Chemical analyses on late antique glass finds from Histria, Romania

Bugoi, Roxana; Țârlea, Alexandra; Szilágyi, Veronika; Harsányi, Ildikó; Cliante, Laurenţiu; Kasztovszky, Zsolt

doi:10.1111/arcm.12732

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…The results of elemental analysis have been used to derive ML-based models for the classification of glass fragments. − ,− Thus, Tallon–Ballesteros and Riquelme used several ML algorithms such as random forest (RF), Bayes classifiers, artificial neural networks (ANNs), and nearest-neighbor methods to classify glass fragments into six classes based on their elemental composition. Similarly, Park and Carriquiry determined that better associations between the specimen and source could be obtained with RF and Bayesian Additive Regression Trees than with other algorithms.…”

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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