人工智能在食品新污染物分析中的应用潜力研究进展

Research progress on the application potential of artificial intelligence in the analysis of emerging contaminants in food

中文关键词:  食品安全  人工智能  新污染物  检测分析

英文关键词:food safety  artificial intelligence  emerging contaminants  detection and analysis

中文摘要:

      持久性有机污染物(persistent organic pollutants, POPs)、微塑料(microplastics, MPs)、内分泌干扰物、抗生素等目前全球关注的新污染物(emerging contaminants, ECs)已成为威胁食品安全、损害公众健康的重大风险因素, 而传统食品安全分析方法在应对未知、复杂的新污染物方面面临技术瓶颈。近年来, 机器学习(machine learning, ML)等人工智能(artificial intelligent, AI)技术由于数据处理能力强、模式识别性能优异, 因而为解决该难题提供了全新、有力的工具。本文阐述了AI技术在优化样品前处理流程、提高仪器分析效率、构建筛查数据库、挖掘确证信息等检测分析关键技术环节中的突出优势, 评估了其在食品中新污染物分析的应用潜力。尽管应用前景广阔, 该领域仍面临数据质量、模型可解释性及标准化等技术与制度和机制挑战。总体而言, AI技术正在切实推动食品中新污染物的检测分析向智能化、可预测的方向发展。

英文摘要:

      Persistent organic pollutants (POPs), microplastics (MPs), endocrine disruptors, antibiotics and other emerging contaminants (ECs) are currently major risk factors threatening food safety and harming public health. Traditional food safety analytical methods face technical bottlenecks in addressing unknown and complex new contaminants. In recent years, artificial intelligence (AI) technologies, such as machine learning (ML), have provided a novel and powerful tool to tackle this challenge due to their strong data processing capabilities and excellent pattern recognition performance. This article elaborated on the prominent advantages of AI technology in optimizing sample pre-treatment processes, improving instrumental analysis efficiency, constructing screening databases and extracting confirmatory information in key analytical processes, and assessed its potential applications in the analysis of emerging contaminants in food. Despite broad prospects, this field still faced challenges related to data quality, model interpretability, standardisation and institutional and procedural mechanisms. Overall, AI technology is effectively promoting the development of intelligent and predictive analysis of emerging contaminants in food.