In this research, ultrasound assisted osmotic dehydration of courgette rings using sorbitol/sucrose solution under different temperature (5, 25 and 50 °C for 2 h) was investigated. Sucrose (35%, w/v) and sorbitol solutions (5, 10 and 15%, w/v) were used for osmotic dehydration processes. The reliability of using an artificial neural network (ANN) approach for predicting the osmotic dehydration properties of courgette was investigated. Immersion time, type of treatment, osmotic solution temperature and concentration were selected as input variables and solid gain and water loss were chosen as the outputs of the network. Results showed that all processing factors had a significant effect on the solid gain and water loss (P<0.01). Increasing osmotic solution concentration and temperature lead to increases in water loss and solid gain for both samples of ultrasonicated and non-ultrasonicated treatments. The results of ANN indicated that, tanh activation function with 46 neurons in first and second hidden layers was selected as the best activation function. This network was able to predict solid gain and water loss with R2 value equals to 0.938 and 0.985, respectively.
