Rapid Identification of the Species of Bloodstain Based on Near Infrared Spectroscopy and Convolutional Neural Network-Support Vector Machine Algorithm

Abstract

As one of the most important types of evidence at the scene of the crime, the rapid identification of the human bloodstain is of great significance to solve the criminal case. In this paper, the spectral data of different species of bloodstain samples including human, chicken and pig were acquired by using a hand-held near-infrared spectrometer. Then, the training models were established via convolutional neural network-support vector machine algorithm. Meanwhile, the traditional support vector machine, genetic algorithm-back propagation and random forest classification algorithms were also compared. The results showed that the prediction accuracy of convolutional neural network-support vector machine algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a handheld near-infrared spectrometer and convolutional neural network-support vector machine algorithm could identify the species of bloodstain efficiently, non-destructively, quickly and accurately and it provided a new technical reference for bloodstains detection and identification.