Analysis of gunshot residue from nontoxic ammunition by laser-induced breakdown spectroscopy and machine learning algorithms.

Gunshot residue (GSR) is defined as particles generated upon the discharge of ammunition from a firearm. The main components of ammunition include the primer, cartridge case, and bullet. GSR particles originated from a combination of these components as well as from internal firearm parts. For conventional ammunition, GSR can be reliably identified by detecting Pb, Ba, and Sb using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). In contrast, GSR from nontoxic ammunition lacks these markers, making SEM-EDS detection ineffective. Laser-induced breakdown spectroscopy (LIBS) was used to analyze GSR-NTA particles collected directly from shooters' hands to identify potential chemical fingerprints. Spectra were acquired across two spectral ranges (186-1042 nm and 186-570 nm), and elements such as H, N, O, C, Ti, Zn, Cu, Ba, Sr, Fe, Mg, and Al were detected. Multivariate analysis and machine learning (ML) algorithms were applied. The dataset was divided into training and external validation sets, with linear discriminant analysis (LDA) achieving 100 % classification accuracy. Spectral analysis revealed that Zn, Ti, Cu, and Fe were the primary elements responsible for sample differentiation, with minor contributions from Ba and Sr. In conclusion, the combination of LIBS and ML shows potential as a forensic tool for identifying GSR-NTA particles on the hands of individuals who have, or have not, discharged a firearm.