Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA.
Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA.
Environ Toxicol Chem. 2023 Nov;42(11):2336-2349. doi: 10.1002/etc.5727. Epub 2023 Aug 22.
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 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.
原油的暴露特征描述，尤其是在溢油和灾害等时间敏感的情况下，是分析化学领域的一个众所周知的挑战。气相色谱-质谱联用通常用于溢油的“指纹”和来源追踪；然而，这种方法既耗时又缺乏分离共洗脱化合物的分辨率。最近，使用高分辨率分析技术分析石油物质的方法学进展表明，该方法既提高了分辨率，又提高了通量。其中一种方法，离子淌度谱-质谱联用（IMS-MS），特别有前途，因为它既快速又高通量，能够分辨高度同源的烃类分子。先前将 IMS-MS 应用于原油分析的应用仅限于少数样本，并且没有对化学成分进行详细的特征描述。我们使用 IMS-MS 分析了 195 个原油样本的多样性库，并应用了计算工作流程将分子公式分配给各个特征。这些油来自 12 个地理和地质起源组：非美国（1 组）、美国陆上（3 组）和美国墨西哥湾近海（8 组）。我们假设通过 IMS-MS 数据获得的信息将提供更有信心的分组，并产生额外的指纹信息。来自 IMS-MS 的化学组成数据用于无监督层次聚类，以及基于机器学习的有监督分析，以预测每个样本的地理和源岩类别；后者还产生了几个用于指纹识别的新的潜在多环芳烃生物标志物。我们发现，IMS-MS 数据在不同原油的指纹识别和特征描述方面具有互补优势，并且所提出的多环芳烃生物标志物可用于快速暴露特征描述。环境毒理学与化学 2023；42:2336-2349。版权所有 © 2023 作者。环境毒理学与化学由 Wiley 期刊出版公司代表 SETAC 出版。
