The application of 'Big Data Analysis' techniques in GSR research: a novel approach to an ever evolving forensic discipline

Although the use of SEM/EDX equipment for GunShot Residue (GSR) analysis was already introduced in the 1980's and has since been used for decades in the search for microscopic primer particles, the technique has continuously evolved. Part of the drive for this ongoing development comes from the continuous changes in the composition of ammunition primers. Recently, munition manufacturers are progressing away from the 'classic' compositions, containing (heavy) metals, with the introduction of primers containing no metallic elements. Especially this recent innovation poses severe problems for modern analysis systems. The commercial GSR analysis software depends on the BackScattered Electron signal of the metal GSR particles to set them apart from the Environmental Particles (EP), which are present in abundance on any sampler. However, as the mean Z of these metal-free GSR particles will approach that of the EP, the standard procedures and the parameter settings of these search algorithms will probably fail. Although as a partial solution other signals could be used for the detection of the relevant particles, such as Secondary Electrons or Cathode Luminescence, a much larger number of potential GSR particles will have to be analysed because a number of EP will also be selected as potential GSR particles. Finally, the EDX classification algorithms may encounter problems in discerning GSR particles from EP because of their similar chemical composition. The use of Big Data Analysis (BDA) techniques is a novel approach in the GSR field, which may yield a solution for a number of the problems posed by these new primers. In order to implement these BDA techniques, a database of the GSR particles is compiled, together with databases of EP. Against these 'Ground Truth' databases, a test sample's particle populations can be compared as a group, which will potentially yield a shortlist of munition types which produce similar particle groups. In order to develop and test these techniques, databases were compiled using classic munition data which was readily available from case samples. In this presentation, the preliminary results of this study, which involves researchers from three Belgian universities, all working within the iMinds ICON BAHAMAS project (2015-2016), will be discussed.

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