Apricot osmotic drying modeling using genetic algorithm - Artificial Neural Network

Iran is the second highest apricot production in the world and studying the factors affecting the drying of this fruit and their impact is essential. Then¡ in this study the effect of osmotic solution temperature in the range 25°C to 65° C¡ at 30-120 minutes and osmotic solution concentration in the range of 30 to 60 % (w/w) on the parameters of weight reduction¡ water loss and solids gain during osmotic drying of apricots were studied. Results of osmotic drying showed that the three parameters mentioned are effective on weight reduction¡ water loss and solids gain. With increasing osmotic process time from 30 minutes to 120 minutes¡ weight reduction¡ water loss and solids gain¡ increase 21.78¡ 50.64 and 157.31 percent¡ respectively. In this study also¡ process modeling were done with the genetic algorithm–artificial neural network (GA-ANN) method with 3 inputs and 3 outputs for predictors of weight reduction¡ water loss and solids gain. The results of the modeling results using a GA-ANN showed a network with 14 neurons in the hidden layer with hyperbolic tangent activation function can be well predicted the weight reduction (R=0.98)¡ water loss (R=0.97) and solid gain (R=0.96) in osmotic drying process of apricot. Sensitivity analysis results by optimum ANN showed the osmotic solution temperature was the most sensitive factor to controlling the weight reduction¡ water loss and solids gain from apricot particle.