Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data.

Comparative gunshot residue analysis addresses relevant forensic questions such as 'did suspect X fire shot Y?'. More formally, it weighs the evidence for hypotheses of the form H1: gunshot residue particles found on suspect's hands are from the same source as the gunshot residue particles found on the crime scene and H2: two sets of particles are from different sources. Currently, experts perform this analysis by evaluating the elemental composition of the particles using their knowledge and experience. The aim of this study is to construct a likelihood-ratio (LR) system based on representative data. Such an LR system can support the expert by making the interpretation of the results of electron microscopy analysis more empirically grounded. In this study we chose statistical models from the machine learning literature as candidates to construct this system, as these models have been shown to work well for large and high-dimensional datasets. Using a subsequent calibration step ensured that the system outputs well-calibrated LRs. The system is developed and validated on casework data and an additional validation step is performed on an independent dataset of cartridge data. The results show that the system performs well on both datasets. We discuss future work needed before the method can be implemented in casework.

Conduction of a round-robin test on a real sample for the identification of gunshot residues by SEM/EDX.

A round-robin test on the identification of GSR particles by SEM/EDX and involving eleven Institutes was conducted on a real sample, in order to evaluate the possibilities/limitations of using such sample to get additional information (compared to the analysis of the usual synthetic sample used within the framework of the ENFSI proficiency test) about the performances of the SEM/EDX systems. Each Institute was asked to analyse this sample following its own standard operating procedure, and by using all the systems in house, whenever available. Between each Institute, a check of the sample was performed by the organizing Institute (NICC), in order inter alia to monitor any degradation and/or contamination of the sample. A total of about 30 analyses were performed on the sample. For each particle of interest identified on the real sample, the detection effectiveness was monitored, as well as the classification allotted by each Institute. The Institutes were also asked to report some of their measurement parameters, and to send the results as they would have been communicated in their own case report. A quite good agreement was observed with regard to the classification of the particles of interest, since a broad consensus was reached for approximately 75% of these particles. A different classification risk exists for some classes, the barium/antimony classes being probably the most critical, as traces of lead may cause the particles to shift (or not) from the consistent with GSR upper-class to the characteristic of GSR upper-class; in the end, the decision to shift from one class to another strongly depends on local rules. At the end of the campaign, a survey sent to collect experience and lessons learned from this exercise showed that analysing a real sample definitively offers an added value, especially in terms of classification process (during the automatic run and when performing the manual review) of particles.

Large area imaging of forensic evidence with MA-XRF.

This study introduces the use of macroscopic X-ray fluorescence (MA-XRF) for the detection, classification and imaging of forensic traces over large object areas such as entire pieces of clothing and wall paneling. MA-XRF was sufficiently sensitive and selective to detect human biological traces like blood, semen, saliva, sweat and urine on fabric on the basis of Fe, Zn, K, Cl and Ca elemental signatures. With MA-XRF a new chemical contrast is introduced for human stain detection and this can provide a valuable alternative when the evidence item is challenging for conventional techniques. MA-XRF was also successfully employed for the chemical imaging and classification of gunshot residues (GSR). The full and non-invasive elemental mapping (Pb, Ba, Sr, K and Cl) of intact pieces of clothing allows for a detailed shooting incident reconstruction linking firearms and ammunition to point of impact and providing information on the shooting angle. In high resolution mode MA-XRF can even be used to provide information on the shooting order of different ammunition types. Finally, by using the surface penetration of X-rays we demonstrate that the lead signature of a bullet impact can be easily detected even if covered by multiple layers of wall paint or human blood.