ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM AND ARTIFICIAL NEURAL NETWORK ESTIMATION OF APPARENT VISCOSITY OF ICE-CREAM MIXES STABILIZED WITH DIFFERENT CONCENTRATIONS OF XANTHAN GUM

Abstract

An adaptive neuro-fuzzy inference system (ANFIS) was used to accurately model the effect of gum concentration (GC) and shear rate (SR) on the apparent viscosity (eta) of the ice-cream mixes stabilized with different concentrations of xanthan gum. ANFIS with different types of input membership functions (MFs) was developed. Membership function the gauss2 generally gave the most desired results with respect to MAE, RMSE and R-2 statistical performance testing tools. The ANFIS model was compared with artificial neural network (ANN) and multiple linear regression (MLR) models. The estimation by ANFIS was superior to those obtained by ANN and MLR models. The ANFIS and ANN model resulted in a good fit with the observed data, indicating that the apparent viscosity values of the ice-cream can be estimated using the ANFIS and ANN models. Comparison of the constructed models indicated that the ANFIS model exhibited better performance with high accuracy for the prediction of unmeasured values of apparent viscosity eta parameter as compared to ANN although the performance of ANFIS and ANN were similar to each other. Comparison of the constructed models indicated that the ANFIS model exhibited better performance with high accuracy for the prediction of unmeasured values of apparent viscosity eta parameter as compared to ANN although the performance of ANFIS and ANN were similar to each other.