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Background: Oil spills are widespread and can cause devastating environmental consequences. Rapid oil identification is critical to find the origin of the spill, monitor the environment, and lead to informed mitigation measures. The current standard methods in oil spill identification are precise and reliable, but require extensive sample preparation, long instrument runs, and time-consuming data processing. Direct analysis in real time time-of-flight mass spectrometry (DART-ToF MS) has been employed to screen for spilled petroleum oils, with results obtained in mere hours. The present study introduced an innovative, simple, and fast oil sampling method using hydrophobic filter paper and demonstrated its compatibility with DART-ToF MS analysis. Motor oils, jet fuels, marine diesels, crude oils, intermediate fuel oils, heavy fuel oils, and diluted bitumen were collected using the filter paper sampling method. Classification models were constructed from the spectral data by heat map inspection followed by principal component analysis (PCA) and discriminant analysis of principal components (DAPC). Oil slicks and weathered oil slicks were prepared from five oil types, and samples from each slick were collected using filter paper. Results: The filter paper technique allowed for effective oil sampling and data acquisition by DART-ToF MS for diluted source oils, oil slicks and weathered oil slicks. Classification via the constructed DAPC models indicated that the DART-ToF MS instrument in tandem with filter paper sampling and multivariate statistics can accurately identify common oil types, with significant improvement of sample collection and turnaround time. Conclusions: The promising classification results, simple sample collection, and rapid data analysis illustrate the potential use of hydrophobic filter paper and DART-ToF MS as tools in managing large scale oil spill emergency situations. [ABSTRACT FROM AUTHOR]

Oil spill forensic investigations are often challenging due to many confounding variables such as sample weathering, oil composition complexities, and the quality or quantity of collected materials, but the difficulty is further compounded when dealing with mixed oils. In this case, well-established oil fingerprinting techniques become inadequate, including gas chromatography-flame ionization detection (GC/FID) and gas chromatography-mass spectrometry (GC/MS) diagnostic ratio analysis. In dealing with mixtures of highly refined lubricating (lube) oils, GC/FID analysis often yields inconclusive results, while diagnostic ratio analysis can be compromised by missing or low response biomarker compounds. The present study explored the feasibility of addressing the challenges of mixed lube oil analysis through a multi-tiered analytical approach. This analysis supplemented traditional GC/FID and GC/MS diagnostic ratio analyses with multivariate statistics to rapidly screen large data sets. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) proved to be effective and intuitive qualitative methods for visualizing, differentiating, and characterizing four highly similar lube oil mixtures. Non-linear mixing patterns that were significant in the diagnostic ratio analysis were far less evident through LDA. Overall, these findings lay the groundwork for promising future study involving multivariate statistical approaches to complex mixed oil forensic cases. [ABSTRACT FROM AUTHOR]