Utilization of accelerator and reactor based nuclear analytical techniques for chemical characterization of automobile windshield glass samples and potential of statistical analyses using trace elements towards glass forensics

Prediction and classification of chemical composition of ancient glass objects based on generalized Shapley functions

Ancient glass products have suffered from the baptism of time and experienced changes in the burial environment and weathering, resulting in a change in the proportions of their chemical composition and interfering with their accurate identification by later generations. In this paper, the chemical composition of ancient glass products is predicted and identified. First, the multivariate statistical ANOVA test is applied to explore the relationship between whether the cultural relics samples are weathered or not and the glass type, decoration, and color to derive a law of chemical composition of the cultural relics and to analyze the correlation and difference among the four factors. Second, compared with the relevant data of the existing glass products, the missing values are processed by using the method of filling in the plurality. The weathering condition of the sampling points of the samples whose surfaces are not weathered is judged by the "distance discrimination method." Combined with the characteristics of the lead-barium glass and the high-potassium glass, the law of the chemical composition content on the surface of the samples, weathered or not, is explored. The modeling of the gray prediction method was applied again to predict the chemical composition content before weathering. Finally, the generalized Shapley function of fuzzy measurement was used to analyze the correlation between indicators and the chemical compositions and their differences. The scheme proposed in this paper can solve the difficult problem of category judgment in archeology, which is of great significance in promoting the smooth progress of archaeological work.

Addressing forensic science challenges with nuclear analytical techniques - A review

We review the application of Nuclear Analytical Techniques (NATs) to forensic problems for the first time. NATs include neutron activation analysis (NAA), carried out in nuclear reactors for elemental analysis; accelerator-based techniques, mainly Ion Beam Analysis (IBA) for elemental and molecular analysis; and Accelerator Mass Spectrometry (AMS) for dating of traces of forensic interest by "radiocarbon dating" and other related methods. Applications include analysis of drugs of abuse, food fraud, counterfeit medicine, gunshot residue, glass fragments, forgery of art objects and documents, and human material. In some applications only the NATs are able to provide relevant information for forensic purposes. This review not only includes a wide collection of forensic applications, but also illustrates the wide availability worldwide of NATs, opening up opportunities for an increased use of NATs in routine forensic casework.

Neutron Activation Analysis: An Excellent Nondestructive Analytical Technique for Trace Metal Analysis

For proper functioning of the human body, several metals are required in different concentrations but if their concentration slightly elevates, because of any metal-contaminated environment or of other food sources, which leads to high toxicity and different chronic health issues. Different analytical techniques like atomic absorption spectroscopy, X-ray fluorescence, inductively coupled plasma- mass spectroscopy (ICP-MS) and flame atomic absorption spectroscopy are used for metals analysis present in different samples in different fields but nowadays neutron activation analysis (NAA) is preferred over other analytical techniques because it is an efficient, multi-elemental, nondestructive analytical technique having an ultralow minimum detection limit, therefore it can detect heavy metals (HMs) even if at a very trace level parts per billion (ppb) with a quite simple sample preparation technique. This technique is known as "referee technique" because of its accuracy and trustworthiness. There is a widespread use of this technique in biomedical science like in Alzheimer's disease, cancer, arthritis, metabolism study, brain tumor and in many more conditions where metals are actively present. For its typical sample sizes and due to a multitude of additional benefits, it also helps in mapping of pathophysiology of the disease. Besides all, mainly in biomedical science the biological samples can easily be analyzed irrespective of any form. In recent years NAA is preferred over other analytical techniques in several research fields, so this article focuses on the analytical technique, its general principle and recent applications.

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.