Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics-Informed Neural Networks

Food insecurity is a major global challenge. Food preservation, particularly through drying, presents a promising solution to enhance food security and minimize waste. Fruits and vegetables contain 80%–90% water, and much of this is removed during drying. However, structural changes across multiple length scales occur during drying, compromising stability and affecting quality. Understanding these changes is essential, and several modeling techniques exist to analyze them, including empirical modeling, physics-based computational methods, purely data-driven machine learning approaches, and physics-informed neural network (PINN) models. Although empirical methods are straightforward to implement, their limited generalizability and lack of physical insights have led to the development of physics-based computational methods. These methods can achieve high spatiotemporal resolution without requiring experimental investigations. However, their complexity and high computational costs have prompted the exploration of data-driven machine learning models for drying processes, which involve comparatively lower computational costs and are more straightforward to execute. Nonetheless, their poor predictive ability with sparse data has restricted their application, leading to a hybrid modeling approach: PINN, which merges physical insights with data-driven machine learning techniques. This method still holds significant potential for advancements in food drying modeling. Therefore, this study aims to conduct a comprehensive literature review of state-of-the-art conventional drying modeling techniques, such as empirical, physics-based computational, and pure data-driven machine learning techniques, and explores the potential of the PINN approach for overcoming the limitations associated with conventional drying modeling strategies.