Chemical comparison of Semen Euphorbiae and Semen Euphorbiae Pulveratum by UPLC-Q-TOF/MS coupled with multivariate statistical techniques
Huinan Wang1, Jingzhen Zhang1, Yuexin Cui1, Siyu Wang1, Hui Gao1, Yao Zhang1, Xinjie Wang1, Ziye Yang1, Mengyu Chen1,Peihua Wang1,Guimei Zhang1, Yingzi Wang1*,Chao Zhang2*
1. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing102488, China
2. School of Pharmaceutical Sciences, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
Abstract: In the present study, we aimed to assess the chemical composition changes of Semen Euphorbiae (SE) and Semen Euphorbiae Pulveratum (SEP) by UPLC-Q-TOF/MS and multivariate statistical methods. The UPLC-Q-TOF/MS method and SIMCA-P software were used to analyze the changes of chemical components of SE and SEP based on PCA and PLS-DA multivariate statistical methods. A “component-target-disease” network model was constructed by Intelligent Platform for Life Sciences of traditional Chinese medicine (TCM) to predict potential related diseases. The differences of chemical composition were significant between SE and SEP. Under positive ion mode, the amounts of Euphorbia factor L2, L3, L7a, L8, L9 and lathyrol were obviously decreased after processing. Under negative ion mode, the amounts of aesculetin, bisaesculetin, ingenol and cetylic acid were increasedobviously, while Euphorbia factor L1, L4 and L5 were decreased obviously after processing, and the components of euphobiasteroid, aesculetin, lathyrol and linoleic acid among the 14 differentiated compounds were closely related to the SE-related diseases through the “component-target-disease” network model. UPLC-Q-TOF/MS technology in combination with multivariate statisticalmethods had certain advantages in studying the complex changes of chemical composition before and after manufacturing pulveratumof SE. It provided a basis for clarifying the toxicity-attenuated mechanisms of SE manufacturing pulveratum, and laid the foundation for its further development and utilization.
Keywords: Semen Euphorbiae; Semen Euphorbiae Pulveratum; UPLC-Q-TOF/MS; Multivariate statistical techniques; Chemical constituents; Manufacturing pulveratum
CLC number: R284 Document code: A Article ID: 1003–1057(2020)7–470–10
Semen Euphorbiae (SE), the seed of Euphorbia lathyris L., which belongs to genus Euphorbia, is a kind of poisonous traditional Chinese medicine (TCM) and has been used for the treatment of hydropsy, ascites, anuresis, constipation, amenorrhea and scabies. Although SE has a limited therapeutic range, it has been reported to have a significant effect on some cancers, such as leukemia and lung cancer[2,3]. In addition, SE has several adverse drug reactions, such as irritation and inflammation intense on the skin, mouth and gastrointestinaltract irritation[4,5]. Semen Euphorbiae Pulveratum (SEP), which is another type of Euphorbia lathyris that is commonly used in TCM practice, is obtained by removing the oil from the seed. Traditionally, SEP has been known to reduce the toxicity and ease diarrhea.
“Pao zhi” is a common practice that usually occurs before most herbs are prescribed whereby during processing. Under the guidance of TCM theory, the role of “Pao zhi” is to enhance drug efficacy, eliminate or reduce the toxicity, facilitate the preparation and storage of drugs[7,8]. The main mechanisms underlying herb processing have been found to be mainly related to the changes in the composition and the activity of the components in the herbs.
Ultra-high performance liquid chromatography-quadrupoletime-of-flight mass spectrometry (UPLC-TOF/MS) combined with multivariate spectrometry techniques, such as principal component analysis (PCA), is a newlydeveloped hyphenated technique to analyze the constituentsof TCM, because of its advantage of efficient separation capability, high resolution, high sensitivity and strongcharacterization capability. In recent years, (U) HPLC-Q-TOF/MS has been increasingly used for rapid chemical profiling of medicinal raw and processed herbs[9,11–14].
To the best of our knowledge, no study has been conducted to comprehensive analyze chemical constituents of SE and SEP, and few chemometric approaches have been employed to distinguish raw from processed herbs. In this study, a method using UPLC-Q-TOF/MS combined with multivariate statistical analysis was developed to rapidly find potential chemical markers for studying the processing mechanism of SE and SEP. In addition, a multivariate statistical analysis composed of PCA and PLS-DA based on all chemical informationwas conducted to find potential chemical markers. Also, the processing mechanism of SE was elucidated according to the results of chemical markers for SE and SEP. This feasible study could be valuable for rapidly exploring potential chemical markers and studying processing mechanisms of herbs.
2. Materials and methods
2.1. Chemicals, reference compounds and samples
Acetonitrile (ACN, HPLC-MS grade) and formic acid (HPLC grade) were purchased from Fisher, and Ultra-pure water was prepared using ELGA Pure lab Classic-UVF (ELGA, UK). Other solvents and chemicalswere of analytical grade. Samples of SE were purchasedfrom Huqiao prepared pieces of Chinese medicine factory (batch number: 1203070692; Bo Zhou, Anhui Province, China). The raw SE was obtained from the seeds of Euphorbia lathyris L. These samples were named SE-01–SE-03, and then preparation of processed SEP was carried out according to Chinese Pharmacopoeia (CP) (2015). These samples were named SEP-01–SEP-03. All the samples were authenticated by Chunsheng Liu, the professor of Beijing University of Chinese Medicine.
2.2. Sample preparation of SE and SEP
SEP was pressured out of the oil based on the method of pressing in laboratory. According to Chinese Pharmacopoeia, the seeds coat of SE was removed and crushed, followed by slight heating process. It was pressed to remove most of the oil and made into loose powder until meeting the requirements (the oil content of SEP was 18%–20%; the oil content of SEP samples made by our lab was 20.0%, 20.0% and 19.2%, respectively).
Each SE and SEP samples were accurately weighed (approximately 0.5 g), put in flat bottomed flask with cover, respectively and refluxed with 25.0 mL of methanol for 30 min. After the extraction, sample solution was weighed again. The lost volume was added with methanol. The solution was shaken well and filtered through a 0.22 µm membrane filter before UPLC-Q-TOF/MS analysis.
2.3. Instrument analysis
UPLC-Q-TOF-MS analysis was performed by using an Agilent 1290-6520 (Agilent, Wilmington, America) instrument. Water–formic acid (99.9:0.1, v/v, solvent A) and ACN–formic acid (99.9:0.1, v/v, solvent B) wereused as mobile phase. The separation was performed on an RP-C18 column (Agilent Zorbax Eclipse Plus, 100 mm×2.1 mm, 1.8 µm), 0–1 min: 5% B; 1–4 min: 5%–50% B; 4–15 min: 50%–80% B; 15–20 min: 80%–95% B; 20–22 min: 95% B;22–22.1 min: 95%–5% B; 22.1–25 min: 5% B. Flow rate was set at 0.4 mL/min. Separation was performed at 30 ºC with a sample injection volume of 5 µL.
Mass spectrometry workstation was performed using Mass Hunter (Agilent, Wilmington, America). Mass spectrometry analysis was performed using Agilent 6520 mass spectrometer equipped with an electrospray ionization (ESI) source. Samples were injected twice: once in positive ESI mode and once in negative ESI mode. The data acquisition range was m/z of 50–1200. The capillary voltages were set to 3.0 kV, and the cone voltages were set to 35 V. The MS source temperature was set at 120 ºC with atomized gas (N2) flow of 10 L/h, and the desolvation temperature was 450 ºC with a gas (N2)flow of 10 L/h. The collision energies (CE) were set to 30 V. The correction mass was m/z of 121.050873 and 922.009798.
2.4. Data processing and multivariate statistical analysis
The UPLC-Q-TOF/MS data of all samples were analyzed using Agilent MassHunter workstation softwarewith Qualitative Analysis B.04.00. The mass spectra peak in both positive (ES+) and negative (ES–) electrospray ionization modes were analyzed, and the resulting three-dimensional data comprising of retentiontime (RT), m/z value and ion intensity were stored in Microsoft® Office Excelfile as the data. Data were further exported to SIMCA-P software (version 11.5, Umetrics AB, Umea, Sweden) for multivariate statisticalanalysis. The datasets were normalized at first. Both PCA and PLS-DA were applied to investigate the metabolicprofiles of the samples. If variable importance for the projection was more than 2 (VIP>2), variable tentatively was selected as the potential chemical markers to obtain the information of retention time and m/z value.
3. Results and discussion
The differences of components between SE and SEP in positive and negative ion modes were analyzed by UPLC-Q-TOF/MS. As shown in Figure 1, the chromatograms had a good resolution, and the baselinecould be satisfactorily separated within 30 min, indicatingthat the sensitivity of MS under the optimized conditions was adequate for this study. The sensitivities of the components in SE and SEP were found to be higher in the negative ion mode. All data monitored in both negative and positive ion modes were used for the multivariate statistical analysis and component characterization.
Figure 1. The representative base peak chromatograms of SE and SEP by UPLC-Q-TOF/MS in the positive and negative ESI modes, respectively. (A) Positive mode; (B) negative mode. The peak numbers represent the same meanings as in Table 1.
To compare the differences between SE and SEP, PCA and PLS-DA were performed to classify and identify different metabolites in order to evaluate variation among complex datasets. After Pareto scaling with mean-centering, the data were displayed as scores plot (Fig. 2). An obvious separation trend could be observed, and the determined samples were clearly clustered into two groups in the PCA score plot, indicating that the processing procedures caused considerably alteration in the composition and the content of components in Euphorbia lathyris L. The large variations in the intensity of the marker ions related to SE samples mightindicate a big difference of samples. However, the processed samples of SEP were closely clustered.
Figure 2. PCA/Scores plot of raw and processed Euphorbia lathyris samples obtained using Pareto scaling with mean centering: (A) negative and (B) positive ion mode.
Deep differences between SE and SEP as well as the potential biomarkers were investigated by the loading plot of supervised PLS-DA. As shown in Table 1, mass spectrometry signals responsible for differentiation were characterized by the values of the VIP value (where a VIP value of >2 was regarded as significant) from the PLS-DA analysis[12,16]. The greater VIP value, the greater contribution of the corresponding variables to the classification. Figure 3 displays the result of the PLS-DA model using metabolic data. According to the values of VIPs and the corresponding PLS-DA loading plots, 302 compounds with VIP value more than 2 in positive (ES+) electrospray ionization mode and 133 compounds with VIP value more than 2 in negative (ES–)electrospray ionization mode were selected. Among them, 14 metabolites were identified and selected as potential biomarkers to distinguish striking difference (as shown in Table 1) by comparing retention time and MS data (accurate mass, isotopic distribution and fragmentation patterns in positive ion mode) of compounds with diterpenoid compounds reported in literature[1,3–5,17–19] and found in public online databases. As mentioned above, 14 potential biomarkers between SE and SEP were identified (Table 1). These potential biomarkers included the lathyrane diterpenoid type compounds (Fig. 4A): Euphorbia factor L1, L2, L3, L7a, L8 and L9; ingenane diterpenoid compounds (Fig. 4B): Euphorbia factor L4, L5; coumarin (Fig. 4C): aesculetin, euphorbetin;diterpenoid alcohol (Fig. 4D): ingenol, lathyrol; fatty acid (Fig. 4E): palmitic acid, oleic acid.
Table 1. Identification to molecular ion peak of significantly different characteristic compounds in SE and SEP.
Figure 3. VIP value of variables of PLS-DA. Values for VIPs were calculated by the formula described in the user’s guide of SIMCA-P.
Figure 4. Graphical representation of 14 potential markers between SE and SEP (X: component name; Y: ionic strength. (A) Lathyrane diterpenoids; (B) Ingenane diterpenoids; (C) Coumarin; (D) Diterpenoid alcohol; (E) Fatty acid).
Based on “like dissolves like” theory, coumarins with high water solubility show a low solubility in fatty oil. Through processing, a little of coumarins was decreased when fatty oil was removed. Therefore, the content of coumarins in SEP was relatively increased. In other words, coumarins in the SEP were also reduced, while the ratio was less than other components. Therefore, the proportion of SEP was also increased accordingly. Simultaneously, diterpene esters with low water solubility were easily removed along with fatty oil. Ionic strength of the diterpene esters, which were characteristic compounds with significant differences between SE and SEP, was significantly decreased.
Euphorbia factor L4 and L5 are isomers. Euphorbia factor L4 is 20-O-hexadecanoyl-ingenol, Euphorbia factor L5 is 3-O-hexadecanoyl-ingenol. Their molecular weight was similar. It was found that they generated the same fragmentation ions in MS mass spectral experiments done in the past. However, ionic strength of each fragmentation was slightly different. The phenomenon was the samewith the change trend of ionic strength of Euphorbia factor L4 and L5 in Figure 4. Additionally, they were further confirmed through the method of standard sample comparison. Therefore, RT of Euphorbia factor L5 was shorter than Euphorbia factor L4 by RP-UPLC. Euphorbia factor L4 and L5, which belonged to ingenanediterpene ester, had skin irritation and aided carcinogenesisactivity. The modern studies have shown that C-20 hydroxylis the prerequisite of causing irritation. 3-O-Palmityl ingenol (Euphorbia factor L5) is the strongest irritation in the ingenane diterpene ester, but its maternal polyol ingenol has no skin irritation and auxiliary carcinogenic effect[21,22]. Content of Euphorbia factor L4 and L5 was significantly decreased in SEP. The reduction of Euphorbia factor L4 and L5 content effectively contributed to the distinction between the SE and SEP. VIP value was 12.2975 and 3.48921, respectively. This study also found that the content of ingenol and palmitic acid were modestly increased in SEP. Based on this, it could be deduced that there was another way in which the content of Euphorbia factor L4 and L5 were reduced, because long palmacyl carbon chain was easy to degrade into ingenol and palmitic acid during the process of thermal treatment. In addition, change of oleic acid content had significant contribution to discrimination of SE from SEP.
On the basis of our study, we analyzed changes in metabolic markers to better understand the mechanism of attenuated toxicity. This understanding helped us more clearly explain the “Pao zhi” process. It is known to all that SE contains poisonous diterpene esters[5,23]. When SE is prepared to become SEP, the main components of diterpene esters are changed. However, the specific change process and metabolite conversion mechanism are still unknown. Figure 4 and Table 1 show that compared with SE, SEP had lower Euphorbia factor L1, L2, L3, L4, L5, L7a, L8 and L9 and higher ingenol, lathyrol, aesculetin, euphorbetin and palmitic acid. The results could be clarified that the meanings of processing of SE to attenuate toxicity are chiefly as follows. (1) Quantity of lathyrane diterpenoids which was related to diarrhea effect, such as Euphorbia factor L1, L2, L3, L7a, L8 and L9, was removed with the process of SEP preparation. (2) The content of ingenane diterpenoids causing irritation,such as euphorbia factor L4 and L5, was reduced significantly. One reason is that they can be removed partly with processed SEP. Another reason is that palmitate bond of long carbon chain is easy to degrade during the process of thermal treatment. (3) Modern pharmacological action studies have shown that the coumarin-like compound aesculetin has anti-inflammatory and diuretic effects. Animal tests show that aesculetin could inhibit the increase of capillary permeability caused by histamine and increase the urine volume and uric acid excretion in rats and rabbits, which is consistent with the effect of TCM in reducing swelling. The results of this study indicated that the content of aesculetin and other coumarins was relatively increased, and it was speculated that the “retention effect” or “enhancement effect” of SE after frosting might be related to the change of coumarins. These results showed that “Pao zhi” could play a key role in detoxification.
In conclusion, we proposed a proven strategy to rapidly determine potential chemical markers for the discrimination and quality control of raw and processedChinese medicinal herbs by UPLC-Q-TOF/MS coupledwith multivariate statistical analysis. Multivariate analysis successfully identified specific metabolite changes between SE and SEP. As a result, 14 key biomarkers responsible for the detoxifying actions of “Pao zhi” were discovered, and the possible processing mechanism was discussed thoroughly. Unlike conventional phytochemical approaches, which require the tedious and time-consuming characterization of large numbers of components, this new approach provided a feasible study and methodology to identify the complicated components from various complex mixtures, such as crude TCM, processed TCM and biological samples, which can avoid replication in isolation, purification and identification of the identical components in both raw and processed herbs, and it is therefore a cost-effective way to determine potential chemical markers of processed herbs. It could be used as a valid and efficient analytical method for further understanding the processing mechanism of toxic TCM, along with the intrinsic quality control of TCM, providingmore accurate characterizations of traditional “Pao zhi”detoxification. This analytical strategy was also expected to be applied in the quality evaluation of Euphorbia lathyris L. products with different localities, cultivation practices, harvest times and storage conditions.
A total of 14 kinds of compounds were screened and identified in this paper, including eight diterpenoid alcoholesters, two coumarins, two diterpenoid alcohols and two fatty acids. Through Intelligent Platform of Life Sciences of TCM, 714 diseases related to the search for SE were studied by using the method of combination of TCM as well as bio-information and intelligence analysis. According to the degree of correlation, breast cancer, which was the most related disease, was selected for network pharmacological analysis. “Component-target-disease” network model (Fig. 5) was constructed, and we found that the components of euphobiasteroid, aesculetin, lathyrol and linoleic acid were closely related to it[24,25]. The study screened and identified a variety of compounds that changed before and after manufacturing pulveratum, predicted the potentially related diseases of SE and speculated the chemical components related to the disease through the “component-target-disease” model. It clarified the mechanism of medicinal action of SE and provided a reference for its further development and utilization.
Figure 5. The “Component-target-disease” network.
This work was supported by the Beijing Natural Science Foundation (Grant No. 7182097), National Natural Science foundation of China (Grant No. 81673597) and National Key Research and Development Program of China (Grant No. 2018YFE0197900).
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王慧楠1, 张景珍1, 崔曰新1, 王思雨1, 高慧1, 张瑶1, 王新杰1,杨子烨1, 陈梦雨1, 王佩华1, 张桂梅1, 王英姿1*, 张超2*
1. 北京中医药大学 中药学院, 北京 102488
2. 山东中医药大学 药学院, 山东 济南 250355
摘要: 本文建立了一种超高效液相色谱-飞行时间-质谱联用技术(UPLC-Q-TOF/MS)全面快速地分析千金子制霜前后的化学成分变化的方法, 为进一步阐明千金子制霜减毒机理提供参考。采用UPLC-Q-TOF/MS技术分析了千金子制霜前后成分变化, 并借助SIMCA-P软件,基于主成分分析法(PCA)和偏最小二乘判别法(PLS-DA)多变量统计方法分析千金子制霜前后化学成分量的变化。结果发现, 千金子制霜前后化学成分差异显著, 从中推断出14个潜在的化学标记物,正离子模式下, 千金子制霜后千金子素L2、L3、L7a、L8、L9和续随子醇的量明显下降;负离子模式下, 霜品中秦皮乙素、双七叶内酯、巨大戟醇和棕榈酸的量明显升高,千金子素L1、L4和L5的量明显降低。UPLC-Q-TOF/MS技术结合多变量统计方法研究千金子制霜前后化学成分复杂变化具有一定的优势, 为阐明其制霜减毒机理提供了重要科学依据。
关键词: 千金子; 千金子霜; UPLC-Q-TOF/MS; 多变量统计; 化学成分; 炮制机理
Received: 2020-03-28; Revised: 2020-04-16; Accepted: 2020-05-18.
Foundation items: Beijing Natural Science Foundation (Grant No. 7182097), National Natural Science foundation of China (Grant No. 81673597) and National Key Research and Development Program of China (Grant No. 2018YFE0197900).
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