Development and evaluation of portable NIR technology for the identification and quantification of Australian illicit drugs.

Fursman, H; Morelato, M; Chadwick, S; Coppey, F; Esseiva, P; Roux, C; Stojanovska, N

Development and evaluation of portable NIR technology for the identification and quantification of Australian illicit drugs.

Fursman, H Morelato, M

Chadwick, S

Coppey, F Esseiva, P Roux, C Stojanovska, N

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Publisher:: ELSEVIER IRELAND LTD
Publication Type:: Journal Article
Citation:: Forensic Sci Int, 2024, 362, pp. 112179
Issue Date:: 2024-09

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Field	Value	Language
dc.contributor.author	Fursman, H
dc.contributor.author	Morelato, M https://orcid.org/0000-0001-7616-0623
dc.contributor.author	Chadwick, S https://orcid.org/0000-0003-2006-9841
dc.contributor.author	Coppey, F
dc.contributor.author	Esseiva, P
dc.contributor.author	Roux, C
dc.contributor.author	Stojanovska, N
dc.date.accessioned	2024-09-04T23:13:54Z
dc.date.available	2024-07-02
dc.date.available	2024-09-04T23:13:54Z
dc.date.issued	2024-09
dc.identifier.citation	Forensic Sci Int, 2024, 362, pp. 112179
dc.identifier.issn	0379-0738
dc.identifier.issn	1872-6283
dc.identifier.uri	http://hdl.handle.net/10453/180620
dc.description.abstract	The efficient and accurate analysis of illicit drugs remains a constant challenge in Australia given the high volume of drugs trafficked into and around the country. Portable drug testing technologies facilitate the decentralisation of the forensic laboratory and enable analytical data to be acted upon more efficiently. Near-infrared (NIR) spectroscopy combined with chemometric modelling (machine learning algorithms) has been highlighted as a portable drug testing technology that is rapid and accurate. However, its effectiveness depends upon a database of chemically relevant specimens that are representative of the market. There are chemical differences between drugs in different countries that need to be incorporated into the database to ensure accurate chemometric model prediction. This study aimed to optimise and assess the implementation of NIR spectroscopy combined with machine learning models to rapidly identify and quantify illicit drugs within an Australian context. The MicroNIR (Viavi Solutions Inc.) was used to scan 608 illicit drug specimens seized by the Australian Federal Police comprising of mainly crystalline methamphetamine hydrochloride (HCl), cocaine HCl, and heroin HCl. A number of other traditional drugs, new psychoactive substances and adulterants were also scanned to assess selectivity. The 3673 NIR scans were compared to the identity and quantification values obtained from a reference laboratory in order to assess the proficiency of the chemometric models. The identification of crystalline methamphetamine HCl, cocaine HCl, and heroin HCl specimens was highly accurate, with accuracy rates of 98.4 %, 97.5 %, and 99.2 %, respectively. The sensitivity of these three drugs was more varied with heroin HCl identification being the least sensitive (methamphetamine = 96.6 %, cocaine = 93.5 % and heroin = 91.3 %). For these three drugs, the NIR technology provided accurate quantification, with 99 % of values falling within the relative uncertainty of ±15 %. The MicroNIR with NIRLAB infrastructure has demonstrated to provide accurate results in real-time with clear operational applications. There is potential to improve informed decision-making, safety, efficiency and effectiveness of frontline and proactive policing within Australia.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER IRELAND LTD
dc.relation	Australian Federal Police
dc.relation.ispartof	Forensic Sci Int
dc.relation.isbasedon	10.1016/j.forsciint.2024.112179
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Legal & Forensic Medicine
dc.subject.mesh	Illicit Drugs
dc.subject.mesh	Spectroscopy, Near-Infrared
dc.subject.mesh	Australia
dc.subject.mesh	Humans
dc.subject.mesh	Substance Abuse Detection
dc.subject.mesh	Machine Learning
dc.subject.mesh	Methamphetamine
dc.subject.mesh	Heroin
dc.subject.mesh	Humans
dc.subject.mesh	Methamphetamine
dc.subject.mesh	Heroin
dc.subject.mesh	Spectroscopy, Near-Infrared
dc.subject.mesh	Substance Abuse Detection
dc.subject.mesh	Australia
dc.subject.mesh	Machine Learning
dc.subject.mesh	Illicit Drugs
dc.title	Development and evaluation of portable NIR technology for the identification and quantification of Australian illicit drugs.
dc.type	Journal Article
utslib.citation.volume	362
utslib.location.activity	Ireland
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Forensic Science (CFS)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-09-04T23:13:43Z
pubs.publication-status	Published
pubs.volume	362

Abstract:

The efficient and accurate analysis of illicit drugs remains a constant challenge in Australia given the high volume of drugs trafficked into and around the country. Portable drug testing technologies facilitate the decentralisation of the forensic laboratory and enable analytical data to be acted upon more efficiently. Near-infrared (NIR) spectroscopy combined with chemometric modelling (machine learning algorithms) has been highlighted as a portable drug testing technology that is rapid and accurate. However, its effectiveness depends upon a database of chemically relevant specimens that are representative of the market. There are chemical differences between drugs in different countries that need to be incorporated into the database to ensure accurate chemometric model prediction. This study aimed to optimise and assess the implementation of NIR spectroscopy combined with machine learning models to rapidly identify and quantify illicit drugs within an Australian context. The MicroNIR (Viavi Solutions Inc.) was used to scan 608 illicit drug specimens seized by the Australian Federal Police comprising of mainly crystalline methamphetamine hydrochloride (HCl), cocaine HCl, and heroin HCl. A number of other traditional drugs, new psychoactive substances and adulterants were also scanned to assess selectivity. The 3673 NIR scans were compared to the identity and quantification values obtained from a reference laboratory in order to assess the proficiency of the chemometric models. The identification of crystalline methamphetamine HCl, cocaine HCl, and heroin HCl specimens was highly accurate, with accuracy rates of 98.4 %, 97.5 %, and 99.2 %, respectively. The sensitivity of these three drugs was more varied with heroin HCl identification being the least sensitive (methamphetamine = 96.6 %, cocaine = 93.5 % and heroin = 91.3 %). For these three drugs, the NIR technology provided accurate quantification, with 99 % of values falling within the relative uncertainty of ±15 %. The MicroNIR with NIRLAB infrastructure has demonstrated to provide accurate results in real-time with clear operational applications. There is potential to improve informed decision-making, safety, efficiency and effectiveness of frontline and proactive policing within Australia.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180620