Influence of temperature, frequency and moisture content on honey viscoelastic parameters - Neural networks and adaptive neuro-fuzzy inference system prediction

The aim of this study is to evaluate the influence of temperature, moisture and frequency on nine honeys from viscoelastic (complex viscosity, η∗, loss modulus, G″, and storage modulus, G′) point of view using artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The temperature has ranged between 5 and 40 °C, the moisture content 16.04 and 17.82% and the frequency 0.1 and 10 Hz. Artificial neural networks (Multilayer perceptron - MLP, Probabilistic neural network - PNN, Radial basis function network - RBF and Recurrent network - RN) have been used to evaluate their model of prediction usefulness. Keeping into account the statistical parameters values, it seems that the ANNs methodology predicts better the evolution of viscoelastic parameters of honey in function of temperature, frequency and moisture content than ANFIS.