Genetic Algorithm-Artificial Neural Network Modeling of Moisture and Oil Content of Pretreated Fried Mushroom

In this research, the effect of different pretreatments (osmotic dehydration and gum coating) on moisture and oil content of fried mushroom was investigated, and artificial neural network and genetic algorithm were applied for modeling of these parameters during frying. Osmotic dehydration was performed in solution of NaCl with concentrations of 5% and 10%, and methyl cellulose was used for gum coating. Either pretreated or control samples were fried at 150, 170, and 190 degrees C for 0.5, 1, 2, 3, and 4 min. The results showed that osmotic dehydration and gum coating significantly decreased (0-84%, depending upon the processing conditions) oil content of fried mushrooms. However, moisture content of fried samples diminished as result of osmotic pretreatment and increased by gum coating. An artificial neural network was developed to estimate moisture and oil content of fried mushroom, and genetic algorithm was used to optimize network configuration and learning parameters. The developed genetic algorithm-artificial neural network (GA-ANN) which included 17 hidden neurons could predict moisture and oil content with correlation coefficient of 0.93 and 96%, respectively. These results indicating that GA-ANN model provide an accurate prediction method for moisture and oil content of fried mushroom.