The evaluation of evidence for microspectrophotometry data using functional data analysis.

Evidence evaluation Functional data analysis Likelihood ratio Microspectrophotometry

Journal

Forensic science international
ISSN: 1872-6283
Titre abrégé: Forensic Sci Int
Pays: Ireland
ID NLM: 7902034

Informations de publication

Date de publication:
Dec 2019
Historique:
received: 06 07 2019
revised: 01 10 2019
accepted: 21 10 2019
pubmed: 23 11 2019
medline: 23 11 2019
entrez: 23 11 2019
Statut: ppublish

Résumé

Microspectrophotometry data arise in the study of many forensically applicable situations. The situations here are those of ink and fibres. In a criminal investigation, data associated with a crime scene are compared with data associated with a person of interest. Methods based on the likelihood ratio are often used to evaluate such evidence. A technique known as functional data analysis for determining likelihood ratios using the full spectrum is described. It provides support comparing a proposition of common source with a proposition of different sources for data from the crime scene and from the person of interest. Data are available from ink, woollen and cotton fibres. The effectiveness of the method is assessed using false positive and false negative rates and Tippett plots in the comparison of data from known sources.

Identifiants

pubmed: 31756683
pii: S0379-0738(19)30419-0
doi: 10.1016/j.forsciint.2019.110007
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110007

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Colin Aitken (C)

School of Mathematics and Maxwell Institute, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK. Electronic address: c.g.g.aitken@ed.ac.uk.

Ya-Ting Chang (YT)

School of Mathematics and Maxwell Institute, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK. Electronic address: ytchang.sabrina@ed.ac.uk.

Patrick Buzzini (P)

Department of Forensic Science, Sam Houston State University, Huntsville, TX, USA. Electronic address: patrick.buzzini@shsu.edu.

Grzegorz Zadora (G)

Institute of Forensic Research, Krakow, Poland; Institute of Chemistry, The University of Silesia in Katowice, Katowice, Poland. Electronic address: gzadora@ies.krakow.pl.

Genevieve Massonnet (G)

School of Criminal Justice, University of Lausanne, Switzerland. Electronic address: genevieve.massonet@unil.ch.

Classifications MeSH