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.

Effect of Three Nanoparticles (Se, Si and Cu) on the Bioactive Compounds of Bell Pepper Fruits under Saline Stress

“…Fortunately, there are novel methods that can help to understand the functioning of nanoparticles in crops and their impacts in the medium and long term. These include the application of artificial intelligence, optimization algorithms, and artificial neural network models [ 58 , 59 , 60 , 61 , 62 ] that can be excellent tools for the study of nanotechnology in agriculture and in fruit quality.…”

Drying kinetics of basil seed mucilage in an infrared dryer: Application of GA‐ANN and ANFIS for the prediction of drying time and moisture ratio

“…They reported that these approaches are able to estimate the drying kinetics of various fruits and vegetables with high precision. It has been shown that nonlinear approaches based on ANN are far better in generalization and estimation in comparison to empirical models (Bahramparvar et al, 2014;Salehi, 2020b;Zhang et al, 2014). Determination of the best number of neurons in hidden layers of ANN models is generally carry out by trial and error.…”

“…Genetic algorithm is the search technique for optimal value, mimicking the mechanism of biological evolution. It has a high capability to find the optimum value of a complex objective function, without falling into local optima (Ramzi et al, 2015;Salehi, 2020b).…”

“…The main strength of ANFIS models is that they are universal approximators with the ability to solicit interpretable If-Then rules. One approach to the derivation of the fuzzy rule base is to use the self-learning features of ANN, to define the membership functions (MF) based on input-output data (Ramzi et al, 2015;Salehi, 2020b). Fuzzy clustering technique is the strategy used to identify the MF by organizing data samples into clusters so that the data samples within clusters have the most similarity to each other.…”

“…ANFIS model was used to predict the drying kinetics (MR) of different fruits and vegetables (Salehi, 2020b). Lertworasirikul (2008) performed a comparative study on the drying kinetics of semi-finished cassava crackers using empirical, ANN, and ANFIS models, and predicted the dynamic drying parameters.…”