&lt;title&gt;International forensic automotive paint database&lt;/title&gt;

Bishea, Gregory A.; Buckle, J.L.; Ryland, Scott

doi:10.1117/12.339194

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Paint samples are often recovered from collisions where damage to vehicles or injury or death to a pedestrian has occurred. The Royal Canadian Mounted Police (RCMP) has shown that automobiles can be identified by comparing the color, layer sequence and chemical composition of each individual layer of the recovered paint sample to known automotive paint systems . To make these comparisons possible, the RCMP has developed the paint data query (PDQ) database , which contains over 16,000 samples (street samples and factory panels) that correspond to over 60,000 individual paint layers, representing the paint systems used in most domestic and foreign vehicles sold in North America.…”

Section: Introductionmentioning

confidence: 99%

Search prefilters for mid‐infrared absorbance spectra of clear coat automotive paint smears using stacked and linear classifiers

Lavine

Fasasi

Mirjankar

et al. 2014

Journal of Chemometrics

View full text Add to dashboard Cite

By using stacked partial least squares classifiers and genetic algorithms for feature selection and classification, it is demonstrated that search prefilters can be developed to extract investigative lead information from clear coat paint smears. The results obtained in this study also show that identifying specific wavelengths or wavelet coefficients in IR spectral data is superior to identifying informative wavelength windows when applying pattern recognition techniques to IR spectra from the paint data query (PDQ) database when differentiating paint samples by assembly plant. Search prefilters developed using specific wavelengths or wavelet coefficients outperformed search prefilters that utilized spectral regions. Clear coat paint spectra from the PDQ database may not be well suited for stacking as there are few spectral intervals that can reliably distinguish the different sample groups (i.e., assembly plants) in the data. The information contained in the IR spectra about assembly plant may not be highly compartmentalized in an interval, which also works against stacking. The similarity of the IR spectra within a plant group and the noise present in the IR spectra may also be obscuring information present in spectral intervals.

show abstract

“…Infrared (IR) spectroscopy is one of the main tools used by forensic laboratories to characterize, differentiate, and identify automotive paint evidence. 1–3 Typically, each individual layer of the paint system is isolated and analyzed as a thin section in a diamond anvil cell, or it is pressed onto a window of a diamond compression cell or a salt plate. The paint data query (PDQ) system is a searchable database of original automotive finish systems 4,5 consisting of paint binder, pigment, and color information (text-based search), as well as transmission spectra of each individual layer (clear coat, mid-coat for a tricoat system, base coat, and one or more undercoats to include an electrodeposition primer).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Attenuated Total Reflection Infrared Absorbance Spectra: Applications to Automotive Clear Coat Forensic Analysis

et al. 2014

View full text Add to dashboard Cite

Attenuated total reflection (ATR) is a widely used sampling technique in infrared (IR) spectroscopy because minimal sample preparation is required. Since the penetration depth of the ATR analysis beam is quite shallow, the outer layers of a laminate or multilayered paint sample can be preferentially analyzed with the entire sample intact. For this reason, forensic laboratories are taking advantage of ATR to collect IR spectra of automotive paint systems that may consist of three or more layers. However, the IR spectrum of a paint sample obtained by ATR will exhibit distortions, e.g., band broadening and lower relative intensities at higher wavenumbers, compared with its transmission counterpart. This hinders library searching because most library spectra are measured in transmission mode. Furthermore, the angle of incidence for the internal reflection element, the refractive index of the clear coat, and surface contamination due to inorganic contaminants can profoundly influence the quality of the ATR spectrum obtained for automotive paints. A correction algorithm to allow ATR spectra to be searched using IR transmission spectra of the paint data query (PDQ) automotive database is presented. The proposed correction algorithm to convert transmission spectra from the PDQ library to ATR spectra is able to address distortion issues such as the relative intensities and broadening of the bands, and the introduction of wavelength shifts at lower frequencies, which prevent library searching of ATR spectra using archived IR transmission data.

show abstract

<title>International forensic automotive paint database</title>

Cited by 5 publications

References 0 publications

Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

Pattern Recognition-Assisted Infrared Library Searching of Automotive Clear Coats

Search prefilters for mid‐infrared absorbance spectra of clear coat automotive paint smears using stacked and linear classifiers

Simulation of Attenuated Total Reflection Infrared Absorbance Spectra: Applications to Automotive Clear Coat Forensic Analysis

Contact Info

Product

Resources

About