Evaluation of organic and inorganic gunshot residues in various populations using LC-MS/MS

• NCJ Number: 304108
• Journal: Forensic Chemistry Volume: 27 Dated: March 2022
• Author(s): William Feeney; Korina Menking-Hoggatt; Luis Arroyo; James Curran; Suzanne Bell ; Tatiana Trejos
• Date Published: March 2022
• Grant Number(s): 2018-DU-BX-0186; 2019-R2-CX-0044; 2020-DQ-BX-0010
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Fast Screening of Firearm Discharge Residues by Laser-based Spectrochemical Methods, Electrochemical Sensors, and Chemometrics; Validation of a Single Instrument, Single Sample Protocol for the Detection of the Inorganicand Organic Constituents of Firearms Discharge Residue; Comprehensive Assessment of Novel Reference Materials and Analytical Methods for the Analysis and Interpretation of Organic and Inorganic Gunshot Residues

Annotation

This research investigated the prevalence of organic and inorganic gunshot residue within two main subpopulations: 1) non-shooters, including groups with low- and high-risk of potentially containing GSR-like residues; and 2) individuals involved in a firing event (shooters, bystanders, and shooters performing post-shooting activities).

Abstract

The study analyzed just over 400 samples via a liquid chromatography-mass spectrometry (LC-MS/MS) methodology with complexing agents. Exploratory statistical tools and machine learning algorithms (neural networks, NN) were used to evaluate the resulting mass spectral and quantitative data. This study observed lower occurrences of OGSR compounds in the non-shooter populations compared to IGSR analytes. The presence of GSR on authentic shooters versus other potential sources of false positives, such as bystanders and professions including police officers, agricultural workers, and mechanics, were further assessed by using machine-learning algorithms trained with the observed OGSR/IGSR traces. The probability of false negatives was also estimated with groups who performed regular activities after firing. In addition, the low-risk background set enabled documentation of GSR occurrence in the general population. The probabilistic outputs of the neural network models were used to calculate likelihood ratios (LR) to measure the weight of the evidence. Using both the IGSR and OGSR profiles, the NN model’s accuracy ranged from 90 to 99 percent, depending on the subpopulation complexity. The log-LR histograms and Tippet plots show the method can discriminate between each sub-population and low rates of misleading evidence, suggesting that the proposed approach can be effectively used for a probabilistic interpretation of GSR evidence. (publisher abstract modified)