Recent developments of artificial intelligence in drying of fresh food: A review

Abstract

Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

“…Therefore, artificial neural networks (ANNs) technique as a powerful tool provides a platform where solve these problems with logical precision and low computation times instead of mathematical modeling in drying process (Afkhamipour, Mofarahi, Borhani, & Zanganeh, ). Extensive investigations have been reported by means of ANNs for modeling and predicting the purposes in food science (Abbaspour‐Gilandeh, Jahanbakhshi, & Kaveh, ; Sun, Zhang, & Mujumdar, ).…”

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

“…Multiple linear regression (MLR), stepwise MLR, principal component regression (PCR), and partial least squares (PLS) regression have also been used for quantitative purposes. More sophisticated pattern recognition algorithms such as support vector machine (SVM), and the biologically inspired artificial neural network (ANN) and fuzzy logic are currently on the rise [ 28 , 29 , 30 , 31 ].…”

Intelligent drying systems

“…Most published research deals with "black-box" computing, such as artificial neural network (ANN), fuzzy logic (FL) or evolutionary algorithms (EA). [1] These black-box approaches clearly demonstrate ability to manage hidden uncertainty and non-linearity of process/product parameters, but they do not contribute to the improvement of our knowledge about drying process and optimal control strategies.…”