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 quantitative assessment of the extent and distribution of textile fibre transfer to persons involved in physical assault.

Knowledge of the number of fibres transferred during a particular activity is essential for the interpretation of findings in similar criminal cases. In this regard, violent contacts and physical assaults still present a challenge, due to a lack of robust published data. Hereby, we present the outcome of an empirical study where different assault activities were simulated by a Jiu Jitsu team and participants were asked to play either the role of an aggressive 'assailant' or a defensive 'victim', wearing cotton garments (i.e., Gi's). Four different scenarios were simulated in replicates (n = 5), each of them involving different intensity levels (low and high) and duration times (30 and 60 s). Results showed that approximately 1,000 to 44,000 fibres were cross-transferred between the participants' garments, with noticeable differences between the different scenarios. These values were significantly larger than those published in previous studies and, therefore, suggested the possibility of a current underestimation of the number of fibres transferred in physical assaults. Furthermore, statistical analysis by ANOVA indicated that the all the variables tested (i.e., intensity level, duration time, and participants role) had a significant effect on the number of transferred fibres (p < 0.001) and, consequently, that some knowledge of the case circumstances may be important to make more educated estimations. This is the first time that such a methodology has been applied for the quantitative assessment of fibre transfer between participants in assault activities. Data are expected to help practitioners with the interpretation of findings in real casework and lead to a more robust evidential assessment.

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.

A study on contactless airborne transfer of textile fibres between different garments in small compact semi-enclosed spaces.

Interpretation of fibre evidence at activity level requires extensive knowledge of all the possible transfer mechanisms that may explain the presence of fibres on a recipient surface of interest. Herein, we investigate a transfer method that has been largely understudied in previous literature: contactless transfer between garments through airborne travel. Volunteers were asked to wear UV-luminescent garments composed of different textile materials and situate themselves in a semi-enclosed space (elevator) for a pre-determined period of time with other participants, who wore non-luminescent recipient garments. The latter were then inspected for fibres using UV-luminescent photographic techniques. Results showed that contactless transfer between garments is possible. Indeed, a number of fibres were observed after most of the experiments. As many as 66 and 38 fibres were observed in the experiments involving cotton and polyester donor garments, compared to 2 and 1 fibres in those involving acrylic and wool donor garments, respectively. In this regard, the type of donor garment was found to be a significant factor. Multifactorial ANOVA supported these observations (p<0.001) and further indicated a statistically significant influence of elevator door opening/closing (p<0.001), people entering/exiting (p=0.078) and the recipient garment (p=0.030). Therefore, contactless transfer of fibres between garments can occur and can do so in (ostensibly) high numbers. This should be taken into consideration when interpreting fibre evidence at activity level and may have a major implication for the assignment of evidential values in some specific cases.

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.

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%, tR %RSDs <0.4% and δm/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.

Comparative Assessment of a Novel Photo-Anthropometric Landmark-Positioning Approach for the Analysis of Facial Structures on Two-Dimensional Images.

Positioning landmarks in facial photo-anthropometry (FPA) applications remains today a highly variable procedure, as traditional cephalometric definitions are used as guidelines. Herein, a novel landmark-positioning approach, specifically adapted for FPA applications, is introduced and, in particular, assessed against the conventional cephalometric definitions for the analysis of 16 landmarks on ten frontal images by two groups of examiners (with and without professional knowledge of anatomy). Results showed that positioning reproducibility was significantly better using the novel method. Indeed, in contrast to the classic approach, very low landmark dispersions were observed for both groups of examiners, which were usually below the strictest clinical standards (i.e., 0.575 mm). Furthermore, the comparison between the two groups of examiners highlighted higher dispersion consistencies, which supported a higher robustness. Thus, the use of an adapted landmark-positioning approach proved to be highly advantageous in FPA analysis and future work in this field should consider adopting similar methodologies.

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.

Prediction of bioconcentration factors in fish and invertebrates using machine learning.

The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23-0.73 and 0.34-1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.

DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models.

The field of DNA intelligence focuses on retrieving information from DNA evidence that can help narrow down large groups of suspects or define target groups of interest. With recent breakthroughs on the estimation of geographical ancestry and physical appearance, the estimation of chronological age comes to complete this circle of information. Recent studies have identified methylation sites in the human genome that correlate strongly with age and can be used for the development of age-estimation algorithms. In this study, 110 whole blood samples from individuals aged 11-93 years were analysed using a DNA methylation quantification assay based on bisulphite conversion and massively parallel sequencing (Illumina MiSeq) of 12 CpG sites. Using this data, 17 different statistical modelling approaches were compared based on root mean square error (RMSE) and a Support Vector Machine with polynomial function (SVMp) model was selected for further testing. For the selected model (RMSE = 4.9 years) the mean average error (MAE) of the blind test (n = 33) was calculated at 4.1 years, with 52% of the samples predicting with less than 4 years of error and 86% with less than 7 years. Furthermore, the sensitivity of the method was assessed both in terms of methylation quantification accuracy and prediction accuracy in the first validation of this kind. The described method retained its accuracy down to 10 ng of initial DNA input or ∼2 ng bisulphite PCR input. Finally, 34 saliva samples were analysed and following basic normalisation, the chronological age of the donors was predicted with less than 4 years of error for 50% of the samples and with less than 7 years of error for 70%.

Suspect screening of halogenated carboxylic acids in drinking water using ion exchange chromatography - high resolution (Orbitrap) mass spectrometry (IC-HRMS).

Retrospective in silico screening of analytical data for the identification of new or emerging disinfection by-products in drinking waters could be useful to assess quality and potential hazards, as well as help implement mitigation procedures more rapidly. Herein, the first study coupling ion exchange chromatography (IC) with high resolution mass spectrometry (HRMS) for the determination of halogenated carboxylic acid disinfectant by-products is reported. Separation was achieved using a Metrohm A Supp 5 column and a Na2CO3/NaHCO3 gradient eluent from 1/0.31 to 10/3.1mM. A variety of solid phase extraction (SPE) sorbents were tested for added selectivity to organic ions and Isolute ENV+ cartridges were selected because of their best overall extraction performance. Method LODs were in the µgL-1 concentration range, with R2 ≥ 0.99 for all the analytes, and isobaric ions could be easily discriminated using HRMS. The method was applied to municipal drinking water. Targeted quantitative analysis revealed the presence of 10 haloacetic acids at levels not exceeding the limits set by WHO and USEPA. Furthermore, suspect screening for additional halogenated carboxylic acids via retrospective HRMS data analysis also indicated the presence of other iodinated HAAs and chlorinated propionic acids, of which one (i.e. monochloropropionic acid) is discussed here for the first time. Most importantly, several potential suspects could be eliminated from further consideration through HRMS data analysis alone. To our knowledge, this represents the first time that a retrospective IC-HRMS screen of halogenated carboxylic acids in drinking water has been reported.