Main Article Content
adulteration, oil quality, Fourier transfer infrared spectroscopy, least-squares support vector machine
Fourier transfer infrared (FTIR) spectroscopy is a fast and reliable technique for the authentication of adulteration in the extra-virgin olive oil (EVOO) for quality control and market management. To verify the authenticity of EVOO, the feasibility of FTIR spectroscopy coupled with multivariate calibration has been investigated. Three different multivariate calibrations including linear discrimination analysis, back propagation artificial neural network and least-squares support vector machine (LS-SVM) were studied to make models with the acquired spectra and compared by correlation coefficients in the prediction set. The results demonstrated that the LS-SVM model was superior to others based on its optimum discrimination rates of 100 and 92.5% in the training and prediction set respectively. Besides, all misclassified samples were low-level adulterated EVOO ones with concentrations of 2.5%. This work demonstrates that the FTIR spectroscopy technique combined with an appropriately selected multivariate calibration could be promising to detect of adulterations in EVOO.
Callao, M.P. and Ruisánchez, I., 2018. An overview of multivariate qualitative methods for food fraud detection. Food Control 86: 283-293.
De, L.M.-E.P., Bosque-Sendra, J.M., Bro, R. and Cuadros-Rodríguez, L., 2011. Discriminating olive and non-olive oils using HPLC-CAD and chemometrics. Analytical & Bioanalytical Chemistry 399: 2083-2092.
El-Abassy, R.M., Donfack, P. and Materny, A., 2010. Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration. Journal of Raman Spectroscopy 40: 1284-1289.
Fadzlillah, N.A., Man, Y.B.C. and Rohman, A., 2014. FTIR spectroscopy combined with chemometric for analysis of sesame oil adulterated with corn oil. International Journal of Food Properties 17: 1275-1282.
Fyh, K., Chen, Q., Hassan, M.M., Yang, M., Sun, H. and Rahman, M.H., 2017. Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chemistry 240: 231-238.
Garrido-Delgado, R., Muñoz-Pérez, M.E. and Arce, L., 2018. Detection of adulteration in extra virgin olive oils by using UV-IMS and chemometric analysis. Food Control 85: 292-299.
Georgouli, K., Rincon, J.M.D. and Koidis, A., 2016. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry 217: 735-742.
Gonzálvez, A., Armenta, S. and Guardia, M.D.L., 2010. Adulteration detection of argan oil by inductively coupled plasma optical emission spectrometry. Food Chemistry 121: 878-886.
Jiang, H., Liu, G., Xiao, X., Yu, S., Mei, C. and Ding, Y., 2012. Classification of Chinese soybean paste by Fourier Transform Near-Infrared (FT-NIR) spectroscopy and different supervised pattern recognition. Food Analytical Methods 5: 928-934.
Jiménez-Carvelo, A.M., Osorio, M.T., Koidis, A., González-Casado, A. and Cuadros-Rodríguez, L., 2017. Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy. LWT – Food Science and Technology 86: 174-184.
LermaGarcía, M.J., RamisRamos, G., HerreroMartínez, J.M. and SimóAlfonso, E.F., 2010. Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy. Food Chemistry 118: 78-83.
Lohumi, S., Lee, S., Lee, H. and Cho, B.K., 2015. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science and Technology 46: 85-98.
Mu, T., Chen, S., Zhang, Y., Chen, H., Guo, P. and Meng, F., 2015. Classification of motor oil using laser-induced fluorescence and phosphorescence. Analytical Letters 49(8): 1233-1239.
Oussama, A., Elabadi, F., Platikanov, S., Kzaiber, F. and Tauler, R., 2012. Detection of olive oil adulteration using FT-IR spectroscopy and PLS with variable importance of projection (VIP) scores. Journal of the American Oil Chemists Society 89: 1807-1812.
Ouyang, Q., Zhao, J. and Chen, Q., 2014. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. Analytica Chimica Acta 841: 68-76.
Rohman, A. and Man, Y.B.C., 2010. Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil. Food Research International 43: 886-892.
Su, W.H. and Sun, D.W., 2018. Fourier Transform Infrared and Raman and Hyperspectral Imaging techniques for quality determinations of powdery foods: a review. Comprehensive Reviews in Food Science & Food Safety 17(1): 104-112.
Vann, K.R., Sedgeman, C.A., Jacob, G., Avi, G.G. and Neil, O., 2015. Effects of olive metabolites on DNA cleavage mediated by human type II topoisomerases. Biochemistry 54: 4531-4541.
Vapnik, V.N. and Chervonenkis, A.Y., 2015. On the Uniform convergence of relative frequencies of events to their probabilities. Springer International Publishing, New York, NY, USA, pp. 264-280.
Vasconcelos, M., Coelho, L. and Barros, A., 2015. Study of adulteration of extra virgin olive oil with peanut oil using FTIR spectroscopy and chemometrics. Cogent Food and Agriculture 1: 1-13.
Wang, C.W. and You, W.H., 2013. Boosting-SVM: effective learning with reduced data dimension. Applied Intelligence 39: 465-474.
Xu, Y., Kutsanedzie, F.Y.H., Sun, H., Wang, M., Chen, Q., Guo, Z. and Wu, J., 2017. Rapid Pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy. Food Analytical Methods 11(4): 1199-1208.
Xu, Y., Li, H., Chen, Q., Zhao, J. and Ouyang, Q., 2015. Rapid detection of adulteration in extra-virgin olive oil using three-dimensional fluorescence spectra technology with selected multivariate calibrations. International Journal of Food Properties 18: 2085-2098.
Xu, Z., Morris, R.H., Bencsik, M. and Newton, M.I., 2014. Detection of virgin olive oil adulteration using low field unilateral NMR. Sensors (Basel) 14(2): 2028-2035.
Yang, M., Chen, Q., Kutsanedzie, F.Y.H., Yang, X., Guo, Z. and Ouyang, Q., 2017a. Portable spectroscopy system determination of acid value in peanut oil based on variables selection algorithms. Measurement 103: 179-185.
Yang, R., Dong, G., Sun, X., Yang, Y., Liu, H., Du, Y., Jin, H. and Zhang, W., 2017b. Discrimination of sesame oil adulterated with corn oil using information fusion of synchronous and asynchronous two-dimensional near-mid infrared spectroscopy. European Journal of Lipid Science and Technology 119(9): 1600459.
Yang, Y., Ferro, M.D., Cavaco, I. and Liang, Y., 2013. Detection and identification of extra virgin olive oil adulteration by GC-MS combined with chemometrics. Journal of Agricultural & Food Chemistry 61: 3693-3702.
Zhao, X., Dong, D., Zheng, W., Jiao, L. and Lang, Y., 2015. Discrimination of adulterated sesame oil using mid-infrared spectroscopy and chemometrics. Food Analytical Methods 8: 2308-2314.