عنوان مقاله [English]
In this research, the ability of a machine vision system was evaluated to monitor the oxidation period of olive oil. For this purpose, the olive oil samples studied during accelerated storage for 24 days in an oven and then image processing processes was carried out to extract parameters related to color spaces RGB, HSV and L*a*b*. The performance of artificial neural network and decision tree techniques compared to determine the olive oil oxidation rate. In each of the models color parameters were used as inputs and different stages in the olive oil oxidation period were considered as output. According to the results, the highest classification accuracy (94.44%) and the lowest RMSE (0.0696) are related to the decision tree technique. The proposed machine vision system combined with artificial intelligence techniques as non-destructive and efficient tool will offer for monitoring and quality control during oil storage.
 Esterbauer, H., Schaur, R.J., Zollner, H. (1991). Chemistry and biochemistry of 4-hydroxynonenal, malonaldehyde and related aldehydes. Free Radic. Biol. Med., 11(1), 81-128.
 Nogala-Kalucka, M., Korczak, J., Dratwia, M., Lampart-Szczapa, E., Siger, A., Buchowski, M. (2005). Changes in antioxidant activity and free radical scavenging potential of rosemary extract and tocopherols in isolated rapeseed oil triacylglycerols during accelerated tests. Food Chem., 93(2), 227-235.
 Casal, S., Malheiro, R., Sendas, A., Oliveira, B.P., Pereira, J.A. (2010). Olive oil stability under deep-frying conditions. Food Chem. Toxicol., 48(10), 2972-2979.
 Servili, M., Selvaggini, R., Esposto, S., Taticchi, A., Montedoro, G., Morozzi, G. (2004). Health and sensory properties of virgin olive oil hydrophilic phenols: agronomic and technological aspects of production that affect their occurrence in the oil. J. Chromatogr. A, 1054(1), 113-127.
 Fernandez, L., Castillero, C., Aguilera, J. (2005). An application of image analysis to dehydration of apple discs. J. Food Eng., 67(1), 185-193.
 Valadez-Blanco, R., Virdi, A., Balke, S., Diosady, L. (2007). In-line colour monitoring during food extrusion: Sensitivity and correlation with product colour. Food Res. Int., 40(9), 1129-1139.
 Pedreschi, F., Leon, J., Mery, D., Moyano, P. (2006). Development of a computer vision system to measure the color of potato chips. Food Res. Int., 39(10), 1092-1098.
 Iqbal, A., Valous, N.A., Mendoza, F., Sun, D.-W., Allen, P. (2010). Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Sci., 84(3), 455-465.
 Boskou, D., Blekas, G., Tsimidou, M. (2006). Olive oil composition, in: Boskou, D. (Ed.), Olive oil: Chemistry and technology, 2nd ed, AOCS press, Champaign, Illinois, pp 41-72.
 Bendini, A., Cerretani, L., Di Virgilio, F., Belloni, P., Lercker, G., Toschi, T.G. (2007). In‐process monitoring in industrial olive mill by means of FT‐NIR. Eur. J. Lipid Sci. Technol., 109(5), 498-504.
 Mahesar, S.A., Bendini, A., Cerretani, L., Bonoli‐Carbognin, M., Sherazi, S.T.H. (2010). Application of a spectroscopic method to estimate the olive oil oxidative status. Eur. J. Lipid Sci. Technol., 112(12), 1356-1362.
 Cayuela Sánchez, J.A., Moreda, W., García, J.M. (2013). Rapid determination of olive oil oxidative stability and its major quality parameters using Vis/NIR transmittance spectroscopy. J. Agric. Food Chem., 61(34), 8056-8062.
 Poulli, K.I., Mousdis, G.A., Georgiou, C.A. (2009). Monitoring olive oil oxidation under thermal and UV stress through synchronous fluorescence spectroscopy and classical assays. Food Chem., 117(3), 499-503.
 Du, C.-J., Sun, D.-W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol., 15(5), 230-249.
 de Melo Milanez, K.D.T., Pontes, M.J.C. (2015). Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis. Anal. Methods, 7(20), 8839-8846.
 Kadiroğlu, P., Korel, F. (2015). Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils. J. Am. Oil Chem. Soc., 92(9), 1235-1242.
 Ram, T., Wiesman, Z., Parmet, I., Edan, Y. (2010). Olive oil content prediction models based on image processing. Biosystems Eng., 105(2), 221-232.
 Moyano, M., Meléndez-Martínez, A.J., Alba, J., Heredia, F.J. (2008). A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (II): CIELUV and CIELAB uniform colour spaces. Food Res. Int., 41(5), 513-521.
 Moyano, M., Meléndez-Martínez, A.J., Alba, J., Heredia, F.J. (2008). A comprehensive study on the colour of virgin olive oils and its relationship with their chlorophylls and carotenoids indexes (I): CIEXYZ non-uniform colour space. Food Res. Int., 41(5), 505-512.
 Marchal, P.C., Gila, D.M., García, J.G., Ortega, J.G. (2013). Expert system based on computer vision to estimate the content of impurities in olive oil samples. J. Food Eng., 119(2), 220-228.
 Taghadomi‐Saberi, S., Omid, M., Emam‐Djomeh, Z., Ahmadi, H. (2014). Evaluating the potential of artificial neural network and neuro‐fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing. J. Sci. Food Agric., 94(1), 95-101.
 Shantha, N.C., Decker, E.A. (1994). Rapid, sensitive, iron-based spectrophotometric methods for determination of peroxide values of food lipids. J. AOAC Int., 77(2), 421-424.
 Sanaeifar, A., Mohtasebi, S.S., Ghasemi-Varnamkhasti, M., Siadat, M. (2014). Application of an electronic nose system coupled with artificial neural network for classification of banana samples during shelf-life process. In: Proceeding of the 2nd Int. Conf. on Control Decision and Information Technologies (CoDIT) (pp. 753-757), IEEE, Metz, France.
 Hykin, S. (1999). Neural Networks: A Comprehensive Foundation, 2nd ed., Printice-Hall, Inc., New Jersey, pp 120-134.
 L Gupta, D., K Malviya, A., Singh, S. (2012). Performance Analysis of Classification Tree Learning Algorithms. Int. J. Comput. Appl., 55(6), 39-44.
 Witten, I.H., Frank, E., Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, Burlington, pp 187-194.
 Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceeding of the 14th Int. Joint Conf. on Artificial Intelligence (IJCAI) (pp. 1137-1143), Morgan Kaufmann, San Francisco.
 Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation, in L. Liu, M.T. ÖZsu (Eds.), Encyclopedia of Database Systems, Springer US, Boston, pp 532-538.
 Keramat, M., Golmakani, M.T., Aminlari, M., Shekarforoush, S.S. (2016). Comparative Effect of Bunium persicum and Rosmarinus officinalis Essential Oils and Their Synergy with Citric Acid on the Oxidation of Virgin Olive Oil. Int. J. Food Prop., 19(12), 2666-2681.
 Minguez-Mosquera, M.I., Rejano-Navarro, L., Gandul-Rojas, B., SanchezGomez, A.H., Garrido-Fernandez, J. (1991). Color-pigment correlation in virgin olive oil. J. Am. Oil Chem. Soc., 68(5), 332-336.
 Frankel, E.N. (2012). Lipid Oxidation, 2nd ed., Woodhead Publishing Ltd., Cambridge, UK, pp 485.
 O'Brien, R.D. (2008). Fats and Oils: Formulating and Processing for Applications, 3rd ed, Taylor and Francis Group, CRC Press, Boca Raton, pp 540-551.
 Kohavi, R. (1996). Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid, In: Proceeding of the 2nd Int.Conf. on Knowledge Discovery and Data Mining (pp. 202-207), AAAI Press, Cambridge.
 Grigoriadou, D., Tsimidou, M.Z. (2006). Quality control and storage studies of virgin olive oil: Exploiting UV spectrophotometry potential. Eur. J. Lipid Sci. Technol., 108(1), 61-69.
 López-Feria, S., Cárdenas, S., García-Mesa, J.A., Valcárcel, M. (2008). Simple and rapid instrumental characterization of sensory attributes of virgin olive oil based on the direct coupling headspace-mass spectrometry. J. Chromatogr. A, 1188(2), 308-313.
 Soltani, M., Omid, M. (2015). Detection of poultry egg freshness by dielectric spectroscopy and machine learning techniques. LWT- Food Sci. Technol., 62(2), 1034-1042.