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Improving a Database To Help Identify a Vehicle by Using Paint Fragments

NCJ Number
250764
Date Published
Author(s)
National Institute of Justice
Annotation
This article reports on a project with the goal of improving current approaches to data interpretation of forensic paint examinations and to assist in evidential significance assessment, both at the investigative-lead stage and at the courtroom- testimony stage.
Abstract
For years, investigators have relied on the Paint Data Query database developed by the Royal Canadian Mounted Police in identifying the make of a vehicle by matching the physical attributes, chemical composition, and infrared spectrum of the paints, primer, and clear coating layers. Although the Paint Data Query database is extensive, it uses text to code the chemistry of each layer of a paint sample. The text coding is generic and leads to a large number of spurious “hits” that impair the accuracy of a search. Another issue with the Paint Data Query database is that modern automotive paints have a thin color coat, which means microscopic fragments left at a crime scene may be too thin to obtain accurate chemical and topcoat color information. Another concern with using the Paint Data Query database to identify a paint fragment is that many forensic laboratories use attenuated total reflection (ATR) spectroscopy for infrared analysis of automotive paints. The infrared spectrum of an automotive paint sample obtained by ATR exhibits distortions and consequentially hinders specific identification. In addressing the aforementioned issues, the current project developed a search prefilter to resolve the text coding issues in the Paint Data Query. The project also developed a correction algorithm to allow ATR spectra to be matched using the infrared transmission spectra of the Paint Data Query database.
Date Created: May 15, 2017