Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR).

Evidence association in forensic cases involving gunshot residue (GSR) remains very challenging. Herein, a new in silico approach, called quantitative profile-profile relationship (QPPR) modelling, is reported. This is based on the application of modern machine learning techniques to predict the pre-discharge chemical profiles of selected ammunition components from those of the respective post-discharge GSR. The obtained profiles can then be compared with one another and/or with other measured profiles to make evidential links during forensic investigations. In particular, the approach was optimised and successfully tested for the prediction of GC-MS profiles of smokeless powders (SLPs) from organic GSR in spent cases, for nine ammunition types. Results showed a high degree of similarity between predicted and experimentally measured profiles, after adequate combination and evaluation of fourteen machine learning techniques (median correlation of 0.982). Areas under the curve (AUCs) of 0.976 and 0.824 were observed after receiver operating characteristic (ROC) analysis of the results obtained in the comparisons between predicted-predicted and predicted-measured profiles, respectively, in the specific case that the ammunition types of interest were excluded from the training dataset (i.e., extrapolation). Furthermore, AUCs of 0.962 and 0.894 were observed in interpolation mode. These values were close to those of the comparison of the measured SLP profiles between themselves (AUC = 0.998), demonstrating excellent potential to correctly associate evidence in a number of different forensic scenarios. This work represents the first time that a quantitative approach has successfully been applied to associate a GSR to a specific ammunition.

Characterization of volatile organic gunshot residues in fired handgun cartridges by headspace sorptive extraction.

In forensic investigation of firearm-related cases, determination of the residual amount of volatile compounds remaining inside a cartridge could be useful in estimating the time since its discharge. Published approaches are based on following the decrease of selected target compounds as a function of time by using solid phase micro-extraction (SPME). Naphthalene as well as an unidentified decomposition product of nitrocellulose (referred to as "TEA2") are usually employed for this purpose. However, reliability can be brought into question given their high volatility and low reproducibility of the extracted procedure. In order to identify alternatives and therefore develop improved dating methods, an extensive study on the composition and variability of volatile residues in nine different types of cartridges was carried out. Analysis was performed using headspace sorptive extraction (HSSE), which is a more exhaustive technique compared to SPME. One hundred sixty six compounds were identified (several of which for the first time), and it was observed that the final compositional characteristics of each residue were strongly dependent on its source. Variability of single identified compounds within and between different types of cartridge as well as their evolution over time were also studied. Many explosion products containing up to four aromatic rings were found to be globally present in high proportions. Twenty-seven of them (excluding naphthalene) also presented detectable decreases during the first 24 h. Therefore, they could be used as complementary target analytes in future dating methods.

A new quantitative method for gunshot residue analysis by ion beam analysis.

Imaging and analyzing gunshot residue (GSR) particles using the scanning electron microscope equipped with an energy dispersive X-ray spectrometer (SEM-EDS) is a standard technique that can provide important forensic evidence, but the discrimination power of this technique is limited due to low sensitivity to trace elements and difficulties in obtaining quantitative results from small particles. A new, faster method using a scanning proton microbeam and Particle Induced X-ray Emission (µ-PIXE), together with Elastic Backscattering Spectrometry (EBS) is presented for the non-destructive, quantitative analysis of the elemental composition of single GSR particles. In this study, the GSR particles were all Pb, Ba, Sb. The precision of the method is assessed. The grouping behaviour of different makes of ammunition is determined using multivariate analysis. The protocol correctly groups the cartridges studied here, with a confidence >99%, irrespective of the firearm or population of particles selected.