A novel approach for forensic identification of automotive paints using optical coherence tomography and multivariate statistical methods. Issue 6 (1st August 2022)

• Record Type: Journal Article
• Authors: Wu, Di; Wu, Qiong; Lu, Yifan; Wang, Chengming; Yv, Siyi; Wang, Lei; Zeng, Haoran; Sun, Yijian; Li, Zhigang; Gao, Shuhui; Zhang, Ning
• Is Part Of: Journal of forensic sciences. Volume 67:Issue 6(2022)
• Page Start: 2253
• Page End: 2266
• Publication Date: 2022-08-01
• Subjects: automotive paints -- Bayes discriminant analysis -- forensic identification -- multivariate statistical method -- optical coherence tomography -- principal component analysis; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.15114
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: Automotive paint is one of the most important evidence in solving vehicle‐related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral‐domain optical coherence tomography system (SD‐OCT). Two‐dimensional cross‐sectional OCT images and three‐dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A‐scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross‐sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A‐scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi‐layer perceptron (MLP), k‐nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.