Evaluation of the performance of modern X-ray fluorescence spectrometry systems for the forensic analysis of glass

Corzo, Ruthmara; Ernst, Troy; Insana, Joseph; Martinez-Lopez, Claudia; Webb, Jodi; Haase, Emily; Weis, Peter; Trejos, Tatiana

doi:10.1016/j.forc.2022.100447

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…In an inter-laboratory comparison by Corzo et al the relative merits of using μXRF equipped with modern silicon drift detectors rather than the traditional lithium doped silicon detectors were evaluated. 291 The standard test method for the forensic analysis of glass using μXRF (ASTM E2926) gives recommendations for the number of replicate measurements to characterise a known source. It also provides the criteria for the comparison between the known and questioned samples.…”

Section: Forensic Analysismentioning

confidence: 99%

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Clough,

Fisher,

Gibson

et al. 2023

J. Anal. At. Spectrom.

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Section: Forensic Analysismentioning

confidence: 99%

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Clough,

Fisher,

Gibson

et al. 2023

J. Anal. At. Spectrom.

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show abstract

“…These standards are constantly under scrutiny and improvement. For example, Corzo et al 30 found that E2926 comparison criteria developed for μ-XRF systems are no longer appropriate for newer, more sensitive μ-XRF systems equipped with silicon drift detectors (SDDs) and high-intensity X-ray optics. As a remedy, they suggest to increase the number of fragments collected and to modify the recommended comparison criteria.…”

Section: Introductionmentioning

confidence: 99%

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

Kaspi

Israelsohn-Azulay²,

Zidon³

et al. 2022

J. Chem. Inf. Model.

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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.

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A novel standard for forensic elemental profiling of polymers by LA-ICP-TOF-MS

et al. 2023

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Evaluation of the performance of modern X-ray fluorescence spectrometry systems for the forensic analysis of glass

Cited by 5 publications

References 20 publications

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

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

A novel standard for forensic elemental profiling of polymers by LA-ICP-TOF-MS

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