Author:
Tikkisetty Krishnaja,McCallum Paige,Filewood Taylor,Yan Jeffrey,Kwok Honoria,Brunswick Pamela,Cody Robert,Shang Dayue
Abstract
Abstract
Background
The biomarker diagnostic ratio analysis outlined by the European Committee for Standardization is considered the current gold standard in oil forensic analysis. However, it has a major limitation as an emergency response procedure in the case of a large scale oil spill due to the high number of samples collected, long GC/MS instrument run time, and the time-consuming data processing required. This current study utilized direct analysis in real time time-of-flight mass spectrometry to develop a rapid spilled oil screening method. An exploratory search of biomarkers and synthetic additives was conducted on reference oil samples of various types. To build a robust yet swift procedure for oil typing, specific heat maps were built with extensive reference sample modelling. These heat maps were then used to select relevant ions from which principal component analysis and discriminant analysis of principal component models were constructed to result in defensible oil classifications.
Results
The initial exploratory search of biomarkers and additives in the various reference oil samples resulted in promising preliminary matches. The heat map and multivariate statistical analysis oil typing method was applied to three unknown samples, all of which were classified accurately.
Conclusion
The merit of direct analysis in real time time-of-flight mass spectrometry on oil forensic was confirmed with the detected biomarkers compound class starting members and lubricating additives along with the successful application of heat maps and multivariate statistical analysis, providing a swift yet reliable screening tool for oil spill environmental monitoring and impact surveying.
Publisher
Springer Science and Business Media LLC
Reference32 articles.
1. Abdi H, Williams LJ (2010) Principal component analysis. WIREs Comp. Stat 2:433–459. https://doi.org/10.1002/wics.101
2. Brody TM, Di Bianca P, Krysa J (2012) Analysis of inland crude oil spill threats, vulnerabilities and emergency response in the midwest United States. Risk Anal 10:1741–1749. https://doi.org/10.1111/j.1539-6924.2012.01813.x
3. Brunswick P, Cuthbertson D, Yan J, Chua C, Duchesne I, Isabel N, Evans P, Gasson P, Kite G, Bruno J, van Aggelen G, Shang D (2021) A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis. Environ Adv 5:100089. https://doi.org/10.1016/j.envadv.2021.100089
4. CEN (2021) Oil spill identification–Petroleum and petroleum related products–Part 2: analytical method and interpretation of results based on GC-FID and GC-low resolution-MS analyses. J Hazard Mater 435:129027
5. Chua CC, Brunswick P, Kwok H, Yan J, Cuthbertson D, van Aggelen G, Helbing CC, Shang D (2020) Enhanced analysis of weathered crude oils by gas chromatography-flame ionization detection, gas chromatography-mass spectrometry diagnostic ratios, and multivariate statistics. J Chromatogr 1634:461689. https://doi.org/10.1016/j.chroma.2020.461689
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献