RESUMEN
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.
Asunto(s)
Criminales , Heridas por Arma de Fuego , Medicina Legal , Mano , Humanos , Aprendizaje AutomáticoRESUMEN
Currently, forensic drug experts are facing chemical identification challenges with the increasing number of new isomeric forms of psychoactive substances occurring in case samples. Very similar mass spectra for these substances could easily result in misidentification using the regular GC-MS screening methods in combination with colorimetric testing in forensic laboratories. Building on recent work from other groups, this study demonstrates that GC-VUV is a powerful technique for drug isomer differentiation, showing reproducible and discriminating spectra for aromatic ring-isomers. MS and VUV show complementary selectivity as VUV spectra are ring-position specific whereas MS spectra are characteristic for the amine moieties of the molecule. VUV spectra are very reproducible showing less than 0.1 deviation in library match scores and therefore small spectral differences suffice to confidently distinguish isomers. In comparison, MS match scores gave over 10 deviation and showed significant overlap in match score ranges for several isomers. This poses a risk for false positive identifications when assigning compounds based on retention time and GC-MS mass spectrum. A strategy was developed, based on Kernel Density Estimations of match scores, to construct Receiver Operating Characteristic (ROC) curves and estimate likelihood ratios (LR values) with respect to the chemical differentiation of drug related isomers. This approach, and the added value of GC-VUV is demonstrated with the chemical analysis of several samples from drug case work from the Amsterdam area involving both compounds listed in Dutch drug legislation (3,4-MDMA; 3,4-MDA; 4-MMC; 4-MEC and 4-FA) as well as their unlisted and thus uncontrolled isomers (2,3-MDMA; 2,3-MDA; 2- and 3-MMC; 2- and 3-MEC and 2- and 3-FA).