Optimization of de‐bittering process of mosambi (Citrus limetta) peel: Artificial neural network, Gaussian process regression and support vector machine modeling approach

Abstract

AbstractThe process of di‐bittering of mosambi peel powder (MPP) using sodium chloride solution (0, 5, and 10%) was optimized. The optimization was based on the acceptable taste, maximum total polyphenol content, and maximum antioxidant properties of peel powder. Support vector machine (SVM)‐based classification model was developed for sensory evaluation. Artificial neural network (ANN) and Gaussian process regression (GPR) was used for development of regression models to predict total polyphenol and total antioxidant properties. The two combinatorial models of SVM–ANN and SVM–GPR were developed for optimization using genetic algorithm. The optimized powder had total polyphenol content and antioxidant properties of 0.65 ± 0.10% and 57.05 ± 0.08%, respectively, while taste was in acceptable limits and showed good, swelling power, water binding capacity, oil binding capacity, emulsion stability, and emulsion activity. The optimized dibittered powder also showed good results for in vitro glucose retardation index and showed antimicrobial activity against some pathogenic microorganisms.Practical ApplicationsMosambi peel powder is rich in dietary fiber and other bioactive compounds. However, its utilization is limited due to the presence of some bitter compounds. The present study deals with the optimization of di‐bittering process of mosambi peel. The optimized powder had convincing functional properties and hence opens the doors for its commercial application in food industry. Mosambi peel powder being rich in dietary finder can enhance the fiber content of same fiber deficient foods.