Centre for Forensic Science, University of Technology Sydney, PO Box 123 Broadway NSW 2007, Australia.
Centre for Forensic Science, University of Technology Sydney, PO Box 123 Broadway NSW 2007, Australia; Faculty of Science, UNSW Sydney, Kensington, NSW 2052, Australia.
Forensic Sci Int. 2021 Feb;319:110651. doi: 10.1016/j.forsciint.2020.110651. Epub 2020 Dec 5.
Illicit drug trafficking and in particular amphetamine-type stimulants continue to be a major problem in Australia. With the constant evolution of illicit drugs markets, it is necessary to gain as much knowledge about them to disrupt or reduce their impact. Illicit drug specimens can be analysed to generate forensic intelligence and understand criminal activities. Part of this analysis involves the evaluation of similarity scores between illicit drug profiles to interpret the link value. Most studies utilise one of two prominent score evaluation approaches, i.e. deterministic or Bayesian. In previous work, the notion of a dual approach was suggested, which emphasised the complementary nature of the two mentioned approaches. The aim of this study was to assess the operational capability of a dual approach in evaluating similarity scores between illicit drug profiles. Utilising a practical example, link values were generated individually from both approaches, then compared in parallel. As a result, it was possible to generate more informed hypotheses, relating to specimen linkage, due to the greater wealth of information available from the two approaches working concurrently. Additionally, it was shown that applying only one approach led to less information being generated during analysis as well as potentially important links between illicit drug specimens being missed.
非法药物贩运，特别是苯丙胺类兴奋剂，在澳大利亚仍然是一个主要问题。随着非法药物市场的不断演变，有必要尽可能多地了解这些市场，以破坏或减少其影响。可以对非法药物样本进行分析，以生成法医学情报并了解犯罪活动。分析的一部分涉及评估非法药物特征之间的相似性得分，以解释关联值。大多数研究使用两种突出的评分评估方法之一，即确定性或贝叶斯方法。在以前的工作中，提出了双重方法的概念，强调了这两种方法的互补性质。本研究的目的是评估双重方法在评估非法药物特征之间相似性得分方面的操作能力。利用一个实际案例，分别从两种方法中单独生成关联值，然后并行比较。结果表明，由于两种方法同时工作提供了更丰富的信息，因此可以生成更有根据的假设，这些假设与样本关联有关。此外，结果还表明，仅应用一种方法会导致分析过程中生成的信息量减少，并且可能会错过非法药物样本之间的重要关联。
