Comparative Modeling of Drying Kinetics for Potato Slices: AI-Based vs. Empirical and Semi-Empirical Approaches

Articles in Press

Document Type : Research Article-en

Authors

• R. Raesi
• M. Moradi
• A. Dehghani

Biosystems Engineering Department, School of Agriculture, Shiraz University, Shiraz, Iran

10.22067/jam.2025.92739.1358

Abstract

Drying is a vital preservation method in the food industry, reducing moisture content while maintaining product quality and extending shelf life. This process involves complex heat and mass transfer mechanisms, necessitating accurate predictive models. This study compares various modeling approaches, including regression models, semi-empirical, and artificial intelligence (AI)-based methods, to simulate the drying process of potato slices. Experimental drying trials were run at 40°C, 50°C, and 60°C, both with and without phase change materials (PCM) and infrared radiation (IR). AI models (ANN, SVM, and RF) were trained and validated using experimental data. Their performance was evaluated against conventional and semi-empirical models using R², RMSE, MAE, and MBE. Results indicate that ANN achieved the highest predictive accuracy (R²= 0.998, RMSE= 0.0656 g water g^-1 dry matter), outperforming other models. SVM also demonstrated strong predictive capability, while RF performed slightly lower. Among semi-empirical models, the Midilli model provided the best fit but was less accurate than AI-based models. These findings highlight the superiority of AI-driven approaches, particularly ANN, in optimizing drying processes for the food industry.

Keywords

• AI Modeling
• Drying rate
• Random forest
• Semi-Empirical
• SVM

Main Subjects

• Post-harvest technologies

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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