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.
|State||Published - 1 Nov 2021|
Bibliographical noteFunding Information:
The work was carried out under the IAEA Coordinated Research Project on “Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensic Sciences” (F11021). The authors would like to thank Ms. Osnat Israelsohn Azulay (Israeli DIFS) for her help with the collection of the specimens. We also would like to thank Prof. Yossi Almog for the helpful discussions.
© 2021 Elsevier B.V.
- Glass fragments
- Machine Learning
- Random forest