التفاصيل البيبلوغرافية
| العنوان: |
Machine Learning Tools Can Pinpoint High-Risk Water Pollutants †. |
| المؤلفون: |
Sepman, Helen, Peets, Pilleriin, Jonsson, Lisa, Malm, Louise, Posselt, Malte, MacLeod, Matthew, Martin, Jonathan, Breitholtz, Magnus, McLachlan, Michael, Kruve, Anneli |
| المصدر: |
Proceedings (2504-3900); 2024, Vol. 92, p68, 2p |
| مصطلحات موضوعية: |
WATER pollution, MACHINE learning, MACHINE tools, EMERGING contaminants |
| مستخلص: |
This article discusses the use of machine learning tools in identifying high-risk water pollutants. Liquid chromatography–high-resolution mass spectrometry (LC/HRMS) is a powerful technique for detecting chemicals in low concentrations. However, the risk assessment process for these chemicals can be time-consuming. Machine learning models have been developed to predict toxicity and ionization efficiency, but they often require a chemical structure as input. The authors of this study developed a workflow that estimates the risk of unidentified chemicals detected in non-target screening based on their MS2 data. The models were trained using structural fingerprints and applied to wastewater analysis, showing promising results in pinpointing high-risk chemicals. The authors conclude that this approach provides a way to evaluate the risk of unidentified LC/HRMS features and prioritize high-risk chemicals for identification. [Extracted from the article] |
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| قاعدة البيانات: |
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