Predicting mass transfer activation energy and physicochemical properties of dried onion using numerical modeling and artificial intelligence.

The quality of the onion slices was statistically evaluated based on the variables of drying conditions, considering the following characteristics: drying time, color, shrinkage, water activity, and rehydration ratio, critical parameters in evaluating food processing and preservation methods. The onion slices were subjected to drying under the ensuing settings: infrared powers of 1500, 3500, and 5500 W/m; airflow rates of 0.3, 0.7, and 1.0 m/s; and air temperatures of 40, 50, and 60 °C. The findings indicated that the drying time increased with air velocity to 0.7 and 1.0 m/s at a constant infrared intensity of 5500 W/m, resulting in a significant increase of 26 and 37%. The water activity values for the sample ranged from 0.40 to 0.49. The maximum vitamin C content was 24 mg/g, followed by an air temperature of 40 °C and 0.3 m/s air velocity under 1500 W/m. Conditions of 1.5 m/s, 40 °C, and 1500 W/m indicate the lowest rehydration ratio. The ANN model proved to be a robust tool for predicting and optimizing drying parameters, including drying duration, energy consumption, and quality. Additionally, it highlights the potential of advanced heating technologies to optimize food drying approaches, providing important insights for the food sector in its quest for increased efficiency.