This article reports on a project that focused on developing versatile and fast methodologies for detecting gunshot residue on hands and other surfaces, and on applying statistical methods for the probabilistic interpretation of GSR evidence; the article also reports on the project’s development of the most extensive GSR dataset ever reported from a single study.
Analysis of gunshot residue (GSR) can provide valuable investigative information in cases involving firearms: homicides, suicides, accidental shootings, and terrorism. But when it comes to the traditional methods of GSR detection, there are persistent concerns regarding the speed of analysis, the accuracy of results, and the applicability to new ammunition types. There is, consequently, a critical need to develop methods that can improve response time and increase certainty to protect the public and help investigators make informed decisions based on reliable data. Establishing methods for GSR analysis that have demonstrable error rates is challenging. The residue comprises organic components, which arise from the propellant and lubricant; and inorganic components, which arise from the primer, bullet, and cartridge casing. However, current methods using scanning electron microscopy and energy dispersive spectroscopy only analyze the inorganic components, require expensive laboratory instrumentation, and require hours to scan a single sample. A research team supported by the National Institute of Justice found that two screening techniques—electrochemical detection and laser-induced breakdown spectroscopy—were quicker than current laboratory-based and field tests, permitting the identification of organic and inorganic residues with 92-99% accuracy. Moreover, the techniques are practically non-destructive of the samples, allowing follow-up with confirmatory techniques to validate the results.