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Discrimination and Classification among Common Items of Evidence using Particle Combination Profiles

NCJ Number
255718
Journal
Forensic Science International Volume: 289 Dated: 2018 Pages: 92-107
Author(s)
D. A. Stoney; C. Neumann; P. L. Stoney
Date Published
2018
Length
16 pages
Annotation

This article reports on a project that used established analytical tools and statistical methods to determine the evidential value of very small particle (VSP) profiles found on handguns, cell phones, drug packaging, and ski masks.

 

Abstract

Sampling protocols were designed, tested, and used to sample VSP from evidence items from a single county-level crime laboratory: 30 handguns, 31 cell phones, 36 drug packaging specimens, and 32 ski masks. Specimens were prepared for analysis employing established protocols for semi-automated scanning electron microscopy with elemental characterization by energy dispersive x-ray analysis (SEM/EDS). Statistical methods of particle combination analysis were applied to (1) remove particle “noise” from the datasets; (2) define a set of highly discriminating target particle types; (3) measure the strength of correspondence between profiles; and (4) measure the potential of VSP as an evidence type under defined experimental conditions. Most (84 percent) of the VSP specimens recovered from common evidence items showed sufficient variety and complexity in their VSP profiles to enable meaningful classification among closed sets of approximately 30 specimens. Correct associations were achieved for 93.5 percent of test specimens (drug packaging, 97.2 percent; cell phones, 92.6 percent; handguns, 92.9 percent; and ski masks. 88.2 percent). Test specimens with VSP numbers greater than 125 showed predominantly correct classifications. These findings establish (1) that VSP are present on the surfaces common items of physical evidence, (2) that the VSP can be efficiently recovered, prepared and analyzed by computer-assisted SEM/EDS analysis, (3) that the variety of particles is sufficient for the definition of classifiers based on reference sources, and (4) that the classifiers perform very well for these particle sets, showing that VSP recovery, analytical methods and computational methods are working effectively. (publisher abstract modified)

 

Date Published: January 1, 2018