We demonstrate a novel system for the detection and discrimination of varying levels of exposure to gunshot residue from subjects in various control scenarios. Our aim is to address the key challenge of minimizing the false positive identification of individuals suspected of discharging a firearm. The chemometric treatment of voltammetric data from different controls using Canonical Variate Analysis (CVA) provides several distinct clusters for each scenario examined. Multiple samples were taken from subjects in controlled tests such as secondary contact with gunshot residue (GSR), loading a firearm, and postdischarge of a firearm. These controls were examined at both bare carbon and gold-modified screen-printed electrodes using different sampling methods: the 'swipe' method with integrated sampling and electroanalysis and a more traditional acid-assisted q-tip swabbing method. The electroanalytical fingerprint of each sample was examined using square-wave voltammetry; the resulting data were preprocessed with Fast Fourier Transform (FFT), followed by CVA treatment. High levels of discrimination were thus achieved in each case over 3 classes of samples (reflecting different levels of involvement), achieving maximum accuracy, sensitivity, and specificity values of 100% employing the leave-one-out validation method. Further validation with the 'jack-knife' technique was performed, and the resulting values were in good agreement with the former method. Additionally, samples from subjects in daily contact with relevant metallic constituents were analyzed to assess possible false positives. This system may serve as a potential method for a portable, field-deployable system aimed at rapidly identifying a subject who has loaded or discharged a firearm to verify involvement in a crime, hence providing law enforcement personnel with an invaluable forensic tool in the field.