Integrating drying kinetics, quality attributes and machine learning for red pepper drying in a greenhouse solar dryer.

This study investigates the performance of three drying methods (open sun, shade and Greenhouse Solar Dryer (GSD)) in terms of drying kinetics, bioactive compound retention, antioxidant activity and predictive modeling using machine learning techniques. Experimental data were collected under controlled environmental conditions, including temperature, solar radiation and humidity, while monitoring moisture ratio changes during the drying process. The GSD method demonstrated superior drying efficiency compared to open sun and shade drying, achieving faster moisture removal while preserving high levels of total phenolic content, total flavonoid content and antioxidant activity. Mathematical modeling revealed that the Midilli and Kucuk model best described the drying behavior across all methods, particularly under GSD conditions. Critically, this study presents the first application and rigorous validation of Gaussian Process Regression (GPR) for mass prediction in red pepper drying within a GSD, achieving accuracy (R² = 0.98, MAPE = 4.86%) through k-fold cross-validation method. These findings highlight the advantages of GSD in enhancing both drying performance and product quality, making it a promising alternative for sustainable and efficient pepper drying. The integration of kinetic modeling and machine learning further supports the development of smart drying systems for real-time monitoring and optimization.