Optimization of Processing Conditions of Garlic Flavoring Powder Based on GA-BP Neural Network

The effects of drying temperature, slice thickness and drying time on the flavor quality of garlic flavoring were studied in order to optimize the preparation conditions of garlic flavoring powder, and high-quality garlic flavoring powder was prepared by using hot air drying method. Box-Behnken design and response surface method were used to experiment with sensory score as response value. The results of the response surface method were mutually verified by the combination of artificial neural network and genetic algorithm. The results indicated that the optimal conditions for the preparation of garlic flavoring powder were as follows: the drying temperature was 61℃, the drying time was 5.9 h, and the slice thickness was 2.7 mm. The sensory score of the product obtained under this condition was 18.70 points and the relative error from the model's predicted value was 0.74%. The predicted value calculated by the GA-BP artificial neural network model was 18.5622 points. This means that the experimental value fits well with the predicted value of the model. Therefore, artificial neural network combined with genetic algorithm is a reasonable and feasible method for the preparation conditions of flavoring garlic powder. © 2020, Editorial Office of Journal of CIFST. All right reserved.