Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel

Mass transfer of kaffir lime peel during osmotic dehydration was investigated in this paper. Processing factors were solute concentrations, process temperatures, and immersion time. The results showed that increasing solute concentration and process temperature resulted in a higher reduction in moisture contents of kaffir lime peel and increase in water loss and solid gain rates. Analysis of variance showed significant effects (P < 0.05) of all processing factors except process temperatures for water loss. Multilayer feedforward neural network (MFNN) was proposed to predict percentages of water loss and solid gain of kaffir lime peel during osmotic dehydration based on three processing factors as inputs. The best network with the lowest average mean squared error (MSE) of 0.0066 and the highest average regression coefficient (r2) of 0.9725 from normalized training and validating data sets was composed of one hidden layer with five hidden neurons and used Levenberg–Marquardt algorithm as a training algorithm. A simulation test showed good generalization of the successfully trained MFNN model with the average MSE of 6.5813 and 5.9340, and average r2 of 0.9745 and 0.9632, respectively, for water loss and solid gain. Compared with multiple linear regression models, MFNN was found to be more suitable for predicting water loss and solid gain during the OD process of kaffir lime peel.