Machine learning algorithm for estimating and optimizing the phytochemical content and physicochemical properties of okra slices in an infrared heating system.

This study investigates how different air temperatures and infrared intensities affect the physicochemical properties of dried okra at different airflow rates. The model was developed using machine learning, and Okra's physicochemical properties were optimized using a self-organizing map (SOM). The results showed that higher infrared intensity and air temperature improved rehydration and colour but reduced water activity and vitamin C levels.