Rapid Identification of Bloodstain Based on Near Infrared Spectroscopy and Extreme Learning Machine Algorithm
Bloodstain is one of the most important forensic evidences in criminal cases.How to identify the bloodstains and obtain some potential evidence is of great significance to solve the criminal case.In this paper,a hand-held near-infrared(NIR)spectrometer was used to collect the spectral data of different species of bloodstains samples on cotton fabrics with different colors including human blood,chicken blood and pig blood.After collecting the spectral data,standard normal variables(SNV)pre-processing operation was implemented on the spectral data to eliminate the common offset and scaling effects.Then,the training models were established via extreme learning machine(ELM)algorithm to identify the species of bloodstain.Next,the testing samples were predicted by means of using the built specie identification bloodstain model.Meanwhile,the traditional support vector machine(SVM)and genetic algorithm-back propagation(GA-BP)classification algorithms were also used to build the identification model and the prediction results were also compared with ELM algorithm.The experimental results showed that the prediction accuracy of ELM algorithm was 98.48%,which was higher than that of GA-BP algorithm(84.62%)and SVM algorithm(73.84%).Meanwhile,the precision,sensitivity and specificity of the prediction results using ELM algorithm were also much higher than those of SVM and GA-BP algorithms.The above results showed that the accuracy of the identification model built by ELM algorithm was the highest and the overall performance of the model was the best.The research results of the paper showed that he rapid detection method based on a handheld NIR spectrometer and ELM algorithm could identify the types of the bloodstains efficiently,non-destructively,quickly and accurately and it provided a new technical reference for bloodstains detection and identification in criminal cases.
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