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

Chanaka P Batuwatta Gamage; Hyogu Jeong; Helambage C.P. Karunasena; Muhammad A Karim; Charith Rathnayaka; Y T Gu

doi:10.48550/arXiv.2501.09034

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

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

Chanaka P Batuwatta Gamage, Hyogu Jeong, Helambage C.P. Karunasena, Muhammad A Karim, Charith Rathnayaka and Y T Gu

arXiv, Vol.14 January 2025

Cornell University

2025

DOI: https://doi.org/10.48550/arXiv.2501.09034

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Abstract

Food drying

Heat Transfer

Mass transfer

Shrinkage

Physics-informed machine Learning

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.

Details

Title: Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights
Authors: Chanaka P Batuwatta Gamage - Queensland University of Technology
Hyogu Jeong - Queensland University of Technology
Helambage C.P. Karunasena - Queensland University of Technology
Muhammad A Karim - Queensland University of Technology
Charith Rathnayaka - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Y T Gu - Queensland University of Technology
Publication details: arXiv, Vol.14 January 2025
Publisher: Cornell University
Date published: 2025
DOI: 10.48550/arXiv.2501.09034
ISSN: 2331-8422
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991103846102621
Output Type: Preprint

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