Recent Advances in the Modeling and Predicting Quality Parameters of Fruits and Vegetables during Postharvest Storage: A Review

Abstract

Artificial neural network (ANN), genetic algorithm (GA), fuzzy logic (FL), and adaptive neurofuzzy inference system (ANFIS) have been applied in every aspect of food science in the recent years. These models are useful tools for fruit and vegetable monitoring; grading and classification; modeling the respiration rate; predicting and modeling quality properties; modeling of microbial growth; and forecasting chemical, physical, and sensorial characteristics during processing and postharvest storage. These models hold an enormous deal of promise for modeling difficult task;s in practice control and simulation and in the use of machine perception including machine vision system and electronic nose for fruit and vegetable quality control. In addition, these models were used for different fruit and vegetable storage process modeling, for detecting chilling injury, to detect defects, for controlling various drying process, and for improving climate control. The present study reviews the efficiency and applications of ANN, GA, FL, and ANFIS models to predict and control the quality parameters of various fruits and vegetables during postharvest storage.