Spectral Pattern Recognition of Cardiac Tissue in Electric Shock Death and Post-Mortem Electric Shock

In forensic identification, cases involving electrocution death are frequent and the identification of electric shock before and after death remains one of the difficult problems in forensic pathology identification. The experiments were carried out to classify and identify electrocution death and post-mortem electrocution behavior from the viewpoint of heart tissue through Fourier transform infrared spectroscopy fused with machine learning model. 30 rats were subjected to electrocution death, post-mortem electrocution and control treatment, and their heart tissue spectra were scanned by a spectrometer, and a total of 70 spectral feature wavelength points were extracted using a competitive adaptive re-weighting algorithm, and a random forest model was established to identify the feature wavelength The results showed that the accuracy of model classification recognition before and after feature wavelength extraction was 34.9% and 73.7% respectively, which verified the effectiveness and necessity of the feature wavelength extraction method, and the partial least squares model, traditional support vector machine and support vector machine model optimized by particle swarm algorithm and grey wolf algorithm were established for classification recognition. The results showed that the accuracy of the models was 61.07%, 34.48%, 100% and 98.46% respectively, and the particle swarm optimized support vector machine model with feature extraction was found to be the most effective. In order to exclude the interference of the biological death period, 60 rats were treated in the same way, and each group was divided into two subgroups: 0.5 h and 1 h post-mortem, and the spectral data were scanned again by Fourier transform infrared spectrometer-SVM model analysis, the results showed that the method could achieve an accuracy of 80.85% for classification and identification. It provides a new research idea and method for forensic identification in electrocution death. It shows that FTIR combined with machine learning models can be an essential research significance as a complementary tool to provide relatively objective judgements.