Excluding sunscald from long-term storage of ‘Granny Smith’ apples

Abstract

Sunscald is a postharvest apple and pear peel disorder primarily caused by excessive light exposure during the growing season. Symptoms are not apparent at-harvest but develop during storage as superficial brown or gray discolored patches on the exposed portion of the apple. A novel protocol using hyperspectral imaging (HSI) was developed to identify apples at- harvest that are more likely to develop the disorder and reduce sunscald in the cold chain by eliminating at-risk apples from long-term storage. PLS-DA and LOGISTIC models and an index summarizing the chlorophyll to carotenoid ratio were tested for this purpose using two populations selected for different sun exposure levels and four populations harvested from whole trees. The accuracy of models varied seasonally. However, detection of sunburn and estimation of sunscald risk based on index values was a more consistent approach. Summarizing peel area according to this index was the basis for categorizing whole apples as rejected (sunburned), high (sunscald) risk, or low-risk. Categorization segregated apples nearly entirely into these categories with only an insignificant frequency of misclassification. The frequency of high-risk apples without sunburn symptoms in the populations harvested from whole trees fell below 20 %, although actual sunscald incidence within this category ranged from 55 % to 81 % among populations harvested from whole trees, highlighting that other factors beyond elevated sun exposure contribute to sunscald incidence. The classification model for low and high-risk categories had an accuracy of 83–93 % in four independent populations, with 99.9 % sensitivity to classify non-sunscalded fruit into the low-risk category. Sorting by sunburn presence and sunscald risk allows for apples that have sunburn symptoms to be excluded and those that are marketable but most likely to develop sunscald to be pushed to market rather than storage. With the appropriate source/sensor array, this approach is readily adaptable to sorting lines or even robotic harvesters. Challenges include adapting this approach for other apple and pear cultivars and extending capabilities for additional uses, including sorting fruit into additional categories representative of relative sunlight exposure.