Zhong REN, Tao LIU, Guo-dong LIU. Classification and Identification of Real or Fake Blood Based on OPO Pulsed Laser Induced Photoacoustic Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(9): 2734
- Spectroscopy and Spectral Analysis
- Vol. 41, Issue 9, 2734 (2021)
Fig. 1. Structure of WNN network
Fig. 2. Flow chart of GA algorithm
Fig. 3. Diagram of experimental set-up
Fig. 4. Experimental samples
(a): Horse blood; (b): Cow blood; (c): Rabit blood;(d): Props fake blood; (e): Red ink
(a): Horse blood; (b): Cow blood; (c): Rabit blood;(d): Props fake blood; (e): Red ink
Fig. 5. Time-resolved photoacousic signals of different blood samples at 740 nm
Fig. 6. Photoacoustic peak-to-peak mean spectral of five different blood samples at 700~1 064 nm
Fig. 7. Effects of parameters on the classification and identification of real or fake blood for WNN and WNN-GA
(a): Effect of neuron numbers in hidden layer; (b): Effect of learning factorη ; (c): Effect of learning factor γ ; (d): Effect of iteration number
(a): Effect of neuron numbers in hidden layer; (b): Effect of learning factor
Fig. 8. Results of classification and identification of real or fake blood for WNN and WNN-GA
(a): Effect of training time on the mean square error;(b): Results of classification and identification of test bloods
(a): Effect of training time on the mean square error;(b): Results of classification and identification of test bloods
Fig. 9. Effects of parameters on the classification and identification of real or fake blood gor PCA-WNN-GA
(a): Effect of neuron numbers in hidden layer; (b): Effect of learning factorη ; (c): Effect of learning factor γ ; (d): Effect of iteration number
(a): Effect of neuron numbers in hidden layer; (b): Effect of learning factor
Fig. 10. Results of classification and identification of real or fake blood for PCA-WNN-GA under different principle components
(a): Effect of training time on the mean square error;(b): Results of classification and identification of test bloods
(a): Effect of training time on the mean square error;(b): Results of classification and identification of test bloods
Fig. 11. Correction rate of classification and identification of real or fake bloods for seven different algorithms
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