Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR)
File(s)
Published version
Author(s)
Gallidabino, MD
Barron, LP
Weyermann, C
Romolo, FS
Type
Journal Article
Abstract
Evidence association in forensic cases involving gunshot residue (GSR) remains very challenging. Herein, a new in silico approach, called quantitative profile-profile relationship (QPPR) modelling, is reported. This is based on the application of modern machine learning techniques to predict the pre-discharge chemical profiles of selected ammunition components from those of the respective post-discharge GSR. The obtained profiles can then be compared with one another and/or with other measured profiles to make evidential links during forensic investigations. In particular, the approach was optimised and successfully tested for the prediction of GC-MS profiles of smokeless powders (SLPs) from organic GSR in spent cases, for nine ammunition types. Results showed a high degree of similarity between predicted and experimentally measured profiles, after adequate combination and evaluation of fourteen machine learning techniques (median correlation of 0.982). Areas under the curve (AUCs) of 0.976 and 0.824 were observed after receiver operating characteristic (ROC) analysis of the results obtained in the comparisons between predicted-predicted and predicted-measured profiles, respectively, in the specific case that the ammunition types of interest were excluded from the training dataset (i.e., extrapolation). Furthermore, AUCs of 0.962 and 0.894 were observed in interpolation mode. These values were close to those of the comparison of the measured SLP profiles between themselves (AUC = 0.998), demonstrating excellent potential to correctly associate evidence in a number of different forensic scenarios. This work represents the first time that a quantitative approach has successfully been applied to associate a GSR to a specific ammunition.
Date Issued
2019-02-21
Date Acceptance
2018-11-02
Citation
Analyst, 2019, 144 (4), pp.1128-1139
ISSN
0003-2654
Publisher
The Royal Society of Chemistry
Start Page
1128
End Page
1139
Journal / Book Title
Analyst
Volume
144
Issue
4
Copyright Statement
This journal is © The Royal Society of Chemistry 2019 This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
License URL
Identifier
http://www.scopus.com/inward/record.url?scp=85061287530&partnerID=8YFLogxK
Publication Status
Published
Date Publish Online
2018-11-02