Tolulope Bayode Abejide 1 , Graham Souch 2 , David Olayemi Alebiosu 2 , 1University of Derby, United Kingdom, 2University of Derby , United Kingdom, 3Sunway University, Malaysia.
Machine Learning (ML) algorithms have become essential for forensic science, helping with the more accurate and objective analysis of evidence. The current study discusses the use of ML algorithms in classifying gunshot residue (GSR) particles by analysing spectral data acquired with scanning electron microscopy in combination with energy-dispersive X-ray spectroscopy (SEM-EDX). Traditionally, GSR analysis is based on finding inorganic particles that contain Lead (Pb), Barium (Ba), and Antimony (Sb), indicating discharge from firearm primer in Forensic science.
Twelve Spectra were generated as samples from four hand swabs taken from a simulation where a suspect just shot some bullets using a firearm. Control samples produced no result. GSR were examined to evaluate elemental distribution and ML-assisted classification. In addition, a statistical analysis of SEM-EDX spectra was performed along with probabilistic modelling, and likelihood ratios (LRs) were used to measure the evidential weight between two hypotheses. ML algorithms assisted in separating typical GSR particles from those belonging to other types.
The research showed considerable differences in elemental composition between the samples, demonstrating the necessity of applying appropriate statistical tools. Three out of the four classes of swab samples provided a solid ground for proving GSR presence and produced high LRs in favour of the prosecution hypothesis. At the same time, only one sample failed to comply with the model assumptions, demonstrating the risk of misinterpretation when using elemental signatures alone. All in all, the application of ML in combination with probabilistic modelling can provide a solid basis for forensic science analysis of gunshots, but there should be no room for neglecting methodology issues.
GSR, Pb-Ba-Sb, SEM-EDX, likelihood ratios, Machine Learning.
