Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis

Abstract

The quality of postharvest apples is greatly affected by storage temperatures. In this paper, the sensory qualities, such as flavor, texture, color, and taste change of apples during storage at 4 °C and 20 °C were investigated. After correlation analysis, the partial least squares (PLS) and artificial neural network (ANN) techniques were used to build a shelf-life prediction model. The results showed that lower temperature storage can better maintain the color, flesh hardness, and release of volatile compounds of apples. The acidity of apples stored at 20 °C decreased much faster than that at 4 °C. The PLS models were successful in predicting the apple shelf life. When modeling using PLS with a single type index, the order of accuracy of the prediction model was texture, color, and flavor. As a nonlinear algorithm, the ANN model was also an effective predictive tool of apple shelf life at both temperatures.