Study on Quality and Safety Prediction of Meat Products Based on CNN

Abstract

Compared with traditional data mining technology, depth learning technology has more powerful feature learning and feature expression ability, and it has great application value and potential in the field of food safety. In this paper, meat products inspection data, originating from national food safety casual inspection platform, as the object of study, first of all, the data would be some operations, such as data preprocessing, feature extraction, feature selection, feature classification and so on. Then, the key food safety risk early warning model was constructed by using convolution neural technology. Finally, a comparative experiment was conducted to predict the quality and safety of meat products. Experimental results show that, compared with SVM and BP neural networks, the food safety prediction method based on convolution neural network proposed in this paper has better prediction accuracy and algorithm stability, the feasibility and effectiveness of this research were verified, and a new idea was provided for food safety prediction research.