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

Omer Kaspi, Osnat Israelsohn-Azulay, Zidon Yigal, Hila Rosengarten, Matea Krmpotić, Sabrina Gouasmia, Iva Bogdanović Radović, Pasi Jalkanen, Anna Liski, Kenichiro Mizohata, Jyrki Räisänen, Zsolt Kasztovszky, Ildikó Harsányi, Raghunath Acharya, Pradeep K. Pujari, Molnár Mihály, Mihaly Braun, Nahum Shabi, Olga Girshevitz, Hanoch Senderowitz

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)87-100
Number of pages14
JournalJournal of Chemical Information and Modeling
Volume63
Issue number1
DOIs
StatePublished - 9 Jan 2023

Bibliographical note

Publisher Copyright:
© 2022 American Chemical Society.

Fingerprint

Dive into the research topics of 'Toward Developing Techniques─Agnostic Machine Learning Classification Models for Forensically Relevant Glass Fragments'. Together they form a unique fingerprint.

Cite this