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

Kaspi, Omer; Israelsohn-Azulay, Osnat; Zidon, Yigal; Rosengarten, Hila; Krmpotić, Matea; Gouasmia, Sabrina; Radović, Iva Bogdanović; Jalkanen, Pasi; Liski, Anna; Mizohata, Kenichiro; Räisänen, J.; Girshevitz, Olga; Senderowitz, Hanoch

doi:10.1016/j.forsciint.2022.111216

Cited by 7 publications

(19 citation statements)

References 14 publications

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“…Then, for the purpose of consistency, features (e.g., elements) that were not measured by all laboratories were discarded, leaving only four features (Si, Al, Ca, and Fe). This step, while necessary, may well compromise the performances of subsequent models as was already noted by Kaspi et al This is particularly true when combining different techniques that potentially measure a significantly different number of elements. Indeed, a significant loss of potential information was observed for BARC that uses PIGE and INAA measurements and retained only 18% of its original set of features.…”

Section: Resultsmentioning

confidence: 98%

“…We hypothesized that discarding data from laboratories whose individual model Using principal component analysis (PCA) as a dimensionality reduction method, we have previously demonstrated that the overall shape of the distribution of car manufacturers in the space of the measured elements is maintained across all laboratories that used the PIXE technique. 25 To test whether this observation still holds across elemental compositions measured by other techniques, we have subjected the unified and filtered dataset (284 glass fragments each characterized by six elements) to a similar analysis. The results are presented in Figure 4 and demonstrate that following labwise normalization, samples belonging to the same vehicle manufacturer are located in the same region of the PC plot, irrespective of the lab.…”

Section: ■ Resultsmentioning

confidence: 99%

“…Recent works , demonstrated the usefulness of a workflow designed to derive ML-based models for the reliable classification of glass fragments’ original car manufacturer. This workflow is based on the elemental composition of fragments taken from cars’ windshields measured by the PIXE technique.…”

Section: Methodsmentioning

confidence: 99%

“…Another analytical approach to study glass fragments, which is gaining popularity among law enforcement agencies, , is based on elemental analysis. Elemental analysis techniques include, but are not limited to, laser ablation inductively coupled plasma mass spectrometry − (LA-ICP-MS), particle-induced X-ray emission , (PIXE), 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 activation analysis (PGAA). In-depth description of the different methods with emphasis on forensic analysis could be found in several excellent reviews. , …”

Section: Introductionmentioning

confidence: 99%

“…Under this scenario, any laboratory could optimize its classification performances without investing additional resources (time or money) simply by uploading data pertaining to a specific specimen and classifying it using previously derived, technique-agnostic models. For this purpose, we first subjected the same set of glass fragments considered in our previous works , to elemental analysis by multiple techniques including LA-ICP-MS, PGAA, PIGE, INAA, and SEM-EDS to develop lab-specific models and subsequently derived a set of rules for the successful combinations of data from different laboratories. We demonstrate that following these rules gives rise to improved models, and conversely, violating them leads to poor-performing models.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 98%

Section: ■ Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kaspi

Israelsohn-Azulay²,

Zidon³

et al. 2022

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

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Applications of Energy Dispersive X‐Ray Fluorescence Spectrometry and Direct Current Arc Atomic Emission Spectroscopy Methods for Grouping Study of Automobile Windshield Glasses for Glass Forensics

et al. 2023

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A comprehensive evaluation was carried out for elemental characterization of automobile glasses having potential forensic application by Energy Dispersive X‐ray Fluorescence (ED‐XRF) spectrometry and Direct Current Atomic Emission Spectroscopy (DC Arc AES). The methods were optimized and validated using geological certified reference materials and glass standard reference material. Total seventeen elements including five major elements (Si, Na, Ca, Mg and Fe) and twelve minor and trace elements (Al, Ti, V, Cr, Mn, Ni, Cu, Zn, Rb, Mo, Zr and Sr) were found to be present. Though DC Arc AES performed better for minor and trace level determination, however, the precision was evaluated as 15–20 % RSD. On the contrary, ED‐XRF performed better for major elements with 5–10 % RSD. The detection limits for the analytes prone to form refractory oxides on heating were found to be poorer in DC Arc AES compared to ED‐XRF. Statistical analyses were also performed to evaluate the reliability of the data obtained by the optimized methods. As a part of forensic applications, the elemental concentrations of minor and trace elements were utilized for the grouping study of the glass samples by cluster analysis using average linkage method.

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Forensic glass examinations—A review focused on elemental spectrochemical analysis

Trejos

2022

WIREs Forensic Science

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Glass is a trace material commonly found at crime scenes that can provide valuable information at early investigative stages and during a trial. The forensic analysis of glass has steadily evolved since the 1970s with numerous technological advances in spectroscopy and spectrometry. This article presents an advanced review of the recent literature concerning the forensic examination of glass evidence and discusses the current state and future opportunities of the discipline. The review focuses on established elemental spectrochemical techniques, including Inductively Coupled Plasma methods (ICP), laser‐ablation, and x‐ray Fluorescence (XRF) methods, and newer applications using Laser Induced Breakdown Spectroscopy (LIBS), Raman Spectroscopy, and accelerator and reactor nuclear techniques. A brief overview of the transfer, persistence, interpretation, and analytical schemes implemented in the field is revised to provide context to the discussion of the capabilities and limitations of the current and emerging technology. This article is categorized under: Forensic Chemistry and Trace Evidence > Trace Evidence

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

Cited by 7 publications

References 14 publications

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

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

Applications of Energy Dispersive X‐Ray Fluorescence Spectrometry and Direct Current Arc Atomic Emission Spectroscopy Methods for Grouping Study of Automobile Windshield Glasses for Glass Forensics

Forensic glass examinations—A review focused on elemental spectrochemical analysis

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