Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

Huo Zhang; Lanjuan Huang; Chuanpei Xu; Chuanpei Xu; Zhi Li; Xianhua Yin; Tao Chen; Yuee Wang; Guanglei Li

doi:10.1364/AO.491777

Applied Optics
Vol. 62,
Issue 19,
pp. 5306-5316
(2023)
•https://doi.org/10.1364/AO.491777

Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

Huo Zhang, Lanjuan Huang, Chuanpei Xu, Zhi Li, Xianhua Yin, Tao Chen, Yuee Wang, and Guanglei Li

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Huo Zhang, Lanjuan Huang, Chuanpei Xu, Zhi Li, Xianhua Yin, Tao Chen, Yuee Wang, and Guanglei Li, "Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy," Appl. Opt. 62, 5306-5316 (2023)

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Table of Contents Category
- Spectroscopy
Optics & Photonics Topics
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About this Article
History
- Original Manuscript: March 27, 2023
- Revised Manuscript: June 8, 2023
- Manuscript Accepted: June 13, 2023
- Published: June 29, 2023

Abstract

This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.

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Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Modeling Methods	Pre-processing	Rc	RMSEc (%)	Rp	RMSEp (%)
PLSR	None	0.5243	5.3347	0.3901	5.7685
PLSR	SGFD	0.9860	1.0451	0.9255	2.3724
PCA-PLSR	SGFD	0.9855	1.0633	0.9292	2.3160
SVR	None	0.5644	5.1718	0.4653	5.5545
SVR	SGFD	0.9935	0.7110	0.9524	1.9107
PCA-SVR	SGFD	0.9995	0.1954	0.9509	1.9398

Modeling Methods	Feature Extraction Methods	Rc	RMSEc (%)	Rp	RMSEp (%)
2DCOS-PLSR	None	0.9910	0.8395	0.9626	1.6978
	PCA	0.9904	0.8642	0.9630	1.6882
	UVE	0.9341	2.2370	0.9167	2.5039
	SPA	0.9667	1.6038	0.9575	1.8080
	CARS	0.9937	0.7002	0.9902	0.8761
2DCOS-SVR	None	0.9995	0.1938	0.9783	1.2981
	PCA	0.9995	0.1930	0.9782	1.3024
	UVE	0.9509	1.9309	0.9224	2.4197
	SPA	0.9981	0.3856	0.9663	1.6129
	CARS	0.9970	0.4885	0.9915	0.8160

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Modeling Methods	Pre-processing	Rc	RMSEc (%)	Rp	RMSEp (%)
PLSR	None	0.5243	5.3347	0.3901	5.7685
PLSR	SGFD	0.9860	1.0451	0.9255	2.3724
PCA-PLSR	SGFD	0.9855	1.0633	0.9292	2.3160
SVR	None	0.5644	5.1718	0.4653	5.5545
SVR	SGFD	0.9935	0.7110	0.9524	1.9107
PCA-SVR	SGFD	0.9995	0.1954	0.9509	1.9398

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Abstract

Data availability

Cited By

Figures (8)

Tables (3)

Equations (8)

Applied Optics

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12

Sample Number	P. notoginseng Content (mg)	Kusnezoff monkshood root Content (mg)	Wallich swallowwort rootContent (mg)	Baiyun ginseng Content (mg)	Proportions of P. notoginseng (%)	Count of Samples
1	0	100	100	100	0	12
2	6	98	98	98	2	12
3	15	95	95	95	5	12
4	24	92	92	92	8	12
5	30	90	90	90	10	12
6	36	88	88	88	12	12
7	45	85	85	85	15	12
8	60	80	80	80	20	12