4.6 Article

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

Journal

JOURNAL OF BIOPHOTONICS
Volume 15, Issue 5, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/jbio.202100309

Keywords

blood authenticity; correct rate; photoacoustic spectroscopy; quantum-behaved particle swarm optimization; wavelet neural network

Funding

  1. Chinese National Natural Science Fund Project [61650402, 51763011, 62165006]
  2. Young top-notch personnel fund project of JXSTNU [2014QNBJRC004]
  3. Doctor start-up fund project of JXSTNU [2017BSQD021]
  4. 2018 Natural Science outstanding youth fund project of Jiangxi Province [2018ACB21006]
  5. Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City [2019-NCZDSY-008]
  6. 2019 Outstanding Young Personnel Training Program of Jiangxi Province [20192BCBL23015]
  7. Natural Science Foundation of Jiangxi Province [20192BAB206016]

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A photoacoustic detection system was established to accurately identify blood authenticity. The overlap of signals and spectra limited the accurate identification. To improve the correct rate, wavelet neural network (WNN) and quantum-behaved particle swarm optimization (QPSO) algorithm were employed, along with dynamic contraction-expansion coefficients and truncated mean stabilization strategy (TMSS) to enhance the performance. The method showed excellent performance in blood authenticity identification.
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.

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