Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification.
Environ Sci Technol
; 58(5): 2458-2467, 2024 Feb 06.
Article
en En
| MEDLINE
| ID: mdl-38270113
ABSTRACT
High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.
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Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Contaminantes Químicos del Agua
/
Fluorocarburos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
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Screening_studies
Límite:
Animals
Idioma:
En
Revista:
Environ Sci Technol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Suecia