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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 ±1

Over the years, forensic science has primarily positioned itself as a service provider for the criminal justice system, following the dominant and traditional reactive law enforcement model. Unfortunately, this focus has limited its capacity to provide knowledge about crime systems and to support other forms of policing styles through forensic intelligence. Although forensic intelligence research has steadily developed over the last few years, it is rarely covered in the core of academic teaching and research programs. Developing forensic intelligence programs would empower graduates with an awareness of forensic intelligence meaning and models, creating great opportunities to shape their future professional activities and progressively shift the dominant paradigm through a bottom-up approach. In this article, the teaching and learning strategies in forensic intelligence developed at the University of Lausanne (Switzerland) and adapted at the University of Technology Sydney (Australia) and the Université du Québec à Trois-Rivières (Canada) are presented. The objective behind the strategy is to reflect on and work on real case scenarios using a progressive teaching and learning approach that builds upon the theory and practical exercise putting students in real-life situations. Through this innovative learning process, students move away from the Court as the sole end purpose of forensic science. They learn to adopt different roles, adopt a proactive attitude as well as work individually and collaboratively. This teaching and learning strategy breaks the current silos observed in the forensic science discipline by focusing on processes and critical thinking. It can be foreseen, through the evolution of crime and policing models, that the learning and teaching strategy described in this article offers and will offer the students with many new job opportunities. The article concludes with the advantages that such teaching and learning programs in forensic intelligence

Providing information about substances injected can reduce the negative impact of illicit drug consumption and support people who inject drugs to make informed decisions. In Australia, information about drugs injected relies largely on periodic self-report surveys. For the first time, the analysis of the residual content of used injecting equipment was conducted in a supervised injecting facility (SIF) located in Sydney, Australia. The aim was to gain a better understanding of the substances injected by clients through: (1) chemical analyses of the content of used syringes; (2) comparison of these results with clients' self-reported drug use; and (3) assessing the usefulness of analysing other injecting equipment to detect substances used. During one week in February 2019, syringes and other injecting equipment were collected at the Sydney SIF. Their residual content was analysed by gas-chromatography/mass-spectrometry. Heroin was the most commonly detected substance (present in 51% of syringes), followed by methamphetamine (22%) and oxycodone (10%). In addition to the main psychoactive substance, cutting agents reported in the literature were also detected in used syringes. The main psychoactive substance identified by laboratory analysis reliably corresponded with users' self-reported drug type. Analytical confirmation of substances injected allows for the provision of better targeted harm reduction messaging based on timely and objective data. The approach used is amenable to clients and feasible in the Australian SIF context. Upscaling and wider implementation could be done through Needle and Syringe Programs, and would support the early detection of harmful substances entering drug markets and better inform harm reduction strategies.