Enhanced analysis of weathered crude oils by gas chromatography-flame ionization detection, gas chromatography-mass spectrometry diagnostic ratios, and multivariate statistics.

Abstract Artificially weathered crude oil “spill” samples were matched to unweathered suspect “source” oils through a three-tiered approach as follows: Tier 1 gas chromatography-flame ionization detection (GC/FID), Tier 2 gas chromatography-mass spectrometry (GC/MS) diagnostic ratios, and Tier 3 multivariate statistics. This study served as proof of concept for a promising and new method of crude oil forensics that applies principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) in tandem with traditional forensic oil fingerprinting tools to confer additional confidence in challenging oil spill cases. In this study, weathering resulted in physical and chemical changes to the spilled oils, thereby decreasing the reliability of GC/FID and GC/MS diagnostic ratios in source attribution. The shortcomings of these traditional methods were overcome by applying multivariate statistical tools that enabled accurate characterization of the crude oil spill samples in an efficient and defensible manner.