Integrating non-target analysis and machine learning: a framework for contaminant source identification

Integrating non-target analysis and machine learning: a framework for contaminant source identification

Abstract Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considera...

Full description

Saved in:
Bibliographic Details
Main Authors: Peng Liu, Ding Pan, Xin-Yi Jiao, Ji-Ning Liu, Peng-Hui Du, Peng-Cheng Li, Meng-Zhu Xue, Yan-Chao Jin, Cai-Shan Wang, Xue-Rong Wang, Ying-Zhi Ding, Guang-Ning Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xin-Hui Liu, Li-Ping Li
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:npj Clean Water
Online Access:https://doi.org/10.1038/s41545-025-00504-z
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considerations of key steps in data processing, pattern recognition, and model validation. The framework provides practical guidance for translating raw NTA data to actionable environmental insights that support informed decision-making.
ISSN:2059-7037

Similar Items