The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.
Keywords: GSR; SPCE; electrochemistry; gunshot residues; inorganic; machine learning algorithms; organic; screen-printed carbon electrodes; simultaneous detection; square-wave anodic stripping voltammetry.
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