Tatiana Trejos

Glass fragments are frequently recovered during criminal investigations as they can provide links between the crime scene, suspects, or victims. The elemental composition of glass is often used to determine if there is a source commonality between glass samples. Micro‐X‐ray fluorescence spectrometry (μXRF) is a standard technique for elemental glass comparisons due to its high informing power, low cost, rapid analysis time, and non‐destructive nature. In recent years, advancements in μXRF technology, such as silicon drift detectors (SDD), have improved precision and analysis time and increased adoption in crime laboratories. Furthermore, the superior precision afforded by SDD technology has been hypothesized to allow for accurate analysis of much thinner glass fragments, expanding its applicability to casework scenarios where smaller fragments are encountered. This study compares results for the μXRF analysis of full‐thickness (≈2 mm) and thin glass fragments (10 to 50 μm) for different types of float glass. The proposed modified 3s comparison criterion results in a false exclusion rate of less than 2.5% and a false inclusion rate of less than 1.5% for full‐thickness fragments. Thin fragments yielded false exclusion and false inclusion rates of less than 12% and 7.5%, respectively. A spectral similarity metric, spectral contrast angle ratio (SCAR), was tested to quantitatively evaluate spectral similarity, achieving accuracies of greater than 98% and 91% for full‐thickness fragments and thin fragments, respectively. These findings show that while full‐thickness fragments produce more precise data, μXRF‐SDD is suitable for fragments as thin as 10 μm.

This study provides novel information about the flow and deposition dynamics of GSR in the environment and on a shooter, bystander, or passerby, which will lead to greater confidence in evidence interpretation.

Several organizations have outlined the need for standardized methods for conducting physical fit comparisons. This study answers this call by developing and evaluating a systematic and transparent approach for examining, documenting, and interpreting textile physical fits, using qualitative feature descriptors and a quantitative metric (Edge Similarity Score, ESS) for the physical fit examination of textile materials. Here, the results from 1027 textile physical fit comparisons are reported. This includes the evaluation of inter and intraanalyst variation when using this method for hand‐torn and stabbed fabrics. ESS higher than 80% and ESS lower than 20%, respectively, support fit and nonfit conclusions. The results show that analyst accuracy ranges from 88% to 100% when using this criterion. The estimated false‐positive rate for this dataset (2% false positives, 10 of 477 true nonfit pairs) demonstrates the importance of assessing the quality of a physical fit during an examination and reveals that potential errors are low, but possible in textile physical fit examinations. The risk of error must be accounted for in the interpretation and verification processes. Further analysis shows that factors such as the separation method, construction, and design of the samples do not substantially influence the ESS values. Additionally, the proposed method is independently evaluated by 15 practitioners in an interlaboratory exercise that demonstrates satisfactory reproducibility between participants. The standardized terminology and documentation criteria are the first steps toward validating approaches to streamline the peer review process, minimize bias and subjectivity, and convey the probative value of the evidence.

A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.

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.