Prevalence of gunshot residue particles on back seats of police vehicles.

The presence of gunshot residue (GSR) in a sample can provide valuable information in forensic investigations by associating a suspect with a shooting incident. However, in order to have confidence in the integrity of the results' interpretation, the possibility of contamination by secondary transfer of GSR occurring during the transportation of a person under custody in a police vehicle should be evaluated. In order to investigate police vehicles as a source for secondary transfer of GSR particles, a total of 51 samples were collected from the rear seats of random police vehicles and used to transport arrested individuals. Results indicated that the type of upholstery of the seats plays a main role in determining the potential for secondary GSR contamination. The potential chance of coming into contact with GSR particles in police vehicles is low. GSR contamination from police vehicles is, maybe, not of a major concern but should be taken into consideration mainly when very few characteristic GSR particles are found on an analyzed sample.

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.

Inter-laboratory workflow for forensic applications: Classification of car glass fragments.

The International Atomic Energy Agency (IAEA) has coordinated a research project titled "Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensics Sciences" (CRP F11021) with the aim of empowering accelerator and reactor based techniques for applications in forensic sciences. One of the key topics of this project was the analysis and classification of forensic glass specimens using Ion Beam Analysis (IBA) techniques and in particular, Particle Induced X-ray Emission (PIXE). To this end, glass fragments from car windows from different car models and manufacturers provided by the Israeli police force were subjected to PIXE measurements at three laboratories to determine their elemental compositions and possible glass corrosion. Major and trace elements were measured and given as an input to machine learning (ML) algorithms in order to develop classification models to determine the origin of the glass samples. First, we have developed ML models based on the results obtained at each lab. These models successfully classified glass fragments into different car models with an accuracy> 80% on external test sets. Next, we demonstrated that following an appropriate pre-processing step, results from different labs could be combined into a single unified database for the derivation of a classification model. This model demonstrates good performances that matches or surpasses the performances of models derived from the individual labs. This finding paves the way towards establishing an international database that is composed of measurements from various PIXE labs. We believe that using this methodology of combining various sources of measurements will improve models' performances and generality and will make the models accessible to law enforcement agencies around the world.

A Mixture Model for the Number of Gunshot Residues Found on Suspects' Hands.

Quantifying the strength of gunshot residue (GSR) evidence requires scientific knowledge about the number of particles expected to be found on individuals who were or were not involved in a shooting. However, controlled experiments demand expensive resources in terms of microscope time and labor, which restricts the data of most studies to only a small group of individuals. We suggest a novel method that exploits data collected routinely on suspects during the daily work of forensic laboratories. These observational data relate to both persons who were involved in a shooting and innocent individuals. We suggest a mixture approach with different models for the number of gunshot residue particles in each group and develop an iterative algorithm to estimate the probabilities of observing the evidence under the defense proposition that the suspect is innocent and under the prosecution assumption that he is not. The method is applied to data of more than 500 suspects collected by the Israel National Police Division of Identification and Forensic Science. The analysis shows that the probability of finding three or more GSR particles on the hands of innocent suspects is very small, less than 1.5 in 1000 cases. Our new method enables researchers to use data on real cases, possibly supplemented by experimental data, in order to estimate the probabilities of a given GSR finding under the defense and prosecution propositions.

Classification Improvements in Automated Gunshot Residue (GSR) Scans.

Classification of particles as gunshot residues (GSRs) is conducted using a semiautomatic approach in which the system first classifies particles based on an automatic elemental analysis, and then, examiners manually analyze particles having compositions which are characteristic of or consistent with GSRs. Analyzing all the particles in the second stage is time consuming with many particles classified by the initial automated system as being potentially GSRs excluded as such by the forensic examiner. In this paper, a new algorithm is developed to improve the initial classification step. The algorithm is based on a binary tree that was trained on almost 16,000 particles from 43 stubs used to sample hands of suspects. The classification algorithm was tested on 5,900 particles from 23 independent stubs and performed very well in terms of false positive and false negative rates. A routine use of the new algorithm can reduce significantly the analysis time of GSRs.

A mixed composition particle highlights the formation mechanism of the weapon memory effect phenomenon.

A shooting event involved several types of ammunition that were all shot from a single firearm. GSR analysis of samples taken from the suspect's hands, hair and from his clothes was carried out. Mixed compositions particles were found among other GSR particles, attributed to the weapon memory effect. SEM/EDX analysis of these particles highlighted a particle with defined domains containing distinct groups of elements. Some of these elements were absent in the primers mixtures of the ammunition used in the shooting event. X-ray mapping indicated that these domains might have been incorporated into the particle within several shooting cycles. This combination of compositions created an opportunity to shed light on the formation mechanism of "memory effect" particles. The aim of the study is to highlight such particles way of formation.

Effect of Cutting with an Abrasive Saw on Swarf Composition.

Crime scene investigators are often asked to examine swarf from break-in sites and compare it with swarf found on a suspect. Occasionally, elemental composition of swarf from these two sources does not match. It is generally assumed that cutting metal does not induce changes in chemical composition. However, this study shows that, if a matrix contains iron, chromium, and nickel, composition of swarf produced from cutting the matrix with an abrasive saw may indeed alter. In this study, we cut a stainless steel sheet using a standard cutoff saw. Swarf was sorted and chemically analyzed by EDS/SEM, and the following relationships between chip morphology and modified composition were discovered: elongated chips remained unchanged; changes in the irregular chips appeared to be random, possibly because they are formed in a number of different ways; composition of spherical chips changed with size.

Chemical contamination of soft drinks in sealed plastic bottles by environmental stress cracking.

A contamination of soft drinks in sealed bottles by organic solvents is reported: closed bottles full of soft drinks were accidentally placed on a cardboard soaked with thinner and the organic fluid subsequently fissured the bottom of the bottles and penetrated into the soft drinks without any apparent leakage of the soft drinks. Experiments were carried out to simulate the process: the penetration of different organic solvents into soft drinks through the bottom of closed bottles was tested. The penetration occurred only when the closed bottles contained carbonated soft drinks (CSD), indicating that inner pressure is a necessary condition for the fissuring of the bottles. This paper discusses environmental stress cracking of polyethylene terephthalate (PET) bottles by organic solvents and migration of chemicals to CSD. Experiments were conducted to determine the conditions in which PET can be permeable to poisoning organic products.