Moisture Content in Food Production Process Based on Wavelet Neural Network

Abstract

Water content not only affects the taste and hardness of food, but also affects the shelf life of food, which determines the profit and loss of enterprises to a certain extent. Therefore, in the process of food production, the detection of moisture content is very necessary. In this paper, wavelet theory and wavelet neural network are introduced briefly, and make necessary modification on this basis, it is applied to the actual food production process to achieve real-time prediction of food moisture content. The alarm is raised when the predicted value exceeds the pre-set moisture range, so as to remind staff to adjust raw material ratio in advance. Finally, through a group of data in MATLAB programming environment to study the feasibility of the algorithm, we can find that the error between the prediction results and the actual results is small, the method can better achieve the prediction of food moisture content. In this way, the rate of defective products in the production process can be reduced as much as possible. Meanwhile, the waste of raw materials can be reduced and the economic benefits of enterprises can be improved.