Citation

Matzen T, Kukurin C, van de Wetering J, Ariëns S, Bosma W, Knijnenberg A, Stamouli A, Ypma RJ. Forensic Sci. Int. 2022; 335: e111293.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.forsciint.2022.111293

PMID

35462180

Abstract

Comparative gunshot residue analysis addresses relevant forensic questions such as 'did suspect X fire shot Y?'. More formally, it weighs the evidence for hypotheses of the form H(1): gunshot residue particles found on suspect's hands are from the same source as the gunshot residue particles found on the crime scene and H(2): two sets of particles are from different sources. Currently, experts perform this analysis by evaluating the elemental composition of the particles using their knowledge and experience. The aim of this study is to construct a likelihood-ratio (LR) system based on representative data. Such an LR system can support the expert by making the interpretation of the results of electron microscopy analysis more empirically grounded. In this study we chose statistical models from the machine learning literature as candidates to construct this system, as these models have been shown to work well for large and high-dimensional datasets. Using a subsequent calibration step ensured that the system outputs well-calibrated LRs. The system is developed and validated on casework data and an additional validation step is performed on an independent dataset of cartridge data. The results show that the system performs well on both datasets. We discuss future work needed before the method can be implemented in casework.

Language: en

Keywords

Machine learning; Casework data; Comparative GSR analysis; Evidence evaluation; Gunshot residue; Likelihood ratio (LR)