Abstract
This work was undertaken to analyze the ripening process of avocados variety Hass (Persea americana Mill.) by image processing (IP) methodology. A set of avocados (10 samples) was used to follow the changes in image features during ripening by applying a computer vision system, extracting color and textural parameters. Other 16 avocados were used to evaluate the firmness and mass loss. Three maturity stages of avocados were established, and a classification was obtained by applying principal component analysis and k-nearest neighbor algorithm. During the ripening process (12 days), avocado firmness decreased from 75.43 to 2.63 N, while skin color values kept invariable during 6 days; after that, a decrement in the peel green color (a*) was observed (−9.68 to 2.32). Image features showed that during ripening the color parameters (L*, a*, and b*), entropy (4.29 to 4.00), angular second moment (0.287 to 0.360), and fractal dimension (2.58 to 2.44) had a similar path as compared to mass loss, a*, and firmness ripening parameters, respectively. Relationships between image features and ripening parameters were obtained. The parameter a* was the most useful digital feature to establish an acceptable percentage of avocado classification (>80%) in three different maturity stages found. Results obtained by means of IP could be useful to evaluate, at laboratory level, the ripening process of the avocados.
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References
Butz, P., Hofmann, C., & Tauscher, B. (2005). Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis. Journal of Food Science, 70(9), R131–R141.
Cox, K. A., McGhie, T. K., White, A., & Wolf, A. B. (2004). Skin colour and pigment changes during ripening of Hass avocado fruit. Postharvest Biology and Technology, 31(1), 287–294.
Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.
Du, C., & Sun, D. W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science and Technology, 15(1), 230–249.
Farrera-Rebollo, R. R., Salgado-Cruz, M. P., Chanona-Pérez, J., Gutiérrez-López, G. F., Alamilla-Beltrán, L., & Calderón-Domínguez, G. (2011). Evaluation of image analysis tools for characterization of sweet bread crumb structure. Food and Bioprocess Technology. doi:10.1007/s11947-011-0513-y.
Goulao, L., & Oliveira, C. (2008). Cell wall modifications during fruit ripening: when a fruit is not the fruit. Trends in Food Science and Technology, 19(1), 4–25.
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K. (2003) kNN model-based approach in classification. In: Proceedings of On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. pp 986–996.
Haralick, R., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics SMC, 3(6), 610–621.
Hertog, M. L. A. T. M., Nicholson, S. E., & Whitmore, K. (2003). The effect of modified atmospheres on the rate of quality change in ‘Hass’ avocado. Postharvest Biology and Technology, 29, 41–53.
Jollife, I. T. (2002). Principal component analysis. Series: Springer Series in Statistics (2nd ed.). New York: Springer.
León, K., Mery, D., Pedreschi, F., & León, J. (2006). Color measurement in L*a*b* units from RGB digital images. Food Research International, 39(1), 1084–1091.
Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285–295.
Mendoza, F., Dejmek, P., & Aguilera, J. (2007). Colour and image texture analysis in classification of commercial potato chips. Food Research International, 40(1), 1146–1154.
Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., et al. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology, 46(2), 99–118.
Ochoa, S., Hertog, M., & Nicolaï, B. (2009). Modelling the transient effect of 1-MCP on Hass avocado softening: a Mexican comparative study. Postharvest Biology and Technology, 51(1), 62–72.
Quevedo, R., Mendoza, F., Aguilera, J., Chanona, J., & Gutierrez, G. (2008). Determination of senescent spotting in banana (Musa cavendish) using fractal texture Fourier image. Journal of Food Engineering, 84(1), 509–515.
Toivonen, P., & Brummell, D. (2008). Biochemical bases of appearance and texture changes in fresh-cut fruit and vegetables. Postharvest Biology and Technology, 48, 1–14.
Yu, X., Liu, K., Wu, D., & He, Y. (2011). Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food and Bioprocess Technology. doi:10.1007/s11947-011-0531-9.
Acknowledgments
Israel Arzate-Vázquez wishes to thanks CONACyT and PIFI-IPN-México for the scholarship provided. We want to thank the financial support of the Mexican Government (COFAA, EDI, and SIP of IPN, and CONACyT) to carry out this project.
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Arzate-Vázquez, I., Chanona-Pérez, J.J., Perea-Flores, M. et al. Image Processing Applied to Classification of Avocado Variety Hass (Persea americana Mill.) During the Ripening Process. Food Bioprocess Technol 4, 1307–1313 (2011). https://doi.org/10.1007/s11947-011-0595-6
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DOI: https://doi.org/10.1007/s11947-011-0595-6