Application of Ion Mobility Spectrometry-Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples.
Alexandra C CordovaJames N DoddsHan-Hsuan D TsaiDillon T LloydAlina T Roman-HubersFred A WrightWeihsueh A ChiuThomas J McDonaldRui ZhuGalen NewmanIvan RusynPublished in: Environmental toxicology and chemistry (2023)
Exposure 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 (GC-MS) 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 coeluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques 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 perform detailed characterization of chemical constituents. The current study 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 will provide a more confident grouping and yield additional fingerprints. 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 has complementary advantages for fingerprinting and characterization of diverse crude oils and that the proposed polycyclic aromatic hydrocarbon (PAH) biomarkers can be used for rapid exposure characterization.
Keyphrases
- mass spectrometry
- high resolution
- liquid chromatography
- gas chromatography
- machine learning
- ms ms
- multiple sclerosis
- gas chromatography mass spectrometry
- high performance liquid chromatography
- electronic health record
- capillary electrophoresis
- high throughput
- big data
- tandem mass spectrometry
- fatty acid
- dna damage
- healthcare
- deep learning
- simultaneous determination