Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

Abstract

Abstract Knowledge of rheological properties of honey is of great interest to honey handlers, processors and keepers. In this study, genetic algorithm–artificial neural network (GA–ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the viscosity of four types of honey, two poly floral (Mountain, Forest) and two monofloral (Sunflower, Ivy). The GA–ANN and ANFIS were fed with 3 inputs of water content (15.25–19.92%), temperature (10–30 °C) and shear rate (1–42 s−1). The developed GA–ANN, which included 11 hidden neurons, could predict honey viscosity with correlation coefficient of 0.997. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.999). Sensitivity analysis results showed that temperature was the most sensitive factor for prediction of honey viscosity. Both GA–ANN and ANFIS models predictions agreed well with testing data sets and could be useful for understanding and controlling factors affecting viscosity of honey.