Study on Establishment of Near-Infrared Quantitative Model for Salvianolic Acid B in Naoxintong Capsule Based on the System Modeling Idea

Rui-lin GAO; Peng-shuo YANG; Gang XU; Xiao-wen WU; Chang YANG; Xin-yuan SHI

doi:10.3964/j.issn.1000-0593(2020)11-3573-06

Journals >Spectroscopy and Spectral Analysis >Volume 40 >Issue 11 >Page 3573 > Article

Spectroscopy and Spectral Analysis
Vol. 40, Issue 11, 3573 (2020)

Study on Establishment of Near-Infrared Quantitative Model for Salvianolic Acid B in Naoxintong Capsule Based on the System Modeling Idea

Rui-lin GAO^1、1、*, Peng-shuo YANG^1、1, Gang XU^1、1, Xiao-wen WU^1、1, Chang YANG^1、1, and Xin-yuan SHI^1、1

Author Affiliations

¹[in Chinese]

¹1. School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China

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DOI: 10.3964/j.issn.1000-0593(2020)11-3573-06 Cite this Article

Rui-lin GAO, Peng-shuo YANG, Gang XU, Xiao-wen WU, Chang YANG, Xin-yuan SHI. Study on Establishment of Near-Infrared Quantitative Model for Salvianolic Acid B in Naoxintong Capsule Based on the System Modeling Idea[J]. Spectroscopy and Spectral Analysis, 2020, 40(11): 3573 Copy Citation Text

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Fig. 1. The chromatograms of salvianolic acid B reference standard (a) and sample solution (b)
1: Salvianolic acid B

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Fig. 2. The raw spectra of Salvia miltiorrhiza samples

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Fig. 3. Correlation between the prediction and reference values of salvianolic acid B

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Variable		Type	Levels
X₁	K-S	Continuous	1:1 4:1
X₂	LVs	Discrete	1 2 3 4 5 6 7 8 9 10
X₃	P	Nominal	Raw 2D MSC SNV SG9 2D-snv
X₄	V	Nominal	siPLS iPLS mwPLS Full
Response variables			$R_{cal}^{2}$ RMSEC $R_{pre}^{2}$ RMSEP RPD

Table 1. Experimental factor levels and response variables for the D-optimal design

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run	X₁	X₂	X₃	X₄	$R_{cal}^{2}$	RMSEC	$R_{pre}^{2}$	RMSEP	RPD
1	1.45	1	2D-snv	sipls	0.176 8	0.016 0	0.080 1	0.017 0	0.944 4
2	3.10	7	SG9	full	0.983 0	0.002 9	0.990 0	0.002 4	6.823 7
3	4.00	10	2D	sipls	0.994 1	0.001 8	0.998 7	0.003 2	5.239 6
4	2.58	5	MSC	mwpls	0.986 3	0.002 7	0.987 7	0.003 3	4.905 4
5	1.66	8	RAW	ipls	0.980 4	0.003 3	0.969 4	0.004 3	3.937 4
6	1.00	1	SG9	full	0.647 8	0.017 9	0.460 0	0.015 6	1.126 2
7	3.50	9	MSC	full	0.993 4	0.001 9	0.995 1	0.001 7	9.868 5
8	2.56	6	2D-snv	full	0.992 2	0.002 0	0.993 2	0.002 1	7.467 3
9	3.33	3	MSC	ipls	0.966 2	0.004 2	0.990 2	0.002 3	7.075 0
10	3.92	8	2D	mwpls	0.981 3	0.003 1	0.995 6	0.003 0	5.478 0
11	1.00	1	SNV	sipls	0.259 4	0.017 8	0.309 7	0.014 7	1.229 3
12	4.00	2	2D-snv	ipls	0.831 5	0.008 8	0.865 3	0.009 2	1.753 3
13	1.00	1	2D	mwpls	0.322 9	0.016 8	0.441 5	0.016 8	0.993 8
14	3.10	7	SG9	full	0.983 0	0.002 9	0.990 0	0.002 4	6.823 7
15	1.00	10	MSC	ipls	0.988 7	0.002 7	0.958 0	0.004 4	4.137 8
16	2.50	6	2D-snv	mwpls	0.986 0	0.002 6	0.991 0	0.002 4	6.596 6
17	1.08	2	SNV	ipls	0.469 2	0.015 6	0.429 0	0.013 3	1.356 9
18	2.81	1	SNV	mwpls	0.220 0	0.016 2	0.104 9	0.016 5	0.998 6
19	2.47	5	SG9	ipls	0.982 5	0.003 0	0.961 5	0.004 8	3.365 0
20	1.00	9	RAW	full	0.994 0	0.001 9	0.982 3	0.003 0	5.798 0
21	2.37	5	SG9	sipls	0.946 2	0.005 3	0.943 6	0.006 1	2.722 3
22	4.00	5	SNV	full	0.976 6	0.003 5	0.988 0	0.002 7	6.167 0
23	2.26	9	2D	mwpls	0.981 9	0.003 1	0.992 6	0.003 0	5.499 7
24	2.71	2	2D	sipls	0.776 4	0.010 2	0.830 0	0.009 7	1.694 9
25	1.23	2	MSC	full	0.714 4	0.012 0	0.732 5	0.010 5	1.668 6
26	2.47	5	SG9	ipls	0.982 5	0.003 0	0.961 5	0.004 8	3.365 0
27	3.85	10	2D-snv	sipls	0.996 0	0.001 4	0.995 3	0.002 4	6.605 1
28	4.00	1	2D	mwpls	0.334 9	0.016 8	0.782 7	0.016 7	0.987 3
29	4.00	1	RAW	ipls	0.368 0	0.016 5	0.382 4	0.018 0	0.899 4
30	1.08	9	2D-snv	ipls	0.941 3	0.005 6	0.956 6	0.005 0	3.332 7
31	2.42	6	2D	full	0.990 0	0.001 9	0.992 8	0.002 1	7.880 1
32	1.65	4	2D-snv	ipls	0.881 1	0.007 4	0.939 2	0.006 0	2.665 0
33	2.50	6	2D	ipls	0.974 8	0.003 6	0.988 7	0.003 4	4.804 2
34	3.66	9	SNV	ipls	0.982 7	0.003 0	0.985 2	0.002 9	5.761 3
35	3.45	8	SNV	sipls	0.992 6	0.002 0	0.992 6	0.002 2	7.628 7
36	4.00	1	MSC	sipls	0.126 0	0.016 2	0.033 1	0.016 9	0.966 1
37	3.22	1	RAW	full	0.573 0	0.016 4	0.504 3	0.018 0	0.900 0
38	1.23	3	SNV	full	0.945 4	0.005 6	0.948 9	0.004 8	3.659 8
39	1.68	2	RAW	sipls	0.356 1	0.015 6	0.360 8	0.014 8	1.140 1
40	1.00	10	RAW	mwpls	0.993 8	0.001 9	0.965 6	0.004 2	4.217 6
41	1.00	8	SNV	mwpls	0.993 7	0.002 0	0.961 3	0.004 5	4.017 4
42	1.52	7	MSC	sipls	0.978 7	0.003 5	0.989 7	0.002 8	6.111 6
43	3.63	10	RAW	sipls	0.991 7	0.002 1	0.992 9	0.001 9	8.640 9
44	3.71	1	SG9	mwpls	0.372 9	0.016 5	0.388 8	0.017 8	0.915 5
45	1.00	10	2D	sipls	0.996 8	0.001 3	0.986 6	0.003 2	5.201 0
46	2.37	5	SG9	sipls	0.946 2	0.005 3	0.943 6	0.006 1	2.722 3
47	1.08	8	SG9	mwpls	0.984 8	0.003 0	0.967 8	0.004 0	4.416 5
48	2.02	10	2D-snv	sipls	0.996 7	0.001 3	0.993 3	0.002 9	5.368 7
49	2.27	7	RAW	sipls	0.975 0	0.003 6	0.988 5	0.002 5	6.652 9
50	3.15	10	SNV	full	0.994 3	0.001 7	0.994 5	0.001 8	8.978 0
51	1.89	1	2D	ipls	0.278 3	0.016 0	0.526 4	0.018 0	0.877 9
52	4.00	5	RAW	mwpls	0.982 3	0.003 0	0.956 2	0.004 9	3.329 1
53	2.71	2	2D	sipls	0.776 4	0.010 2	0.830 0	0.009 7	1.694 9
54	2.42	6	2D	full	0.990 0	0.001 9	0.992 8	0.002 1	7.880 1
55	4.00	10	SG9	mwpls	0.985 1	0.002 8	0.969 4	0.004 1	3.925 8

Table 2. Experimental results of D-optimal design

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响应值	模型	P值	R²	$R_{Adj}^{2}$
Y₁	二次模型	<0.000 1	0.995 8	0.977 4
Y₂	二次模型	<0.000 1	0.999 0	0.994 9
Y₃	二次模型	<0.000 1	0.994 7	0.971 5
Y₄	二次模型	<0.000 1	0.998 7	0.993 2
Y₅	二次模型	<0.000 1	0.990 6	0.949 0

Table 3. Model fitting and analysis results

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