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Related Intrinsic Quality Attributes of Astragali radix Significantly Affected under Origin and Extraction Method

Meng Wu, Xizhen Cheng, Yuling Zhang, Mingdong Si, Yuguang Zheng, Huigai Sun*, Donglai Ma*

School of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, 050200, ChinaSchool of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, 050200, China

Abstract: Astragali radix (AR) is a famous traditional Chinese medicine in China. Different AR producing areas and different extraction methods will affect the internal quality of AR. To ensure the internal quality of AR sold in the market and explore the influence of its source and extraction method on the quality of AR, we proposed a method combining fingerprint and stoichiometry to identify AR. UPLC fingerprints of AR methanol extract (AME) and AR water extract (AWE) were established, and common peaks were found. Qualitative and quantitative analyses were carried out using stoichiometric methods, such as similarity analysis (SA), principal component analysis (PCA), and orthogonal partial least squares (OPLS-DA). The chemical composition of AME and AWE was analyzed by UPLC-Q-TOF/MS. In addition, five compounds of AR were quantitatively determined, which provided accurate and useful data for quality evaluation.

Keywords: astragali radix, UPLC-Q-TOF/MS, PCA, OPLS-DA

Received December 05, 2021; Revised January 11, 2022; Accepted January 20, 2022

Citation: Meng Wu, Xizhen Cheng, Yuling Zhang, Mingdong Si, Yuguang Zheng, Huigai Sun, Donglai Ma, Related Intrinsic Quality Attributes of Astragali radix Significantly Affected under Origin and Extraction Method. Journal of Food and Nutrition Research, 10 (2). 114-122. January 2022. doi:10.12691/jfnr-10-2-5

1. Introduction

Astragali radix (AR) is the dry root of the leguminous plant Astragalus membranaceus (Fisch.) Bge. var. Mongholicus (Bge.) Hsiao or A.membranaceus (Fisch.) Bge [1]. It has high health care and medicinal value, and it is also the homologous substance of medicine and food [2]. Studies have shown that the main components of astragalus membranaceus are flavonoids [3], triterpenoids [4,5], and alkaloids [6], which have many pharmacological activities, such as immunomodulatory, antioxidant, anti-inflammatory, antidiabetic, and antiviral activities [7,8,9,10]. AR, as one of the commonly used Chinese medicinal materials in China, has a long medicinal history, extensive clinical application, and large market consumption.

Traditional Chinese medicine decoction is the earliest and most widely used dosage form in China. With the quickening of the pace of modern life, to better adapt to the fast pace of modern life, traditional medical workers developed Chinese medicine formula granules [11]. Traditional decoction is water decocted, but methanol extract is often further processed as liquid medicine when preparing formula granules. The number, type, and content of chemical components in the extracts with different solvents were obviously different, indicating that the extraction solvent greatly influenced the chemical components of plant extracts [12]. Studies have shown that the 70% ethanol ultrasonic extraction method is more effective than water extraction and enzymatic hydrolysis for extracting natural compounds from Angelica sinensis [13]. Therefore, it is necessary to study the differences in chemical components in different extraction methods and AR quality control from different sources.

A fingerprint is a powerful means to evaluate the quality of traditional Chinese medicine (TCM), identify the authenticity, distinguish the origin of species, and ensure the consistency and stability of its components. But the chemical composition of Traditional Chinese medicine is very complex, so it is difficult to determine the correlation between the fingerprints obtained by instrumental analysis. Using Principal Component Analysis (PCA), a statistical analysis of samples can identify specific differences between graphs. The contents of the four main active triterpenoids in Alismatis rhizoma were determined by HPLC, and 20 batches of samples from different sources were distinguished and classified by PCA [14]. Ultrahigh performance liquid chromatography chromatographic quadrupole time of flight mass spectrometry (UPLC-Q-TOF-MS) has the advantages of high speed, resolution, and accuracy [15], which can be used for qualitative analysis of complex TCM components [16]. In recent years, UPLC-Q-TOF-MS has rapidly become an important research method in the field of TCM component analysis [17].

In this study, the fingerprints of different extracts were established, and SA, PCA and OPLS-DA methods were used for quantitative labeling screening of Astragali radix from different sources. UPLC-Q-TOF /MS was used to study the chemical constituents of Chinese herbal medicines, which provided the basis for quality evaluation of Chinese herbal medicines. In summary, a new method was established to explore the influence of the extraction process, climate environment, soil conditions, and other factors on AR quality.

2. Materials and Methods

2.1. Plant Materials and Chemicals

A total of 20 batches of licorice were collected from Xinjiang, Gansu, and Inner Mongolia and were divided into three groups: S1–S5 from Xinjiang, S6–S10 from Gansu, and S11–S13 from Inner Mongolia. The Calycosin 7-O-glucoside (LOT number: R11O9F71937), Calycosin (LOT number: A03GB156838), Methylnissolin-3-O-glucoside (LOT number: P21N9F75533), Ononin (LOT number: M02A11S120167), and Isomucronulatol (LOT number: Y28M10H84303) were purchased from Shanghai yuanye Bio-Technology Co.

2.2. Preparation of Sample Solutions

The medicinal herbs of AR were dried and crushed. We sieved the powder through a 40-mesh sieve. After precision weighing (1 g), 50 ml of methanol was added to the dried and crushed herb, followed by ultrasonic extraction for 1 h. The supernatant was centrifuged, and the solution was stored in the injection vial through a filter membrane with a diameter of 0.22 mm to obtain the methanol extracts of AR. Next, we weighed out 50 g of AR powder, decocted in 1000 ml of boiling water for 2 h, strain, and repeated once. The water extract of AR with the concentration of 0.025 g/mL was prepared by combining two water decoctions, filtration, concentration, and dilution, with pure water. The prepared alcohol extract and water extract were tested by UPLC-Q-TOF/MS.

2.3. UPLC-Q-TOF/MS Analysis

Chromatographic analysis of the AME and the AWE were performed in the Agilent UPLC-Q-TOF/MS system. Chromatographic separation was carried on a Waters CORTECS C18 column (4.6 × 50 mm, 2.7 µm) held at 30°C. To shorten the analysis time while maintaining satisfactory separation, the optimal mobile phase was composed of acetonitrile (A) and 0.1% aqueous formic acid (B) operating under a gradient elution program as follows: 0–5 min, 82–77% B; 5–7 min, 77–70% B; 7–10 min, 70–65% B; 10–12 min, 65–60% B; 12–13 min 60–55% B; and 13–15 min 55–40% B. The flow rate was 0.4 mL/min, the injection volume was 2 µL, and the chromatogram detector was set at 260 nm. The mass spectrometry detector was equipped with an electrospray ionization (ESI) source. The ESI source was operated in both negative and positive polarities. The optimal MS parameters were as follows: the capillary temperature was set at 270°C and the vaporization temperature at 300°C. The sheath gas flows were set at 35 µL/min, the auxiliary gas flows were set at 5 µL/min. The analyses were conducted in full scan mode, and the Q-TOF acquisition rate was 0.1 s, with a mass range between 100 and 1500 m/z.

2.4. Data Processing

The chromatographic fingerprints were evaluated by chemometrics, including Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (version 2004A, National Committee of Pharmacopoeia, China), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) was performed by SIMCA 13.0 software. The post-acquisition mass spectrometry data are processed by Qualitative Analysis 10.0 (Agilent).

3. Results and Discussion

3.1. Establish the Fingerprint of AR

Twenty batches of AR from different producing areas were determined by UPLC with different extraction methods, as shown in Figure 1a and 1b. Fingerprints of alcohol extraction and water extraction were established, and standardized treatment was carried out using the similarity evaluation system of fingerprint of traditional Chinese medicine chromatography (2004A edition). According to fingerprint analysis, 21 common peaks were identified from 20 batches of methanol extract samples of AR (Figure 1c). Eleven common peaks were delineated from the water extract sample (Figure 1d), showing a high degree of separation. For reference chromatogram.

Figure 1. Twenty AR methanol extracts were fingerprinted. (a) Twenty AR water extracts were fingerprinted; (b) The characteristic peaks of methanol extracts of AR were selected; and (c) The characteristic peaks of water extracts of AR were selected

3.2. Similarity Analysis

Supplemental Table S1 and S2 show the correlation analysis of 20 ethanol and water extraction samples batches, respectively. The results of AME showed that the similarity of the samples from the Shanxi region was greater than 0.974, the similarity of the samples from the Gansu region was greater than 0.914, and the similarity of the samples from Inner Mongolia was greater than 0.814, except for the two samples which were 0.565 and 0.703. The AWE results showed that the similarity values of all samples from Shanxi were greater than 0.973, the similarity values of all samples from Gansu were greater than 0.831 except sample S17, and the similarity values AR from Inner Mongolia were greater than 0.969 except sample S19 and S20.

Table S1 shows that the correlation between S6 and S5, S6 and S7, S10 and S9, S10 and S11, S10 and S12 is higher than other sample sources. The correlation between S13 and S8, S13 and S9, S16 and S8, and S16 and S9 were lower than other samples. The contents of characteristic peaks 6, 7, 8, and 11 in S6 and S10 were significantly higher than those in other samples, while the contents of characteristic peaks 5, 6, 7, 10, and 14 in S13 and S16 were significantly lower than those in other samples. The results in Table S2 show that the correlation between S4 and S2, S4 and S17, S4 and S13, S15 and S16, and S15 and S1 is higher than the similarity of other sample sources. The correlations between S20 and S7, S20 and S9, S18 and S19, and S18 and S10 samples were lower than other samples. It was found that the content of characteristic peaks 4, 8, and 11 in S15 was significantly higher than that in other samples, while the content of characteristic peaks 2, 3, 5, and 7 in S20 was significantly lower than that in other samples.

3.3. The Score Plot of the PCA Analysis

According to the PCA score chart of AME (Figure 2a), PC1 and PC2 accounted for 56.8% and 16.7% of the total variation of the original observations, respectively. In the PCA score diagram of AWE (Figure 2b), PC1 and PC2 accounted for 53.7% and 19.7% of the total change in the original observations, respectively. We found that AR from three different regions was clearly distinguished through two PCA score charts. The results showed that the regional differences had a certain influence on the quality of AR.

3.4. The Loadings Plot of PCA Analysis

Figure 2c and 2d are PCA loading diagrams of AR. In the load analysis, the further away the sample is from the origin, the greater the weight of the variable. As can be seen from the AME (Figure 2b), peaks 2, 4, 16, 21, and 20 had the greatest influence on the quality of geographical difference. Similarly, it can be seen from the load diagram of AWE (Figure 2e) that peaks 1, 4, 8, and 11 have a great influence on the quality of geographical difference. The loading plot shows the multivariate variables that influence the differences between the samples. It can be seen that these common peaks affect the quality of AR.

3.5. OPLS-DA Analysis

Figure 2e and 2f are the score plot of the OPLS-DA. The results show that the established AME model R²X = 0.808. For the AWE model, R²X = 0.729 and Q² are all greater than 0.5. It shows that the model data is in good condition and has the good predictive ability. The samples were divided into three groups according to the origin. OPLS-DA model analysis showed that the samples of each group could be significantly distinguished under methanol extraction and water extraction conditions, indicating that there were significant differences between different producing areas of AR.

Figure 2. PCA score plot of 20 methanol extracts of AR (a); PCA score plot of 20 AR water extracts (b); PCA loading diagram of 21 characteristic peaks of alcohol extracts of AR (c); PCA loading diagram of 11 characteristic peaks of AR water extract (d); Orthogonal partial least squares discriminant analysis (OPLS-DA) score of 20 AR methanol extracts (e); Orthogonal partial least squares discriminant analysis (OPLS-DA) score of 20 AR water extracts (f)

The differential compounds were screened according to the VIP value of each index component in the model. Within the confidence interval of 0.95, the compounds with VIP > 1.0 were selected as the differential compounds to further analyze the overall difference of the data of abbreviations and the predicted values of the order of importance of variable weights (VIP) were obtained, as shown in Table 1. The effects of methanol extract were the peaks 16, 2, 20, 4, 5, 21, 19, and 11. The VIP influence of water extract was in the order of peaks 8, 1, 5, 2, and 11 (Table 2).

Table 1. VIP values of 21 characteristic peaks of Astragali Radix methanol extract

Table 2. VIP values of 11 characteristic peaks of Astragali Radix water extract

3.6. The Main Chemical Constituents of Astragali Radix

The most important clue for TOF-MS characterization is the high-resolution molecular ion peak. Combined with the isotope model, the molecular formula of the compound can be inferred. Finally, various methods (MS/MS fragmentation information, retention index, UV absorption, and literature retrieval) were used to qualitatively identify the inferred compounds. In this study, the water and alcohol extracts of AR were qualitatively identified by using TOF-MS to obtain accurate relative molecular weight information of excimer ion peak, isotopic distribution, and secondary fragments information obtained by MS/MS, combined with retention time. The unknown compounds were identified from fingerprint and similarity analysis (21 characteristic peaks of alcohol extraction and 11 characteristic peaks of water extraction).

Table 3. MS data for characterization of compounds in methanol extract of AR by UPLC/Q-TOF-MS

Table 4. MS data for characterization of compounds in water extract of AR by UPLC/Q-TOF-MS

Through OPLS-DA combined with UPLC-Q-TOF/MS analysis, it was found that the Koparin, Feruloyl C1-glucuronide, Isomucronulatol, Sucrose, Biochanin A 7-O-beta-d-glucoside-6''-O-malonate, and Choline were differential genes of AME. The Choline, Methylnissolin-3-O-glucoside and 2, 5-Dimethoxycinnamic acid were differential genes of AWE.

3.7. Quantitative Analysis and Methodological Verification

3.7.1. Investigation of a Linear Relation

The reference substance under item 2.1 was accurately weighed, and the methanol was put into a 10 mL volumetric flask to obtain the mixed reference substance solution. Mixed reference solutions 1, 1.5, 2, 2.5, and 3 mL were accurately absorbed and placed in a 5 mL volumetric flask. UPLC determination was performed according to the chromatographic conditions in item 2.3. The linear regression equation was established, and the results are shown in Table S3. The R² values were greater than 0.999, indicating a good linear relationship.

3.7.2. Precision, Repeatability, STABILITY

We prepared the test solution and analyzed the precision, repeatability, and stability by UPLC. We recorded the peak area and calculated the RSD value. The results are shown in Supplemental Table S4. RSD of precision repeatability and stability were lower than 5%, indicating that the instrument precision met the requirements and the sample was stable.

3.8. Quantitative Research

Contents of characteristic compounds under different extraction methods were determined, as shown in Supplemental Tables S5 and S6. In AME, the CalyCosin-7-O-beta-D-Glucoside contents of samples 1–7 were between 0.0863% and 0.0928%; the contents of samples 8–12 and 17–18 were between 0.0696% and 1.387%, and the contents of samples 13–16 and 19–20 were between 0.0581% and 0.0781%. In AWE, the calyCosin-7-O-beta-D-glucoside content of samples 1–7 was between 0.0570% and 0.0674%, and the contents of samples 8–12 and 17-18 were between 0.0559% and 0.0846%. The contents of samples 13–16 and 19–20 ranged from0.0572% to 0.0839%. The content of each sample in the table is different. However, there are significant differences in different regions. This shows that different growing environments and different extraction conditions would affect the content of medicinal materials.

4. Discussion

AR is a common Chinese medicine in traditional decoction and modern granule [18,19,20]. Unlike traditional dosage forms, methanol is used as an extraction solvent for further preparation. Therefore, the chemical composition of AR is significantly different when extracted with different solvents. AME and AWE were analyzed by UPLC, and a fingerprint was established to find 21 common peaks of AME and 11 common peaks of AWE. The results show that the composition and quantity of AR extracted by different methods differed. Studies have shown that aroma profiles recorded by GC-MS showed that the resulting odor profile varies widely in different preparation methods, Capsicum Annuum L. [21]. Similarity analysis results showed significant differences between AR from different sources and different extraction processes. For example, the low similarity among the different origins may be due to the differences in the chemical composition of R. rugosa caused by different ecological environments [22]. Therefore, different origins and extraction methods will affect the quality of AR.

PCA, OPLS-DA, and other stoichiometric methods showed that samples from the same area clustered together, and there were significant differences among different areas. This result is confirmed by UPLC and similarity analysis. The results in Figure 2b showed a large difference between samples S11 and S15 in the water extract. Combined with peak area analysis, the peak area of all common peaks in sample S11 was larger than that of the other five batches of Gansu samples, and the peak area of five common peaks in sample S15 was larger than that of other Inner Mongolia samples. Zizhou astragalus is famous for its good quality [23], and most of the ancient origin of AR is in Gansu Province. The growth environment of the S11 sample is similar to that of Zizhou astragalus, so the quality of S11 is better. The S15 samples cultivated in Inner Mongolia are sandy soil suitable for the growth of Astragali radix, which is the main reason for the high quality [24].

5. Conclusion

In this study, AR with different extraction methods was analyzed by UPLC and fingerprint was established. The differences in the chemical composition of AR from different producing areas and different extraction methods were analyzed by UPLC-Q-TOF/MS. The fingerprint was combined with a similarity evaluation system, PCA, OPLS-DA, and other stoichiometric methods to distinguish and screen characteristic peaks of AR from different producing areas, and then quantitative analysis was conducted. We found that AR from different sources (Shanxi, Inner Mongolia, Gansu) gather together separately. The composition and content of AME and AWE were also significantly different. The results showed that different growing environments and different extraction methods had a significant influence on the inner quality of AR. Fingerprint and stoichiometry were used to confirm the results more comprehensively. The combination of fingerprint and stoichiometry can explain more effectively the influence of different growing environment and extraction process on the quality of medicinal materials.

Acknowledgements

This study was financed in part by the Natural Science Foundation of Hebei Province of China (grant 19276414D); and Hebei Province University Students' Science and Technology Innovation Ability Cultivation Project (grant 2021H011608).

Conflict of Interest

The authors declare that they have no conflicts of interest.

References

Supplementary Material

Table S1. Similarity analysis of 20 methanol extracts of Astragali radix

Table S2. Similarity analysis of water extracts of 20 Astragali radix

Table S3. Regression equation of 5 compounds in Astragali radix

Table S4. Precision, repeatability and stability of five compounds

Table S5. Contents of five characteristic compounds in methanol extract of Astragali radix from different producing areas

Table S6. Contents of four characteristic compounds in water extracts of Astragali radix from different origins

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