The evaluation of evidence relating to traces of cocaine on banknotes

Amy Wilson, Colin Aitken*, Richard Sleeman, James Carter

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Banknotes can be seized from crime scenes as evidence for suspected association with illicit drug dealing. Tandem mass spectrometry data are available from banknotes seized in criminal investigations, as well as from banknotes from general circulation. The aim of the research is to evaluate the support provided by the data gathered in a criminal investigation for the proposition that the banknotes from which the data were obtained are associated with a person who is associated with a criminal activity related to cocaine in contrast to the proposition that the banknotes are associated with a person who is not associated with a criminal activity involving cocaine. The data considered are the peak area for the ion count for cocaine product ion m/z 105. Previous methods for assessment of the relative support for these propositions were concerned with the percentage of banknotes contaminated or assume independence of measurements of quantities between adjacent banknotes. Methods which account for an association of the quantity of drug on a banknote with that on adjacent banknotes are described. The methods are based on an autoregressive model of order one and on two versions of a nonparametric approach. The results are compared with a standard model which assumes measurements on individual banknotes are independent; there is no autocorrelation. Performance is assessed using rates of misleading evidence and a recommendation made as to which method to use. (C) 2014 Published by Elsevier Ireland Ltd.

Original languageEnglish
Pages (from-to)67-76
Number of pages10
JournalForensic Science International
Publication statusPublished - Mar 2014

Keywords / Materials (for Non-textual outputs)

  • Autoregressive model
  • Banknotes
  • Cocaine
  • Evidence evaluation
  • Likelihood ratio
  • Nonparametric density estimation


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