A probabilistic approach to heroin signatures

D. Brynn Hibbert, Danielle Blackmore, Jianfeng Li, Diako Ebrahimi, Michael Collins, Sasha Vujic, Paul Gavoyannis

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The probability density functions of amount ratios of compounds (total codeine/total morphine, 6-monoacetylemorphine/total morphine, papaverine/total morphine, and noscapine/total morphine) from the analysis of seized heroin, originating from known world regions (South East Asia, South West Asia, South America, Mexico) allows calculation of likelihood ratios for 'unknown' samples. Application of Bayes Theorem with a suitable prior probability, for example the frequency of a particular region in the database, leads to the probability that a particular profile comes from a given target region. Data from 2549 seizures of heroin at Australia's border illustrates the method, and results are compared with simple HS1 ratio approaches for assigning geographical origin. The method can be implemented in a spreadsheet and gives more refined intelligence of the origins of seized drugs than simple ranges.

Original languageEnglish (US)
Pages (from-to)765-773
Number of pages9
JournalAnalytical and Bioanalytical Chemistry
Volume396
Issue number2
DOIs
StatePublished - Jan 1 2010

Keywords

  • Bayes' theorem
  • Bayesian classification
  • Codeine
  • Diacetyl morphine
  • Drug identification
  • Heroin
  • Heroin analysis
  • Heroin signature program
  • Illicit drug analysis
  • Likelihood
  • Monoacetyl morphine
  • Morphine
  • Probability density function

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