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

This article focuses on the use of ion mobility spectrometry–mass spectrometry (IMS–MS) for rapid chemical fingerprinting and geographic/source rock classification of crude oils. Analyzing a diverse library of 195 crude oil samples from 12 geographic and geological groups, the study applied IMS–MS combined with computational workflows and machine learning to assign molecular formulas and identify predictive polycyclic aromatic hydrocarbon (PAH) biomarkers. While unsupervised clustering showed limited ability to distinguish oils by origin due to inherent compositional variability, supervised classification achieved moderate accuracy (~55–57%) in predicting geographic and source rock groups, with PAH features being most informative. Spatial interpolation of predictive features across the Gulf of Mexico revealed that biomarker abundance patterns corresponded more closely to source rock formations than to broad geographic groupings, demonstrating IMS–MS's potential as a rapid screening tool for exposure characterization and oil spill response.