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

Lavine, Barry K.; White, Collin G.; Allen, M. D.

doi:10.1016/j.microc.2016.06.024

Cited by 17 publications

(4 citation statements)

References 19 publications

(20 reference statements)

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“…Lavine et al have studied search prefilters for PDQ searches of FT-IR spectroscopic data extensively. They have used these prefilters to assist in correct manufacturer information, to enhance leads, and for model Ford vehicles made between 2000 and 2006. − Many other types of chemometrics, other than prefilter settings, have been used. Thoonen et al used automated color analysis and chemometrics to aid in the classification of car paint using microscopy .…”

Section: Paintmentioning

confidence: 99%

Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis

et al. 2018

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Section: Paintmentioning

confidence: 99%

Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis

et al. 2018

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“…Chemometric methods for Raman spectroscopy include preprocessing, [16][17][18][19][20][21][22][23] pattern recognition, [24][25][26][27] multivariate calibration, 28,29 and instrument standardization. [30][31][32][33][34][35] There are fluorescent backgrounds and noises in Raman spectra of complex samples, so preprocessing is needed before further statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Mixture analysis using non‐negative elastic net for Raman spectroscopy

Zeng

Hou²,

Ni³

et al. 2020

Journal of Chemometrics

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Raman spectrum is wealth of structural information, which can be used as molecular fingerprint to identify compounds. There are relations between the intensities of Raman peaks and the concentrations of compound, and they can be used to estimate relative concentrations of the compounds in mixture. However, it is still challenging to interpret the Raman spectrum of mixture. The non-negative lasso (NN-LASSO) has been used to solve this problem, but it failed to identify highly correlated compounds. The quadratic term of nonnegative elastic net (NN-EN) can ensure the stability of the fitted model. Therefore, a novel mixture analysis method was developed based on NN-EN in this study. It has been applied to analyze the simulated, liquid, powder, tablet, and quantitative mixture datasets. Results showed that NN-EN can identify the compounds in mixture with high accuracy and estimate their relative concentrations with small deviation. Furthermore, NN-EN was more stable than NN-LASSO when the spectra of some compounds are highly correlated. It is a promising approach for analyzing Raman spectra of mixtures.

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“…Previous studies of search prefilters applied to the PDQ database to identify the automotive manufacturer were limited to General Motors, Chrysler, and Ford. [16][17][18] The manufacturer search prefilter for PDQ described in these previous studies consisted of a single discriminant to solve a three-way classification problem where each class was a specific vehicle manufacturer spanning a limited production year range (2000)(2001)(2002)(2003)(2004)(2005)(2006). The significance of the current study in relation to the previously published studies on search prefilters for manufacturer arises from the complexity of the pattern recognition problem encountered when a larger number of automotive manufacturers are considered.…”

Section: Introductionmentioning

confidence: 99%

“…Previously published studies on the use of search prefilters for the PDQ database were largely restricted to the identification of the assembly plant of the vehicle. These studies typically involved only a single manufacturer and a single layer (clear coat layer) [9][10][11][12][13][14] or multiple layers (clear coat, surfacer-primer, and e-coat layer) [15][16][17][18] of automotive paint.…”

Section: Introductionmentioning

confidence: 99%

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

2017

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Pattern recognition techniques have been applied to the infrared (IR) spectral libraries of the Paint Data Query (PDQ) database to differentiate between nonidentical but similar IR spectra of automotive paints. To tackle the problem of library searching, search prefilters were developed to identify the vehicle make from IR spectra of the clear coat, surfacer-primer, and e-coat layers. To develop these search prefilters with the appropriate degree of accuracy, IR spectra from the PDQ database were preprocessed using the discrete wavelet transform to enhance subtle but significant features in the IR spectral data. Wavelet coefficients characteristic of vehicle make were identified using a genetic algorithm for pattern recognition and feature selection. Search prefilters to identify automotive manufacturer through IR spectra obtained from a paint chip recovered at a crime scene were developed using 1596 original manufacturer's paint systems spanning six makes (General Motors, Chrysler, Ford, Honda, Nissan, and Toyota) within a limited production year range (2000-2006). Search prefilters for vehicle manufacturer that were developed as part of this study were successfully validated using IR spectra obtained directly from the PDQ database. Information obtained from these search prefilters can serve to quantify the discrimination power of original automotive paint encountered in casework and further efforts to succinctly communicate trace evidential significance to the courts.

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

Cited by 17 publications

References 19 publications

Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis

Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis

Mixture analysis using non‐negative elastic net for Raman spectroscopy

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints

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