Hybrid Thin-Layer and Deep Learning Modeling for One-Step-Ahead Prediction of Solar Drying Kinetics of Whole Charal (Chirostoma spp.) Under Field-Realistic Scenarios

Charal (Chirostoma spp.) is a small pelagic fish of high nutritional and economic importance in central Mexico. However, its high moisture content and rapid post-harvest deterioration result in substantial losses in artisanal fisheries. Solar drying represents a sustainable preservation alternative, particularly in regions with limited access to refrigeration. This study investigates the drying kinetics of whole charal under field-realistic mild-to-moderate solar drying scenarios, including forced convection, natural convection, and open-air exposure. Experimental drying curves were modeled using classical thin-layer formulations, and neural network models were evaluated as complementary one-step-ahead predictors of experimental moisture ratio. Among the evaluated thin-layer models, the Modified Page formulation consistently provided the most reliable empirical description of the drying curves, with coefficients of determination greater than 0.97. An ablation-style comparison of ANN, CNN, LSTM, and CNN-LSTM architectures showed that the CNN model achieved the highest global predictive accuracy in the present dataset, with R2 = 0.987 and MSE = 4.3 × 10−4. Because the dataset contained a limited number of independent drying curves, the deep-learning results are interpreted as exploratory and complementary to thin-layer modeling rather than as a replacement for classical empirical models. The proposed framework may support future drying-endpoint estimation and decision-support tools for artisanal fish processing, provided that additional validation is performed with standardized sample masses, environmental covariates, and product-quality indicators.