Discerning expiration status of edible vegetable oils based on color changes during oxidation process: Using digital image and linear discriminant analysis in both primary and secondary oxidations

مجله پژوهشهای علوم و صنایع غذایی ایران

Article 6, Volume 15, Issue 6 - Serial Number 60, January and February 2020, Pages 145-158   

Document Type: Research Article-en

DOI: 10.22067/ifstrj.v1396i0.70907

Authors

Olga Azimi¹; Mohebbat Mohebbi²; Reza Farhoosh^{* 2}; Mahdi Saadatmand-Tarzjan³

¹Department of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad, International Campus, Mashhad, Iran.

²Department of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

³Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Mashhad, Iran

Abstract

Discerning the expiration status (non-rejected and rejected) of edible vegetable oils is very significant because of the hazardous primary and secondary oxidation products. Therefore, it is of outmost importance to monitor the quality and safety of these oils. Based on previous literature, reports and experimental observation, the oil color changes during oxidation. Thus, the present study investigates the use of image processing and linear discriminant analysis (LDA) for the classification of non-rejected and rejected edible vegetable oils during oxidation process at 85°C, with respect to the induced period in both primary and secondary oxidation of four oil type (Olive, Sunflower, Palm and Soybean). The purpose of this study was to find less costly and quicker methods with environmental protection, by using the color spaces (RGB, HSI, L*a*b* with Grayscale) instead of chemical analyses to determine the expiration status of edible vegetable oils. Results of this study indicated that the best classification for expiration status of known oils according to induced period of peroxide value in  each color space, was achieved with LDA model were for palm with 100% (HSI and Grayscale), olive with 84.61% (L*a*b* and RGB), soybean with 95% (Grayscale) and sunflower with 100% (RGB and HSI), also in induced period of carbonyl value test, the best classification performance was achieved in palm with 100% (L*a*b*), olive with 100% (L*a*b*), soybean with 89.47% and sunflower with 95% (HSI).

Keywords

Edible Vegetable Oil; oxidation; Peroxide value; Carbonyl value; Linear Discriminant Analysis; imaging

References

B. Zhang, W. Huang,J. Li, C. Zhao, S. Fan, J. Wu, C. Liu, Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res Int. 62, 326-343 (2014)

S. Gomez-Alonso, V. Mancebo-Campos, M. Desamparados Salvador, G. Fregapane, Oxidation kinetics in olive oil triacylglycerols under accelerated shelf-life testing (25–75 °C). EUR J LIPID SCI TECH. 106, 369–375 (2004)

M. Yin, S. Tang, M. Tong, Identification of edible oils using terahertz spectroscopy combined with genetic algorithm and partial least squares discriminant analysis. Anal. Methods. 8, 2794-2798 (2016)

A.H.El-Hamdy, N.K. El-Fizga,Detection of olive oil adulteration by measuring its authenticity factor using reversed-phase high-performance liquid chromatography. J. Chromatogr. A. 708, 351–355 (1995)

E. Guzm´an, V. Baeten, J.A.F. Pierna, Garcia-Mesa J.A. Evaluation of the overall quality of olive oil using fluorescence spectroscopy. Food Chem. 173, 927–934 (2015)

N. Vlachos, Y. Skopelitis, M. Psaroudaki, V. Konstantinidou, A. Chatzilazarou, E. Tegou, Applications of Fourier transform-infrared spectroscopy to edible oils. Anal. Chim. Acta. 2573–574, 459–465 (2006)

E. Chiavaro, E. Vittadini, M.T. Rodriguez-Estrada, Cerretani L, Bendini A. Differential scanning calorimeter application to the detection of refined hazelnut oil in extra virgin olive oil. Food Chem. 110, 248–256 (2008)

8.D.S. Lee,B.S. Noh, S.Y. Bae, K. Kim, Characterization of fatty acids composition in vegetable oils by gas chromatography and chemometrics. Anal. Chim. Acta. 358, 163–175 (1998)

R. Farhoosh, S. Pazhouhanmehr, Relative contribution of compositional parameters to the primary and secondary oxidation of canola oil. Food Chem. 114 (3), 1002-1006 (2009)

B. Reindl, H.J. Stan, Determination of volatile aldehydes in meat as 2, 4-dinitrophenylhydrazones using reversed-phase high-performance liquid chromatography. J. Agric. Food Chem. 30, 849–854 (1982)

F. Farhoosh, J. Tavakoli, M.M.H. Khodaparast, Chemical Composition and Oxidative Stability of Kernel Oils from Two Current Subspecies of Pistacia atlantica in Iran. J Am Oil Chem Soc. 85(8), 723–729 (2008)

R. Farhoosh, M.M.H.Khodaparast, A. Sharif, S.A.Rafiee, Olive oil oxidation: Rejection points in terms of polar, conjugated diene, and carbonyl values. Food Chem. 131 (4), 1385 – 1390 (2012)

K.D.T.M. Milanez, M.J.C. Pontes, Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis. Anal. Methods.7, 8839-8846 (2015)

G. Dalen, Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Res. Int. 37, 51–58 (2004)

F. Kong, J. Tan, DietCam: Automatic dietary assessment with mobile camera phones. Pervasive Mob Comput. 8, 147–163 (2012)

V. Briones, J.M. Aguilera, Image analysis of changes in surface colour of chocolate. Food Res. Int. 38, 87–94 (2015)

C.J. Du, D.W. Sun, Pizza sauce spread classification using colour vision and support vector machines. J Food Eng. 66,137–145 (2004)

A. Antonelli, M. Cocchi, P. Fava, G. Foca, G.C. Franchini, D. Manzini, A. Ulrici,Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Anal. Chim. Acta. 515, 3–13 (2004)

M. Mohebbi, M.R Akbarzadeh-T, F. Shahidi, M. Moussavi, H.B. Ghoddusi,Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp. Comput Electron Agric. 69 (2), 128-134 (2009)

H.K. Mebatsion, J. Paliwal, D.S. Jayas, Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Comput Electron Agric. 90, 99–105 (2013)

M. Fathi, M. Mohebbi, S.M.A. Razavi, Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit. FOOD BIOPROCESS TECH. 4, 1357–1366 (2011)

W.T.S, Vilar, R.M. Aranha, E.P. Medeiros, M.J.C. Pontes, Classification of Individual Castor Seeds Using Digital Imaging and Multivariate Analysis. J. Braz. Chem. Soc. 26, 102–109 (2015)

J.K. Fernandes, T. Umebara, M.K. Lenzia, E.T.S. Alves, Image analysis for composition monitoring. Commercial blends of olive and soybean oil. ACTA SCI-TECHNOL.35, 317–324 (2013)

P.C. Marchal, D.M. Gila, J.G. Garcia, J.G. Ortega, Expert system based on computer vision to estimate the content of impurities in olive oil samples. J Food Eng.119(2), 220-228 (2013)

K.D.T.M. Milanez, M.J.C. Pontes. Classification of edible vegetable oil using digital image and pattern recognition techniques. Microchem J. 113, 10–16 (2014)

F. Mendoza, J.M. Aguilera, Application of image analysis for classification of ripening bananas. J. Food Sci. 69, 471–477 (2004)

K. Leon, D. Mery, F. Pedreschi, J. Leon, Color measurement in L*a*b* units from RGB digital images. Food Res Int. 39(10), 1084-1091 (2006)

S.E. Papadakis, S. Abdul-Malek, R.E. Kamdem, K.L. Yam, A versatile and inexpensive technique for measuring color of foods. Food Technol. 54(12), 48–51 (2000)

S. Segnini, P. Dejmek, R. O¨ste, A low cost video technique for colour measurement of potato chips. LWT - Food Sci and Technol. 32(4), 216–222 (1999)

N.C. Shantha, E.A. Decker, Rapid, sensitive, iron-based spectrophotometric methods for determination of peroxide values of food lipids. J. AOAC Int.77, 421–424 (1994)

Y. Endo,C.M. Li, M. Tagiri-Endo,K. Fugimoto, A modified method for the estimation of total carbonyl compounds in heated and frying oils using 2- propanol as a solvent. J Am Oil Chem Soc. 10, 1021–1024 (2001)

R.A. Fisher, The use of multiple measurements in taxonomic problem. Ann. Eugen. 7, 179–188 (1936)