The Classification of Automotive Paint by Diamond Window Infrared Spectrophotometry Part III Case Histories

Rodgers, P. G.; Cameron, R.; Cartwright, Nick; Clark, William A.; Deak, J. S.; Norman, Elisabeth

doi:10.1080/00085030.1976.10757253

Cited by 22 publications

(3 citation statements)

References 4 publications

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“…Further details about the collection of these IR spectra can be found elsewhere. 24 For pattern recognition analysis, each IR spectrum was normalized to the helium neon laser frequency of 15 798.0 cm -1 using OMNIC (Thermo-Nicolet). After normalization, each IR spectrum (4000 cm -1 to 400 cm -1 ) consisted of 1869 points.…”

Section: Methodsmentioning

confidence: 99%

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

2017

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Pattern recognition techniques have been applied to the infrared (IR) spectral libraries of the Paint Data Query (PDQ) database to differentiate between nonidentical but similar IR spectra of automotive paints. To tackle the problem of library searching, search prefilters were developed to identify the vehicle make from IR spectra of the clear coat, surfacer-primer, and e-coat layers. To develop these search prefilters with the appropriate degree of accuracy, IR spectra from the PDQ database were preprocessed using the discrete wavelet transform to enhance subtle but significant features in the IR spectral data. Wavelet coefficients characteristic of vehicle make were identified using a genetic algorithm for pattern recognition and feature selection. Search prefilters to identify automotive manufacturer through IR spectra obtained from a paint chip recovered at a crime scene were developed using 1596 original manufacturer's paint systems spanning six makes (General Motors, Chrysler, Ford, Honda, Nissan, and Toyota) within a limited production year range (2000-2006). Search prefilters for vehicle manufacturer that were developed as part of this study were successfully validated using IR spectra obtained directly from the PDQ database. Information obtained from these search prefilters can serve to quantify the discrimination power of original automotive paint encountered in casework and further efforts to succinctly communicate trace evidential significance to the courts.

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Section: Methodsmentioning

confidence: 99%

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

2017

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“…Additional details about the sampling conditions used to generate the clear coat paint spectra can be found elsewhere. 28…”

Section: Methodsmentioning

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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“…Since 1973, by agreement, the RCMP has assumed the responsibility of contacting the automobile companies, collecting and distributing the paint panels to the Canadian laboratories. (36,37,38) explaining the diamond cell method and its application to case work. Two more recent papers by these authors (39,40) extend the concept and explain the practical working of the method.…”

Section: Paint Analysismentioning

confidence: 99%

Forensic Chemistry in Canada—In Review and Retrospect—

Clair¹

1978

Canadian Society of Forensic Science Journal

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The Classification of Automotive Paint by Diamond Window Infrared Spectrophotometry Part III Case Histories

Cited by 22 publications

References 4 publications

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

Forensic Chemistry in Canada—In Review and Retrospect—

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