Optimization Of Infrared-Convective Drying Of White Mulberry Fruit Using Response Surface Methodology And Development Of A Predictive Model Through Artificial Neural Network

A comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared-convective drying of white mulberry. The drying experiments were performed at different air temperatures (40 degrees C, 55 degrees C, and 70 degrees C), air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared radiation power (500, 1000, and 1500 W). RSM focuses on the maximization of effective moisture diffusivity (D-eff) and minimization of specific energy consumption (SEC) in the drying process. The optimized conditions were encountered for the air temperature of 70 degrees C, the air velocity of 0.4 m/s, and the infrared power level of 1464.57 W. The optimum values of and were 1.77 x 10(-9) m(2)/s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the feed and cascade-forward back-Propagation neural systems with application of Levenberg-Marquardt training algorithm and topologies of 3-20-20-1 and 3-10-10-1 were the best neural models to predict D-eff and SEC, respectively. This finding suggests that the ANN as an intelligent method with better performance compared to the RSM can be used to predict the drying parameters of the infrared-convective drying of white mulberry fruit.