Comparative Approach of Response Surface Methodology and Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) in Rehydration Ratio Optimization for Bael ( Aegle marmelos (L) Correa) Powder Production

Presently, the response surface methodology (RSM) is one of the most favoured technique used in optimization of food production process. Though, artificial intelligence (AI) algorithm has flourished as an efficient way in the field of optimization and empirical modelling. One of these kinds of prevalent developed methodologies is Particle Swarm Optimization (PSO), which is often practiced in finding the optimized solution of a problem. This study emphasizes on the relative predictive ability of process condition optimization to yield bael powder with maximum rehydration ratio. All the trials were executed accordingly to central composite design (CCD); the response (rehydration ratio) was studied utilizing both the RSM approach and PSO-ANN algorithm. Based on the coefficient of correlation, the model established with the help of PSO-ANN tool was superior over the RSM model. The findings show that PSO-ANN provides an amended rehydration ratio yield for optimal input process parameter during bael powder processing through different drying methods.