TY  - JOUR
PY  - 2018//
TI  - Classification improvements in automated gunshot residue (GSR) scans
JO  - Journal of forensic sciences
A1  - Mandel, Micha
A1  - Israelsohn Azulay, Osnat
A1  - Zidon, Yigal
A1  - Tsach, Tsadok
A1  - Cohen, Yaron
SP  - 1269
EP  - 1274
VL  - 63
IS  - 4
N2  - 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.<br><br>© 2017 American Academy of Forensic Sciences.<p />  <p>Language: en</p>
LA  - en
SN  - 0022-1198
UR  - http://dx.doi.org/10.1111/1556-4029.13711
ID  - ref1
ER  -