Method development for forensic oil identification by direct analysis in real time time-of-flight mass spectrometry

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Language of the publication
English
Date
2023-11-02
Type
Submitted manuscript
Author(s)
  • Tikkisetty, Krishnaja
  • Filewood, Taylor
  • Yan, Jeffrey
  • Kwok, Honoria
  • Brunswick, Pamela
  • Cody, Robert
  • Shang, Dayue
Publisher
Royal Society of Chemistry

Abstract

The current well established chromatography and mass spectrometry based oil spill identification procedures, such as those outlined by the European Committee for Standardization, are highly reliable as methods, highly defensible in the court of law, and widely applicable to majority of oil spill situations. Nevertheless, the methodology is time consuming and labour intensive, which may not be ideal when dealing with emerency oil spill situation. In this study, direct analysis in real time time-of-flight mass spectrometry (DART/TOFMS) was used to successfully develop an efficient oil identification method. To confirm the accuracy of this method the spilled oil samples were tested from five previous years of blind Round Robin testing organized by the oil spill identification network of experts (OSINET) under the Bonn Agreement. Heatmap inspection, principal component analysis and finally discriminant analysis of principal components were used to arrive final predictions regarding the identities of the spilled oil samples. The results were compared with previous gas chromatography flame ionization detection (GC/FID) and gas chromatography triple quadrupole mass spectrometry (GC/MS/MS) analyses of the same oils. While taking only about a tenth of the time, the DART/TOFMS analysis produced results similar to that of classical GC/FID and GC/MS/MS procedures. The ability of DART/TOFMS to display this level of validity exemplifies its potential a new tool for supplementing classical analyses for oil spill forensics.

Subject

  • Nature and environment,
  • Science and technology

Rights

Pagination

9 pages

Peer review

No

Open access level

Green

Identifiers

ISSN
1759-9679

Article

Journal title
Analytical Methods
Journal volume
15
Journal issue
44
Accepted date
2023-10-16
Submitted date
2023-07-25

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Collection(s)

Economy and industry

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