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

Forensic profiling of smokeless powders (SLPs) by gas chromatography-mass spectrometry (GC-MS): a systematic investigation into injector conditions and their effect on the characterisation of samples

Smokeless powders (SLPs) are composed of a combination of thermolabile and non-thermolabile compounds. When analysed by GC-MS, injection conditions may therefore play a fundamental role on the characterisation of forensic samples. However, no systematic investigations have ever been carried out. This casts doubt on the optimal conditions that should be adopted in advanced profiling applications (e.g. class attribution and source association), especially when a traditional split/splitless (S/SL) injector is used. Herein, a study is reported that specifically focused on the evaluation of the liner type (Ltype) and inlet temperature (Tinj). Results showed that both could affect the exhaustiveness and repeatability of the observed chemical profiles, with Ltype being particularly sensitive despite typically not being clarified in published works. Perhaps as expected, degradation effects were observed for the most thermolabile compounds (e.g. nitroglycerin) at conditions maximising the heat transfer rates (Ltype = packed and Tinj ≥ 200 °C). However, these did not seem to be as influential as, perhaps, suggested in previous studies. Indeed, the harshest injection conditions in terms of heat transfer rate (Ltype = packed and Tinj = 260 °C) were found to lead to better performances (including better overall %RSDs and LODs) compared to the mildest ones. This suggested that implementing conditions minimising heat-induced breakdowns during injection was not necessarily a good strategy for comparison purposes. The reported findings represent a concrete step forward in the field, providing a robust body of data for the development of the next generation of SLP profiling methods.

A feasibility study of direct analysis in real time-mass spectrometry for screening organic gunshot residues from various substrates

This study reports the use of direct analysis in real time-mass spectrometry (DART-MS) for the detection of organic gunshot residues (OGSR) in a variety of matrices of interest for forensics, customs, and homeland security. Detection limits ranged from (0.075 to 12) ng, with intra- and inter-day reproducibility below 0.0012% CV. The collection of mass spectra at multiple in-source collision-induced dissociation (is-CID) voltages produced distinctive mass spectral signatures with varying levels of fragmentation and allowed differentiation of isomers. To test method performance, a collection of 330 authentic specimens from various substrates were analyzed - (1) neat smokeless powders, (2) spent cartridge cases, (3) burnt particles removed from clothing via carbon stubs or (4) with tweezers, and hand samples from (5) non-shooters, and (6) shooters. A subset of hand specimens (n = 80) was further analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) for confirmation and comparison. Seven types of ammunition from five manufacturers and two calibers were monitored for OGSR profiles with similar compositions observed for paired sets (e.g., unburnt smokeless powder and the respective residues on spent cartridges, clothing, and hands). No false positives were observed across all datasets. A 100% true positive rate (TPR) was observed for all substrates except the shooters' hands. Depending on the ammunition type and classification criteria, the shooters' hands exhibited a TPR ranging from 19% to 73%. The results show that DART-MS is feasible and versatile for fast screening of OGSR across various substrates but may benefit from alternative approaches to improve detection at trace levels.

Elemental Characterization of Leaded and Lead-Free Inorganic Primer Gunshot Residue Standards Using Single Particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

This study describes the use of single particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) for the detection and classification of inorganic gunshot residue (IGSR) particles. To establish reliable multi-element criteria to classify IGSR particles, leaded and lead-free IGSR reference materials were analyzed, and the elemental compositions of the individual particles were quantified. The results suggest that expanded element compositions may be used to classify IGSR particles via spICP-TOFMS compared to those used in conventional IGSR analysis using scanning electron microscopy energy dispersive X-ray spectroscopy (SEM-EDS). For spICP-TOFMS analysis of leaded IGSR particles, classification may be based on the presence of lead (Pb), antimony (Sb), and barium (Ba) just as in SEM-EDS; however, additional particle types, such as lead-copper (Pb-Cu) particles, contribute significantly (∼30%) to the leaded IGSR particle population. In lead-free IGSR particles, the dominate multi-metal particle composition found is titanium-zinc (Ti-Zn) with a conserved Zn:Ti ratio of 1.4:1, but other elements, such as copper (Cu), are also characteristic. In mixtures of the two IGSR reference materials, we were able to classify over 80% of the multi-metal particles detected with no false-positive particle-type assignments. With spICP-TOFMS, particles smaller than those typically measured by SEM-EDS are detected, with estimated median diameters for leaded and lead-free IGSR of 180 and 320 nm, respectively. Through measuring these smaller particles, up to ∼two times more particles per mL are recorded by spICP-TOFMS compared to that found by SEM-EDS. Overall, high-sensitivity and high-throughput analysis using spICP-TOFMS enables quantitative, rapid multi-elemental characterization, and classification of individual IGSR particles.

Should inorganic or organic gunshot residues be analysed first?

Gunshot residues (GSR) collected during the investigation of firearm-related incidents can provide useful information for the reconstruction of the events. Two main types of GSR traces can be targeted by forensic scientists, the inorganic (IGSR) and the organic GSR (OGSR). Up to now, forensic laboratories have mainly focused on the detection of inorganic particles on the hands and clothes of a person of interest using carbon stubs analysed by scanning electron microscopy coupled with energy dispersive X-ray spectrometry (SEM/EDS). Several approaches have been proposed to also analyse the organic compounds since they might bring additional information for the investigation. However, implementing such approaches might disrupt the detection of IGSR (and vice versa depending on the applied sequence of analysis). In this work, two sequences were compared for the combined detection of both types of residues. One carbon stub was used for collection, and the analysis was performed either by targeting the IGSR or the OGSR first. The aim was to evaluate which one allows maximum recovery of both types of GSR while minimising losses that might occur at different stages of the analysis process. SEM/EDS was used for the detection of IGSR particles while an ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used for the analysis of OGSR compounds. Extracting OGSR first required the implementation of an extraction protocol that did not interfere with the IGSR particles present on the stub. Both sequences allowed good recovery of the inorganic particles since no significant difference was observed in the detected concentrations. However, OGSR concentrations were lower after IGSR analysis than before for two compounds (ethyl and methylcentralite). Thus, it is advised to extract rapidly the OGSR before or after IGSR analysis to avoid losses during the storage and analysis processes. The data also indicated that there was a low correlation between IGSR and OGSR highlighting the potential of a combined detection and analysis of both types of GSR.

Comparison of four commercial solid-phase micro-extraction (SPME) fibres for the headspace characterisation and profiling of gunshot exhausts in spent cartridge casings

Headspace solid-phase micro-extraction (SPME) is a promising technique for the characterisation and profiling of gunshot exhausts in spent cartridge casings, especially for health and environmental risk assessments, as well as forensic purposes. To date, however, no comprehensive investigation has been carried out to objectively assess the kinds of compound released during a discharge that can be recovered by this approach, the selectivity of the main commercially available fibres, and their relative performances for the analysis of gunshot exhausts and the discrimination of different ammunition types. This study aimed to fill this gap. Gunshot exhausts in spent cartridge casings from four different ammunition types were analysed by GC–MS, after extraction with four different commercial fibres: 100 μm polydimethylsiloxane (PDMS), 85 μm polyacrylate (PA), 65 μm polydimethylsiloxane/divinylbenzene (DVB), and 85 μm carboxen/polydimethylsiloxane (CAR). Results showed that, overall, a total of 120 analytes could be observed across the cartridges, but the different tested fibres also displayed distinct performances, which were, to some extent, complementary for the characterisation of gunshot exhausts. DVB, in particular, recovered the most compounds simultaneously. On the other hand, the observed variability between measurements was also high, making it a poor candidate for (semi-)quantitative applications (e.g. estimation of time since discharge and/or source profiling). In this regard, PA demonstrated the highest potential for broad use and implementation in multi-purpose methods.

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.

Rapid analysis of gunshot residues with single-particle inductively coupled plasma time-of-flight mass spectrometry

Gunshot residues (GSRs) from different types of ammunition have been characterized using a new method based on single-particle inductively coupled plasma time-of-flight mass spectrometry (sp-ICP-TOF-MS). This method can analyze thousands of particles per minute enabling rapid sample screening for GSR detection with minimal sample preparation. GSR particles are multi-elemental nanoparticles that are mainly defined by the elements lead, barium, and antimony. Sp-ICP-TOF-MS was also used to identify other elements contained in GSR particles while standard particle classification protocols do not consider the complexities of GSR compositions and can therefore miss out on valuable information. The proposed method can be used to support existing GSR detection methods, especially when lead-free, antimony-free, or tagged ammunition has been used; it also provides a possibility for multi-elemental fingerprinting of GSR particles.

Time since last discharge of firearms and spent ammunition elements: state of the art and perspectives

The estimation of the time since last discharge of firearms or spent ammunition elements (e.g., casings) may provide crucial information in the investigation of a shooting incident and, eventually, the following trial. Herein, an exhaustive review of the methods described in the literature is reported, with the aim to evaluate their potential and limitations from a forensic perspective. This work, in particular, highlighted the fact that a number of investigations have been carried out in the field during the last century (with an especially high rate in the last 30 years), but the implementation of related procedures in forensic laboratories is still rare. The situation has been discussed and a series of propositions have been forwarded, in order to overcome challenges and facilitate the implementation of dating approaches in real casework.

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

Targeted and non-targeted forensic profiling of black powder substitutes and gunshot residue using gradient ion chromatography - high resolution mass spectrometry (IC-HRMS)

A novel and simplified gradient IC-HRMS approach is presented in this work for forensic profiling of ionic energetic material residues, including low-order explosives and gunshot residue (GSR). This new method incorporated ethanolic eluents to facilitate direct coupling of IC and HRMS without auxiliary post-column infusion pumps that are traditionally used to assist with gas phase transfer. Ethanolic eluents also enabled better integration with an in-service protocol for direct analysis of high-order organic explosives by IC-HRMS, without requiring solvent exchange before injection. Excellent method performance was achieved, enabling both full scan qualitative and quantitative analysis, as required. In particular, linearity for 19 targeted compounds yielded R² > 0.99 across several orders of magnitude, with trace analysis possible at the low-mid pg level. Reproducibility and mass accuracies were also excellent, with peak area %RSDs <10%, t_R %RSDs <0.4% and δ_m/z < 3 ppm. The method was applied to targeted analysis of latent fingermarks and swabbed hand sweat samples to determine contact with a black-powder substitute containing nitrate, benzoate and perchlorate. When combined with principal component analysis (PCA), the effect of time since handling on recorded signals could be interpreted further in order to support forensic investigations. In a second, non-targeted application, PCA using full scan IC-HRMS data enabled classification of GSR from three different types of ammunition. An additional 20 markers of GSR were tentatively identified in silico, in addition to the 15 anions detected during targeted analysis. This new approach therefore streamlines and adds consistency and flexibility to forensic analysis of ionic energetic material. Furthermore, it also has implications for targeted, non-targeted and suspect screening applications in other fields by expanding the separation space to low molecular weight inorganic and organic anions.