Characterization of Binary Edible Oil Blends Using Color Histograms and Pattern Recognition Techniques

Document Type : Research Paper


1 Azarbaijan Shahid Madani University

2 Department of Chemistry, Tarbiat Modares University, Tehran, Iran

3 Department of Chemistry, Azarbaijan Shahid Madani University, Tabriz, Iran


Nutritional value and quality features of oils are the most important factors that should be considered in food industry. There is no pure edible oil with appropriate oxidative stability and nutritional properties. Therefore, vegetable oils are blended to improve their applications and to enhance their nutritional quality. Characterization of edible oils is important for quality control and identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and non-destructive approach for characterization of different types of vegetable oil blends according to the corresponding color histograms. Regression models were applied on four datasets of binary edible oil blends including; Palm Olein-Rapeseed, Palm Olein-Sunflower, Soybean-Sunflower and Soybean-Rapeseed. In all of the aforementioned data sets, despite the high performances of Support Vector Regression (SVR) and Levenberg-Marquardt Artificial Neural Network (LMANN) regression models in terms of coefficient of determination, Bayesian Regularized Artificial Neural Networks (BRANN) provided better results up to 97% for HSI color histograms in both the training and test sets. In order to reduce the numbers of independent variables for modelling, principle component analysis (PCA) algorithm was used. Finally, the results of image analysis were compared with those obtained by processing of FT-IR spectra of mixtures of edible oils. The results revealed that image analysis of mixtures of edible oils yield comparable results to those obtained by processing of FT-IR spectra for characterization of edible oils. Our results suggest that the proposed method is promising for characterization of different binary blends of edible oils.


[1]           F. Gunstone, Vegetable Oils in Food Technology: Composition, Properties and Uses, Wiley-Blackwell, 2011.
[2]           F. Hashempour-Baltork, M. Torbati, S. Azadmard-Damirchi, G.P. Savage, Trends Food Sci. Tech. 57 (2016) 52.
[3]           N.A. Idris, N.L.H.M. Dian, Asia Pac. J. Clin. Nutr. 14 (2005) 396.
[4]           S.M. Downs, V. Gupta, S. Ghosh-Jerath, K. Lock, A.M. Thow, A. Singh, BMC Public Health 13 (2013) 1139.
[5]           R.V. Rios, M.D.F. Pessanha, P.F. de Almeida, C.L. Viana, S.C. da S. Lannes, Food Sci. Technol. Campinas 34 (2014) 3.
[6]           T.-T. Xu, J. Li, Y.-W. Fan, T.-W. Zheng, Z.-Y. Deng, Int. J. Food Prop. 18 (2015) 1478.
[7]           R.S. Farag, M.A. El-Agaimy, B.S.A. El Hakeem, Food Nutr. Sci. 1 (2010) 24.
[8]           M. Abdulkarim, M.W. Myat, H.M. Ghazali, K. Roselina, K.A. Abbas, J. Agr. Sci. 2 (2010) 18.
[9]           E. De Marco, M. Savarese, C. Parisini, I. Battimo, S. Falco, R. Sacchi, Eur. J. Lipid Sci. Tech. 109 (2007) 237.
[10]        H. Sakurai, J. Pokorný, Eur. J. Lipid. Sci. Technol. 105 (2003) 769
[11]        M. Naghshineh, A.A. Ariffin, H.M. Ghazali, H. Mirhosseini, A.S. Mohammad, A. Kuntom, J. Food Lipids 16 (2009) 554.
[12]        R.C. Gonzalez, R.E. Woods, Digital Image Processing,) 3rd Edition, Prentice Hall, 2008.
[13]        M.A. Domínguez, P.H.G.D. Diniz, M.S. DiNezio, M.C.U. Araújo, M.E. Centurión, Microchem. J. 112 (2014) 104.
[14]        U.T.C.P. Souto, M.F. Barbosa, H.V. Dantas, A.S. Pontes, W.S. Lyra, P.H.G.D. Diniz,   M.C.U. Araújo, E.C. Silva, Food Anal. Methods 8 (2015) 1515.
[15]        V.E. Almeida, G.B. Costa, D.D.S. Fernandes, P.H.G.D. Diniz, D. Brandão, A.C.D. Medeiros, G. Véras, Anal. Bioanal. Chem. 406 (2014) 5989.
[16]        P.H.G.D. Diniz, H.V. Dantas, K.D.T. Melo, M.F. Barbosa, D.P. Harding, E.C.L. Nascimento, M.F. Pistonesi, B.S.F. Band, M.C.U. Araújo, Anal. Methods 4 (2012) 2648.
[17]        K.D.T.M. Milanez, M.J.C. Pontes, Microchem. J. 113 (2014) 10.
[18]        G.B. Costa, D.D.S. Fernandes, V.E. Almeida, T.S.P. Araújo, J.P. Melo, P.H.G.D. Diniz, G. Véras, Talanta 139 (2015) 50.
[19]        G.D. Pierini, D.D.S. Fernandes, P.H.G.D. Diniz, M.C.U. De Araújo, M.S. Di Nezio, M.E. Centurión, Microchem. J. 128 (2016) 62.
[20]        S. Ahmadi, A. Mani-Varnosfaderani, B. Habibi, J. AOAC Int. 101 (2018) xx.
[21]        K. Plataniotis, A.N. Venetsanopoulos, Color Image Processing and Applications. Springer Science and Business Media, 2013.
[22]        N.R. Draper, H. Smith, Applied Regression Analysis, John Wiley and Sons, 2014
[23]        A.J. Smola, B. Scholkopf, Stat. Comput. 14 (2004) 199.
[24]        Y. Anzai, Pattern Recognition and Machine Learning, Elsevier, 2012.
[25]        A.J.C. Andersen, Refining of Oils and Fats for Edible Purposes, Elsevier, 2016.
[26]        A. Choodum, P. Kanatharana, W. Wongniramaikul, N.N. Daeid, Talanta 115 (2013) 143.
[27]        C. Duchesne, J. Liu, J.F. Mac Gregor, Chemometr. Intell. Lab. 117 (2012) 116.
[28]        S.W. Lyra, L.F. Almeida, F.A.S. Cunha, P.H.G.D. Diniz, V.L. Martins, M.C.U. Araújo, Anal. Methods 6 (2014) 1044.
[29]        C.L.M. Morais, K.M.G. Lima, Talanta 126 (2014) 145.
[30]        P.M. Santos, E.R. Pereira-Filho, Anal. Methods 5 (2013) 3669.
[31]    R.V. Deursen, L.C. Blum, J. Reymond, J. Chem. Inf. Model 50 (2010) 1924.
[32]    D. Hiristozov, T.I. Oprea, J. Gasteiger, J. Chem. Inf. Model 47 (2007) 2044.
[33]    M. Jalali-Heravi, A. Mani-Varnosfaderani, Mol. Inform. 31 (2012) 63.
[34]    L.P.J. Veelenturf, Analysis and Applications of Artificial Neural Networks. Prentice-Hall, 1995.
[35]    Vladimir N. Vapnik, The Nature of Statistical Learning Theory, Springer-VerlagBerlin, Heidelberg ©1995.
[36]    B. Scholkopf, A.J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.
[37]    J. Li, IEEE Trans. Instrum. Meas. 59 (2010) 2094.
[38]    R.M. Balabin, E.I. Lomakina, Analyst 136 (2011) 1703.
[39]    F.R. Burden, D.A. Winkler, J. Med. Chem. 42 (199) 3183.
[40]    M. Jalali-Heravi, A. Mani-Varnosfaderani, Mol. Inform. 28 (2009) 946.
[41]    M. Jalali-Heravi, M. Asadollahi-Baboli, P. Shahbazikhah, Eur. J. Med. Chem. 43 (2008) 548.
[42]    D.J. MacKay, Neural Comput. 4 (1992) 415.