Unification of fruit water sorption isotherms using artificial neural networks.
Myhara R. M., Shyam Sablani
Author Affiliation: Department of Food Security, Natural Resources Institute, The University of Greenwich, Chatham Maritime ME4 4TB, UK.
Drying Technology 19 : 1543-1554
Abstract : The chemical composition, water activity, temperature and equilibrium moisture content (EMC) for 10 selected fruits (date, raisin, figs, prunes [Prunus], apricot, strawberry, pineapple, currant [Ribes], mango and guava) were determined. Two methods of water sorption modelling, the GAB equation and the artificial neural network (ANN) method, were compared for their ability to predict water sorption behaviour. Unlike the GAB equation, which uses only physical data for modelling, the ANN method uses both physical and chemical compositional data to make predictions. The ANN was superior, in most cases, to that of the GAB equation, in predicting EMC. This superiority was due to the availability of the additional chemical compositional information. The ANN method could predict EMC with a mean relative error of 9.85% and a standard error of 1.59% EMC. The correlation coefficient of the relationship between the actual and predicted values of equilibrium moisture content obtained by the ANN was 0.9938. The ANN model was able to show a temperature dependent crossing of water sorption isotherms, due to the dissolution of sugar crystals in the fruit. The ANN was also able to predict the extent of crossing, depending upon differences in the individual fruit chemical composition.