生态与农村环境学报2025，Vol.41Issue(9)：1180-1187,8.DOI:10.19741/j.issn.1673-4831.2025.0335
机器学习技术在新污染物可疑和非靶向筛查分析中的应用
Application of Machine Learning Techniques in Non-Targeted Screening Analysis of Emerging Contaminants
摘要
Abstract
The structural diversity of emerging contaminants and the absence of analytical standards for certain compounds limit the capability of traditional targeted approaches to detect substances beyond predefined reference standards.Conse-quently,the application of high-resolution mass spectrometry(HRMS)-based suspect and non-targeted screening has be-come indispensable for comprehensive identification of emerging contaminants in environmental matrices.However,tradi-tional analysis methods are difficult to process the massive data obtained by HRMS,and complex mass spectrometry data a-nalysis and substance identification have become the core challenges of environmental analytical chemistry.As a powerful data processing and pattern recognition tool,machine learning provides great application potential in improving the efficien-cy and accuracy of suspect and non-targeted screening of emerging contaminants.In this paper,we systematically review the innovative applications and recent advances of machine learning techniques in the full process analysis of suspect and non-targeted screening,focusing on key aspects such as raw mass spectrometry data pre-processing,intelligent molecular formula assignment,retention time prediction and quantitative concentration analysis,and comprehensively illustrate the role of conventional machine learning and deep learning algorithms in improving the efficiency and accuracy of screening.Future efforts should prioritize the integration of machine learning into the entire suspect and non-targeted screening work-flow to enable more holistic investigations into the environmental exposure characteristics of emerging contaminants.关键词
新污染物/机器学习/高分辨质谱/非靶向筛查/可疑筛查Key words
emerging contaminants/machine learning/high-resolution mass spectrometry/non-targeted screening/sus-pect screening分类
资源环境引用本文复制引用
穆洪新,张后虎,陈玲,吴兵,卜元卿..机器学习技术在新污染物可疑和非靶向筛查分析中的应用[J].生态与农村环境学报,2025,41(9):1180-1187,8.基金项目
中央级公益性科研院所基本科研业务费专项(GYZX240401) （GYZX240401）
江苏省卓越博士后计划(2024ZB855) （2024ZB855）
国家自然科学基金联合基金项目(U2340202) （U2340202）
国家重点研发计划项目(2023YFC3706603) （2023YFC3706603）
