Search Prefilters for Mid-Infrared Absorbance Spectra of Clear Coat Automotive Paint Smears Using Stacked and Linear Classifiers

• NCJ Number: 248812
• Journal: Journal of Chemometrics Volume: 28 Issue: 5 Dated: May 2014 Pages: 385-394
• Date Published: May 2014
• Author(s): Barry K. Lavine; Ayuba Fasasi; Nikhil Mirjankar; Mark Sandercock; Steven D. Brown
• Length: 10 pages
• Grant Number(s): 2010-DN-BX-K17
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Grant Sponsored)

Annotation

By using stacked partial least squares classifiers and genetic algorithms for feature selection and classification, this project demonstrated that search prefilters can be developed to extract investigative-lead information from clear coat paint smears.

Abstract

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. (Publisher abstract modified)