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New applications of gas chromatography and gas chromatography-mass spectrometry for novel sample matrices in the forensic sciences: A literature review

Gould, Oliver; Nguyen, Ngoc; Honeychurch, Kevin C.

New applications of gas chromatography and gas chromatography-mass spectrometry for novel sample matrices in the forensic sciences: A literature review Thumbnail


Authors

Ngoc Nguyen



Abstract

The investigation of novel sample matrices in the forensic sciences offers several possible advantages, such as allowing for results to be obtained in cases where common sample types are absent. This review focuses on the application of gas chromatography and gas chromatography–mass spectrometry (GC-MS) for the determination of drugs in alternative sample matrices, including hair, sweat, meconium, breast milk, and vitreous humour. Less common sample types are also reported including air, cerumen, insects, and their larvae and pupae. The application of pyrolysis GC-MS (Py GC-MS) is also reviewed, showing the possibility of determining high molecular weight drugs which would commonly be unattainable by GC-MS. The application of Py GC-MS for the simulation and investigation of the underlying chemistry and the products formed in the smoking of drugs is also reported.

Citation

Gould, O., Nguyen, N., & Honeychurch, K. C. (2023). New applications of gas chromatography and gas chromatography-mass spectrometry for novel sample matrices in the forensic sciences: A literature review. Chemosensors, 11(10), Article 527. https://doi.org/10.3390/chemosensors11100527

Journal Article Type Review
Acceptance Date Oct 3, 2023
Online Publication Date Oct 7, 2023
Publication Date Oct 7, 2023
Deposit Date Oct 23, 2023
Publicly Available Date Oct 24, 2023
Journal Chemosensors
Electronic ISSN 2227-9040
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 10
Article Number 527
DOI https://doi.org/10.3390/chemosensors11100527
Keywords Physical and Theoretical Chemistry; Analytical Chemistry
Public URL https://uwe-repository.worktribe.com/output/11170987

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