Artificial Neural Networks in Modeling Osmotic Dehydration of Foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84. Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively. PRACTICAL APPLICATIONS Artificial neural networks (ANN) models are suitable alternatives for modeling osmotic dehydration of plant materials. It has previously been used as a modeling tool in several foods processing applications and had demonstrated to perform better than conventional tools which were based on regression, statistical or parametric models. This research is applicable for ANN modeling in estimation of the mass transfer in osmotic dehydration of any plant material. Further, it can be applied in estimation of the mass transfer for any other pre-treatment process for plant materials. The radial basis function (RBF) network with a Gaussian function is an efficient application to employ for variable operating conditions for all pre-treatment process on any other plant material.