Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

Figures & data

Figure 1. The basic structure of WNN.

Figure 2. Main chart of QPSO-WNN algorithm.

Table 1. Dynamic ranges of five kinds of DSC strategies.

αendI, αendII, αendIII, αendIV, αendV	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1
αI, αII, αIII, αIV, αV	1→0.1	1→0.2	1→0.3	1→0.4	1→0.5	1→0.6	1→0.7	1→0.8	1→0.9	1

Figure 3. Function profiles of the novel nonlinear DSC strategy (α_V) and other four DSC strategies (α_I, α_II, α_III, α_IV).

Figure 4. Schematic diagram of experimental setup (a), part of experimental setup (b), prepared skin phantom and vessel phantom (c), and rabbit whole blood specimen (d).

Figure 5. The time-domain echo signal (a) and frequency spectrum (b) of the ultrasonic transducer.

Figure 6. Real-time OA signals (a), and the OA PPVs spectra (b) of the blood glucose for rabbit whole blood; the enlarged view with error bars in the zoom region of real-time OA signals (c); the OA PPVs spectra with error bars.

Figure 7. The characteristic wavebands optimization of blood glucose based on iPLSR model. (a) RMSECV of BGL for full-wavelengths under different PLS principle components; (b) RMSECV distributions of BGL in different sub-wavebands; (c) the chosen characteristic wavebands of blood glucose; (d) established PLSR model from 740 nm to 775 nm.

Figure 8. Characteristic wavelengths optimization of blood glucose based on LSF model under different wavelengths.

Figure 9. The real-time OA signals of blood glucose under different combinations of five factors. (a) different temperatures, (b) different flow rates, (c) different concentrations and vessel depths, (d) different irradiation energies and vessel depths.

Figure 10. OA PPVs of blood glucose samples under the comprehensive influence of multiple factors. (a) under temperature and concentration; (b) under concentration and irradiation energy; (c) under temperature and irradiation energy; (d) under flow rates and vessel depth.

Figure 11. The effects of neurons number in hidden layer (a), the learning rate factor η (b), and the learning rate factor λ (c) on the MSE values of predicting BGL under the influence of five factors based on WNN, PSO-WNN and QPSO-WNN algorithms.

Figure 12. The effects of the acceleration factor (c₁) (a), and the acceleration factor (c₂) (b), the inertia weight factor (w) (c) on the MSE values of predicting BGL based on PSO-WNN algorithm.

Figure 13. The effects of training number (a) and iteration number (b) on the MSE of BGL based on PSO-WNN algorithm, and the Clarke error grid analysis (c).

Figure 14. Effect of shrinkage coefficient (a), training number (b), and iteration number (c) on the MSE value of predicting BGL based on QPSO-WNN algorithm.

Figure 15. Clarke error grid analysis of testing set blood based on QPSO-WNN algorithm.

Figure 16. MSE values of predicting BGL for testing set blood based on QPSO-WNN with different DSC strategies.

Figure 17. The effects of training number (a) and iteration number (b) on the MSE values of BGL for the training set blood and the testing set blood based on the QPSO-WNN algorithm with the novel nonlinear DSC strategy (α_V), and Clarke error grid analysis (c).

Figure 18. MSE of predicting BGL for 125 testing set blood based on several different algorithms.

Table 2. Comparisons for predicting glucose concentration between our work and other research works.

Paper	Technology	Object	Wavelength	Algorithm	Influence	MSE, accuracy, R^2, etc.
Ref.^[^15^]	Photoacoustic spectroscopy	Glucose solution and in vivo whole blood	905 and 1550 nm	GKR^a	Not considered	MSE = 0.424 mmol/L (glucose solution), 1.081 mmol/L (in vivo)
Ref.^[^19^]	Photoacoustic spectroscopy	Glucose solution	980 nm	OLS and machine leanring (SVR^b, KNNR^c, etc.)	Not considered	Accuracy: 97%
Ref.^[^14^]	Photoacoustic spectroscopy	Pig blood and intralipid	532 and 1064 nm	OLS and nonlinear fitting	Not considered	R^2 = 0.9747(intralipid), R^2=0.9976(pig blood, 532 nm), R^2 = 0.9749 (pig blood, 1064 nm)
Ref.^[^16^]	Differential continuous wave photoacoustic spectroscopy	In vivo human body	1382 and 1610 nm	OLS fitting	Not considered	SD^f = 2.667 mmol/L, R^2= 0.8
Ref.^[^1^]	NIR spectroscopy	Glucose solution	485, 645, 860 and 940 nm	MLR^d	Not considered	Accuracy: 99.75%, R^2= 0.91
Ref.^[^2^]	NIR spectroscopy	In vivo human body	1050–1609 nm	PLSR combined with DNN^e	Not considered	Accuracy: 97.96%, R^2= 0.9216
Ref.^[^6^]	Raman spectroscopy	In vivo pig ear	830 nm	PLSR	Not considered	R^2= 0.91
Ours	Optoacoustic spectroscopy	In vitro rabbit whole blood	750 nm	IQPSO-WNN	Considered five factors	MSE = 0.3088 mmol/L

*GKR^a: Gaussian kernel-based regression; SVR^b: support vector machine regression; KNNR^c: K-nearest neighbour regression; MLR^d: multivariate linear regression; DNN^e: deep neural network; SD^f: standard deviation.

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