Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights

TL;DR

This systematic review explores computational models, experimental insights, and physics-informed machine learning approaches to understand microscale cellular changes during plant-based food drying, highlighting current challenges and future research directions.

Contribution

It provides a comprehensive analysis of existing experimental and computational models, emphasizing the emerging role of physics-informed machine learning in microscale food drying research.

Findings

Data-driven models face limitations in generalization and dataset acquisition.

Physics-informed machine learning offers promising advantages over traditional models.

Current methodologies have gaps in capturing cellular-level phenomena accurately.

Abstract

This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematically analyse experimental studies in PBFM drying, highlighting their contributions and limitations in capturing cellular-level phenomena, including challenges in data acquisition and measurement accuracy under varying drying conditions. The evolution of computational models for microstructural investigations is thoroughly examined, from traditional numerical methods to contemporary state-of-the-art approaches, with specific focus on their ability to handle the complex, nonlinear properties of plant cellular materials. Special attention is given to the emergence of data-driven models and their limitations in predicting microscale cellular behaviour during PBFM drying, particularly addressing challenges in dataset acquisition and model generalization. The review provides an in-depth analysis of Physics-Informed Machine Learning (PIML) frameworks, examining their theoretical foundations, current applications in related fields, and unique advantages in combining physical principles with neural network architectures. Through this comprehensive assessment, we identify critical gaps in existing methodologies, evaluate the trade-offs between different modelling approaches, and provide insights into future research directions for improving our understanding of cellular-level transformations during PBFM drying processes. The review concludes with recommendations for integrating experimental and computational approaches to advance the field of food preservation technology.