Development of a UHPLC method for the detection of organic gunshot residues using artificial neural networks

Abstract

The introduction of lead and heavy-metal free ammunition to the market challenges the current protocol for gunshot residue (GSR) investigations, which focuses on the inorganic components. Future proofing GSR analysis requires the development and implementation of new methods for the collection and analysis of organic GSR (OGSR) into operational protocols. This paper describes the development and optimisation of an ultra high performance liquid chromatography method for the analysis of 32 compounds potentially present in OGSR. An artificial neural network was applied to predict the retention times of the target analytes for various gradients for rapid determination of optimum separation conditions. The final separation and analysis time for the 32 target analytes was 27 minutes with limits of detection ranging from 0.03 to 0.21 ng. The method was applied to the analysis of smokeless powder and samples collected from the hands of a shooter following the discharge of a firearm. The results demonstrate that the method has the potential for use in cases involving GSR.