scholarly journals Roan, ticked and clear coat patterns in the canine are associated with three haplotypes near usherin on CFA38

2021 ◽  
Vol 52 (2) ◽  
pp. 198-207
Author(s):  
L. Brancalion ◽  
B. Haase ◽  
H. Mazrier ◽  
C. E. Willet ◽  
K. Lindblad‐Toh ◽  
...  
Keyword(s):  
Clear Coat

scholarly journals Evaluation of evidence value of car primer using pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and chemometrics

2021 ◽  
Vol 15 (1) ◽  
pp. 43
Author(s):  
Raja Zubaidah Raja Sabaradin ◽  
Rozita Osman
Keyword(s):  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Clear Coat ◽  
Pyrolysis Gas ◽  
Paint Sample ◽  
Cathodic Electrodeposition ◽  
And Cluster Analysis ◽  
Evaluation Of Evidence ◽  
Paint Analysis ◽  
Cluster 2

The car paint system consisted of four different layers; namely cathodic electrodeposition (CED), primer, the basecoat, and clear coat. Each of these layers may offer valuable information in an analysis of car paint. However, the recovery of a small amount of car paint from a crime scene may not consist of all four layers. Thus, this study is conducted to evaluate the evidence value of car primer in the presence of basecoat and absence of clear coat. In this study, 80 car paint samples, consisting of eight different red basecoats and ten types of primers were analyzed using Py-GC-MS to evaluate the contribution of the primer layer in the analysis of car paint sample. The chromatographic dataset obtained was subjected to chemometric techniques namely principal component analysis (PCA) and cluster analysis (CA). 22 principal components were rendered from PCA with a total variance of 81.23%. CA’s three clusters are cluster 1 and 3 which was based on the shades of red basecoat while cluster 2 was based on the type of primer. This observation showed that the car primer might have a significant contribution to the analysis of car paint using Py-GC-MS. Keywords: Car primer, car paint analysis, Py-GC-MS, chemometric

Effects of Alumina and Silica Nanoparticles on Automotive Clear-Coat Properties

2009 ◽  
pp. 220-235 ◽  
Author(s):  
Bryce R. Floryancic ◽  
Lucas J. Brickweg ◽  
Raymond H. Fernando
Keyword(s):  
Silica Nanoparticles ◽  
Clear Coat

Development of Anti Scratch Clear Coat

2008 ◽  
Author(s):  
Junya Ogawa ◽  
Kazuyuki Kuwano ◽  
Yoshiyuki Noritake
Keyword(s):  
Clear Coat

Investigating the changes in properties when a hydrophobic nanosilica is added to a two-pack polyurethane clear coat

2010 ◽  
Vol 5 (3/4) ◽  
pp. 310
Author(s):  
Hamed Dastmalchian ◽  
Siamak Moradian ◽  
Mohammad Mahdi Jalili ◽  
Ali Karbasi
Keyword(s):  
Clear Coat

“Green” automotive clear coat technology

2009 ◽  
Vol 38 (2) ◽  
Keyword(s):  
Clear Coat

Spies Hecker launches new clear coat

2010 ◽  
Vol 39 (3) ◽  
Keyword(s):  
Clear Coat

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

2014 ◽  
Vol 28 (5) ◽  
pp. 385-394 ◽  
Author(s):  
Barry K. Lavine ◽  
Ayuba Fasasi ◽  
Nikhil Mirjankar ◽  
Mark Sandercock ◽  
Steven D. Brown
Keyword(s):  
Clear Coat ◽  
Mid Infrared ◽  
Linear Classifiers ◽  
Automotive Paint ◽  
Absorbance Spectra

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

2017 ◽  
Vol 72 (3) ◽  
pp. 476-488 ◽  
Author(s):  
Barry K. Lavine ◽  
Collin G. White ◽  
Tao Ding
Keyword(s):  
Pattern Recognition ◽  
Ir Spectra ◽  
General Motors ◽  
Discrete Wavelet ◽  
Vehicle Manufacturer ◽  
Clear Coat ◽  
Discrimination Power ◽  
Paint Systems ◽  
Automotive Paint ◽  
Spectral Libraries

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.

Sign in / Sign up

Export Citation Format

Share Document