Wavelets and genetic algorithms applied to search prefilters for spectral library matching in forensics.

Currently, the identification of the make, model and year of a motor vehicle involved in a hit and run collision from only a clear coat paint smear left at a crime scene is not possible. Search prefilters for searching infrared (IR) spectral libraries of the paint data query (PDQ) automotive database to differentiate between similar but nonidentical Fourier transform infrared (FTIR) paint spectra are proposed. Applying wavelets, FTIR spectra of clear coat paint smears can be denoised and deconvolved by decomposing each spectrum into wavelet coefficients which represent the sample's constituent frequencies. A genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients for underdetermined data that are characteristic of the model and manufacturer of the automobile from which the spectra of the clear coats were obtained. Even in challenging trials where the samples evaluated were all the same manufacturer (Chrysler) with a limited production year range, the respective models and manufacturing plants were correctly identified. Search prefilters for spectral library matching are necessary to extract investigative lead information from a clear coat paint smear; unlike the undercoat and color coat paint layers, which can be identified using the text based portion of the PDQ database.

[1]  B K Lavine,et al.  Source identification of underground fuel spills by solid-phase microextraction/high-resolution gas chromatography/genetic algorithms. , 2000, Analytical chemistry.

[2]  Barry K. Lavine,et al.  Genetic algorithm for fuel spill identification , 2001 .

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  N. S. Cartwright,et al.  A Proposed Data Base for the Identification of Automotive Paint , 1976 .

[5]  Barbara Hubbard,et al.  The World According to Wavelets , 1996 .

[6]  Nikhil Mirjankar,et al.  Pattern recognition analysis of differential mobility spectra with classification by chemical family. , 2006, Analytica chimica acta.

[7]  Barry K. Lavine,et al.  Spectral Pattern Recognition Using Self-Organizing MAPS , 2004, J. Chem. Inf. Model..

[8]  Brian Caddy,et al.  Forensic Examination of Glass and Paint : Analysis and Interpretation , 2001 .

[9]  J. C. W. G. Bink,et al.  Classification of organic compounds by infrared spectroscopy with pattern recognition and information theory , 1983 .

[10]  Thomas L. Isenhour,et al.  Infrared Library Search on Principal-Component-Analyzed Fourier-Transformed Absorption Spectra , 1987 .

[11]  Barry K. Lavine,et al.  Raman Spectroscopy and Genetic Algorithms for the Classification of Wood Types , 2001 .

[12]  P G Rodgers,et al.  The forensic microanalysis of paints, plastics and other materials by an infrared diamond cell technique. , 1974, Forensic science.

[13]  James S. Walker,et al.  A Primer on Wavelets and Their Scientific Applications , 1999 .

[14]  Peter C. Jurs,et al.  New index for clustering tendency and its application to chemical problems , 1990, J. Chem. Inf. Comput. Sci..

[15]  W. H. Clark,et al.  The Classification of Automotive Paint by Diamond Window Infrared Spectrophotometry Part III Case Histories , 1976 .

[16]  Barry K. Lavine,et al.  One stop shopping: feature selection, classification and prediction in a single step , 2011 .

[17]  G. W. Small.  Automated spectral interpretation , 1987 .

[18]  Jean-Raymond Abrial,et al.  On B , 1998, B.

[19]  Barry K. Lavine,et al.  Electronic van der Waals Surface Property Descriptors and Genetic Algorithms for Developing Structure-Activity Correlations in Olfactory Databases , 2003, J. Chem. Inf. Comput. Sci..

[20]  DeLyle Eastwood,et al.  Pattern Recognition, Chemometrics, and Imaging for Optical Environmental Monitoring , 1999 .

[21]  J. L. Buckle,et al.  PDQ—Paint Data Queries: The History and Technology Behind the Development of the Royal Canadian Mounted Police Forensic Laboratory Services Automotive Paint Database , 1997 .

[22]  Gordon Fettis,et al.  Automotive Paints and Coatings , 1994 .

[23]  I. Jolliffe.  Principal Component Analysis , 2002 .

[24]  A. J. Moores,et al.  Genetic algorithms for spectral pattern recognition , 2002 .

[25]  Stephen R. Lowry,et al.  Data base development and search algorithms for automated infrared spectral identification , 1985, J. Chem. Inf. Comput. Sci..

[26]  Barry K. Lavine,et al.  AUTHENTICATION OF FUEL SPILL STANDARDS USING GAS CHROMATOGRAPHY/PATTERN RECOGNITION TECHNIQUES , 2001 .

[27]  W. H. Clark,et al.  The Classification of Automotive Paint by Diamond Window Infrared Spectrophotometry Part II Automotive Topcoats and Undercoats , 1976 .

[28]  A. J. Moores,et al.  Innovative genetic algorithms for chemoinformatics , 2002 .

[29]  W. H. Clark,et al.  A Computerized System for the Identification of Suspect Vehicles Involved in Hit and Run Accidents , 1982 .

[30]  Yachao Zhang,et al.  Detection and identification of bacteria using antibiotic susceptibility and a multi-array electrochemical sensor with pattern recognition. , 2007, Biosensors & bioelectronics.

[31]  B K Lavine,et al.  Fuel spill identification using solid-phase extraction and solid-phase microextraction. 1. Aviation turbine fuels. , 2001, Journal of chromatographic science.

[32]  W. H. Clark,et al.  The Classification of Automobile Paint by Diamond Window Infrared Spectrophotometry Part I: Binders and Pigments , 1976 .