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Drying is one of the most effective methods to preserve foods and guarantee their supply for more people. Mathematical modeling is an important tool that aids the design, development, optimization and control of food drying systems. Driven by the increasing needs to dry more foods with higher efficiency, sustainability, better food quality and safety, various novel drying technologies have been developed, and food drying modeling approaches have also evolved significantly. In this review, progresses and advancements in empirical, mechanistic, and machine learning (ML) modeling approaches in food drying processes toward Multiphysics, multiphase, multidimensional, and intelligent directions are overviewed. Several challenges, needs, and future trends are identified and discussed, which includes multiscale modeling, shape change and structure deformation, coupling of food reaction kinetics, intelligent control and digitalization, translation, and dissemination of modeling results. The materials presented here aim to emphasize the importance of food drying modeling for addressing the key food challenges and attract more food scientists and engineers to contribute to improve food drying models and a more sustainable food production system.
Chen et al. (Thu,) studied this question.