Forensic analysis of automotive paints using a pattern recognition assisted infrared library searching system: Ford (2000–2006)

Abstract

A prototype infrared (IR) library search system for the paint data query (PDQ) database has been further developed to determine the make, line and model of an automotive vehicle from the clear coat, surfacer-primer, and e-coat layers in an effort to improve discrimination capability in automotive paint comparisons involving intact paint chips. Search prefilters for the IR spectral library of PDQ were developed from the clear coat, surfacer-primer and e-coat layers for 1179 manufacturer paint systems within a limited production year range (2000–2006) to identify vehicle manufacturer (Ford, Chrysler, and General Motors). For each make (i.e., manufacturer), search prefilters were developed to identify the assembly plant of the vehicle using a hierarchical classification scheme. A cross correlation library search algorithm that performed both forward and backward searching was then used to identify the line and model of the vehicle from the truncated IR spectral library of PDQ identified by the search prefilters. Samples assigned to the same line and model by both a forward and backward search of the IR spectral data were always correctly matched, always correlated well on an individual basis to a specific library sample and were well represented in the truncated PDQ spectral library identified by the search prefilters. The performance of the prototype IR library searching system (search prefilters and cross-correlation library search algorithms) for the PDQ database was benchmarked against commercial library searching algorithms. Only the results for Ford are reported here.