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Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database To Enhance Lead Information From Automotive Paint Trace Evidence

NCJ Number
Applied Spectroscopy Volume: 71 Issue: 3 Dated: 2017 Pages: 480-495
Date Published
16 pages

This article describes the features, uses, and a recently developed search engine tool for improving searches of the paint data query (PDQ) database, which enables the forensic automotive paint examiner to compare the layer sequence and color, texture, and composition of a sample to paint systems of the original equipment manufacturer (OEM).


Modern automotive paints have a thin color coat, however, and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach that uses pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts. (Publisher abstract modified)

Date Published: January 1, 2017