Proceedings (Nov 2023)
Machine Learning Tools Can Pinpoint High-Risk Water Pollutants
- Helen Sepman,
- Pilleriin Peets,
- Lisa Jonsson,
- Louise Malm,
- Malte Posselt,
- Matthew MacLeod,
- Jonathan Martin,
- Magnus Breitholtz,
- Michael McLachlan,
- Anneli Kruve
Affiliations
- Helen Sepman
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91 Stockholm, Sweden
- Pilleriin Peets
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91 Stockholm, Sweden
- Lisa Jonsson
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91 Stockholm, Sweden
- Louise Malm
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91 Stockholm, Sweden
- Malte Posselt
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
- Matthew MacLeod
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
- Jonathan Martin
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
- Magnus Breitholtz
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
- Michael McLachlan
- Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
- Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91 Stockholm, Sweden
- DOI
- https://doi.org/10.3390/proceedings2023092068
- Journal volume & issue
-
Vol. 92,
no. 1
p. 68
Abstract
Liquid chromatography–high-resolution mass spectrometry (LC/HRMS) is a powerful tool for detecting chemicals that are present in low concentrations [...]
Keywords
- high-resolution mass spectrometry
- toxicity
- quantification
- machine learning
- non-targeted screening
- suspect screening
