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                            Library & Multimedia
 Transmission Infrared Microscopy and Machine Learning Applied to the Forensic Examination of Original Automotive Paint
NCJ Number
              307458
Journal

*Applied Spectroscopy* Volume: 76 Issue: 1 Dated: 2022 Pages: 118-131
Author(s)
          Francis Kwofie;
                        Undugodage Don Nuwan Perera;
                        Kaushalya Sharma Dahal;
                        George P. Affadu-Danful;
                        Koichi Nishikida;
                        Barry K. Lavine
Date Published

2022
Length
              14 pages
Annotation
              This study analyzed alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint throughthe use of machine learning methods to improve forensic automotive paint examination.
Abstract
              Alternate least squares (ALS) reconstructions of the infrared (IR) spectra of the individual layers from original automotive paint were analyzed using machine learning methods to improve forensic automotive paint examination. Applying a correction algorithm to both the ALS reconstructed spectra and in-house IR library spectra, large peak shifts previously encountered with some vibrational modes were successfully mitigated. Using machine learning methods to identify the manufacturer and the assembly plant of the vehicle from which the OEM paint sample originated, twenty-six cross-sectioned automotive paint samples were correctly classified as to the “make” and model of the vehicle and was also matched to the correct paint sample in the in-house IR spectral library. Twenty-six original equipment manufacturer (OEM) paints from vehicles sold in North America between 2000 and 2006 served as a test bed to validate the ALS procedure developed in a previous study for the spectral reconstruction of each layer from IR line maps of cross-sectioned OEM paint samples. An examination of the IR spectra from an in-house library (collected with a high-pressure transmission diamond cell) and the ALS reconstructed IR spectra of the same paint samples (obtained at ambient pressure using an IR transmission microscope equipped with a BaF2 cell) showed large peak shifts (approximately 10 cm−1) with some vibrational modes in many samples comprising the cohort. These peak shifts are attributed to differences in the residual polarization of the IR beam of the transmission IR microscope and the IR spectrometer used to collect the in-house IR spectral library. To solve the problem of frequency shifts encountered with some vibrational modes, IR spectra from the in-house spectral library and the IR microscope were transformed using a correction algorithm previously developed by our laboratory to simulate ATR spectra collected on an iS-50 FT-IR spectrometer. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2014-DN-BX-K087


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Application of Infrared Imaging and Chemometrics to Facilitate the Forensic Examination of Automotive Paints
    
  
  
Date Published: January 1, 2022
    
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      PatentsRelated Topics
        
                Research and development
        Evidence analysis and processing
        Paint analysis
        Trace evidence
        Research and Development in Forensic Science for Criminal Justice Purposes



  
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