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.

人工风化的原油“溢出”样品通过以下三层方法与未风化的可疑“来源”油进行匹配：方法1：气相色谱-火焰电离检测（GC / FID）；方法2：气相色谱-质谱（GC / MS）诊断率和方法3的多元统计量。这项研究为有前途和新颖的原油取证方法提供了概念验证，该方法将主成分分析（PCA）和偏最小二乘判别分析（PLSDA）与传统的取证油指纹识别工具结合使用，可为挑战性溢油赋予更多信心案件。在这项研究中，风化导致溢油发生物理和化学变化，从而降低了源归因中GC / FID和GC / MS诊断比率的可靠性。