Application of Artificial Neural Network in the Baking Process of Salmon

Abstract

The global production of farmed Atlantic salmon amounts to over 2 million tons per year. Consumed all over the world, salmon is not only delicious but also nutritious. This paper deals with the relationship between moisture content, low-field nuclear magnetic resonance (LF-NMR), scanning electron microscope (SEM), and sensory evaluation in the baking process of salmon. An artificial neural network (ANN) model has been established to simulate the change of moisture content and energy consumed in the baking process. Through the study of LF-NMR, SEM, and sensory evaluation, it was found that the change of sensory indexes was consistent with the results observed by LF-NMR and SEM. With the increase of temperature, muscle fibers contracted, the interstices increased, the rate of water loss increased, and the sensory score decreased. Initial moisture content, baking time, baking temperature, baking humidity, and baking air velocity were employed as the baking control parameters for the ANN. ANN can be used to determine the moisture content and energy consumed of baking salmon. The best network topology occurred with 5 input layer neurons, 17 hidden layer neurons, and 2 output layer neurons, and the MSE was 0.00153, and Rall was 0.99661. According to the experiment, it was demonstrated that the ANN is a reliable software-based method.