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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

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  • 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

摘要

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.