Classification Improvements in Automated Gunshot Residue (GSR) Scans

Micha Mandel, Osnat Israelsohn Azulay, Yigal Zidon, Tsadok Tsach, Yaron Cohen

Research output: Contribution to journalComment/debate


Classification of particles as gunshot residues (GSRs) is conducted using a semiautomatic approach in which the system first classifies particles based on an automatic elemental analysis, and then, examiners manually analyze particles having compositions which are characteristic of or consistent with GSRs. Analyzing all the particles in the second stage is time consuming with many particles classified by the initial automated system as being potentially GSRs excluded as such by the forensic examiner. In this paper, a new algorithm is developed to improve the initial classification step. The algorithm is based on a binary tree that was trained on almost 16,000 particles from 43 stubs used to sample hands of suspects. The classification algorithm was tested on 5,900 particles from 23 independent stubs and performed very well in terms of false positive and false negative rates. A routine use of the new algorithm can reduce significantly the analysis time of GSRs.

Original languageAmerican English
Pages (from-to)1269-1274
Number of pages6
JournalJournal of Forensic Sciences
Issue number4
StatePublished - Jul 2018


  • elemental analysis
  • forensic science
  • gunshot residue conformation
  • regression tree
  • supervised learning

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Genetics


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