Machine Learning-Driven Optimization for Digital Transformation in Non-thermal Food Processing

Abstract

Abstract Non-thermal food processing has opened up new space and has emerged as a promising alternative to conventional thermal methods of food processing. These foods meet the growing consumer demands for high-quality, convenient, and minimally processed foods. The idea of proposing a machine learning (ML) strategy for finding the optimum process parameters and kinetics in food processing applications is new and challenging, but this new innovative approach requires considerable scientific effort. This review presents the applications of ML in the optimization of non-thermal food processing technologies such as high-pressure processing (HPP), pulsed light (PL), ultrasound (US), pulsed electric fields (PEF), cold plasma (CP), and irradiation (IR). These technologies have exhibited conspicu