Review of the most common chemometric techniques in illicit drug profiling

[Display omitted] •The most common chemometric techniques in drug profiling are presented in this review.•Applications of comparison metrics, pattern recognition & class prediction are explored.•The outcomes from these techniques can be used to support intelligence-led policing. The information...

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Bibliographic Details
Published in:Forensic science international 2019-09, Vol.302, p.109911-109911, Article 109911
Main Authors: Popovic, Ana, Morelato, Marie, Roux, Claude, Beavis, Alison
Format: Article
Language:English
Subjects:
Adhesives
Amphetamines
Capillary electrophoresis
Chromatography
Cocaine
Comparison metrics
Drugs
Forensic intelligence
Forensic sciences
Fourier transforms
Heroin
Laboratories
Mass spectrometry
Methods
Pattern recognition
Ratios
Scientific imaging
Sensors
Solvents
Writing
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Summary:[Display omitted] •The most common chemometric techniques in drug profiling are presented in this review.•Applications of comparison metrics, pattern recognition & class prediction are explored.•The outcomes from these techniques can be used to support intelligence-led policing. The information generated through drug profiling can be used to infer a common source between one or several seizures as well as drug trafficking routes to provide insights into drug markets. Although well established, it is time-consuming and ineffective to compare all drug profiles manually. In recent years, there has been a push to automate processes to enable a more efficient comparison of illicit drug specimens. Various chemometric methods have been employed to compare and interpret forensic case data promptly. The intelligence that is produced can be used by decision-makers to disrupt or reduce the impact of illicit drug markets. This review highlights the most common chemometric techniques used in drug profiling and more specifically, the most efficient comparison metrics and pattern recognition techniques outlined in the literature.
ISSN:0379-0738
1872-6283
DOI:10.1016/j.forsciint.2019.109911