Mathematical Modeling and Optimization of Ultrasonic Pre-Treatment for Drying of Pumpkin (Cucurbita moschata)

Innovations in food drying processes are usually aimed at reducing drying time and improving the overall properties of dried products. These are important issues from an economic and environmental point of view and can contribute to the sustainability of the whole process. In this study, the effects of ultrasonic treatment on the drying kinetics of pumpkin pulp are investigated, and mathematical models to predict the drying kinetics are analyzed and optimized. The results show that ultrasonic pretreatment significantly reduces drying time from 451 to 268 min, with optimal processing parameters at 90% of the maximum ultrasonic power and a processing time of 45 min. The total color change of the samples was the lowest at the obtained optimal processing parameters. Based on the values (RMSE and R-2) of the investigated mathematical drying models, it was found that the Weibull model is the best fit for the experimental data and is considered suitable for the drying kinetics of ultrasonically pretreated pumpkin samples. In this study, an artificial neural network with 15 neurons in hidden layers was also used to model the drying process in combination with ultrasound pretreatment. The network had a performance of 0.999987 and the mean square error was 8.03 x 10(-5), showing how artificial neural networks can successfully predict the effects of all tested process variables on the drying time/moisture ratio.