Classification and discrimination of real and fake blood based on photoacoustic spectroscopy combined with particle swarm optimized wavelet neural networks.

In this work, photoacoustic spectroscopy was employed to distinguish real blood from fake blood rapidly, accurately, and recoverably. To achieve this goal, a photoacoustic detection system for blood was established in the forward mode. In the experiments, four kinds of animal blood and two kinds of fake blood in a total of 150 groups were used. The time-resolved photoacoustic signal and peak-to-peak values (PPVs) of all blood were captured in 700-1064 nm with intervals of 5 nm. Experimental results show that the amplitudes, profiles, peak-point time, and PPVs are different between real and fake blood. Although the PPVs of real blood are larger than those of the fake ones at 700-850 nm, the differences in PPVs are not obvious at 850-1064 nm, especially when there are spectral overlaps of PPVs. To accurately classify and discriminate real and fake blood, a wavelet neural network (WNN) was used to train 120 groups of blood and test 30 groups of blood. Moreover, the particle swarm optimization (PSO) algorithm was used to optimize the weights and thresholds, as well as the translation and scale factors of the Morlet-liked wavelet basis function of the WNN. Under optimal parameters, the correct rate of the WNN-PSO algorithm was improved from 63.3% to 96.7%. Next, principal component analysis (PCA) was combined into the WNN-PSO algorithm to further improve the correct rate. The results indicate that the correct rate of the PCA-WNN-PSO algorithm with 10 principal components reaches 100 %. Therefore, photoacoustic spectroscopy combined with the PCA-WNN-PSO algorithm exhibits excellent performance in the classification and discrimination of real and fake blood.