Water Sorption Isotherms of Dates: Modeling Using GAB Equation and Artificial Neural Network Approaches

Abstract

Water sorption isotherms at 15, 25 and 45°C were determined for two date varieties. Water sorption modeling was carried out using the five-parameter Guggenheim-Anderson-de Boer (GAB) equation, a modified-GAB equation and a novel artificial neural network (ANN) approach. Modeling using the GAB equations used physical data as input, while the ANN approach used both physical and chemical compositional data. The five-parameter GAB equation had a lower mean relative error (approximately 7%) than the modified-GAB equation (approximately 16%), in predicting equilibrium moisture content (EMC). The effects of temperature on the water sorption isotherms were not evident with the five-parameter GAB equation. Although the temperature effects on water sorption isotherms were evident with the modified GAB equation, the overall error was very high. Neither GAB equation could predict water sorption isotherm crossing, an effect observed in the experimental data. An ANN model, optimized by trial and error, was superior to both GAB equations. It could predict EMC with a mean relative error of 4.31% and standard error of moisture content of 1.36 g -05/ -05kg. The correlation coefficients (r2) of the relationships between the actual and predicted values of equilibrium moisture content and date varieties obtained by the ANN were 0.9978 and 0.9999 respectively. The ANN model was able to capture water sorption isotherm crossing due to temperature effects. Water activity and chemical compositional data, however, had more impact upon the water sorption isotherms than temperature.