Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice

Abstract

Response surface methodology (RSM) is a frequently used method for empirical modeling and prediction in the processing of biological media. The artificial neural network (ANN) has recently grown to be one of the most efficient methods for empirical modeling and prediction, especially for nonlinear systems. This article presents comparative studies between an ANN and RSM in the modeling and prediction of quality parameters of spray-dried pomegranate juice. In this study, the effects of the carrier type, carrier concentration, and concentration of crystalline cellulose in a pomegranate juice spray-drying process were investigated on five quality parameters—drying yield, solubility, color change, total anthocyanin content, and antioxidant activity—using RSM and ANN methods. A central composite rotatable experimental design (CCRD) and a feed-forward multilayered perceptron (MLP) ANN trained using back-propagation algorithms for three independent variables were developed to predict the five outputs. The final selected ANN model (3-10-8-5) was compared to the RSM model for its modeling and predictive abilities. The predictive abilities of both the ANN and RSM were compared using a separate dataset of 18 unseen experiments based on RMSE (root mean square error), MAE (mean absolute error), and R2 (correlation coefficient) for each output parameter. The results indicate the superiority of a properly trained ANN in capturing the nonlinear behavior of the system and the simultaneous prediction of five outputs.