Drying of Pomegranate Arils: Comparison of Predictions from Mathematical Models and Neural Networks

Abstract

Ten semi-theoretical and empirical models were fitted to the experimental data to evaluate and select the best model for thin-layer drying of pomegranate arils. Experiments were conducted at six temperature levels of 45, 50, 55, 60, 65 and 70 °C and three levels of air velocity (0.5, 1 and 1.5 m/s). Microwave pretreatments were used for samples and the results were compared to those of control (no pretreatments). Regression analysis of mathematical models showed that the Midili model fitted best to the measured data. However, regarding R2 and MSE criteria, neural network modeling yielded a better prediction of pomegranate arils moisture ratio during drying of arils compared to all the mathematical models studied.