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.

The influence of a possible contamination of the victim's clothing on the estimation of shooting distance in mutual shooting incidents.

One of the tools for estimating shooting distance is examination of smokeless powder particle distribution on the target area. Components of the powder that are utilized for this purpose are nitrite anions. The traditional method for detecting nitrite anions mainly involves applying the Griess Test. A known-distance test firing is performed with shootings done at several distances from different targets. The color pattern corresponding to nitrite anions from the crime scene is then compared to the patterns obtained from known distances of the test firing. When a mutual shooting takes place at the crime scene, and a shooter also becomes a shootout victim, it is possible that when examining the shooter as a victim (i.e. target), additional nitrite-containing particles, resulting from his/her shooting, will be present on the shooter-victim clothing. This kind of addition may affect the estimation and practically give a shorter-distance estimation comparing to the actual distance. In this paper, an experimental setup was designed in order to understand if nitrite-containing particles were added to a victim as a consequence of him/her being also a shooter. All of the experiments were predominantly designed to try and minimize the effects of other influencing factors and variables in order to examine if the additions resulting from the firing action affect distance estimation. The experiments involved various types of pistols and distances. The results show that in such a scenario, there are marginal additions of nitrite signals on the victim's shirt. Although the forensic expert's final assessment was within the tolerance interval at all distances, caution should be exercised when attempting to estimate shooting distances in scenarios where the victim also shoots.

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.

Finding GSR evidence on used towels.

When a firearm is discharged, gunshot residue (GSR) is produced and may be deposited on a shooter's body and his close vicinity. The tendency of GSR to drop off easily from the shooter may hinder forensic detection; this well-known phenomenon is accelerated by various common physical activities so that the number of particles detected on a suspect decreases over time. After shooting incidents, suspects will often try to cover their tracks by taking a shower. In these cases, it was assumed that no GSR will be detected upon examining the suspect's hand and hair. In the present study, we provide a way to overcome this loss of evidence by taking advantage of another occurrence, namely secondary transfer. Our participants were asked to take a shower after shooting a firearm. Samples were thereafter collected from the used bath towels and were found to contain up to a few dozen particles characteristic of GSR, including very large particles (>45 µm). The detection of GSR on a suspect's towel may provide significant forensic evidence aiding an investigation. When a shooter tries to remove evidence by taking a shower, sampling the towels that he may have used can preserve important evidence and connect a suspect to a shooting incident.

The risk of inter-stub contamination during SEM/EDS analysis of gunshot residue particles.

Detecting gunshot residue (GSR) particles on samples collected from individuals or their belongings can connect them to a shooting event. Scanning electron microscopy combined with energy-dispersive X-ray spectrometry (SEM/EDX) is currently the most common forensic method for detecting and characterizing GSR. At the forensic laboratory of the Israel Police, one inch (25 mm) diameter sticky stubs are used to collect samples from suspects' hands, hair, clothes and vehicles. To maximize testing capacity, stubs of samples collected from several different cases and persons may be analyzed side by side in a single run. This has raised concern in court that a clean sample taken from an innocent person may be contaminated during the analysis by GSR particles from an adjacent sample transferred inside the SEM chamber. several experiments were conducted where stubs that were known to contain GSR particles were run adjacent to stubs that were known to be clean. Not a single event of GSR particle transfer was detected, even when a clean stub was surrounded on all sides by stubs containing a total of over 100,000 particles. Thus, the probability of transfer of a single particle is at most 1:100,000. Since the total number of GSR particles found per run is usually three orders of magnitude lower than 100,000, we conclude that the risk of inter-stub contamination is highly negligible.

Photographic Image Comparison of Vegetation.

Images and videos are common types of evidence in crime scene investigations and laboratory analysis. Images may be taken by the suspect and/or by crime scene investigators and may serve as crucial elements in forensic laboratory analysis. Forensic photographic image comparison is the process of comparing one or more objects or persons in photographs/images when at least one image is known to be related to a crime. The forensic examiner usually compares the images in order to determine whether or not an association between the exhibits in the images can be made. This paper proposes an extension to the currently prevalent photographic image examination method. The extension introduces comparison of landscape and vegetation over time. It is revealed that similarities between images may still be found between the period of time the suspect photograph was taken and the period the crime scene investigator took the photograph from the same area. In this case report, two suspects to be involved in growing a marijuana field were arrested by the police. The forensic experts were asked to examine images taken by the crime scene investigators and to compare them to the images found in the suspects' phones. They then tried to determine whether the suspects could be linked to the specific locations. While applying techniques derived from morphological comparison methodologies, the plants at the scene provided significant additional information. A tree trunk, branches, and twigs on a hedge in the photographs revealed specific individual characteristics that led the examiner to reach a conclusive decision.