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Title: Quantitative Structure Retention Relationships to Identify Per-/Poly-Fluoroalkyl Substances and Other Contaminants of Emerging Concern
Nontarget analysis using liquid chromatography–high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structure-retention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been successful in identification of per- /poly-fluoroalkyl substances (PFAS) and other contaminants. Nontarget analysis using liquid chromatography– high resolution mass spectrometry (LC–HRMS) is a valuable approach in characterizing for contaminants of emerging concern (CECs) in the environment. However, identification of these analytes can be quite costly or taxing without proper analytical standards. To circumvent this problem we utilize Quantitative structureretention relationships (QSRR) models to predict elution order and retention times. Properties calculated from density functional theory (DFT) and the conductor-like screening model for real solvents (COSMO-RS) theory are used to produce our QSRR models, which can be calculated for virtually any analyte. We show that this methodology has been  more » « less
Award ID(s):
1904825
NSF-PAR ID:
10345229
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Annual meeting abstracts
ISSN:
1087-8939
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Methods

    Gas chromatography coupled to Orbitrap™ mass spectrometry (GC/Orbitrap™ MS) was used to simultaneously perform suspect screening (using in‐house database) and unknown screening (using vendor databases) of extracts from wristbands worn by volunteers. The goal of this study was to optimize a workflow that allows detection of low levels of priority pollutants, with high reliability. In this regard, a data processing workflow for GC/Orbitrap™ MS was developed using a mixture of 123 environmentally relevant standards consisting of pesticides, flame retardants, organophosphate esters, and polycyclic aromatic hydrocarbons as test compounds.

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    The optimized unknown screening workflow using a search index threshold of 750 resulted in positive identification of 70 analytes in validation samples, and a reduction in the number of false positives by over 50%. An average of 26 compounds with high confidence identification, 7 level 1 compounds and 19 level 2 compounds, were observed in worn wristbands. The data were further analyzed via suspect screening and retrospective suspect screening to identify an additional 36 compounds.

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    This study provides three important findings: (1) a clear evidence of the importance of sample cleanup in addressing complex sample matrices for unknown analysis, (2) a valuable workflow for the identification of unknown contaminants in silicone wristband samplers using electron ionization HRMS data, and (3) a novel application of GC/Orbitrap™ MS for the unknown analysis of organic contaminants that can be used in exposomics studies.

     
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