Modeling of Blackberry Drying Process by Double sided infrared System using Genetic Algorithm–Artificial Neural Network Method

Black mulberry is a very important and rich source of polyphenols and anthocyanin’s that dry and maintain can be valuable. The use of infrared radiation as a new method in drying preserves the quality of the final product, which increases the speed of drying and reduces the cost of the process. The effect of 120, 180, 240 W of infrared power, 5, 10 and 15 cm of sample distance from infrared source and drying time on black mulberry drying were investigated. Modeling of black mulberry drying was used to predict the output of this study using genetic algorithm-artificial neural network with four inputs (infrared power, sample distance from infrared source and drying time) and one output (weight loss percentage). It was determined that by increasing the infrared power and reducing the distance between the samples and the infrared lamp, the drying rate of black mulberry was significantly increased by two-way infrared (P<0.05). The sigmoid activation function was selected as the activation function in the hidden layer and output layer due to the lower error value than other functions. According to the test method and the fault method, 15% of the data was used for training to achieve the best learning conditions for the relationships between inputs and outputs by the network. 15% of the data was used for the trained network test, and 60% of the remaining data was used to evaluate the network. According to the results, it can be concluded that using the sigmoid activation function and the network with 8 neurons in a hidden layer, we can well analyze the percentage of weight loss using the genetic algorithm-artificial neural network during the black mulberry drying process was predicted by two-way infra-red method (R2=0/999). Drying time was introduced as the most effective factor for controlling black mulberry weight loss using sensitivity analysis by optimal neural network.