Quality Evaluation of <i>Saposhnikovia divaricata</i> (Turcz.) Schischk from Different Origins Based on HPLC Fingerprint and Chemometrics

Chen, Yuqiu; Xu, Zhefeng; Gao, Siying; Zhang, Tao; Chen, Changbao

doi:https://doi.org/10.1155/2022/1155650

Journal of Chemistry

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 1155650 | https://doi.org/10.1155/2022/1155650

Quality Evaluation of Saposhnikovia divaricata (Turcz.) Schischk from Different Origins Based on HPLC Fingerprint and Chemometrics

Yuqiu Chen,¹Zhefeng Xu,¹Siying Gao,¹Tao Zhang,¹and Changbao Chen¹

Academic Editor: Eulogio J. Llorent Mart nez

Received12 Oct 2021

Revised29 Dec 2021

Accepted11 Jan 2022

Published28 Jan 2022

Abstract

A valid and encyclopedic evaluation method for assessing the quality of Saposhnikovia divaricata has been set up based on the analysis of a high-performance liquid chromatography (HPLC) fingerprint combined with the cluster analysis (CA), principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and similarity analysis. 15 peaks of the common model were obtained and used for the similarity analysis, CA analysis, PCA analysis, and PLS-DA analysis. The fingerprint of S. divaricata was established, and 15 common peaks were calibrated. The four common peaks were identified as prim-o-glucosylcimifugin, 4-O-β-D-glucosyl-5-O-methylvisamminol, cimifugin, and sec-o-glucosylhamaudol by comparison with the reference substance. The similarity of the fingerprints of the 33 batches of S. divaricata is above 0.9. Cluster analysis divides the 33 batches of S. divaricata into 2 categories. Principal component analysis (PCA) roughly divides them into 4 categories. Partial least squares method-discriminant analysis (PLS-DA) screened to obtain 2 differential markers, the different components were designated by the reference substance as 4-O-β-D-glucosyl-5-O-methylvisamminol and cimifugin. The fingerprint established by this study combined with chemometrics analysis is reasonable, effective, accurate, and simple, which makes the information more comprehensive and can provide a scientific basis and reference for quality control and quality evaluation of S. divaricata.

1. Introduction

Saposhnikovia divaricata, the dried root of Saposhnikovia divaricata (Turcz.) Schischk., was first listed in the Shennong Bencao Jing as a top grade. Its main chemical constituents are chromone, coumarin, polysaccharide, volatile oil, and so on [1–4]. Modern pharmacological studies have shown that S. divaricata has antipyretic, anti-inflammatory, sedative, analgesic, and antitumor pharmacological activities. It is often used clinically to treat rheumatoid arthritis, rubella pruritus, startle convulsions, and tetanus [5–9]. It is commonly called Guan Fangfeng and is mainly produced in Jilin, Liaoning, Heilongjiang, Inner Mongolia, and other authentic regions of China. S. divaricata is a traditional Chinese medicine with a long history and extensive clinical application. However, due to the lack of wild resources in recent years, wild S. divaricata mainly produced in Inner Mongolia, Outer Mongolia, the northeast, and other places has been in short supply in the market, and home-grown S. divaricata has gradually become the mainstream of the market [10]. However, the quality of home-grown S. divaricata on the market is uneven, and there are many bad behaviors, such as shoddy quality and mixed selling of genuine and fake SR, which seriously affect the quality of the medicinal materials and directly lead to a significant decline in clinical efficacy. Therefore, ensuring the quality of S. divaricata is of great significance to guarantee clinical efficacy. The HPLC fingerprint has been widely used in quality evaluation and new drug development due to its high efficiency, high speed, and high sensitivity [11]. In recent years, the evaluation model combining HPLC fingerprint with pattern recognition analysis has been widely used in the quality control of Chinese medicinal materials (CMMs) [12]. Unsupervised pattern recognition analysis includes cluster analysis (CA), principal component analysis (PCA), and so on. Supervised pattern recognition analysis includes discriminant analysis (DA) and partial least squares-discriminant analysis (PLS-DA). [13]. At present, there are few studies on the HPLC fingerprint of S. divaricata. At the same time, there is no literature on the quality control of SR in different producing areas with the combination of HPLC fingerprint and multiple chemical pattern recognition techniques. In this manuscript, 33 sets of fingerprints of S. divaricata from different areas were established by the high-performance liquid chromatography (HPLC) method. Through the similarity evaluation combined with three pattern recognition analyses, the quality of S. divaricata was comprehensively evaluated, which provided a data reference for further improving the quality control of S. divaricata.

2. Materials and Methods

2.1. Chemicals and Reagents

The following instruments were used for the study: the CLF-10C sealed grinder (Zhejiang Wenling Chuangli Medicinal Material Equipment Factory), the electric blast drying oven (Shanghai Hengyi Scientific Instrument Co., Ltd.), the ME204 electronic balance (Mettler-Toledo Instrument Co., Ltd.), the KQ- 500DA CNC ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd.), the HH-8 digital display constant temperature water bath (Changzhou Jiangnan Experimental Instrument Factory), the Agilent-1200 high-performance liquid chromatograph, and the Elite Supersil ODS2 5 μm chromatographic column (4.6 mm × 250 mm, 5 μm). 4-O-β-D-glucosyl-5-O-methylvisamminol, cimifugin, prim-o-glucosylcimifugin, and sec-o-glucosylhamaudol were all supplied by Shanghai YUANYE Biotechnology Co., Ltd. The purity of each ingredient was greater than 98% as determined by HPLC. Methanol of HPLC grade, pure water, and methanol of analytical grade were purchased from Fisher Scientific (USA), WAHAHA (Hangzhou, China), and KESHI (Cheng Du, China).

2.2. Materials

Thirty-three batches of S. divaricata were collected from different regions of China and were authenticated by professor Chang-Bao Chen of Changchun university of Chinese medicine (Changchun, China). The origin, latitude, and longitude of 33 batches of S. divaricata are shown in Table 1.

2.3. Preparation of Sample and Standard Solutions

First, 1 g of sample powder (passed through a No. 2 sieve) was placed in a centrifuge tube, 20 ml of methanol was added, and the supernatant was filtered after 30 minutes of ultrasound. Then, 20 ml of methanol was added to the centrifuge tube after 30 minutes of ultrasound. After the second filtration, the combined filtrate was poured into an evaporating dish. When the liquid in the evaporating dish has evaporated to about 5 ml, it is transferred to a 10 ml volumetric flask and methanol is added to make the volume up to 10 ml. Finally, the solution was filtered using a 0.22 μm microporous membrane to obtain the sample solution. Appropriate amounts of prim-o-glucosylcimifugin, cimifugin, 4-O-β-D-glucosyl-5-O-methylvisamminol, and sec-o-glucosylhamaudol were precisely weighed, and then methanol was added. The standard solution was prepared to contain 2 mg of prim-o-glucosylcimifugin, 2.3 mg of cimifugin, 3 mg of 4-O-β-D-glucosyl-5-O-methylvisamminol, and 0.7 mg of sec-o-glucosylhamaudol in each 1 mL of the solution. The specific concentration of the mixed standard solution of prim-o-glucosylmifugin was 2 mg/ml, cimifugin was 2.3 mg/ml, 4-β-D-glucosyl-5-O-methylvisamminol was 3 mg/ml, and sec-o-glucosylhamaudol was 0.7 mg/ml.

2.4. Chromatographic Procedures

HPLC analyses were performed on an Agilent 1200 HPLC instrument (Agilent, USA) that was equipped with a diode array detector (DAD) and an Elite Supersil ODS2 5 μm column (250 mm × 4.6 mm, 5 μm). The mobile phase consisted of a mixture of methanol (A; Thermo Fisher Scientific, USA) and purified water (B; WAHAHA, China). The gradient elution program was as follows: 0–20 min, 30% A; 20–25 min, 45% A; 25–50 min, 60% A; 50–55 min, 90% A; and 55–60 min, 100% A. The flow rate was 1 mL·min⁻¹; the column temperature was 30°C; the injection volume was 10 μL; and the UV detection wavelength was 254 nm.

2.5. Content Determination and Moisture Determination

The 33 batches of S. divaricata samples were used to prepare the standard sample and the test solution according to the method under “2.3,” and the HPLC content was determined according to the chromatographic conditions under “2.4.” 2 g of samples were placed in a constant weight weighing bottle, the thickness of which was not more than 5 mm, and were carefully weighed. The samples were then placed in an oven at 105°C for 5 hours, then moved to a dryer and cooled for 30 minutes. It is then dried in an oven at 105°C for another 1 h and cooled in a dryer for another 30 minutes to be quickly and precisely weighed until the difference between the two successive weighing operations does not exceed 5 mg. Finally, the water content (%) of the sample is calculated according to the weight loss [1].

2.6. Data Analysis

The data were analyzed and evaluated by a similarity evaluation system for chromatographic fingerprint of TCM (Version 2012 A) which was recommended by the SFDA of China for evaluating similarities of chromatographic profiles of TCM. The Past software is used for cluster analysis (CA), SIMCA14.1 is used for principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), and Origin 2019b is used for drawing.

3. Results and Discussion

3.1. Method Validation

3.1.1. Linearity and Investigation of Chromatographic Conditions

A mixed solution containing all reference substances was prepared and diluted with methanol to obtain 4 reference substance solutions of different concentrations. Mixed reference solutions of different concentrations were used to draw the standard curve. As shown in Table 2, a good calibration curve was obtained for the four compounds, with a high correlation coefficient (R² > 0.997) and good linearity over a wide range. The HPLC chromatogram of the S. divaricata reference solution is shown in Figure 1(a), and the HPLC chromatogram of the sample solution is shown in Figure 1(b).

(a)

(b)

3.1.2. Precision, Stability, and Repeatability

The same mixed standard solution of 10 μl was injected six consecutive times under chromatographic conditions, and their RSDs were calculated. The RSDs of prim-o-glucosylcimifugin, 4-O-β-D-glucosyl-5-O-methylvisamminol, and cimifugin sec-o-glucosylhamaudol were 0.88%, 0.75%, 0.79%, and 0.87%, respectively, which indicated that the developed method had a good precision. The stability of the sample solutions was analyzed at 0, 2, 4, 8, 12, and 24 h at room temperature. The RSD values of the peak area were 1.7%, 4.9%, 0.5%, and 4.1%, respectively, which indicated that the sample solutions were stable within 24 h. To confirm the repeatability of the method, six independently prepared solutions from the same batch were analyzed. The RSD values of the peak area were 0.67%, 4.72%, 0.9%, and 0.99%, respectively. The results indicated the method is reproducible (RSD ≤5.0%).

3.2. Results of Determination of Water Content and Chromone Components in S. divaricata from Different Habitats by HPLC

The results are shown in Table 3. The water content of 33 batches of S. divaricata from different production areas is between 5.78% and 9.97%, which all meet the requirement of the 2020 edition of ‘Chinese Pharmacopoeia’ that the water content of diffracts should not exceed 10%, of which the lowest water content is S14 in Heilongjiang and the highest water content is S24 in Hebei. The HPLC method was used to determine the active components in S. divaricata, and the contents of prim-o-glucosylcimifugin, 4-O-β-D-glucosyl-5-O-methylvisamminol, cimifugin, and sec-o-glucosylhamaudol were calculated. The Chinese Pharmacopoeia (2020 edition) stipulates that the total content of prim-o-glucosylcimifugin (C₂₂H₂₈O₁₁) and 4-O-β-D-glucosyl-5-O-methylvisamminol (C₂₂H₂₈O₁₀) in S. divaricata should not be less than 0.24% [1]. The results showed that the content of SR varied with the batch number. Therefore, the average content of each producing area is calculated, as shown in Table 1. As can be seen from Table 1, the total amount of prim-o-glucosylcimifugin and 4-O-β-D-glucosyl-5-O-methylvisamminol in the above 33 batches of SR is more than 0.24%, which meets the standards of China Pharmacopoeia (2020 edition). The contents in Heilongjiang and Jilin regions were significantly higher than those in other regions. To sum up, the water and chromone content of S. divaricata from different producing areas are also different, and the quality of S. divaricata from different producing areas is also different.

3.3. HPLC Fingerprint and Similarity Analysis

The 33 batches of samples were prepared according to Section 2.3, and 10 μL was injected into the HPLC system according to the chromatographic conditions given under Section 2.4, and then the chromatograms were recorded and entered into the similarity analysis software. S1 was used as a reference fingerprint, and the average correlation coefficient method of 33 batches of samples was used for multipoint calibration. And then, the fingerprints of 33 batches of S. divaricata from different areas were generated (Figure 2). Compared with the reference fingerprint, the similarities of the 33 batches of samples are respectively 0.992, 0.991, 0.992, 0.972, 0.995, 0.964, 0.986, 0.991, 0.958, 0.998, 0.997, 0.912, 0.988, 0.999, 0.966, 0.993, 0.994, 0.992, 0.986, 0.907, 0.990, 0.933, 0.918, 0.978, 0.992, 0.994, 0.906, 0.932, 0.945, 0.974, 0.936, 0.999, and 0.967. The results are all above 0.90, which proves that the similarity is good.

3.4. Cluster Analysis (CA)

Cluster analysis (CA) is a kind of analysis method to classify a group of unclassified samples according to their quantitative characteristics. The four common peak areas of 33 batches of S. divaricata were brought into the Past software for cluster analysis, and the results are shown in Figure 3. When the distance is 4000, the 33 batches of windbreak samples can be roughly divided into two categories, of which the Jilin region and part of the Heilongjiang region are clustered into one category, and the three regions of Hebei, Shanxi, and Inner Mongolia are roughly clustered into one category. The reason that the medicinal materials of Jilin and Heilongjiang are grouped may be that the chemical contents of the medicinal materials of Jilin and Heilongjiang are similar due to their similar producing areas. However, some samples such as S15 and S33 are far away from their corresponding origin samples in the dendrogram, which may be related to the larger differences in the samples themselves.

3.5. Principal Component Analysis (PCA)

Principal component analysis (PCA) is a data reduction method proposed by Kail Pearson in 1901 and later developed by Hotelling in 1933. The principal component score (PCA) chart can reflect the differences between samples and classify them [4]. The peak areas of the 4 common chromatographic peaks of 33 batches of fingerprints were formed into a 4 × 33-order matrix and imported into the SIMCA 14.1 software to establish a PCA model. The self-scaled method (UV) was used for scaling, and 3 principal components were extracted to obtain the model. The interpretation rate parameter R²X is 0.992, and the predictive ability parameter Q² is 0.913, indicating that the extracted principal components can explain 99.2% of the original variables, and the predictive ability of the model is 91.3%. The PCA scores of 33 batches of S. divaricata samples are shown in Figure 4. The results show that most of the S. divaricata samples can be divided into 4 categories, which is consistent with the results of cluster analysis.

3.6. Partial Least Square Discriminant Analysis (PLS-DA)

Partial least square discriminant analysis (PLS-DA) is a supervised statistical method of discriminant analysis, which was first used in chemical analysis in the 1980s. The PLS method can be used to analyze the spectrum of complex chemical components in traditional Chinese medicine without the need for pure component information, the analysis process is simpler, and the results are more comprehensive [14–16]. To further search for the markers that cause the differences among different origins of S. divaricata, the common peak areas of four common peaks of 33 batches of S. divaricata were introduced into SIMCA 14.1 software, where the partial least squares discriminant analysis model of supervisory mode was established. The explanatory rate parameters R²X and R²Y were 0.992 and 0.609, respectively, and the predictive ability parameters of the model were 0.556, which indicated that the explanatory capacity of the model to variable X was 99.2%. The PLS-DA score is shown in Figure 5. The results show that 33 batches of SR were well classified into 5 classes. A displacement test (n = 200) was used to verify the current model. The test parameters R² = (0.0, 0.00761) and Q2 = (0.0, −0.228) are shown in Figure 6. The values of R² and Q² generated by random permutations on the left are smaller than the original values on the right. This shows that the PLS-DA model fits well. Variable importance projection (VIP) is an important index for screening differential compounds. The higher the VIP, the greater the influence on the differences between groups. Using VIP >1 as the threshold, 2 common peaks were selected. The results are shown in Figure 7. The results showed that the compounds represented by peak 2 (4-O-β-D-glucosyl-5-O-methylvisamminol) and peak 3 (cimifugin) were the main markers of quality difference among 33 batches of S. divaricata.

3.7. Discussion

The quality of medicinal materials varies from place to place due to the different growing environments. In this study, 33 batches of S. divaricata from different producing areas were studied. The HPLC fingerprint of S. divaricata was established. The similarity of 33 batches of S. divaricata was above 0.9, which showed that the method was accurate. The fingerprints of 33 batches of S. divaricata were further analyzed by cluster analysis and principal component analysis. Two different components, 4-O-β-D-glucosyl-5-O-methylvisamminol and cimifugin, were obtained by PLS-DA analysis, which can be used as quality markers to evaluate the quality of S. divaricata from different habitats. The quality mark can effectively distinguish the different origin of S. divaricata [17, 18].

In this study, four common peaks of fingerprint spectra were identified. Based on the previous studies, the sources of samples were further enriched by cluster analysis, principal component analysis, and partial least squares discriminant analysis. The analysis of different components of S. divaricata from different producing areas was added, and 2 components were selected, which can provide an experimental reference for the selection of the Quality Control Index and the study of quality control methods of S. divaricata.

In the 2020 edition of the Pharmacopoeia of the People's Republic of China [1], only the total amount of prim-o-glucosylcimifugin (C₂₂H₂₈O₁₁) and 4-O-β-D-glucosyl-5-O-methylvisamminol (C₂₂H₂₈O₁₀) should not be less than 0.24% in the quality standard of S. divaricata. In addition to the two components mentioned above, other common and different components can be added to evaluate the quality of S. divaricata more scientifically. In this experiment, we found that apart from prim-o-glucosylcimifugin and 4-O-β-D-glucosyl-5-O-methylvisamminol, the chromatographic peak response of cimifugin was also good. In the future, cimifugin can be added as the determination index to further study the content determination method of multicomponent of S. divaricata, which can provide a reference for the research of quality control methods of S. divaricata [19, 20].

For the determination of water content, we adopted the drying method stipulated in the 2020 edition of the Pharmacopoeia of the People's Republic of China, but after consulting related literature, we found that many methods can be applied to the determination of plant crop moisture, such as near-infrared spectroscopy. This method is efficient and can be studied in depth in the future. At the same time, in the preparation of the sample solution, we perform ultrasonic extraction. After consulting the relevant literature, we found that in addition to ultrasonic extraction, there are many pretreatment methods, such as presoaking, liquid ammonia pretreatment, and other methods. The extraction efficiency of these methods is very high, and in-depth research can be carried out in the future to increase the content of active ingredients in the samples of S. divaricata.

4. Conclusion

Based on these results, the fingerprint established by this study combined with chemometrics analysis is reasonable, effective, accurate, and simple, which makes the information more comprehensive and can provide a scientific basis and reference for quality control and quality evaluation of S. divaricata.

Data Availability

The datasets generated and analysed during the current study are not publicly available due to the confidentiality of our project, but are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the National Key R&D Projects of China (no: 2019YFC1710704) and Key Projects of the Science and Technology Development Plan of Jilin Province, China (no: 20200708028YY).

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Copyright © 2022 Yuqiu Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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