PIXE based, Machine-Learning (PIXEL) supported workflow for glass fragments classification

Omer Kaspi, Olga Girshevitz, Hanoch Senderowitz

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper presents a structured workflow for glass fragment analysis based on a combination of Elemental Analysis using PIXE and Machine Learning tools, with the ultimate goal of standardizing and helping forensic efforts. The proposed workflow was implemented on glass fragments received from the Israeli DIFS (Israeli Police Force's Division of Identification and Forensic Sciences) that were collected from various vehicles, including glass fragments from different manufacturers and years of production. We demonstrate that this workflow can produce models with high (>80%) accuracy in identifying glass fragment's origins and provide a test-case demonstrating how the model can be applied in real-life forensic events. We provide a standard, reproducible methodology that can be used in many forensic domains beyond glass fragments, for example, Gun Shot Residue, flammable liquids, illegal substances, and more.

Original languageEnglish
Article number122608
StatePublished - 1 Nov 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.


  • Forensic
  • Forensoinformatics
  • Glass fragments
  • Machine Learning
  • PIXE
  • Random forest


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