Integrating non-target analysis and machine learning: a framework for contaminant source identification
Peng Liu, Ding Pan, Xin-Yi Jiao, Jining Liu, Penghui Du, Pengcheng Li, Mengzhu Xue, Yanchao Jin, Cai-Shan Wang, Xuerong Wang, Ying-Zhi Ding, Guangming Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xinhui Liu, Leping Li
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.
Topics & Concepts
Identification (biology)Computer scienceMachine learningArtificial intelligenceBotanyBiologyAir Quality Monitoring and ForecastingMachine Learning and Data ClassificationAdvanced Chemical Sensor Technologies