Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks

An artificial neural network (ANN) model was developed for the prediction of water loss (WL) and solid gain (SG) in osmotic dehydration of apple cylinders using a wide variety of data from the literature to make it more general. This model mathematically correlates six processing variables (temperature and concentration of osmotic solution, immersion time, surface area, solution to fruit mass ratio and agitation level) with WL and SG. The optimal ANN consisted of one hidden layer with four neurons. This model was able to predict WL and SG in a wide range of processing variables with a mean square error of 13.9 and 4.4, and regression coefficient of 0.96 and 0.89, respectively, in testing step. This ANN model performs better when compared to linear multi-variable regression. The wide range of processing variables considered for the formulation of this model, and its easy implementation in a spreadsheet using a set of equations, make it very useful and practical for process design and control.