Introduction

Identifying an Unknown Paint System Using the RCMP PDQ Program

Identifying an Unknown Paint System Using the RCMP PDQ Program

This webinar originally occurred on April 8, 2020
Duration: 1.5 hours

Overview

Using a typical OEM paint system, the PDQ Maintenance Team will walk through the best practices to efficiently use the database and spectral libraries to identify the most likely source of an unknown paint system. Details gathered from this process include manufacturing plant, year range, and models.

The Royal Canadian Mounted Police Paint Data Query (PDQ) program is an international OEM automotive paint database used to help identify possible suspect vehicles involved in hit and run incidents based on paint evidence left at the scene of a crime. Paint systems on an automobile typically have three or four layers: a clear coat over a topcoat over one or more undercoats. Each paint layer contains pigments, fillers, and binders. Automotive manufacturing plants often use unique combinations of paint layers, which allow forensic scientists to determine the most likely manufacturing plant, make/model, and year range for the vehicle from which the paint chip originated.

The analytical method used to identify automotive paint relies on the selective absorption of infrared light by the components in the paint. Each paint layer is separated and placed between two diamonds for infrared analysis. Each component has a characteristic fingerprint known as an infrared spectrum. Analysts can identify the automotive manufacturer by comparing the infrared spectrum of each paint layer in a paint system (topcoat and primers) to the spectra in the paint database.

The PDQ Program is comprised of two components: the database that contains the complete color, chemical composition, layer sequence, and sourcing information of more than 23,000 known paint systems and the PDQ Spectral Libraries (currently produced in BioRad’s KnowItAll format) that contain the FTIR spectra of the samples in PDQ.

In this webinar, subject matter experts will review how to use the PDQ Program to identify an unknown paint system from a late model vehicle in under 60 minutes. Using the chemical composition from the FTIR spectra of a clear coat and primer layers of a paint chip, a PDQ team member will perform a live Layer System Query search. In this search, the chemical components of each layer are entered as a minimum parameter to produce a PDQ Hit List, which is a list of known samples from the database that are similar in layer sequence and chemistry to the unknown paint system. Using the spectral image preview function in PDQ, manufacturing plants will be eliminated from the Hit List based on spectral differences in one or more layers, narrowing down the Hit List to a small number of manufacturing plants that will be explored further by doing spectral searches and overlays in BioRad’s KnowItAll software. Once one or more manufacturing plants are identified, a year range will be determined to identify makes/models that would be the most likely source of the unknown paint chip.

There will be time for a question and answer period following the PDQ search. All levels of experience or knowledge in PDQ or automotive paint are welcome.

Detailed Learning Objectives

  1. Perform a Layer System Query search to produce a Hit List
  2. Perform spectral comparisons in PDQ to narrow down a Hit List and determine possible manufacturing plants
  3. Use BioRad’s KnowItAll to perform spectral searches to refine a Hit List to determine most likely make, models and year ranges

Presenters

  • Tamara Hodgins
  • Andrew Ho

Funding for this Forensic Technology Center of Excellence webinar has been provided by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice.

The opinions, findings, and conclusions or recommendations expressed in this webinar are those of the presenter(s) and do not necessarily reflect those of the U.S. Department of Justice.

Contact us at ForensicCOE@rti.org with any questions and subscribe to our newsletter for notifications.


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