Regulation and Analysis of Food Safety Based on Machine Learning

Abstract

Food is the top priority for the people, and safety is the top priority for food. The quality and safety of food are related to the national economy and people's livelihood. With the development of Chinese economy and the improvement of people's quality of life, the scale of the food industry has also grown year by year, and the society and consumers have more stringent requirements on the quality of food production and its own safety. However, food quality and safety incidents occur frequently, making food quality and safety management an important task for improving people's livelihoods. Machine learning has been widely applied in the field of food quality and safety, with strong self-learning ability, good non-linear fitting ability, and fast modeling. Among them, neural network models and supervised learning methods can accurately and quickly detect and control the quality of food in the production process. This article focuses on the research progress of machine learning in the field of food quality and safety, and discusses it in three directions: Food quality inspection, food process traceability, and food safety warning. In order to clarify the focus, advantages and disadvantages, and future development direction of machine learning algorithms in food regulation, and provide theoretical support and technical guidance for the intelligent development of ensuring food quality and safety.

Keywords

• machine learning
• neural networks
• supervised learning
• food regulation
• food safety