Determination of Cherry Color Parameters during Ripening by Artificial Neural Network Assisted Image Processing Technique

Authors
S. Taghadomi-Saberi 1
M. Omid 1
Z. Emam-Djomeh 2
K. Faraji-Mahyari 3
1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
2 Department of Food Sciences and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
3 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Abstract
Among the different classes of physical properties of foods, color is considered the most important visual attribute in quality perception. Consumers tend to associate color with quality due to its good correlation with physical, chemical and sensorial evaluations of food quality. This study used an inexpensive method to predict sweet cherries color parameters by combining image processing and artificial neural network (ANN) techniques. The color measuring technique consisted of a CCD camera for image acquisition, MATLAB software for image analysis, and ANN for modeling. To demonstrate the usefulness of this technique, changes of cherry color during ripening were studied. After designing, training, and generalizing several ANNs using Levenberg-Marquardt algorithm, a network with 7-14-11-3 architecture showed the best correlation (R2= 0.9999) for L*, a* and b* values from Chroma meter and the machine vision system. L* and b* parameters decreased during ripening of cherries and a* parameter increased at first and then decreased. Evaluation of L*, a* and b* values showed the possibility of reliable use of this system for determination of absolute color values of foodstuffs with a much lower cost in comparison with Chroma meter.

Keywords

1. Abbasgholipour, M., Omid, M., Keyhani, A. R. and Mohtasebi, S. S. 2011. Color Image Segmentation with Genetic Algorithm in a Raisin Sorting System Based on Machine Vision in Variable Conditions. Expert Syst. Appl., 38: 3671–3678.
2. Afshari-Jouybari, H. and Farahnaky, A. 2011.Evaluation Photoshop Software Potential for Food Colorimetry. J. Food Eng., 106: 170–175.
3. Anonymous. 2010. Countries by Commodity: 2010. Food and Agriculture Organization of the United Nations. FAOSTAT, 2 October 2012, Available at: www.faostat.fao.org.
4. Behroozi Khazaei, N., Tavakoli Hashjin, T., Ghassemian, H., Khoshtaghaza, M. H. and Banakar, A. 2013. Application of Machine Vision in Modeling of Grape Drying Process. J. Agr. Sci. Tech., 15: 1095-1106.
5. Blasco, J., Aleixos, N. and Molto, E. 2003. Machine Vision System for Automatic Quality Grading Fruit. Biosyst. Eng., 85: 415–423.
6. Brosnan, T. and Sun, D. W. 2003. Improving Quality Inspection Food Products by Computer Vision: A Review. J. Food Eng., 61: 3–16.
7. Du, C. J. and Sun, D. W. 2006. Learning Techniques Used in Computer Vision for Food Quality Evaluation: A Review. J. Food Eng., 72: 39–55.
8. Fadilah, N., Mohamad-Saleh, J., Abdul Halim, Z., Ibrahim, H. and Syed Ali, S.S. 2012. Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch. Sensors. 12: 14179-14195.
9. Fils-Lycaon, B. and Buret, M. 1990. Loss Firmness and Changes in Pectic Fractions during Ripening and Over-ripening of Sweet Cherry. J. Am. Soc. Hortic. Sci., 25: 777-778.
10. Fu, L., Okamoto, H., Kataoka, T. and Shibata, Y. 2011.Colorbased Classification for Berries Japanese Blue Honeysuckle. Int. J. Food Eng., 7: 1-12.
11. Huber, D. J. 1983. The Role Cell Wall Hydrolyses in Fruit Softening. Hortic. Rev., 5: 169–219.
12. Iqbal, A., Valous, N. A., Mendoza, F., Sun, D. W. and Allen, P. 2010. Classification Presliced Pork and Turkey Ham Qualities Based on Image Color and Textural Features and Their Relationships with Consumer Responses. Meat Sci., 84: 455–465.
13. Kılıç, K., Onal-Ulusoy, B., Yıldırım, M. and Boyacı, I. H. 2007. Scanner-based Color Measurement in Lab Format with Artificial Neural Networks (ANN). Eur. Food Res. Technol., 226: 121–126.
14. MATLAB R2009a, Matlab user’s guide. The MathWorks Inc., Massachusetts, USA: Natick, 2009.
15. Mollazade, K., Omid, M. and Arefi, A., 2012. Comparing Data Mining Classifiers for Grading Raisins Based on Visual Features. Comput. Electron. Agr., 84: 124–131.
16. Momenzadeh, L., Zomorodian, A. and Mowla, D. 2012.Applying Artificial Neural Network for Drying Time Prediction of Green Pea in a Microwave Assisted Fluidized Bed Dryer. J. Agr. Sci. Tech., 14: 513-522.
17. Omid, M., Khojastehnazhand M. and Tabatabaeefar, A. 2010a. Estimating Volume and Mass Citrus Fruits by Image Processing Technique. J. Food Eng., 100: 315–321.
18. Omid, M., Mahmoudi A. and Omid, M. H. 2010b. Development Pistachio Sorting System Using Principal Component Analysis (PCA) Assisted Artificial Neural Network (ANN) Impact Acoustics. Expert Syst. Appl., 37: 7205–7212.
19. Otsu, N. 1979. A Threshold Selection Method from Gray-level Histograms. IEEE Transactions Systems, Man, Cybernetics, 9: 62–66.
20. Pedreschi, F., Leόn, J., Mery, D. and Moyano, P. 2006. Development of a Computer Vision System to Measure the Color Potato Chips. Food Res. Int., 39: 1092–1098.
21. Polder, G., van der Heijden, G.W. A. M. and Young, I. T. 2003. Tomato Sorting Using Independent Component Analysis on Spectral Images. Real-Time Imaging, 9: 253–259.
22. Taghadomi-Saberi, S., Omid, M., Emam-Djomeh, Z. and 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: 95–101.
23. Serrano, M., Guillean, F., Martı´nez-Romero, D., Castillo, S. and Valero, D. 2005. Chemical Constituents and Antioxidant Activity of Sweet Cherry at Different Ripening Stages. J. Agric. Food Chem., 53: 2741-2745.
24. Serrano, M., Guillean, F., Martı´nez-Romero, D., Castillo, S. and Valero, D. 2005. Chemical Constituents and Antioxidant Activity of Sweet Cherry at Different Ripening Stages. J. Agr. Food Chem., 53: 2741-2745.
25. Taghadomi-Saberi, S., Omid, M., Emam-Djomeh, Z. and Ahmadi, H. 2013. Estimation of Sweet Cherry Antioxidant Activity and Anthocyanin Content during Ripening by Artificial Neural Network–assisted Image Processing Technique. Int. J. Food Sci. Technol., 48: 735–741.
26. Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F. and Debeir, O. 2011. Automatic Grading Bi-colored Spples by Multispectral Machine Vision. Comput. Electron. Agr., 75: 204–212.
27. Valadez-Blanco, R., Virdi, I. S., Balke, S. T. and Diosady, L. L. 2007. In-line Color Monitoring during Food Extrusion: Sensitivity and Correlation with Product Color. Food Res. Int., 40: 1129–1139.
28. Venora, G., Grillo, O. and Saccone, R. 2009. Quality Assessment of Durum Wheat Storage Centres in Sicily: Evaluation Vitreous, Starchy and Shrunken Kernels Using an Image Analysis System. J. Cereal Sci., 49: 429–440.
29. Wu, J. and Chan, J. 2009. Faulted Gear Identification of a Rotating Machinery Based on Wavelet Transform and Artificial Neural Network. Expert Syst. Appl., 36: 8862-8875.
30. Yam, K. L., and Papadakis, S. E. 2004. A Simple Digital Imaging Method for Measuring and Analyzing Color Food Surfaces. J. Food Eng., 61: 137–142.
31. Yoshida, K., Ito, D., Shinkai, Y. and Kondo, T., 2008. Change Color and Components in Sepalschameleon Hydrangea during Maturation and Senescence. Phytochem., 69: 3159–3165.
32. Zhou, T., Harrison, A. D., McKellar, R., Young, J. C., Odumeru J., Piyasena, P., Lu, X., Mercer, D. G. and Karr, S. 2004. Determination Acceptability and Shelf Life Ready-to-use Lettuce by Digital Image Analysis. Food Res. Int., 37: 875–881.
Journal of Agricultural Science and Technology
Volume 17, Issue 3
May and June 2015
Pages 589-600