Non-destructive characterization and discrimination of vehicle bumpers fragments in forensic science using molecular spectral fusion analysis and chemometrics

Abstract

A rapid and non-destructive approach for characterizing and discriminating trace evidences is one of the hotspots and focuses in forensic science. The vehicle bumpers fragments, as a trace evidence, are often found and extracted from the clothing of victims, the crashed vehicle, and the road at the scenes. Micro-laser Raman spectroscopy (MLRM) and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been used with principal component analysis (PCA), Multi-layer perceptron neural network (MLPNN), and Fisher discriminant analysis (FDA) to propose a method for characterizing and discriminating 160 vehicle bumpers fragments from 8 manufacturers. The spectra were pre-treated by automatic baseline correction, multivariate scatter correction, standard normal variate and Savitzky-Golay algorithm. PCA was used to reduce the dimension and extraction of feature variables, while MLPNN, FDA, the several supervised pattern recognition methods, were used as algorithms of constructing classifiers. The results displayed that the discrimination ability of fusion spectra model was stronger than that of single spectra dataset. In MLPNN model, the order of it from high to low was feature-level fusion (93.5%) > data-level fusion (90.3%) > MLRM (74.2%) > ATR-FTIR (64.5%). In FDA model, the order of it from high to low was feature-level fusion (100%) > data-level fusion (93.5%) > MLRM (77.4%) > ATR-FTIR (61.3%). Fisher discriminant analysis model based on feature-level fusion dataset was the more optimal and practical method for discriminating the vehicle bumpers fragments. “Audi”, “Hyundai”, and “Chery” samples, the similar kind from different manufacturers, were also distinguished exactly, which was satisfactory. The designed method represented a potentially rapid and non-destructive approach for differentiation trace evidences in forensic science.