Co-Authors:
Ignat, T., Institute of Agricultural Engineering, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan, Israel
Lurie, S., Department of Postharvest Science, Volcani Center, Agricultural Research Organization, Bet Dagan, Israel
Nyasordzi, J., Department of Postharvest Science, Volcani Center, Agricultural Research Organization, Bet Dagan, Israel
Ostrovsky, V., Institute of Agricultural Engineering, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan, Israel
Egozi, H., Institute of Agricultural Engineering, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan, Israel
Hoffman, A., Institute of Agricultural Engineering, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan, Israel
Friedman, H., Department of Postharvest Science, Volcani Center, Agricultural Research Organization, Bet Dagan, Israel
Weksler, A., Department of Postharvest Science, Volcani Center, Agricultural Research Organization, Bet Dagan, Israel
Schmilovitch, Z., Institute of Agricultural Engineering, Volcani Center, Agricultural Research Organization, P.O. Box 6, Bet Dagan, Israel
Abstract:
Harvest date is generally established by monitoring small batches of fruit prior to harvest for changes in maturity parameters such as firmness, starch index, and total soluble solid content using destructive methods. Substituting this method with nondestructive visible-near infrared (VIS-NIR) spectrophotometers could save manpower hours and improve accuracy for harvesting for prolonged storage. Six hundred apples (Malusdomestica Borkh.) from three different orchards for each of three cultivars, “Granny Smith,” “Pink Lady,” and “Starking” were used to build calibration and validation models in different spectral regions. Two instruments, VIS-NIR (340–1,014 nm) and short-wavelength near-infrared (SWIR) (850–1,888 nm) spectrophotometers, measured the apples at harvest both in a static position and on a moving cell conveyer, and these measurements were used to predict total soluble solid (TSS) content, titratable acidity (TA), and firmness at harvest and after 2, 4, and 6 months of 0 °C storage. Starch was also predicted at harvest. The best R2 values were for TSS and starch (0.86 to 0.91) while TA and firmness predictions were less precise (0.53 to 0.78). The findings of the study indicate that the method offers potential for nondestructive prediction of ripeness and quality parameters of different cultivars of apples originating from different orchards. Moreover, the method enables forecasting of apple internal composition changes during storage based on the spectral signature at the time of harvest. Application of the results of this study could serve as a basis for the development of an automatic system for forecasting of apple internal composition change and of a sorting system. © 2014, Springer Science+Business Media New York.
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