Search

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., (© 2023 The Authors. Journal of Forensic Sciences published by Wiley Periodicals LLC on behalf of American Academy of Forensic Sciences.)

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., (© 2023 The Authors. Journal of Forensic Sciences published by Wiley Periodicals LLC on behalf of American Academy of Forensic Sciences.)

This study assists the interpretation of glass and paint evidence by filling an existing gap in the background occurrence that reflects the socioeconomic and demographic circumstances in the United States. The collection was performed in a college US city (Morgantown, West Virginia) to determine the effect of the type of clothing worn at different seasons on the presence of glass and paint. Tape lifts and sole scrapings (1038) were collected from 210 participants and up to six clothing and footwear areas per individual. Glass fragments were analyzed via polarized light microscopy (PLM), refractive index (RI), micro-X-Ray fluorescence (μXRF), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), while paint specimens were examined by light microscopy and infrared spectroscopy (μFTIR). Higher occurrences of glass and paint were found in the winter season. The winter collection yielded 10 glass fragments and 68 paint particles, whereas the summer collection resulted in one glass fragment and 23 paint particles. The percentage of individuals with traces varied between seasons; 7% of individuals in the winter and 0.9% in the summer had glass, whereas 36% of individuals in the winter and 19% in the summer bore paint. Lastly, when considering the overall garment and footwear areas, glass was detected in 1.4% of the winter set, compared to 0.2% in the summer collection; paint was found in 9.2% of the winter collection, whereas only 4.2% was found in the summer set. There were no instances where both glass and paint were detected on the clothing and footwear of the same individual., (© 2023 American Academy of Forensic Sciences.)

Analysis of gunshot residue currently lacks effective screening methods that can be implemented in real time at the crime scene. Historically, SEM-EDS has been the standard for analysis; however, advances in technology have brought portable instrumentation to the forefront of forensic science disciplines, including the screening of GSR. This study proposes electrochemical methods with disposable screen-printed carbon electrodes for GSR screening at the laboratory and points of care due to their rapid, cost-efficient, and compact platform. GSR residues were extracted from typical aluminum/carbon adhesive collection stubs and analyzed via square-wave anodic stripping voltammetry. Benchtop and portable electrochemical instruments were compared for the assessment and classification of authentic shooter samples by monitoring a panel of inorganic and organic GSR elements and compounds including lead, antimony, copper, 2,4-dinitrotoluene, diphenylamine, nitroglycerin, and ethyl centralite. The evaluation included the assessment of figures of merit and performance measures from quality controls, nonshooter, and shooter data sets. Samples collected from the hands of 200 background individuals (nonshooters), and shooters who fired leaded ammunition (100) and lead-free ammunition (50) were analyzed by the benchtop and portable systems with accuracies of 95.7% and 96.5%, respectively. The findings indicate that electrochemical methods are fast, sensitive, and specific for the identification of inorganic and organic gunshot residues. The portable potentiostat provided results comparable with the benchtop system, serving as a proof-of-concept to transition this methodology to crime scenes for a practical and inexpensive GSR screening that could reduce backlogs, improve investigative leads, and increase the impact of gunshot residues in forensic science., (© 2022 American Academy of Forensic Sciences.)

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management., (© 2020 American Academy of Forensic Sciences.)

This study applies Raman microspectroscopy to differentiate the chemical components in printing inks of different brands, colors, and type using the 532 nm and 785 nm excitation wavelengths. Spectra were collected from 319 inks (78 inkjet, 76 toner, 79 offset, and 86 intaglio) representing various colors. Comparisons were performed to calculate discrimination capability percentages for each ink type. Overall, Raman microspectroscopy differentiates according to the following hierarchy: intaglio (96%), inkjet (92%), offset (90%), and toner (61%). The ability of Raman microspectroscopy to differentiate between same-colored inks from different brands was dependent on the color and ink analyzed. Based on ink color, the discrimination capability ranged from 75 to 94% (inkjet), 0 to 86% (toner), and 0 to 77% (offset). Copper phthalocyanine was detected in cyan inks and various intaglio inks, while carbon black was identified in black inkjet, offset, and intaglio inks., (© 2017 American Academy of Forensic Sciences.)

A searchable printing ink database was designed and validated as a tool to improve the chemical information gathered from the analysis of ink evidence. The database contains 319 samples from printing sources that represent some of the global diversity in toner, inkjet, offset, and intaglio inks. Five analytical methods were used to generate data to populate the searchable database including FTIR, SEM-EDS, LA-ICP-MS, DART-MS, and Py-GC-MS. The search algorithm based on partial least-squares discriminant analysis generates a similarity "score" used for the association between similar samples. The performance of a particular analytical method to associate similar inks was found to be dependent on the ink type with LA-ICP-MS performing best, followed by SEM-EDS and DART-MS methods, while FTIR and Py-GC-MS were less useful in association but were still useful for classification purposes. Data fusion of data collected from two complementary methods (i.e., LA-ICP-MS and DART-MS) improves the classification and association of similar inks., (© 2016 American Academy of Forensic Sciences.)

Improvements in printing technology have exacerbated the problem of document counterfeiting, prompting the need for analytical techniques that better characterize inks for forensic analysis and comparisons. In this study, 319 printing inks (toner, inkjet, offset, and Intaglio) were analyzed directly on the paper substrate using scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). As anticipated, the high sensitivity of LA-ICP-MS pairwise comparisons resulted in excellent discrimination (average of ~ 99.6%) between different ink samples from each of the four ink types and almost 100% correct associations between ink samples known to originate from the same source. SEM-EDS analysis also resulted in very good discrimination for different toner and intaglio inks (>97%) and 100% correct association for samples from the same source. SEM-EDS provided complementary information to LA-ICP-MS for certain ink types but showed limited utility for the discrimination of inkjet and offset inks., (© 2016 American Academy of Forensic Sciences.)

Forensic analysis of glass samples was performed in different laboratories within the NITE-CRIME (Natural Isotopes and Trace Elements in Criminalistics and Environmental Forensics) European Network, using a variety of Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) systems. The main objective of the interlaboratory tests was to cross-validate the different combinations of laser ablation systems with different ICP-MS instruments. A first study using widely available samples, such as the NIST SRM 610 and NIST SRM 612 reference glasses, led to deviations in the determined concentrations for trace elements amongst the laboratories up to 60%. Extensive discussion among the laboratories and the production of new glass reference standards (FGS 1 and FGS 2) established an improved analytical protocol, which was tested on a well-characterized float glass sample (FG 10-1 from the BKA Wiesbaden collection). Subsequently, interlaboratory tests produced improved results for nearly all elements with a deviation of < 10%, demonstrating that LA-ICP-MS can deliver absolute quantitative measurements on major, minor and trace elements in float glass samples for forensic and other purposes.