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.

A likelihood-ratio framework for evaluating results of forensic gunshot-residue analysis.

When reporting results of Gunshot Residue (GSR) analysis from a person suspected to be involved in a recent shooting, most forensic experts only provide the court with the raw results (i.e. the number of GSR particles found) and a disclaimer that a positive finding does not prove that the suspect was involved in a firearm shooting incident whilst a negative finding does not prove that he was not. Probabilistic analysis of the GSR results provides more value to the court, so the present study calculated likelihood ratio (LR) values for finding 0-8 characteristic GSR particles (containing Lead, Barium and Antimony) on a suspect's hands, based on the available GSR data from the published literature as well as studies by the authors. Defense propositions, i.e. modes for GSR acquisition other than involvement in a shooting event, were divided into three broad categories: low, medium and heavy background. For each background level and number of GSR particles found, minimal and maximal LR values were calculated. Thus, for each proposition the defense provides for the presence of GSR on the defendant's hands, the forensic expert can provide a possible set of minimal and maximal LR values, leaving the court to examine the defendant's contention and decide which of the three background modes is more plausible according to the circumstances of the specific case.