Comparative Approach of Artificial Neural Network and Thin Layer Modelling for Drying Kinetics and Optimization of Rehydration Ratio for Bael (Aegle marmelos (L) correa) Powder Production
SARKAR TANMAY, Molla Salauddin, HAZRA SUDIPTA KUMAR, Tanupriya Choudhury, CHAKRABORTY RUNU
Abstract
Bael is a seasonal fruit and available in a particular period in year. To study the drying characteristics of bael pulp in the sun, hot-air, microwave, and freeze-drying process thin layer drying models as well as artificial neural network modeling were adopted. The correlation coefficient and the chi-squared test were performed to describe the aptness of models. The response surface methodology (RSM) is one of the most used approaches cultivated in the optimization of food processing technology. One of met heuristic algorithms of ubiquitous developed practices is Particle Swarm Optimization (PSO), which is frequently experienced in finding the optimized elucidation of a problem. This study focused on the comparative predictive ability of process condition optimization to yield bael powder with the highest rehydration ratio. The results showed that PSO contributed an improved rehydration ratio yield for optimal input process parameters during bael powder production through different cutting edge drying methods.