Photoacoustic identification of blood authenticity based on quantum-behaved particle swarm optimized wavelet neural network.

Abstract

To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time-resolved photoacoustic signals and peak-to-peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak-to-peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum-behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN-PSO algorithms. Results showed the correct rate of WNN-QPSO was increased to 96%. Then, three kinds of dynamic contraction-expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN-QPSO coupled with TMSS and dynamic contraction-expansion coefficient had an excellent performance in the identification of blood authenticity.