Exploration of Habitat-Related Chemical Markers for Stephania tetrandra Applying Multiple Chromatographic and Chemometric Analysis

Abstract

Geo-authentic herbs refer to medicinal materials produced in a specific region with superior quality. Stephania tetrandra S. Moore (S. tetrandra) is cultivated in many provinces of China, including Anhui, Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Guangdong, Hainan, and Taiwan, among which Jiangxi is the geo-authentic origin. To explore habitat-related chemical markers of herbal medicine, an integrated chromatographic technique including gas chromatography-mass spectrometry (GC-MS), ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) and ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) combined with chemometric analysis was established. The established methods manifested that they were clearly divided into two groups according to non-authentic origins and geo-authentic origins, suggesting that the metabolites were closely related to their producing areas. A total of 70 volatile compounds and 50 non-volatile compounds were identified in S. tetrandra. Meanwhile, tetrandrine, fangchinoline, isocorydine, magnocurarine, magnoflorine, boldine, and higenamine as chemical markers were accurately quantified and suggested importance in grouping non-authentic origins and geo-authentic origins samples. The discriminatory analysis also indicated well prediction performance with an accuracy of 80%. The results showed that the multiple chromatographic and chemometric analysis technique could be used as an effective approach for discovering the chemical markers of herbal medicine to fulfill the evaluation of overall chemical consistency among samples from different producing areas.

1. Introduction

S. tetrandra is derived from the dried root of Stephania tetrandra S. Moore (S. tetrandra) [1], a perennial liana plant of the genus Stephania, belonging to the Menispermaceae family [2]. It was first recorded as medicine in Shen Nong’s Herbal Classic and suitable as a treatment for arthralgia associated with rheumatoid arthritis, wet beriberi, eczema, and inflamed sores, which acts as a diuretic, analgesic, and anti-inflammatory [2,3,4]. Currently, the compounds isolated and identified from S. tetrandra are mainly alkaloids which are critical for evaluating their therapeutic effects and quality [5]. However, only two characteristic components, tetrandrine and fangchinoline, were defined as the quantitative indexes recorded in the Chinese Pharmacopoeia 2020 edition [1]. Other major components with high content and extensive pharmacological properties, such as magnoflorine, magnocurarine, isocorydine, higenamine, and boldine, have been less studied so far [2]. Therefore, it was an urgent need to develop more potential chemical markers to better evaluate the holistic chemical features of S. tetrandra from different origins.

It is well accepted that medicinal herbs exert their efficacies through synergistic actions via “multi-components hitting multi-targets” of complex chemicals in the herbs [6,7], and integrating multiple methods simultaneously characterizing different kinds of components has been employed as a comprehensive strategy to evaluate the holistic quality, thus assure the efficacy of medicinal herbs [8]. Moreover, chemometrics provides a variety of good algorithms to explore and obtain more valuable chemical information [9,10,11,12,13,14,15]. Among these, discriminatory analysis is an effective tool for accurate prediction according to various characteristic values. The combination of multiple chromatographic techniques and chemometrics would provide a reliable method for the biomarker screening of herbal medicine.

In this study, an integrated chromatographic technique based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) and gas chromatography-mass spectrometry (GC-MS) was proposed to study the geo-herbalism of S. tetrandra. The habitat-related biochemical markers were further investigated using multiple pattern recognition models. Subsequently, seven main components were quantitatively compared using the validated ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS). A discriminant function equation was established to verify the accuracy of the chemical markers and achieve the prediction of the origin of S. tetrandra. The proposed strategy, which is comprehensive and effective, can be used to assist the application of S. tetrandra as well as related herbal medicine.

2. Results and Discussion

2.1. Method Validation

The relative standard deviations (RSDs) of retention time (Rt) and peak area for precision, repeatability, and stability were less than 1.0% and 7.7% using GC-MS and UHPLC-Q-TOF-MS/MS analysis (Supplementary Table S1), which validated that the established method was precise for differential component analysis of S. tetrandra from different origins.

The UHPLC-MS/MS method was verified in terms of linearity, lower limits of quantification (LLOQs), precision, repeatability, stability, and recovery. The results are shown in Supplementary Tables S2–S4, which revealed that the established method was precise enough for the simultaneous quantitative determination of seven compounds.

2.2. Identification of Volatile Components by GC-MS Analysis

GC-MS analysis was carried out to analyze the volatile components of S. tetrandra samples, and the relative contents of volatile compounds were calculated by peak area normalization. According to the database NIST08 and NIST08s, 70 volatile components, including alkanes, fatty acids, esters, carbonyls, alcohols, and phenols, were identified. Among them, the content of esters was the highest, accounting for 28.44%, of which methyl (9E)-9-octadecenoate, methyl linoleate, and methyl palmitate were 11.48%, 6.50%, and 4.50%, respectively. Alkanes have the most types, with a relative content of 14.36%, of which 2,4-dimethyl-1-heptene was the most abundant, reaching 1.80%. The relative content of fatty acids was 10.26%, among which oleic acid and palmitic acid were the highest, which were 4.54% and 2.94%, respectively. The identification results are shown in

Table 1. Volatile chemicals of S. tetrandra by GC-MS analysis.

No.	Rt (min)	Compound	CAS	Molecular Formula	Molecular Weight	Retention Index	Similarity	Relative Content (%)
1	3.71	2,4-dimethyl-1-heptene	19549-87-2	C9H18	126	819	96	1.80
2	4.05	4-methyloctane	2216-34-4	C9H20	128	852	94	0.21
3	5.62	2-[(2-methylpropan-2-yl) oxy] oxolane	1927-59-9	C8H16O2	144	1024	82	0.07
4	6.12	butyl-2,2-dimethylpropanoate	5129-37-3	C9H18O2	158	999	87	0.07
5	6.26	3,3,5-trimethylheptane	7154-80-5	C10H22	142	867	94	0.67
6	6.33	3,3-dimethyloctane	4110-44-5	C10H22	142	931	94	0.75
7	6.90	5-(2-methylpropyl) nonane	62185-53-9	C13H28	184	1185	88	0.04
8	7.41	8-methylnonyl methacrylate	29964-84-9	C14H26O2	226	1483	88	1.08
9	7.79	4-methylundecane	2980-69-0	C12H26	170	1150	93	0.32
10	7.91	2,6,6-trimethyl-octane	54166-32-4	C11H24	156	966	93	0.31
11	8.18	(5-methyl-2-propan-2-ylhexyl) acetate	40853-55-2	C12H24O2	200	1189	82	0.05
12	8.54	nonadecane	629-92-5	C19H40	268	1910	81	0.02
13	9.23	dodecane	112-40-3	C12H26	170	1214	93	0.03
14	9.54	2,4-dimethylbenzaldehyde	15764-16-6	C9H10O	134	1208	97	0.28
15	10.31	hexadecane	544-76-3	C16H34	226	1612	92	0.29
16	10.68	11-methyldodecan-1-ol	27458-92-0	C13H28O	200	1492	89	1.30
17	10.80	2,4-diethylheptan-1-ol	80192-55-8	C11H24O	172	1229	88	1.73
18	10.92	2-hexyl-1-decanol	2425-77-6	C16H34O	242	1790	86	1.42
19	10.97	4-methyldodecane	6117-97-1	C13H28	184	1249	90	0.53
20	11.10	tetramethyl-2,3,6,7 octane	52670-34-5	C12H26	170	958	91	0.41
21	11.22	4,8-dimethylundecane	17301-33-6	C13H28	184	1185	90	0.58
22	11.55	3,3-dimethylhexane	563-16-6	C8H18	114	732	83	0.03
23	11.63	2,3-dimethyldecane	17312-44-6	C12H26	170	1086	88	0.05
24	12.04	tetradecane	629-59-4	C14H30	198	1413	95	0.06
25	12.77	2,3-dimethyldodecane	6117-98-2	C14H30	198	1285	90	0.33
26	12.88	2,6,10-trimethyldodecane	3891-98-3	C15H32	212	1320	88	0.56
27	13.12	4-methyltetradecane	25117-24-2	C15H32	212	1448	89	0.33
28	13.19	n-heptadecane	629-78-7	C17H36	240	1711	91	0.34
29	13.34	heptacosane	593-49-7	C27H56	380	2705	87	0.05
30	13.43	2,4-di-t-butylphenol	96-76-4	C14H22O	206	1555	95	1.51
31	13.66	3,7,11,15-tetramethylhexadecan-1-ol	645-72-7	C20H42O	298	1996	86	0.71
32	13.75	4,6,8-trimethylnon-1-ene	54410-98-9	C12H24	168	1012	89	1.33
33	14.00	pentatriacontane	630-07-9	C35H72	492	3500	87	0.96
34	14.14	n-heneicosane	629-94-7	C21H44	296	2109	91	0.31
35	14.21	octadecane	593-45-3	C18H38	254	1810	90	0.15
36	14.84	isopropyl dodecanoate	10233-13-3	C15H30O2	242	1615	90	0.13
37	15.35	phytane	638-36-8	C20H42	282	1753	87	0.14
38	15.69	4-ethylhexadecane	25117-26-4	C17H36	240	1647	88	0.17
39	15.76	8-methylheptadecane	13287-23-5	C18H38	254	1746	91	0.42
40	16.34	n-mocosane	629-97-0	C22H46	310	2208	89	0.71
41	16.47	nonahexacontanoic acid	40710-32-5	C69H138O2	998	7236	88	0.39
42	17.12	2-methyldodecane	1560-97-0	C13H28	184	1249	85	0.08
43	17.47	methyl pentadecanoate	7132-64-1	C16H32O2	256	1779	92	0.08
44	18.03	diisobutyl phthalate	84-69-5	C16H22O4	278	1908	81	0.05
45	19.11	methyl palmitate	112-39-0	C17H34O2	270	1878	95	4.50
46	19.41	tetradecyl ether	5412-98-6	C28H58O	410	2880	85	0.26
47	19.64	dotriacontane	544-85-4	C32H66	450	3202	87	0.48
48	19.75	palmitic acid	57-10-3	C16H32O2	256	1968	93	2.94
49	19.96	tetratetracontane	7098-22-8	C44H90	618	4395	89	0.52
50	20.12	tetrapentacontane	5856-66-6	C54H110	758	5389	87	0.36
51	20.22	hexatriacontane	630-06-8	C36H74	506	3600	81	0.22
52	22.35	methyl linoleate	112-63-0	C19H34O2	294	2093	96	6.50
53	22.51	methyl (9E)-9-octadecenoate	1937-62-8	C19H36O2	296	2085	94	11.48
54	22.62	methyl oleate	112-62-9	C19H36O2	296	2085	91	0.51
55	22.99	3-methylheptadecane	6418-44-6	C18H38	254	1746	85	0.31
56	22.99	squalane	111-01-3	C30H62	422	2619	84	0.31
57	23.11	methyl stearate	112-61-8	C19H38O2	298	2077	93	0.49
58	23.21	linoleic acid	60-33-3	C18H32O2	280	2183	92	1.51
59	23.39	oleic acid	112-80-1	C18H34O2	282	2175	93	4.54
60	23.92	alkynyl stearic acid	34450-18-5	C18H32O2	280	2165	81	0.88
61	24.07	ethyl oleate	111-62-6	C20H38O2	310	2185	84	1.47
62	24.64	hexacontane	7667-80-3	C60H122	842	5985	85	0.69
63	25.19	palmityl acetate	629-70-9	C18H36O2	284	1978	93	0.62
64	25.19	octacosyl acetate	18206-97-8	C30H60O2	452	3171	94	0.62
65	27.88	tetracosane	646-31-1	C24H50	338	2407	94	0.42
66	28.55	methyl icosanoate	1120-28-1	C21H42O2	326	2276	88	0.53
67	29.19	oleamide	301-02-0	C18H35NO	281	2228	83	0.20
68	29.62	nonacosane	630-03-5	C29H60	408	2904	85	0.33
69	30.22	2,2’-methylenebis (6-tert-butyl-4-methylphenol	119-47-1	C23H32O2	340	2788	93	3.20
70	30.42	stearyl acetate	822-23-1	C20H40O2	312	2177	84	0.26

, and the corresponding peak of each compound was exhibited in the total ion chromatograms (TIC) diagrams (Supplementary Figure S1).

2.3. Identification of Nonvolatile Components by UHPLC-Q-TOF-MS/MS Analysis

Chromatographic data collected from UHPLC-Q-TOF-MS/MS were imported into Agilent Masshunter Qualitative Workstation Analysis B.07.00 (Agilent Technologies Inc., Santa Clara, CA, USA) for the identification of nonvolatile components of S. tetrandra. The identification of compounds is based on accurate mass, Rt, ion pattern, and MS/MS information. The obtained mass spectrograms were verified by: (a) matching with the instrument-generated molecular formula; (b) analyzing the structural information of metabolites acquired from the Metlin database (http://metlin.scripps.edu, accessed on 29 June 2022); (c) comparing with the fragment information of the standard samples; (d) combining with the compound information of the previous reports. According to the above-mentioned data acquisition and mining strategies, a total of 50 compounds, including 25 alkaloids, six amino acids, four amides, four fatty acids, two phenols, two purine derivatives, one phospholipid, one nucleoside, and five other compounds, were identified in S. tetrandra. The detailed information on the compounds is shown in

Table 2. Possible non-volatile chemicals of S. tetrandra by UHPLC-Q-TOF-MS/MS analysis.

No.	Rt (min)	Loading Form	Molecular Formula	Precursor Ion	Fragment Ions	Difference (ppm)	Possible Compound	Structure Types	Reference
1	1.14	[M+H]+	C6H14N4O2	175.1182	158.0904, 116.0704	4.6	l-arginine	amino acids	[16]
2	1.20	[M+H]+	C5H5N5	136.0611	119.0356	5.1	adenine	purine derivatives	[17]
3	1.40	[M+H]+	C5H9NO2	116.0701	70.0659	4.3	proline	amino acids	[16]
4	1.43	[M+H]+	C5H11NO2	118.0861	58.0672	1.7	valine	amino acids	[16]
5	1.99	[M+H]+	C6H13NO2	132.1017	86.0970, 69.0716	1.5	2-amino-4-methylpentanoic acid	amino acids	[16]
6	2.01	[M+H]+	C9H12N2O6	245.0778	113.0339	−4.1	uridine	nucleoside	[17]
7	2.14	[M+H]+	C5H5N5O	152.0553	135.0292	9.2	guanine	purine derivatives	[17]
8	2.29	[M+H]+	C5H7NO3	130.0493	84.0447	4.6	l-pyroglutamic acid	amino acids	[16]
9	2.86	[M+H]+	C9H11NO2	166.0852	120.0805, 91.0576	6.6	phenylalanine	amino acids	[16]
10	3.34	[M]+	C21H26NO4	356.1847	280.1084	4.2	xanthoplanine	alkaloids	[18]
11	3.47	[M+H]+	C16H17NO3	272.1273	255.1017, 107.0441	2.9	higenamine	alkaloids	[19]
12	3.61	[M+H]+	C23H29NO8	448.1957	269.1175, 237.0916, 107.0438	2.0	O6-methylhigenamine,7-O-D-glucopyranoside	others	[18]
13	3.75	[M+H]+	C24H31NO9	478.2073	316.1557, 192.1000	−0.2	laudanosoline,6-mether,12-O-ß-D-glucopyranoside	others	[18]
14	4.60	[M+H]+	C18H19NO4	314.1382	297.0970, 282.1241, 265.0262, 177.0320	1.6	norisoboldine	alkaloids	[20]
15	4.68	[M+H]+	C18H19NO3	298.1425	269.1156, 254.0966	4.4	n-methylcrotsparine	alkaloids	[21]
16	5.23	[M+H]+	C19H21NO4	328.1541	151.0756, 178.0802	0.6	scoulerine	alkaloids	[22]
17	5.40	[M+H]+	C17H19NO3	286.1429	269.1167, 179.0821, 164.0664, 107.0448,	3.1	coclaurine	alkaloids	[21]
18	5.73	[M+H]+	C19H23NO4	330.1697	192.0893, 175.0211, 137.0146	0.9	reticuline	alkaloids	[23]
19	5.87	[M+H]+	C19H21NO4	328.1545	237.0906	−0.6	boldine	alkaloids	[23]
20	6.01	[M]+	C20H24NO4+	342.1693	297.1117, 282.0895, 265.0853	3.5	magnoflorine	alkaloids	[21]
21	6.42	[M+H]+	C21H23NO5	370.1642	207.0719, 190.0827, 188.0774	1.9	allocryptopine	alkaloids	[22]
22	6.55	[M+H]+	C20H25NO4	344.1842	207.0815, 192.1007	4.1	tembetarine	others	[18]
23	6.83	[M]+	C19H24NO3	314.1751	209.0974, 175.0708	1.6	oblongine	alkaloids	[18]
24	6.83	[M+H]+	C19H23NO3	314.1751	206.1106	0.1	armepavine	alkaloids	[18]
25	6.85	[M]+	C19H24NO3	314.1751	269.1163, 107.0463	1.6	magnocurarine	alkaloids	[24]
26	7.43	[M+H]+	C22H27NO8	434.1806	356.1492	0.7	fenfangjine G	alkaloids	[25]
27	7.56	[M+H]+	C36H38N2O6	595.2800	227.1051	0.5	2-northalrugosine	others	[18]
28	7.63	[M+H]+	C20H23NO4	342.1727	176.0717, 165.0911	−7.9	tetrahydrocolumbamine	alkaloids	[26]
29	7.70	[M+H]+	C20H23NO4	342.1717	311.1442	−5.0	isocorydine	alkaloids	[26]
30	8.58	[M+H]+	C37H40N2O6	609.2966	566.2543, 367.1639	−1.1	fangchinoline	alkaloids	[21]
31	9.57	[M+H]+	C37H38N2O6	607.2796	564.2370, 227.1038	1.2	cepharanthine	alkaloids	[21]
32	10.25	[M+H]+	C21H25NO4	356.1855	251.1071	0.3	glaucine	alkaloids	[17]
33	10.69	[M+H]+	C38H42N2O6	623.3118	580.2693	−0.3	tetrandrine	alkaloids	[18]
34	11.72	[M+H]+	C20H21NO4	340.1535	309.111	2.4	dicentrine	alkaloids	[26]
35	13.39	[M+H]+	C18H19NO2	282.147	191.0823, 207.0874	6.7	o-nornuciferine	alkaloids	[27]
36	15.32	[M+H]+	C19H21NO2	296.1641	235.0810, 191.0858	1.4	nuciferine	alkaloids	[27]
37	15.74	[M+H]+	C19H19NO3	310.1437	235.0759, 247.0758	0.3	fenfangjine F	alkaloids	[25]
38	16.12	[M+H]+	C18H17NO2	280.1319	249.0906, 219.0784, 191.0840	4.6	roemerine	alkaloids	[21]
39	16.34	[M+H]+	C19H19NO2	294.1482	233.0965	2.4	dehydeonuciferine	others	[27]
40	19.57	[M+H]+	C18H39NO3	318.2997	282.2769	1.9	phytosphingosine	phospholipid	[28]
41	25.99	[M+H]+	C18H33NO	280.2626	263.2352, 135.1150, 109.0908	3.2	linoleamide	amides	[29]
42	26.98	[M+H]+	C18H30O2	279.2306	261.2213	4.7	α-linolenic acid	fatty acids	[16]
43	27.51	[M+H]+	C18H35NO	282.2784	83.0867	2.5	oleamide	amides	[30]
44	28.45	[M+H]+	C18H32O2	281.2464	163.1380, 125.0847	3.9	linoleic acid	fatty acids	[30]
45	30.61	[M+H]+	C18H34O2	283.2616	171.1336, 265.2517	5.6	oleic acid	fatty acids	[31]
46	31.03	[M+H]+	C16H33NO	256.2624	102.0933	4.3	palmitamide	amides	[30]
47	34.51	[M+H]+	C18H37NO	284.2931	102.0924, 88.0722	6.0	stearamide	amides	[30]
48	11.78	[M−H]−	C16H18O9	353.0871	191.0633	−3.9	chlorogenic acid	phenols	[21]
49	13.73	[M−H]−	C10H10O4	193.0553	134.0312	2.4	ferulic acid	phenols	[32]
50	30.54	[M−H]−	C18H36O2	283.2665	197.0251	−7.8	stearic acid	fatty acids	[21]

, and the TIC figures of S. tetrandra under positive and negative ion modes are illustrated in Supplementary Figure S2.

2.4. Exploration of Habitat-Related Chemical Markers Based on Global Components

Principal component analysis (PCA) is an unsupervised pattern recognition method that is often used to sort unclassified samples into groups. As displayed in Figure 1, samples from different provinces were separated into two groups. The geo-authentic samples had a more clustered distribution and homogeneous quality, whereas the non-authentic samples had a wide range of interval distribution and large differences in quality. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was subsequently used to filter out random noises, distinguish differences between groups, and improve the validity and analytical ability of the model. The results in Figure 2A–C showed that the medicinal materials were grouped into two groups according to the non-authentic and geo-authentic origins. The model was conducted using 7-fold cross-validation in this research. R² describes the goodness of fit and the cross-validation parameter, Q², represents the predictive ability of the model. The constructed model had good quality, with a cumulative R²Y of 0.997 and Q²Y of 0.491. Permutation tests were conducted 200 times to assess whether the model was overfitted. As shown in Figure 2D–F, the blue regression lines of the Q² points intersected the vertical axis below zero, and the intercepts of all regression lines on the vertical axis were less than 0.5; therefore, the model was reliable and not overfitted.

In addition, based on the above models, the variables with the variable importance in the projection (VIP) > 1 were explicitly detected. To further visualize the components with VIP > 1, OPLS-DA was performed to generate S-plots (Figure 3). Finally, 14 differential volatile components (Supplementary Table S5) and 14 nonvolatile markers (Supplementary Table S6) were characterized, all of which played a significant role in the differentiation of different origins of S. tetrandra.

2.5. Quantitative Analysis of Habitat-Related Chemical Markers Based on Nonvolatile Components

Alkaloids are the main active ingredients and pharmacodynamic substances in S. tetrandra. Among them, tetrandrine, fangchinoline, isocorydine, magnocurarine, magnoflorine, boldine, and higenamine have many pharmacological activities, such as antimicrobial effects, anti-inflammatory, anticancer, immunomodulatory effects, and antiplatelet effects [2,12,13,14,15]. Thus, they were picked as habitat-related chemical markers for further quantitative analysis.

The structural formulas and detailed content of seven analytes in the 16 samples from different origins are exhibited in Figure S3 and Supplementary Table S7. The contents of seven compounds in sample extracts from different origins were inconsistent, which indicated that the different growth conditions, such as climate, and sunlight of different origins, may influence the quality of S. tetrandra. As shown in Figure 4, the total contents of analytes in samples from geo-authentic origins (S11–S16), especially the characteristic components, tetrandrine and fangchinoline, were higher. Besides, the contents of tetrandrine, fangchinoline, isocorydine, and higenamine in samples from geo-authentic origins were highly consistent (Figure 5). In contrast, the compound contents in non-authentic origin samples showed high fluctuation, and the contents of the S1 and S5 samples were low.

2.6. Discriminatory Analysis

Discriminant analysis is a multivariate statistical analysis method to determine the classification of research objects according to various characteristic values under the condition of a classification determination. In this study, establishing the domain U = {X1, X2, …, X11}, representing 11 randomly selected samples of S. tetrandra, and selecting the content of seven characteristic peaks such as tetrandrine, fangchinoline, magnoflorine, higenamine, magnocurarine, isocorydine, and boldine as the discriminant factors to form an 11 × 7 matrix. Then, by SPSS 21.0 (IBM, San Diego, CA, USA), Wilk’s lambda method was used for stepwise discriminant analysis, and a discriminant function for determining the origin of S. tetrandra was obtained. The regression estimation method was used to evaluate the superiority and inferiority of the discriminant function. Finally, the discriminant function equation of S. tetrandra was obtained as follows (S1: tetrandrine, S2: fangchinoline, S3: magnoflorine, S4: higenamine, S5: magnocurarine, S6: isocorydine, S7: boldine):

Y1 = 0.069S1 + 0.073S2 − 0.157S3 − 0.957S4 − 4.415S5 − 79.374S6 − 30.520S7 − 376.515 (non-authentic origins)

Y2 = 0.069S1 + 0.075S2 − 0.155S3 − 1.056S4 − 7.327S5 − 70.871S6 − 30.181S7 − 399.405 (geo-authentic origins)

By taking the content of each characteristic peak after screening into the function equation and comparing the Y values of the function equations from different origins, the value of which is the largest belonging to the origin represented by this equation. Through the test of the regression estimation method, we tested another five batches of S. tetrandra of known origin, the discriminant analysis of the source origin was compared with the actual results, and the correct rate was 80% (

Table 3. Effect evaluation of discriminant function equations of two groups of S. tetrandra samples.

Statistical Magnitude	Original Classification (Origin)	Discriminant Function Prediction Classification (Origin)
		Non-Authentic Origins	Geo-Authentic Origins	Total
Number of samples	non-authentic origins	2	1	3
	geo-authentic origins	0	2	2
Percentage (%)	non-authentic origins	67	33	100
	geo-authentic origins	0	100	100

). This showed that the discriminant function equation established was relatively stable, can achieve the prediction and identification of the origin of S. tetrandra, and is valuable in promotion and application.

3. Materials and Methods

3.1. Chemical Reagents and Materials

Tetrandrine, fangchinoline, magnoflorine, magnocurarine, isocorydine, higenamine, and boldine were collected from Chengdu DeSiTe Biological Technology Co., Ltd. (Chengdu, China). Chromatographic grade methanol and acetonitrile were provided by Thermo Fisher Scientific Co., Ltd. (Shanghai, China). Formic acid was purchased from ROE Co., Ltd. (St. Louis, MO, USA). The Milli-Q system (Millipore, Bedford, MA, USA) was used to obtain purified water.

Sixteen batches of S. tetrandra were collected from seven different provinces (Guangxi, Guangdong, Sichuan, Neimeng, Anhui, Zhejiang, and Jiangxi) in China. The sample information is shown in Supplementary Table S8.

3.2. Preparation of Standard and Sample Solutions

Stock solutions of tetrandrine, fangchinoline, magnoflorine, magnocurarine, isocorydine, higenamine, and boldine were dissolved with methanol at a concentration of 10 mg/mL and serially diluted to plot the standard curves.

All dried samples were grounded, and the powder was passed through a 50-mesh sieve. Powdered samples (1 g) were ultrasonically extracted in 10 mL of n-hexane for 30 min. After cooling, the resulting mixture was centrifuged, and the supernatant filtered through 0.22 µm nylon membranes was collected for GC-MS analysis.

Pulverized samples (0.5 g) were sonicated in 20 mL of 70% methanol for 30 min. After centrifugation, the supernatants were filtered through a 0.22 µm nylon membrane to obtain the sample solutions for LC-MS/MS analysis.

Meanwhile, all equivalent volumes (100 μL) of sample solutions for GC-MS and LC-MS/MS analysis were respectively mixed as quality control (QC) samples. These sample solutions were all stored at 4 °C until analysis.

3.3. GC-MS Analysis

The volatile components were analyzed by a QP 2010 GC-MS (Shimadzu, Kyoto, Japan). Chromatographic separation was performed on a DB-5MS column (0.25 µm × 0.25 mm × 30 m). The temperature program was set as follows: 40 °C for 0 min, 40–190 °C at 10 °C/min, 190–210 °C at 3 °C/min, 210–215 °C at 1 °C/min, 215–240 °C at 6 °C/min.

Mass spectrometry was performed in electron impact (EI) mode and full scan mode at a mass-to-charge ratio (m/z) of 50–1000. The temperatures of the ion source and interface were 230 °C and 250 °C, respectively.

3.4. UHPLC-Q-TOF-MS/MS Analysis

The UHPLC-Q-TOF-MS/MS system is comprised of an Agilent 1290 UHPLC instrument (Agilent Technologies Inc., Palo Alto, CA, USA) and Agilent 6520 Q-TOF mass spectrometer (Agilent Corporation, Santa Clara, CA, USA). Chromatographic separation was performed on a Waters ACQUITY UPLC^®®CSHTM C18 column (2.1 × 100 mm, 1.7 µm) at a temperature of 30 °C. The mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B) at a flow rate of 0.2 mL/min, with a gradient elution program of 5–14% B at 0–5 min, 14–18% B at 5–12 min, 18–34% B at 12–14 min, 34–72% B at 14–22 min, 72–86% B at 22–23 min, 86–95% B at 23–31 min, and 95–95% B at 31–35 min. The injection volume for each sample was 5 μL.

The mass spectrometer was operated in both positive and negative modes with a scanning range of m/z 50–1500 and a scanning rate of 1 spectra/s. High resolution (4 GHz, High Res Mode) was used. The optimized instrumental parameters were as follows: capillary temperature, 350 °C; drying gas (N₂) flow rate, 8 L/min; nebulizer pressure, 25 psi; collision energy, 30 V; fragment voltage, 135 V.

3.5. UHPLC-MS/MS Analysis

Quantitative analysis was performed on an Agilent 1290 UHPLC system (Agilent Technologies Inc., Palo Alto, CA, USA) equipped with an Agilent 6470 Triple quadrupole tandem mass spectrometer (Agilent Technologies, Singapore) with electrospray ionization (ESI) source. A Waters ACQUITY UPLC^®® BEH C18 column (2.1 × 100 mm, 1.7 µm) was used for chromatographic separation, and the column temperature was maintained at 20 °C. The binary gradient elution system consisted of 0.05% formic acid in water (A) and acetonitrile (B). The gradient profile started from 10% B, increased linearly to 23% B within 2 min, and then increased to 50% B within 3 min.

The mass spectrometer was operated in positive mode. The optimized mass conditions were as follows: gas temperature, 320 °C; gas flow rate, 8 L/min; nebulizer, 35 psi; sheath gas temperature, 250 °C; sheath gas flow, 12 L/min; capillary voltage, 4000 V. Multiple reaction monitoring (MRM) mode was applied for the quantitative analysis of different compounds. An MRM diagram is shown in Supplementary Figure S4. The optimal mass spectral parameters and ion patterns are presented in

Table 4. Mass spectrometry parameters of seven target alkaloids.

Compound	Precursor Ion (m/z)	Product Ion (m/z)	Fragmentor (V)	Collision Energy (V)	Ion Mode
tetrandrine	623.2	580.1	80	45	Positive
fangchinoline	609.3	566.2	224	45	Positive
isocorydine	342.2	279.1	80	15	Positive
magnoflorine	342.1	297.1	80	30	Positive
higenamine	272.1	107.0	80	28	Positive
boldine	328.2	265.0	80	20	Positive
magnocurarine	314.2	269.1	113	20	Positive

.

3.6. Method Validation

The GC-MS and UHPLC-Q-TOF-MS/MS methods were validated in terms of precision, repeatability, and stability. The precision was evaluated by observing the intraday variations of the QC sample six consecutive times. The repeatability was accessed by preparing six replicate QC samples. The stability was obtained by detecting one QC sample at 0, 6, 12, 18, and 24 h. Fifteen chromatographic peaks were randomly selected to calculate the RSDs of peak area and RT to investigate precision, repeatability, and stability.

The methodology of UHPLC-MS/MS analysis was verified by determining linearity, LLOQs, precision, repeatability, stability, and recovery. A calibration curve for each alkaloid standard was constructed using a linear regression model, and linearity was verified using correlation coefficients (r). The LLOQs were estimated as the minimum concentration giving signal-to-noise ratios (S/N) of 10. Instrument precision was determined by analyzing six replicates. Repeatability was evaluated by performing six replicate analyses on the same QC sample. In the stability test, the QC sample solutions were stored at room temperature and then analyzed by replicate injections at 0, 2, 4, 8, 12, and 24 h. The recoveries for spiked samples were applied to examine the effect of the extraction method and matrix effect. Blank S. tetrandra samples (0.25 g) and certain amounts of mixed standard solution were dissolved in 20 mL of 70% methanol and then processed by the optimized method, which were regarded as the spiked samples. The recovery rate was calculated using the following formula: recovery rate (%) = (observed amount − original amount)/spiked amount × 100%. Pulverized samples (0.5 g) were sonicated in 20 mL of 70% methanol for 30 min

3.7. Data Preprocessing

The GC-MS and UHPLC-Q-TOF-MS/MS data of S. tetrandra from different origins were exported in MZ format using the GC-MS Postrun (Shimadzu, Kyoto, Japan) and Agilent Masshunter Qualitative Analysis software packages, respectively. The peak finding, alignment, and filtering of the raw data were preprocessed using R 2.7.2 software (R Foundation for Statistical Computing, Vienna, Austria) to obtain the Rt, m/z, and peak strength of each compound. Finally, the obtained data were imported into Simca-P 14.1 (Umetrics, Umea, Sweden) for OPLS-DA. Potential chemical markers to differentiate the S. tetrandra from different origins were screened according to the VIP value. R² and Q² values were used to validate the model. R² implied the explanation capability towards original data, and Q² indicated the prediction ability of the model. The discriminant analysis function equation was established by SPSS 21.0 software.

4. Conclusions

In conclusion, by combing the volatile and nonvolatile components based on multiple chromatographic analyses, the habitat-related chemical markers of S. tetrandra were discovered. Through integrated chemometrics analysis, 14 volatile components and 14 non-volatile oils were screened out as the important contributors to the chemical difference between geo-authentic and non-authentic origins samples. Among these, tetrandrine, fangchinoline, isocorydine, magnocurarine, magnoflorine, boldine, and higenamine as chemical markers with abundant pharmacological activities were quantitatively analyzed by UHPLC-MS/MS. The results showed that the total content of analytes in samples from geo-authentic origins was higher and more consistent. Finally, discriminant analysis was used to simulate the function equation representing the origin of S. tetrandra to trace the origin of medicinal materials, and it also verified the accuracy of the differential components obtained by OPLS-DA. The proposed method could aid the exploration of habitat-related chemical markers for other herbal medicines.