Thesis
Forensic Toxicological Profiling: Streamlined LC-MS and Extraction Protocol for Hair-Based Drug Detection
Murdoch University
Masters by Research, Murdoch University
2025
DOI:
https://doi.org/10.60867/00000051
Abstract
Hair analysis offers a unique advantage in forensic toxicology, enabling retrospective detection of drug use over extended periods. Yet, the chemical diversity of commonly abused and prescribed drugs poses a challenge to developing a unified extraction and analytical method. This study aimed to establish a streamlined extraction protocol and a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the detection of twelve drugs in hair, spanning stimulants, opioids, antidepressants, benzodiazepines, and anticholinergics.
Surface-spiked hair samples of varying pigmentation were used to evaluate extraction efficiency and analytical performance. A methanol-based extraction was optimised, followed by LC-MS/MS analysis. Method validation demonstrated strong linearity, sensitivity, and reproducibility across drug classes, with minor matrix effects observed in select hair types. The LC-MS/MS system, optimised for chromatographic separation and multiple reaction monitoring (MRM) transitions, achieved strong linearity (R² ≥ 0.99) across all analytes. Limits of detection ranged from 0.02 to 0.1 mg/L, with high precision (percentage coefficient of variation generally below 3%). Accuracy was within 5% for most drugs, though morphine showed variability across hair types, suggesting melanin-related matrix effects.
The research findings support the method’s forensic applicability and highlight the need for further validation using in vivo samples and matrix-matched controls. The results lay a foundation for standardised hair testing in forensic contexts, offering a robust approach to reliable multi-drug detection with high sensitivity and specificity.
Details
- Title
- Forensic Toxicological Profiling: Streamlined LC-MS and Extraction Protocol for Hair-Based Drug Detection
- Authors/Creators
- Shahista Rojah
- Contributors
- John Coumbaros (Supervisor) - Murdoch University, School of Medical, Molecular and Forensic SciencesBrendan Chapman (Supervisor) - Murdoch University, School of Medical, Molecular and Forensic Sciences
- Awarding Institution
- Murdoch University; Masters by Research
- Publisher
- Murdoch University
- Identifiers
- 991005851087707891
- Murdoch Affiliation
- School of Medical, Molecular and Forensic Sciences
- Resource Type
- Thesis
- Note
- Accelerated Research Masters with Training
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