A Rapid, Systematic, and Comprehensive Classification System for the Identification and Comparison of Motor Vehicle Paint Samples. I: The Nature and Scope of the Classification System

Audette, R. J.; Percy, Robert F.

doi:10.1520/jfs10908j

Cited by 22 publications

(1 citation statement)

References 24 publications

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“…Paint Data Query is designed as a general text-based search and retrieval system to eliminate the dependency on any one instrument or software system. 7,8 Although direct searching of IR spectra in PDQ does not exist, a text-based search of both the physical and chemical characteristics of each layer of automotive paint can serve as a potent prescreen to a manual IR search because of the relatively large variations in color and chemical formulation of the color coat and undercoat layers of automotive paint. Unfortunately, the use of text to encode chemical information about each paint layer does not allow for searching of clear coats since all modern clear coats applied to automotive substrates have only one of two possible formulations: acrylic melamine styrene or acrylic melamine styrene polyurethane.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

et al. 2015

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Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.

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Section: Introductionmentioning

confidence: 99%