基于机器学习对食品安全的调控与分析

Regulation and Analysis of Food Safety Based on Machine Learning

民以食为天,食以安为先。食品质量及其安全性关系到国计民生。随着中国经济的发展和人民生活质量的提高,食品行业的规模也逐年壮大,社会和消费者对食品生产的质量及其本身安全性有了更加严格的要求。但是,食品质量安全事件时有发生,使得食品质量安全的管理成为了改善民生的重要任务。机器学习已在食品质量与安全领域被广泛应用,它具有自主学习能力强、非线性拟合能力好、建模快速等特点,其中的神经网络模型和监督学习方法能够准确、快速的对食品在生产过程中进行质量检测与过程控制。本文将着重阐述机器学习在食品质量与安全领域中的研究进展,以食品质量检验、食品过程追溯、食品安全预警3个方向进行论述。以期阐明机器学习算法在食品调控环节中的侧重点、优缺点和未来发展方向,为保障食品质量与安全的智能化发展提供理论支持与技术指导。

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.