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
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Optimized workflow for unknown screening using gas chromatography high‐resolution mass spectrometry expands identification of contaminants in silicone personal passive samplers
Silicone wristbands have emerged as valuable passive samplers for monitoring of personal exposure to environmental contaminants in the rapidly developing field of
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.
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.
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.
- Award ID(s):
- 1919594
- PAR ID:
- 10385000
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Rapid Communications in Mass Spectrometry
- Volume:
- 35
- Issue:
- 8
- ISSN:
- 0951-4198
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Per-and polyfluoroalkyl substances (PFAS) are a class of contaminants of emerging concern frequently used in products like aqueous firefighting foams and non-stick coatings due to their stability and surfactant-like qualities. The lack of analytical standards for many emerging PFAS have severely limited our ability to comprehensively identify unknown PFAS contaminants in the environment, especially those that occur as isomers. Annotation of small molecules and identification of unknowns based only on elemental composition and mass fragmentation patterns remain major challenges in nontarget analysis employing liquid chromatography with high-resolution mass spectrometry (LC-HRMS). In this study, chromatographic retention factors (k) and mass spectral fragmentation patterns of 32 known PFAS were determined using our optimized parameters in LC-HRMS. The same method was then used to analyze previously unidentified PFAS in actual environmental samples. Using characteristic ions observed in the MS fragmentation of PFAS, the most probable isomeric structures of the detected PFAS were predicted. To increase confidence in the predicted molecular structure, Density Functional Theory and Conductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations were used to predict physicochemical properties of different constitutional isomers. The DFT calculations facilitated geometric optimization, determination of polarizability, and calculation of the chemical potential the isomers. COSMO-RS uses the chemical potential to predict thermodynamic properties of molecules such as pKa, solubility, and Kow. These properties were then used to make a multi-variable linear regression to predict k values. The model was trained using 32 known PFAS. The properties used were log Kow of the neutral and anion species of the PFAS, and their polarizability. The model was specific enough to predict significantly different k values of unknown compounds with similar structures, which facilitated assignment of isomeric structures of PFAS.more » « less
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