Spectral Analysis and Quantitative Detection of Baicalin Based on Terahertz Radiation

Liu Lingyu; Chang Tianying; Li Ke; Li Yizhang; Zhang Xiansheng; Xu Wenqing

doi:10.3788/CJL202047.0314001

Chinese Journal of Lasers, Volume. 47, Issue 3, 314001(2020)

Spectral Analysis and Quantitative Detection of Baicalin Based on Terahertz Radiation

Liu Lingyu, Chang Tianying^*, Li Ke, Li Yizhang, Zhang Xiansheng, and Xu Wenqing

Author Affiliations

Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250100, China

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Figures & Tables(9)

Fig. 1. Schematic of terahertz time-domain spectroscopy

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Fig. 2. Terahertz absorbance spectra of baicalin, sucrose and corn starch

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Fig. 3. Terahertz absorbance of mixture with different baicalin concentrations. (a) Without baseline correction; (b) after baseline correction

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Fig. 4. 2D correlation spectroscopy of all mixtures' terahertz absorption spectra.(a) Synchronous map; (b) asynchronous map

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$Predicted mass fraction of baicalin using PLS model vs. actual mass fraction$

Fig. 5. Predicted mass fraction of baicalin using PLS model vs. actual mass fraction

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$Predicted mass fraction of baicalin using SVM model versus actual mass fraction$

Fig. 6. Predicted mass fraction of baicalin using SVM model versus actual mass fraction

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Table 1. Component of mixtures of baicalin, sucrose and corn starch
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Table 1. Component of mixtures of baicalin, sucrose and corn starch
Number Mass /g Massfraction ofbaicalin /%
Baicalin Sucrose Cornstarch
1 0.06 1.552 0.388 3
2 0.11 1.672 0.418 5
3 0.22 1.584 0.396 10
4 0.44 1.408 0.352 20
5 0.66 1.232 0.308 30
6 0.88 1.056 0.264 40
7 1.10 0.880 0.220 50

Table 2. Prediction results obtained by PLS and SVM models using random selection method to select the calibration and validation sets

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Table 2. Prediction results obtained by PLS and SVM models using random selection method to select the calibration and validation sets

Model	Calibration set		Validation set
Model	RMSE /%	R	RMSE /%	R
	1.13	0.9978	1.35	0.9969
	1.09	0.9979	1.45	0.9962
PLR	1.03	0.9981	1.56	0.9957
	1.23	0.9975	1.08	0.9979
	0.89	0.9986	1.30	0.9974
	0.89	0.9999	1.15	0.9981
SVM	0.87	0.9987	1.43	0.9967
	0.85	0.9987	1.37	0.9974

Table 3. Prediction results obtained by PLS and SVM models for baicalin mixtures
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Table 3. Prediction results obtained by PLS and SVM models for baicalin mixtures
Model Calibration set Validation set
RMSE /% R RMSE /% R
PLR 1.21 0.9974 1.16 0.9977
SVM 0.90 0.9986 1.14 0.9977

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Liu Lingyu, Chang Tianying, Li Ke, Li Yizhang, Zhang Xiansheng, Xu Wenqing. Spectral Analysis and Quantitative Detection of Baicalin Based on Terahertz Radiation[J]. Chinese Journal of Lasers, 2020, 47(3): 314001

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Paper Information

Category: terahertz technology

Received: Sep. 4, 2019

Accepted: --

Published Online: Mar. 12, 2020

The Author Email: Tianying Chang (tchang2013@163.com)

DOI:10.3788/CJL202047.0314001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Component of mixtures of baicalin, sucrose and corn starch

Table 1. Component of mixtures of baicalin, sucrose and corn starch

Table 2. Prediction results obtained by PLS and SVM models using random selection method to select the calibration and validation sets

Table 2. Prediction results obtained by PLS and SVM models using random selection method to select the calibration and validation sets

Table 3. Prediction results obtained by PLS and SVM models for baicalin mixtures

Table 3. Prediction results obtained by PLS and SVM models for baicalin mixtures