An interlaboratory study evaluating the interpretation of forensic glass evidence using refractive index measurements and elemental composition

Interlaboratory study to evaluate background databases for the calculation of likelihood ratios in the interpretation of vehicle glass evidence using LA-ICP-MS data

Glass samples were analyzed by 13 laboratories participating in an interlaboratory study that used laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) with a standard test method (ASTM E2927-23) for the forensic analysis and comparison of vehicle glass. The aim of this study was to explore the performance of the application of a match criterion described in the standard test method and from likelihood ratio (LR) calculations when reporting the significance of glass evidence comparisons. Five (5) databases populated in different countries and combinations of the databases were used as background data to calculate LRs for two (2) casework scenarios involving vehicle glass comparisons. When the ASTM E2927-23 was used to compare vehicle glass samples that originated from the same source, all laboratories (except one) correctly reported the samples to be indistinguishable thus concluding that the possibility that the glass originated from the same source could not be eliminated. When the LR was calculated for the same comparison, most laboratories obtained large LR values (≈ 10,000) interpreted as "strong support" for same-source proposition. The LR rate of misleading evidence for the same-source (ROME-ss) comparisons was < 2 % for scenario 1. Comparing vehicle glass samples known to originate from different sources resulted in most laboratories reporting the glass to be "distinguishable" when using the ASTM standard method criterion or produced very small LR values (≈ 0.0001) when using the LR comparison criteria, interpreted as "strong (or very strong) support" for different-source proposition. The LR rate of misleading evidence for different-source (ROME-ds) comparisons for scenario 1 was < 21 %, which was due to the number of comparisons of glass samples that are chemically similar (different vehicles but same source of manufacturing). If the chemically similar glass comparisons from the same manufacturer were not treated as "different source", the ROME-ds was reduced to zero. Glass samples that were chemically similar (those that originated from different vehicles but were collected from the same make, model, and year or originated from the same vehicle but a different pane of glass) sometimes resulted in an LR value (≈ 1) interpreted as no support of either proposition or that the possibility that the glass originated from the same source could not be eliminated when using the ASTM match criterion. The laboratories reported approximately 20 % false support for same-source proposition (or "false inclusion") and 7 % false support for different-source proposition (or "false exclusion") when using the ASTM match criterion in the first scenario. All "false inclusions" were derived from the comparison of chemically similar samples, such as inner and outer panes from the same windshield, thus "error rates" on this dataset should not be generalized outside of the context of this study. A database composed of about 2000 background samples originating from different countries and analyzed in different laboratories, produced consistent results. When examined for calibration, all databases and their combinations had "false exclusion" rates below 5 % as well as "false inclusion" rates below 0.5 % for the ASTM calculation. The rate of misleading evidence of LR for same-source comparisons for the databases and their combinations was below 2 % and the rate of misleading evidence for different-source comparisons was below 2 %. An empirical cross entropy (ECE) plot was used to evaluate the calibration of all the databases and their combinations, which resulted in the log-likelihood ratio cost (Cllr) of less than 0.02.

Chemical characterization of automobile windshield glass samples for major, minor, and trace elemental concentration determination by INAA and its comparison with ED-XRF and DC Arc AES in terms of analytical capabilities and possible applications for glass forensics

Automobile (car) windshield glass fragments serve as important forensic evidentiary materials and their chemical characterization mainly at minor and trace concentration levels is a key step in forensic investigations. For such glass analysis as well as for forensics, direct solid sample analysis by suitable analytical technique(s) is very important. In view of this, instrumental neutron activation analysis (INAA) using high flux neutrons from research reactor was utilized for chemical characterization of car windshield glass samples. Energy dispersive X-ray fluorescence (ED-XRF) and direct current arc carrier distillation atomic emission spectroscopy (DC Arc AES) methods were also utilized for the analysis of all glass samples for evaluating their analytical capabilities with respect to INAA. A comparative evaluation was carried out with respect to accuracy, precision, and detection limits under quality assurance/quality control (QA/QC). The methods were validated by analyzing certified reference materials (CRMs) G-2 and RGM-1 from USGS and NIST standard reference material (SRM) of sodalime glass (SRM 610). Concentrations of seventeen elements (Na, Ca, Sc, Cr, Fe, Co, Zn, Rb, Zr, Ba, La, Hf, Ce, Eu, Yb, Sm, and Th) were determined in all analyzed glass samples by INAA at major, minor, and trace concentration levels, indicating its capability for potential applications to forensic studies. Grouping study of these automobile glasses was carried out utilizing concentrations of transition elements and rare earth elements (REEs) in conjunction with statistical cluster analysis. In addition, it has been highlighted that some of the transition elements as well as REEs are important markers/discriminating elements for same brand automobile glasses obtained from two different sources/origins.

Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments

Glass fragments found in crime scenes may constitute important forensic evidence when properly analyzed, for example, to determine their origin. This analysis could be greatly helped by having a large and diverse database of glass fragments and by using it for constructing reliable machine learning (ML)-based glass classification models. Ideally, the samples that make up this database should be analyzed by a single accurate and standardized analytical technique. However, due to differences in equipment across laboratories, this is not feasible. With this in mind, in this work, we investigated if and how measurement performed at different laboratories on the same set of glass fragments could be combined in the context of ML. First, we demonstrated that elemental analysis methods such as particle-induced X-ray emission (PIXE), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE), instrumental neutron activation analysis (INAA), and prompt Gamma-ray neutron activation analysis (PGAA) could each produce lab-specific ML-based classification models. Next, we determined rules for the successful combinations of data from different laboratories and techniques and demonstrated that when followed, they give rise to improved models, and conversely, poor combinations will lead to poor-performing models. Thus, the combination of PIXE and LA-ICP-MS improves the performances by ∼10-15%, while combining PGAA with other techniques provides poorer performances in comparison with the lab-specific models. Finally, we demonstrated that the poor performances of the SEM-EDS technique, still in use by law enforcement agencies, could be greatly improved by replacing SEM-EDS measurements for Fe and Ca by PIXE measurements for these elements. These findings suggest a process whereby forensic laboratories using different elemental analysis techniques could upload their data into a unified database and get reliable classification based on lab-agnostic models. This in turn brings us closer to a more exhaustive extraction of information from glass fragment evidence and furthermore may form the basis for international-wide collaboration between law enforcement agencies.

Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a rapidly developing technique for chemical materials analysis. LIBS is applied for fundamental investigations, e.g., the laser plasma matter interaction, for element, molecule, and isotope analysis, and for various technical applications, e.g., minimal destructive materials inspection, the monitoring of production processes, and remote analysis of materials in hostile environment. In this review, we focus on the element analysis of industrial materials and the in-line chemical sensing in industrial production. After a brief introduction we discuss the optical emission of chemical elements in laser-induced plasma and the capability of LIBS for multi-element detection. An overview of the various classes of industrial materials analyzed by LIBS is given. This includes so-called Technology materials that are essential for the functionality of modern high-tech devices (smartphones, computers, cars, etc.). The LIBS technique enables unique applications for rapid element analysis under harsh conditions where other techniques are not available. We present several examples of LIBS-based sensors that are applied in-line and at-line of industrial production processes.