Back propagation artificial neural network (BP-ANN) for prediction of the quality of gamma-irradiated smoked bacon

Abstract

This study investigated the effect of gamma irradiation on smoked bacon quality during storage and developed a multi-quality prediction model based on gamma irradiation. Gamma irradiation reduced moisture content and improved the microbial safety of smoked bacon. It also accelerated protein and lipid oxidation and altered free amino acids and fatty acids composition. It was effective in slowing down quality deterioration and sensory quality decline during storage. The backpropagation artificial neural network (BP-ANN) model was constructed by using physical and chemical indicators, irradiation dose, and storage time as input variables, and the total number of colonies and sensory scores as output layers. The transfer functions of the input-hidden layer and hidden-output layer were ReLu and Sigmoid, respectively. There were 13 neurons in the hidden layer. Results showed that BP-ANN based on physical and chemical indicators, irradiation dose, and storage time had great potential in predicting the multiple quality of smoked bacon.