drug Administration. (2009). Addressing challenges of economically-motivated adulteration. In Paper presented at the public meeting on economically motivated adulteration, College Park, MD, request for comment. Federal register (Vol. 15497e15499). Ghosh, V., Sugumar, S., Mukherjee, A., & Chandrasekaran, N. (2016). Chapter 67Neem (Azadirachta indica) oils. In V. R. Preedy (Ed.), Essential oils in food preservation, flavor and safety (pp. 593e599). San Diego: Academic Press. Guo, Y., Ni, Y., & Kokot, S. (2016). Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 153, 79e86. Guo, W., Zhu, X., Liu, Y., & Zhuang, H. (2010). Sugar and water contents of honey
B. Elzey et al. / Food Control 68 (2016) 303e309 with dielectric property sensing. Journal of Food Engineering, 97(2), 275e281. Haughey, S. A., Galvin-King, P., Ho, Y.-C., Bell, S. E. J., & Elliott, C. T. (2015). The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye. Food Control, 48, 75e83. €t, E., & Elliott, C. T. (2013). The application of Haughey, S. A., Graham, S. F., Cancoue Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal. Food Chemistry, 136(3e4), 1557e1561. Jakubikova, M., Sadecka, J., & Pavel, M. (2015). Determination of adulterants in adulterant-fruit spirit blends using excitation-emission matrix fluorescence spectroscopy. Acta Chimica Slovaca, 8(1), 52e58. Kurniawati, E., Rohman, A., & Triyana, K. (2014). Analysis of lard in meatball broth using Fourier transform infrared spectroscopy and chemometrics. Meat Science, 96(1), 94e98. Li, B., Wang, H., Zhao, Q., Ouyang, J., & Wu, Y. (2015). Rapid detection of authenticity and adulteration of walnut oil by FTIR and fluorescence spectroscopy: a comparative study. Food Chemistry, 181, 25e30. Malinowski, E. R. (1991). Factor analysis in chemistry. New York: Wiley. Maree, J., Kamatou, G., Gibbons, S., Viljoen, A., & Van Vuuren, S. (2014). The application of GC-MS combined with chemometrics for the identification of antimicrobial compounds from the selected commercial essential oils. Chemometrics and Intelligent Laboratory Systems, 130, 172e181. Markechova, D., Majek, P., & Sadecka, J. (2014). Fluorescence spectroscopy and multivariate methods for the determination of brandy adulteration with mixed wine spirit. Food Chemistry, 159, 193e199. Martens, H., & Naes, T. (1998). Multivariate calibration. New York: Wiley. Morlock, G. E., Meyer, S., Zimmermann, B. F., & Roussel, J.-M. (2014). High-performance thin-layer chromatography analysis of steviol glycosides in Stevia formulations and sugar-free food products, and benchmarking with (ultra) highperformance liquid chromatography. Journal of Chromatography A, 1350, 102e111. Quinones-Islas, N., Meza-Marquez, O. G., Osorio-Revilla, G., & Gallardo-Velazquez, T. (2013). Detection of adulterants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis. Food Research International, 51, 148e154. Ribeiro, R. O. R., M arsico, E. T., Carneiro, C. d. S., Monteiro, M. L. G., Conte Júnior, C., & de Jesus, E. F. O. (2014). Detection of honey adulteration of high fructose corn syrup by Low Field Nuclear Magnetic Resonance (LF 1H NMR). Journal of Food Engineering, 135, 39e43. Oliveira, K., de Almeida, C. E., De Oliveira, L. F., Rodrigues, P. H., Júnior, de Sa Stephani, R., Pinto Mda, S., et al. (2016). FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: classification of samples for the presence of lactose and fraud detection by addition of maltodextrin. Food Chemistry, 196, 584e588. Rohman, A., & Man, Y. B. C. (2012). 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(1), 129e134.
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Rohman, A., Riyanto, S., Sasi, A. M., & Yusof, F. M. (2014). The use of FTIR spectroscopy in combination with chemometrics for the authentication of red fruit (Pandanus conoideus Lam) oil from sunflower and palm oils. Food Bioscience, 7, 64e70. Roman, A., & Che Man, Y. B. (2010). Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil. Food Research International, 43, 886e892. de S a Oliveira, K., de Souza Callegaro, L., Stephani, R., Almeida, M. R., & de Oliveira, L. F. C. (2016). Analysis of spreadable cheese by Raman spectroscopy and chemometric tools. Food Chemistry, 194, 441e446. Salghi, R., Armbruster, W., & Schwack, W. (2014). Detection of argan oil adulteration with vegetable oils by high-performance liquid chromatographyeevaporative light scattering detection. Food Chemistry, 153, 387e392. Santos, P. M., Pereira-Filho, E. R., & Rodriguez-Saona, L. E. (2013). Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis. Food Chemistry, 138(1), 19e24. de Souza, L. M., de Santana, F. B., Gontijo, L. C., Mazivila, S. J., & Borges Neto, W. (2015). Quantification of adulterations in extra virgin flaxseed oil using MIR and PLS. Food Chemistry, 182, 35e40. Spink, J., & Moyer, D. C. (2013). Understanding and combating food fraud. Food Technology, 67, 30e35. Sun, X., Zhang, L., Li, P., Xu, B., Ma, F., Zhang, Q., et al. (2015). Fatty acid profiles based adulteration detection for flaxseed oil by gas chromatography mass spectrometry. LWT - Food Science and Technology, 63(1), 430e436. Tanjuara da Silva, C. E., Filardi, V. L., Pepe, I. M., Chaves, M. A., & Santos, C. M. S. (2015). Classification of food vegetable oils by fluorimetry adn artificial neutral networks. Food Control, 47, 86e91. US Food and Drug Administration. Guidance for industry: Prescription Drug Marketing Act (PDMA) Requirements. http://www.fda.gov/downloads/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/UCM134399.pdf. Uysal, R. S., Boyaci, I. H., Genis, H. E., & Tamer, U. (2013). Determination of butter adulteration with margarine using Raman spectroscopy. Food Chemistry, 141(4), 4397e4403. Wang, S., Guo, Q., Wang, L., Lin, L., Shi, H., Cao, H., et al. (2015). Detection of honey adulteration with starch syrup by high performance liquid chromatography. Food Chemistry, 172, 669e674. Wheatley, V., & Spink, J. (2013). Defining the public health threat of dietary supplement fraud. Comprehensive Reviews in Food Science and Food Safety, 12, 599e613. Williams, A. A., Fakayode, S. O., Lowry, M., & Warner, I. M. (2009). A versatile chiral selector for determination of enantiomeric composition of fluorescent and nonfluorescent chiral molecules using steady-state fluorescence spectroscopy. Chirality, 21, 305e315. Zhu, X., Li, S., Shan, Y., Zhang, Z., Li, G., Su, D., et al. (2010). Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics. Journal of Food Engineering, 101(1), 92e97.
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Food Control journal homepage: www.elsevier.com/locate/foodcont
Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis Brianda Elzey a, David Pollard b, Sayo O. Fakayode a, * a b
Department of Chemistry, North Carolina Agricultural and Technical State University, 1601 East Market Street, Greensboro, NC, 27411, USA Department of Chemistry, Winston-Salem State University, Winston-Salem, NC, 27014, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 January 2016 Received in revised form 5 April 2016 Accepted 7 April 2016 Available online 9 April 2016
Natural oils are increasingly being used in the food, cosmetic, and agrochemical industries in recent years. However, natural oils with high market values are often counterfeited and adulterated with cheap, poor quality oils, with serious economic implications for the food and cosmetic industries, and potential health implications to consumers. This study reports the first combined use of Fourier transform infrared spectroscopy (FTIR) and partial-least-square (PLS) multivariate regression analysis for rapid, accurate, and low cost determination of the % compositions of two natural oils (neem oil (NO) and flaxseed oil (FO)) adulterated either with edible vegetable oil (VO) or extra virgin olive oil (EVOO). The FTIR of the calibration sample sets containing adulterated NO and FO with VO and EVOO at a concentration range of 1e99% w/w were measured and subjected to PLS multivariate regression analyses. The obtained FTIR spectra profile of the adulterated samples are highly dependent on the type of natural oil as well as the type of adulterant oil. The developed PLS models were subsequently used to determine the % compositions of independently prepared validation samples of NO and FO adulterated with VO and EVOO. The figures-of-merit of the PLS regression models were excellent and with good linearity (R2 > 0.998814). The score plots of the PLS regressions revealed interesting and useful information for pattern recognition of adulterated NO and FO samples. The PLS regression models correctly determined % compositions of adulterated NO and FO with VO with low root-mean-square-relative-percent errors (RMS%RE) of determination of 3.02%, and 4.24%, with an overall RMS%RE of 3.63%. The models also correctly determined % compositions of adulterated NO and FO with EVOO with RMS%RE of 7.13% and 2.00%, with an overall RMS%RE of 4.56%. The simplicity and high accuracy of this low-cost study presents an attractive model with potential real-world applications in quality control and quality assurance for consumer products in the food, cosmetics, and agrochemical industries. Published by Elsevier Ltd.
Keywords: Food-analysis Natural oils Adulteration FTIR Multivariate regression analysis Chemometrics Quality assurance
1. Introduction The sale of adulterated natural oil products as a result of great demand of natural oils is problematic, and presents a global challenge to the production of natural oils (European Parliament, 2014; Everstine, Spink, & Kennedy, 2013; FDA, 2009; Public Health and Food Safety, 2013; Spink & Moyer, 2013; Wheatley & Spink, 2013). Unsuspecting consumers may pay higher prices to purchase substandard products of reduced nutritional values with potential negative health effects for humans (European Parliament, 2014; Everstine et al., 2013; FDA, 2009; Public Health and Food
* Corresponding author. E-mail address: [email protected] (S.O. Fakayode). http://dx.doi.org/10.1016/j.foodcont.2016.04.008 0956-7135/Published by Elsevier Ltd.
Safety, 2013; Spink & Moyer, 2013; Wheatley & Spink, 2013). For instance, the adulteration of natural oils often results in dermal irritation in cosmetics and therapeutics, low mortality rates for insecticides, and health issues in the food industry (Do, HadjiMinaglou, Antoniotti, & Fernandez, 2014). Regulatory agencies, including the United States Food and Drug Administration (FDA) and the World Health Organization (WHO), have been very active in the monitoring of the quality and purity of cosmetics, pharmaceuticals, agrochemicals, consumable food products and food supplements to abate counterfeiting and adulteration of consumer goods (European Parliament, 2014; FDA, 2009). The development of food authenticity protocols capable of determining the authenticity of adulterated consumer goods is of a considerable interest for safety and economic reasons. Towards this effort, effective analytical strategies including gas-chromatography
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(Blomberg, Schoenmakers, & Brinkman, 2002; Maree, Kamatou, Gibbons, Viljoen, & Van Vuuren, 2014), high-performance liquid chromatography (Domingues et al., 2014; Morlock, Meyer, Zimmermann & Roussell, 2014; Salghi, Armbruster, & Schwack, 2014; Wang et al., 2015), nuclear magnetic resonance (Ribeiro et al., 2014; Santos, Pereira-Filho, & Rodriguez-Saona, 2013), and electroanalytical techniques (Apetrei & Apetrei, 2014; Guo, Zhu, Liu, & Zhuang, 2010) have been developed for the evaluation of the authenticity of food products. However, some of the protocols suffer inherent drawbacks such as long analyses times, importability, high cost of instrumentation, or specialized training skills. The use of ordinary, inexpensive, and simple analytical spectroscopic methods such as Raman (de Sa Oliveira et al., 2016; Haughey, Galvin-King, Ho, Bell, & Elliott, 2015; Rodrigues Junior, de Sa Oliveira, Almeida, De Oliveira, Stephani, Pinto, Carvalho & Perrone, 2016; Uysal, Boyaci, Genis, & Tamer, 2013), fluorescence (Dankowska, Malecka, & Kowalewski, 2015; Jakubikova, Sadecka, Majek, 2015; Li, Wang, Zhao, Ouyang, & Wu, 2015; Markechova, Majek, & Sadecka, 2014; Tanjuara da Silva, Filardi, Pepe, Chaves, & Santos, 2015), near infrared (Ding, Ni, & Kokot, 2015; Guo, Ni, & €t, & Elliott, 2013; Zhu Kokot, 2016; Haughey, Graham, Cancoue et al., 2010), and Fourier transformed infrared (FTIR) (Kurniawati, Rohman, & Triyana, 2014; Quinones-Islas, Meza-Marquez, OsorioRevilla, & Gallardo-Velazquez, 2013; Rohman & Man, 2012; Rohman, Riyanto, Sasi, & Yusof, 2014; Roman & Che Man, 2010) in conjunction with chemometric multivariate regression analysis has been widely explored recently for accurate analyses of various food products. The use of analytical spectroscopy in combination with multivariate analysis is appealing because it requires a small sample size and also allows for rapid, multicomponent, and simultaneous determination of multiple analytes in complex mixtures with little to no sample preparation. In addition, Raman and IR spectrometers are portable and relatively inexpensive, allowing affordable in-situ field sample analysis. The overreaching goal of this study is therefore to develop a rapid, low cost, and accurate analytical protocol for the detection and simultaneous determination of the % composition of two natural oils (Neem oil (NO) and flaxseed oil (FO)) adulterated with vegetable oil (VO) and extra virgin olive oil (EVOO). NO and FO were selected for this study because of their high nutritional and market values and their applications in the food, cosmetic and agrochemical industries. For instance, NO is plant-based and obtained from the fruits and seeds of the Azadirachta indica evergreen tree in India and is often referred to as ‘the village pharmacy’. Neem oil is used in the preparation of cosmetic products (soaps, hair products, hand creams), for healing skin diseases and inflammations, reducing fevers, helping with rheumatic disorders and as an insecticide (Ghosh, Sugumar, Mukherjee, & Chandrasekaran, 2016). Flaxseed oil originates from the seeds of the flax plant, Linum usitatissimum, L., and contains high concentrations of both omega-3 and omega-6 fatty acids (de Souza, de Santana, Gontijo, Mazivila, & Borges Neto, 2015; Sun et al., 2015). Flaxseed oil is also used for the treatment of a variety of conditions including: high cholesterol, heart disease and constipation (de Souza et al., 2015; Sun et al., 2015).
2.1.2. Sample preparation, FTIR measurement, and chemometrics multivariate regression analysis All glassware used was thoroughly washed with acetone and rinsed with deionized water (Thermo Scientific, GenPure UV-TOC/ UF, Hungary). Calibration samples of varying compositions of NO and FO adulterated with either vegetable oil or extra virgin olive oil ranging from 1 to 90% (wt/wt) were prepared in sample vials. The samples were shaken and kept at room temperature for approximately 48 h in order to equilibrate the samples and to ensure total homogenization. The FTIR spectra of the samples were recorded in reflectance mode using the IRAffinity-1 FTIR spectrometer (Shimadzu Corporation, Kyoto, Japan). The spectrometer was equipped with a MiRacle ZnSe 3B crystal plate attenuated total reflectance device (Pike Technologies, Madison, WI, USA) mounted on the Shimadzu platform. The MiRacle ZnSe 3B crystal plate permits the rapid and accurate measurement of a small sample size with high sensitivity. The FTIR spectrum of each sample was scanned 100 times with a resolution of 4 cm1 over a range of 600 cm1 to 4000 cm1 The FTIR spectrometer was calibrated with a polystyrene standard before each use to ensure wavelength accuracy. Chemometrics and PLS multivariate regression data analysis was performed using the software The Unscrambler (CAMO Software, 9.8, Oslo, Norway).
3. Results and discussion 3.1. FTIR spectra of adulterated NO and FO samples with edible vegetable oil and extra virgin olive oil Fig. 1 is the FTIR spectra of pure unadulterated NO and FO samples showing the characteristic CeH stretch (~2900 cm1), C] O stretch (~1700 cm1), and CeO stretch (~1100 cm1) of triglyceride component of natural NO and FO. Fig. 2A and B are the FTIR spectra of the calibration samples with varying % compositions of NO and FO adulterated with vegetable oil. Fig. 2C and D shows the cross section of FTIR spectra in Fig. 2A and cross section of FTIR spectra in Fig. 2B, respectively of the calibration samples with varying % compositions of NO and FO adulterated with vegetable
2. Experimental 2.1. Materials and methods 2.1.1. Chemicals and supplies Neem oil (NO) was purchased from Dr. Adorable, Inc, Chicago, IL USA. Flaxseed oil (FO) was purchased from New Directions Aromatics Inc., Mississauga, Ontario Canada. Adulterant oils, vegetable oil (VO) and extra virgin olive oil (EVOO) were purchased from a local grocery store in Greensboro, North Carolina, USA.
Fig. 1. FTIR spectra of pure: A. FO, B. NO.
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Fig. 2. FTIR spectra of calibration samples of: A. NO adulterated with vegetable oil, B. FO adulterated with vegetable oil, C. cross section of FTIR spectra in Fig. 2A, D. cross section of FTIR spectra in Fig. 2B. (vegetable oil adulteration ranged between 1 and 90% (wt/wt)).
oil. Considerable differences were observed in the FTIR spectra of NO and FO samples adulterated with vegetable oil. For instance, a more pronounced variation of the FTIR spectra was observed in adulterated NO with vegetable oil at around 1100 cm1 (CeO stretch). In addition, the FTIR spectra profiles and finger print signature of adulterated NO and FO with vegetable oil are notably different between 1400 and 1700 cm1 region. The observed differences in the FTIR spectra of adulterated NO and FO with
vegetable oil suggest differences in interaction of NO and FO with vegetable oil adulterant. The FTIR spectra of the calibration samples of varying % compositions of NO and FO adulterated with extra virgin olive oil are shown in Fig. 3. Once again, the FTIR spectra of adulterated NO and FO with extra virgin olive oil samples is analyte dependent and varied with % composition of NO and FO in the adulterated samples with extra virgin olive oil.
Fig. 3. FTIR spectra calibration samples of: A. NO adulterated with extra virgin olive oil, B. FO adulterated with extra virgin olive oil, C. cross section of FTIR spectra in Fig. 3A, D. cross section of FTIR spectra in Fig. 3B. (extra virgin olive oil adulteration ranged between 1 and 90% (wt/wt)).
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Fig. 4. Typical Score Plot of PLS regression of: A. NO adulterated with vegetable oil; B. FO adulterated with vegetable oil.
Determination of the composition of NO and FO adulterated with VO and EVOO by multivariate PLS regression analysis: Partial-least-square regression analysis was used to correlate changes in FTIR spectra data of NO and FO adulterated with vegetable oil (in Fig. 2) or extra virgin olive oil (in Fig. 3) with w/w % composition of NO and FO of the adulterated samples. Detailed mathematical descriptions of multivariate regression calibrations in analytical spectroscopy for chemical analysis have been reported elsewhere (Adams, 1995; Fakayode, Busch, Bellert, & Busch, 2005; Fakayode, Busch & Busch, 2006; Fakayode, Hordge, McDaniel, & Jones, 2016; Fakayode, Williams, Busch, Busch & Warner, 2006; Malinowski, 1991; Martens & Naes, 1998; Williams, Fakayode, Lowry, & Warner, 2009). In brief, a multivariate regression equation can be simplified and represented by equation (1),
y ¼ b0 þ x1 b1 þ x2 b2 þ x3 b3 þ ………xn bn
(1)
where y is the dependent variable (% oil compositions in this study), x1, x2 … …xn are the independent variables (FTIR intensity at various wavenumbers in this study), b0 is the y-intercept of the regression equation, and b1, b2 … … … bn are the regression coefficients of xvariables. Equation (1) can be expressed in matrix notation as shown in Equation (2),
Y ¼ Xb
(2)
where Y contains the matrix values of the dependent variables for all samples, X is a matrix composed of values of the independent variables of all samples, and b contains the regression vector. The regression vector therefore relates the independent and dependent variables. PLS regression balances two objectives, by seeking factors that explain both response and determinant variation. Accordingly, PLS regression aims to minimize sample response determination error by seeking linear functions of the determinants that explain as much variation in each response as possible, as well as accounting for variation in the determinants. The use of PLS regression is more advantageous because it simultaneously incorporates the
dependent variable in the data compression and decomposition operations, i.e. both X and the Y data are actively involved in the construction of the new basis set made up of PLS components (Fakayode et al., 2005; Fakayode, Busch & Busch, 2006; Martens & Naes, 1998). The PLS regression therefore focuses on those aspects of the data that are most important in determining Y. Multivariate regression analysis is a two-step process. The first step of multivariate regression is known as the calibration phase. In this phase, a model is developed using a calibration set of samples and carefully optimized. The second step of multivariate regression is known as the validation phase. In this phase, the model developed in the calibration phase is tested with an independently prepared test set of validation samples to evaluate the robustness of the model developed in calibration phase. The first task of PLS regression model development in the calibration phase is the selection of the optimum spectral region where the spectral changes (X-variable) correlate most with the Y-variable (% oil compositions in the study). The second critical task of PLS regression model development involves the selection of the appropriate number of PLS required to build the model without overfitting. In theory, (n-1) number of PLS can be used for regression models, where, n is the number of samples. One of the major challenges often encountered in a multivariate regression analysis using original raw spectral data is the collinearity in the spectral data that often reduces the robustness of such regression models. To avoid this problem, the partial-least-square (PLS) component is often used to eliminate collinearity in the spectral data. This is commonly achieved by representing the original raw spectral data in a new orthogonal variance-scaled eigenvector PLS component. The representation of samples in a new orthogonal variance-scaled eigenvector or coordinate system is known as the scores plot. In addition to the elimination of collinearity in the data set, the scores plot can also be used as a data dimensional reduction strategy. Typically, only a few PLS components are required to represent the data in the new orthogonal variance-scaled eigenvector PLS component. Higher PLS can be disregarded as being due to “noise”. Furthermore, the scores plot may provide additional valuable information that may not be apparent from the original data set. Fig. 4 is a representation of the score plots of the first PLS component against the second PLS component resulting from the PLS regression analysis of the FTIR spectra of adulterated NO and FO samples with vegetable oil in Fig. 2. The first two PLS components account for 100% of the variability in the FTIR spectra (x-variable) and 100% of useful information in the y-variable (% compositions of NO) in the vegetable oil samples contributing to the PLS regression model. Similarly, the first two PLS components account for 91% of the variability in the FTIR spectra (x-variable) and 94.4% of the variability in the y-variable (% compositions of FO) in the adulterated vegetable oil samples. Careful examination of the score plots reveals an interesting pattern of the adulterated samples. For instance, adulterated NO samples with larger compositions of vegetable oil adulterant are grouped together on the left side of the
Table 1 Figure of merit of PLS regression analysis of adulterated NO and FO in vegetable oil and extra virgin olive oil. Adulterated oil
R2
Slope
Offset
Adulterated with vegetable oil(VO) FO 0.999702 0.999404 0.029988 NO 0.999373 0.998747 0.069979 Adulterated with extra virgin olive (EVOO) FO 0.999093 0.998188 0.142703 NO 0.998814 0.997630 0.162147
Wavenumber (cm1) 600e715 600e4000 830e950 600e4000
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Fig. 5. FTIR spectra of independent validation samples of: A. NO adulterated with vegetable oil, B. FO adulterated with vegetable oil, C. NO adulterated with extra virgin olive oil, D. FO adulterated with extra virgin olive oil (vegetable oil and extra virgin olive oil adulteration ranged between 1 and 90% (wt/wt)).
score plots (Fig. 4A). However, adulterated NO samples containing larger % compositions of NO in the adulterated NO samples are grouped together on the opposite right side of the score plots in Fig. 4A. Interestingly, pure unadulterated NO that contains no vegetable adulterant was by itself in the first quadrant of the score plot in Fig. 4a. The score plot of adulterated FO with extra vegetable oil adulterant is also interesting. Adulterated FO samples with larger % composition of vegetable oil adulterant are similarly grouped together on the left side of the score plot (Fig. 4B). In contrast, adulterated FO samples containing larger % composition of FO in the adulterated FO are grouped together on the right side of the score plot (Fig. 4B). The obtained score plots of adulterated NO and FO with extra virgin olive oil also show similar grouping of
samples containing larger % composition of NO and FO in the adulterated samples. The results of the score plots are informative and can potentially be used for rapid screening and pattern recognition of the purity, authenticity and possible adulteration of oil samples. Table 1 is the summary of the figure-of-merit of the PLS regression analyses of the FTIR spectral data, including the optimum wavenumber region, the slope, square correlation coefficients, and the offset for adulterated NO and FO with vegetable oil and extra virgin olive oil. The developed PLS models have good linearity (R2 > 0.998814). A perfect model would have a slope of 1, a correlation coefficient of 1, and an offset of 0. While the figure-of-merit of the PLS model was impressive, the
Table 2 The actual and determined % compositions of adulterated NO with VO.
Table 3 The actual and determined % compositions of adulterated FO with VO.
Sample
Determined % NO composition
Actual % NO composition
%RE
Sample
Determined % FO composition
Actual % FO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 RMS%RE
98.181 93.495 88.809 84.123 79.437 74.752 70.066 65.38 60.694 56.008 51.323 46.637 41.951 37.265 32.58 27.894 23.208 18.522 13.836 9.151
96.955 93.273 87.281 83.818 79.339 74.329 69.745 65.026 59.896 54.95 51.893 47.468 42.533 37.876 32.274 27.37 22.309 18.258 12.901 10.093
1.26 0.24 1.75 0.36 0.12 0.57 0.46 0.54 1.33 1.93 1.10 1.75 1.37 1.61 0.95 1.91 4.03 1.45 7.25 9.33 3.02
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 RMS%RE
97.27 93.591 88.204 83.62 79.895 71.839 71.923 64.297 55.519 51.724 45.921 45.581 42.605 37.484 34.306 27.906 23.908 20.321 15.523 11.055
96.717 92.353 87.913 82.863 79.756 74.204 69.265 65.164 56.877 54.602 51.582 47.055 43.401 36.916 33.028 26.739 22.927 18.87 14.606 10.719
0.57 1.34 0.33 0.91 0.17 3.19 3.84 1.33 2.39 5.27 10.9 3.13 1.83 1.54 3.87 4.36 4.28 7.69 6.28 3.13 4.24
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Table 4 The actual and determined % compositions of adulterated NO with EVOO. Sample
Determined % NO composition
Actual % NO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 RMS%RE
85.708 91.393 87.662 80.429 76.848 65.708 64.474 58.42
91.825 87.144 80.145 74.59 70.835 64.855 59.234 55.067
6.66 4.87 9.38 7.83 8.49 1.32 8.85 6.09 7.13
practical utility of any regression model is the ability of the model to correctly determine % compositions of independently prepared adulterated oil validation samples. Fig. 5 is the FTIR spectra of independently prepared test validation samples of NO and FO adulterated either with VO or adulterated with EVOO. It must be highlighted that the % compositions of adulterated NO and FO in the validation samples are different from the % compositions of adulterated NO and FO in the calibration samples used for the PLS regression model development. The result of the validation study of the determined and actual % composition of NO and FO in validation samples adulterated with vegetable oil is shown in Tables 2 and 3 respectively. The capability of the PLS regressions to accurately predict the % composition of NO and FO in the adulterated validation samples were evaluated using a root-mean-square-relative-percent error (RMS%RE) in equation (3).
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ð%REi Þ2 RMS%RE ¼ n
(3)
where, %REi is the percent relative error calculated from the known and determined values for the ith validation sample, and n is the number of validation samples in the set. The PLS regression models correctly determined % compositions of NO and FO adulterated with VO with RMS%RE error of 3.02% and 4.24%, respectively, with an overall RMS%RE 3.63. The summary of the corresponding validation study conducted for the determination of % compositions of NO and FO samples adulterated with virgin olive oil are shown in Tables 4 and 5. Once again, the PLS models correctly determined % compositions of NO and FO in the adulterated virgin olive oil samples with RMS%RE of 7.13% and 2.00%, respectively, with an overall all average RMS%RE of 4.56%.
4. Conclusion The result of a rapid, low cost, and accurate analytical protocol
Table 5 The actual and determined % compositions of adulterated FO with EVOO. Sample
Determined % FO composition
Actual % FO composition
%RE
S1 S2 S3 S4 S5 S6 S7 S8 S9 RMS%RE
97.889 90.6 86.328 80.73 79.627 73.964 57.222 53.382 22.122
97.657 90.678 86.435 80.397 79.553 72.682 60.21 54.922 22.18
0.24 0.09 0.12 0.41 0.09 1.76 4.96 2.80 0.26 2.00
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