Evaluation of mathematical models and artificial neural networks for prediction of sorption isotherm in microwave pre-heated mung bean

Sorption isotherms are essential for understanding the moisture sorption behavior of foods, which is critical for optimizing drying processes, predicting shelf life, and designing effective packaging. This study examined the moisture sorption characteristics of microwave pre-treated mung beans at different temperatures (25, 35, and 45 °C) and relative humidity levels (11–85%) using gravimetric static methods. The experimental data were modeled using BET, GAB, Caurie equations, and artificial neural networks (ANNs) to assess their accuracy in describing sorption behavior. The results revealed Type II isotherms, with equilibrium moisture content increasing with temperature and decreasing with water activity. ANNs demonstrated superior predictive accuracy compared to traditional semi-empirical models. Furthermore, the microwave pre-treated samples exhibited higher predicted moisture content thresholds, limiting microbial growth, suggesting that microwave treatment improves safe storage under higher moisture conditions while preserving quality. This study emphasizes the potential of combining microwave pre-treatment and ANNs to optimize the drying, packaging, and storage of mung beans, enhancing food preservation efforts.