Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network

Abstract

The optimum conditions of extraction time, solid to solvent ratio, and methanol concentration for extracting total phenolic content (TPC) from mangosteen (Garcinia mangostana L.) hull powder have been investigated using response surface methodology. The experimental data obtained was adequately fitted into second-order polynomial models with coefficient of determination (R2) of 0.897. Response surface analysis showed that the optimal extraction parameters which gave a maximum TPC yield of 140.66mg gallic acid equivalent (GAE)/g powder were from a 2h extraction with 0.05 solid to solvent ratio and at 69.77% methanol concentration. Analysis using artificial neural network (ANN) predicted data which showed a higher R2 value of 0.945 and average absolute deviation (ADD) values of 4.01% versus 5.37% for the RSM. This suggests that ANN is a better modelling technique for nonlinear data for predicting TPC extracted from mangosteen hull powder compared to the response surface methodology (RSM).