Recognition of authentic or false blood based on NIR spectroscopy and PCA-WNN-PSO algorithm

To achieve the identification of true and fake blood, the near infrared spectroscopy method was used in this work. The optical absorption spectra of blood samples with 120 groups of training samples and 30 groups of test samples were obtained via a Fourier transform NIR spectroscope. Since the similar spectra profiles and spectra overlap between the blood samples, the accurate identification of true blood and fake blood is difficult from the visual viewpoint. The wavelet neural network was used to train and test the blood samples. The correct rate of identifying true and fake blood is only 23.3%. To improve the correct rate, the particle swarm optimization (PSO) algorithm was used to optimize the weights, two learning rate factors, translation factor and scaling factor of WNN network. At the same time, the effects of the neuron number in the hidden layer, two learning rate factors, two acceleration factors, iteration times and training times on the correct rate and mean square error of identifying blood based on WNN-PSO algorithm were investigated. Under the optimal parameters, the correct rate of WNN-PSO algorithm is improved to 53.3%. Then, the principal component analysis (PCA) method was used to further improve the correct rate. The effect of different principal components on the correct rate of identifying blood based on PCA-WNN-PSO algorithm was also investigated. The results show that the correct rate can reach 96.7% for the identification of blood by using the NIR spectroscopy combined with PCA-WNN-PSO algorithm.