Application of Ion Mobility Spectrometry–Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples

Medientyp: E-Artikel

Titel: Application of Ion Mobility Spectrometry–Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples

Beteiligte: Cordova, Alexandra C.; Dodds, James N.; Tsai, Han‐Hsuan D.; Lloyd, Dillon T.; Roman‐Hubers, Alina T.; Wright, Fred A.; Chiu, Weihsueh A.; McDonald, Thomas J.; Zhu, Rui; Newman, Galen; Rusyn, Ivan

Erschienen: Wiley, 2023

Sprache: Englisch

DOI: 10.1002/etc.5727

ISSN: 1552-8618; 0730-7268

Schlagwörter: Health, Toxicology and Mutagenesis ; Environmental Chemistry

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Beschreibung: AbstractExposure characterization of crude oils, especially in time‐sensitive circumstances such as spills and disasters, is a well‐known analytical chemistry challenge. Gas chromatography–mass spectrometry is commonly used for “fingerprinting” and origin tracing in oil spills; however, this method is both time‐consuming and lacks the resolving power to separate co‐eluting compounds. Recent advances in methodologies to analyze petroleum substances using high‐resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry–mass spectrometry (IMS–MS), is especially promising because it is both rapid and high‐throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS–MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS–MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non‐US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS–MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS–MS was used for unsupervised hierarchical clustering, as well as machine learning–based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS–MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization. Environ Toxicol Chem 2023;42:2336–2349. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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