Optimization of Pectinase Assisted Extraction of Chironji (Buchanania Lanzan) Fruit Juice Using Response Surface Methodology and Artificial Neural Network

The pectinase assisted extraction of Chironji fruit juice was carried out at different combinations of the concentration of pectinase (0.01–0.1 % w/w), temperature for incubation (30–50°C) and time of incubation (20–120 minutes). Conditions for obtaining maximum juice yield was established by deploying Response Surface Methodology (RSM), and Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The optimized conditions acquired through the RSM and ANN-GA technique predicted to yield 70.64% and 72.68% juice, respectively. The juice yield determined experimentally under RSM and ANN-GA optimized conditions were (70.73 ± 0.37) % and (72.52 ± 0.25) %, respectively. Based on the coefficient of determination (R²), absolute average deviation (AAD), and mean squared error (MSE) as well as on physicochemical properties of the juice extracted using optimized conditions, the ANN model was found to be better than RSM model.