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

Omer Kaspi¹, Olga Girshevitz², Hanoch Senderowitz¹

¹Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel.

|August 8, 2021

Summary

This summary is machine-generated.

This study introduces a new workflow combining elemental analysis and machine learning for forensic glass fragment analysis. This method accurately identifies glass origins, aiding forensic investigations and offering a standardized approach for various evidence types.

Keywords:

Forensic Forensoinformatics Glass fragments Machine Learning PIXE Random forest

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Area of Science:

Forensic Science
Analytical Chemistry
Computer Science

Background:

Forensic analysis of glass fragments is crucial for linking suspects to crime scenes.
Current methods for glass analysis can be time-consuming and lack standardization.
Advanced analytical techniques are needed to improve the accuracy and efficiency of forensic investigations.

Purpose of the Study:

To develop and validate a structured workflow for the forensic analysis of glass fragments.
To standardize glass fragment analysis for improved reliability in forensic casework.
To demonstrate the applicability of the workflow in real-world forensic scenarios.

Main Methods:

Elemental Analysis using Particle-Induced X-ray Emission (PIXE) for elemental composition.
Machine Learning (ML) algorithms for pattern recognition and classification of glass fragments.
Implementation of the workflow on authentic glass fragments from the Israeli Police Force's Division of Identification and Forensic Sciences (DIFS).

Main Results:

The developed workflow achieved high accuracy (>80%) in identifying the origins of glass fragments.
A test case demonstrated the practical application of the ML models in forensic event analysis.
The methodology proved to be standard and reproducible for glass fragment analysis.

Conclusions:

The combined PIXE and ML workflow offers a powerful, accurate, and standardized approach to forensic glass fragment analysis.
This methodology can be adapted for the analysis of other forensic evidence types, such as gunshot residue and illegal substances.
The study provides a robust framework to enhance forensic capabilities and support legal proceedings.