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Kernel-based Methods for Source Identification Using Very Small Particles From Carpet Fibers

NCJ Number
251961
Author(s)
D. E. Armstrong, C. Neumann, C. P. Saunders, D. T. Gantz, J. J. Miller, D. A. Stoney
Date Published
January 2017
Length
11 pages
Annotation
This article proposes a method that enables the assignment of a probability distribution to control material from any given source based on its chemical signal and to subsequently infer the source of a trace object using a simple Bayes classifier.
Abstract
The objective comparison of complex signals in chemistry, and more particularly in forensic chemistry, with the view of inferring the source of a particular ‘trace’ object is an ongoing issue. The proposed method takes advantage of the dimension reduction and discriminative powers of kernels, and only requires the estimation of three parameters (once a kernel is chosen). Researchers illustrated the application of the proposed method to the inference of the source of trace objects based on very small particles (VSP) that can be found on their surfaces. VSPs are picked up in the environment(s) where the trace object has been. These VSPs can provide information about the geographic origins of the objects and help discriminate between multiple mass-manufactured objects that would be otherwise identical. This project used VSPs recovered from carpet fibers throughout the United States and applied the method to reduce the complexity of compositional data obtained by SEM/EDS and infer the source of the trace material. This method can be extended to VSPs found on other types of recovered forensic materials, such as weapons, drugs, or IEDs (improvised explosive devices), as well as to other types of chemical signals. (Publisher abstract modified)
Date Published: January 1, 2017