The Multivariate Kernel Likelihood Ratio Method Applied on Comparison of Amphetamine Seizures
2014 (English)Conference paper, Abstract (Other academic)
Comparison of seizures of amphetamine with respect to their origins of illicit manufacturing can be done by investigating the amphetamine impurity pattern. Such an impurity pattern is a result of an incomplete cleaning-up process – typical for illicit manufacturing – when producing the drug. The manufacturing process can be divided into three steps: (1) choosing a recipe for how to produce; (2) producing amphetamine oil; and finally (3) precipitating the amphetamine from the oil.
The impurity pattern of the amphetamine will depend on the recipe itself, the conditions used for the synthesis, the precipitation process and the method of cleaning-up. The impurity profile is a chromatogram of around 150 different contaminants, of these contaminants 26 have been used by several European countries in police intelligence work to link manufacturers of illicit drugs . However, the linkage methods used are investigative and not evaluative.
The issue addressed when two specific seizures are to be compared, and the results are going to be used in the court of law, is whether they originate from the same precipitation batch. When this is true the impurity patterns of the two seizures are in general expected to be similar, at least for stable contaminants. This is a less expected result if the seizures originate from different batches.
Interpretation of observed similarities and differences between the impurity patterns of two seizures is still to a large extent based on subjective judgements where in Sweden the experiences of two forensic experts are used. In this presentation we show how the so-called multivariate kernel likelihood ratio approach  can be used for this interpretation. From a designed experiment comprising several recipes, the variance components for a subset or for a lower-dimensional projection of all contaminants are estimated and likelihood ratios can then be easily calculated. A cross-validatory study shows high sensitivity as well as high specificity of the likelihood ratios.
Place, publisher, year, edition, pages
forensic science, Bayesian inference, multivariate distribution, drugs profiling
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-118545OAI: oai:DiVA.org:liu-118545DiVA: diva2:815460
9th International Conference on Forensic Inference and Statistics (ICFIS2014)