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Improving the PDQ Database To Enhance Investigative Lead Information From Automotive Paints

NCJ Number
Date Published
May 2016
91 pages
In order to develop search prefilters (quick tests to spot dissimilar automotive paint spectra) that avoid a complete spectral comparison of unknown automotive paint samples, this project combined chemical information from Fourier transform infrared (FTIR) spectra of the two primer automotive paint layers and the clear coat layer, followed by analysis using a genetic algorithm (GA) for features selection and pattern recognition.

Spectral features in each FTIR spectrum characteristic of the assembly plant (and hence the manufacturer and model) of the unknown vehicle paint sample were identified by the pattern recognition GA. Assembly plants were first divided into groups of assembly plants based on cluster analysis of the fingerprint region of the clear coat paint layer. Each plant group was then divided into its assembly plants, using both the clear coat and the two undercoat paint layers. The search prefilter system categorizes each unknown paint system by identifying successively smaller sets of vehicles to which the unknown is assigned. The search prefilters have the potential to facilitate spectral library searching, since the size of the library is truncated to those spectra of paint samples obtained from assembly plants identified by the search prefilters. A correction algorithm that allows attenuated total reflection (ATR) spectra to be matched using the IR transmission spectra of the Paint Data Query (PDQ) database was also developed as part of this research project. ATR is a widely used sampling technique in IR spectroscopy, because minimal sample preparation is required. This research enhances current approaches to data interpretation of forensic paint examinations and aids in evidential significance assessment, both in assessing investigative leads and in courtroom testimony. 83 figures, 25 tables, and 37 references

Date Published: May 1, 2016