Origin identification of homemade pepper spray by multivariate data analysis of chemical attribution signatures.

Chemical attribution signatures Chemical forensics Homemade pepper spray Multivariate data analysis

Journal

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

Informations de publication

Date de publication:
Nov 2019
Historique:
received: 21 05 2019
revised: 03 09 2019
accepted: 05 09 2019
pubmed: 1 10 2019
medline: 1 10 2019
entrez: 1 10 2019
Statut: ppublish

Résumé

Riot control agents such as pepper sprays can be misused for antagonistic and criminal purposes. Several web-pages and YouTube videos are available describing how to make homemade pepper spray. In this study, we investigated whether it was possible to identify the origin of homemade pepper sprays based on chemical attribution signatures from thirteen different types of chili acquired from six different vendors analyzed by GC-MS. The results showed that it was possible to differentiate chili based on species, chili type and vendor using OPLS-DA. Application of an external test set of chilies acquired and extracted one year later than development of the models resulted in correct classification in all models. The models displayed high predictability, suggesting their use for prediction of the identity and origin of seized homemade pepper sprays.

Identifiants

pubmed: 31568951
pii: S0379-0738(19)30368-8
doi: 10.1016/j.forsciint.2019.109956
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109956

Informations de copyright

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

Auteurs

Lina Mörén (L)

The Swedish Defence Research Agency, FOI Cementvägen 20, 901 82 Umeå, Sweden.

Sebastian Jonsson (S)

Umeå University, 901 87 Umeå, Sweden.

Tobias Tengel (T)

The Swedish Defence Research Agency, FOI Cementvägen 20, 901 82 Umeå, Sweden.

Anders Östin (A)

The Swedish Defence Research Agency, FOI Cementvägen 20, 901 82 Umeå, Sweden. Electronic address: anders.ostin@foi.se.

Classifications MeSH