Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modeling and Optimization of Pectin Extraction from Banana Peel

Abstract The present study, the influence of three independent variables for extraction of pectin were investigated and optimized using artificial neural network and response surface methodology on the yield and degree of esterification of banana peel pectin obtained using acid extraction method. The results revealed that properly trained artificial neural network model is found to be more accurate in prediction as compared to response surface method model. The optimum conditions were found to be temperature of 82oC, pH of 2 and extraction time of 102 min in the desirable range of the order of 0.977. The yield of pectin and degree of esterification under these optimum conditions was 15.64% and 65.94, respectively. Temperature, extraction time and pH revealed a significant (p < 0.05) effect on the pectin yield and degree of esterification. The extracted banana peel pectin was categorized as high methoxyl pectin, based on the high methoxyl content and degree of esterification. In general, the findings of the study show that banana peel can be explored as a promising alternative for the commercial production of pectin.