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A rapid and nondestructive approach for forensic identification of car bumper splinters using attenuated total reflectance Fourier transform infrared spectroscopy and chemometrics
Author(s) -
Wei Chenjie,
Wang Jifen
Publication year - 2021
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.14606
Subject(s) - chemometrics , support vector machine , linear discriminant analysis , artificial intelligence , principal component analysis , attenuated total reflection , polypropylene , fourier transform , pattern recognition (psychology) , fourier transform infrared spectroscopy , computer science , materials science , mathematics , optics , machine learning , physics , composite material , mathematical analysis
The proper identification of car bumper splinters at hit‐and‐run crime scenes is imperative to forensic investigations, as splinters yield crucial pieces of vehicle information that can lead to subsequent investigation resolution and criminal justice. A method based on attenuated total reflectance Fourier transform infrared spectroscopy (ATR‐FTIR) combined with Fisher discriminant analysis (FDA) and support vector machine (SVM) is reported to classify car bumper splinters. The FDA and SVM models were constructed based on full spectrum, fingerprint spectrum, and characteristic spectrum data from 156 car bumper splinter samples. The characteristic spectrum data were extracted by principal component analysis. The classification results for different types of data were compared, and the classification models were analyzed. In the FDA, the model based on the spectral data of the characteristic spectrum yielded the highest classification accuracy, and the classification accuracy based on 10 brands was 88.5%. For polypropylene type; polypropylene, talcum powder, and calcium carbonate type; and polypropylene and talcum powder type bumper samples, the classification accuracy rate reached 97.4%. The classification results were ideal for the SVM, for 10 brands and 3 types of samples, the classification accuracy of the model constructed based on both full spectrum and characteristic spectrum data reached 100%. The results suggest that the SVM model is superior to the FDA model. The SVM model is also suitable for the classification of high‐dimensional data. ATR‐FTIR combined with the chemometrics methods of FDA and SVM is a rapid, nondestructive, and accurate method for the differentiation of car bumper splinters.