Method for constructing HPLC standard fingerprint of Eupatorium truncatum and method for determining the content of its main components

Abstract

The invention discloses a method for constructing a standard fingerprint of HPLC of yellow flower water lotus and a method for determining the content of its main component. The method comprises obtaining a sample test solution of yellow flower water lotus, collecting HPLC chromatographic data of each test solution by a high performance liquid chromatograph, performing comparative analysis on the HPLC chromatographic data, obtaining a standard fingerprint of HPLC of yellow flower water lotus composed of its common peaks, performing cluster analysis and principal component analysis according to the HPLC chromatographic data, grouping a plurality of yellow flower water lotus samples, constructing Siberian polygala sugar A5 and 3,6'-dierucyl sucrose regression equations, and obtaining Siberian polygala sugar A5 and 3,6'-dierucyl sucrose content in yellow flower water lotus. The method of the invention obtains a standard fingerprint of HPLC of yellow flower water lotus, has the characteristics of strong objectivity and high accuracy, can effectively control the quality of yellow flower water lotus medicinal materials, ensures the clinical efficacy and application safety of yellow flower water lotus, and effectively detects the content of the main component of yellow flower water lotus.

Description

Technical Field

[0001] The invention relates to the technical field of drug analysis, and in particular to a method for constructing a HPLC standard fingerprint of water lily and a method for determining the content of its main components. Background Art

[0002] Polygala Fallax Hemsl. belongs to the Polygalaceae family. The medicinal part of Polygala Fallax Hemsl. is the dried root of Polygala Fallax Hemsl., which is a commonly used medicine in traditional Chinese medicine and Zhuang medicine. Polygala Fallax Hemsl. is mainly used for tonifying, strengthening, dispersing blood stasis, and removing dampness in traditional Chinese medicine. It is often used to treat physical weakness after childbirth or illness, acute and chronic hepatitis, backache and leg pain, uterine prolapse, rectal prolapse, neurasthenia, irregular menstruation, urinary tract infection, rheumatism, and traumatic injuries. In Zhuang medicine, it is mainly used to tonify qi deficiency and regulate the airway, valley, and waterway. It is often used to treat physical weakness, jaundice, Gu disease, malnutrition, tuberculosis, cough, arthralgia, urethritis, edema, insomnia, dysmenorrhea, irregular menstruation, and uterine prolapse. As a commonly used medicine for ethnic minorities such as the Zhuang, Yellow Water Lily is more common in the in-hospital preparations of the Guangxi International Zhuang Medicine Hospital. For example, Yellow Water Lily is added to Fu Zheng Capsules and Gan Shu Capsules.

[0003] Although there are many yellow flower water lotus in traditional Chinese medicine and Zhuang medicine, there are few studies on its quality. Furthermore, although a few provinces have established medicinal material standards, there are no relevant reports on the quality control and content determination of yellow flower water lotus. The quality of Chinese medicine is the key to its efficacy. At present, people judge the authenticity of yellow flower water lotus medicinal materials by their experience based on the appearance, smell and simple physical state of the medicinal materials, which makes it difficult to judge the authenticity of yellow flower water lotus and the accuracy is low, and it is often determined by the experience of the judge. This further makes it difficult to guarantee the quality of yellow flower water lotus, and it is difficult to monitor its clinical efficacy and safety of application.

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for constructing an HPLC standard fingerprint of Glechoma longituba, aiming to overcome the defect that Glechoma longituba has no HPLC standard fingerprint. The traditional method of identifying Glechoma longituba based on experience has low accuracy and cannot effectively achieve the purpose of controlling the quality of Glechoma longituba medicinal materials.

[0006] The present invention also provides a method for determining the contents of Siberian polygala sugar A5 and 3,6'-dicerinoyl sucrose in yellow water lily, aiming to overcome the defects of the prior art that no method for determining the contents of Siberian polygala sugar A5 and 3,6'-dicerinoyl sucrose in yellow water lily has been established, and the quality of the medicinal material is difficult to guarantee.

[0007] To achieve the above object, the present invention provides a method for constructing an HPLC standard fingerprint of Psoralea corylifolia, the construction method comprising the following steps:

[0008] S1, weigh n batches of water lily powder, add methanol, ultrasonically treat, make up the weight loss with methanol, filter and take the filtrate, and obtain n batches of test solutions, where 31≥n>1;

[0009] S2, using a high performance liquid chromatograph to collect HPLC chromatographic data of each test solution, the high performance liquid chromatograph uses a chromatographic column of preset specifications for the experiment, and sets the injection volume of the high performance liquid chromatograph to 10 μL, the column temperature of the chromatographic column to 35° C., the detection wavelength to 250 nm, the mobile phase and the mobile phase elution gradient, wherein the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is 0.05% phosphoric acid-methanol, and the mobile phase B is 0.05% phosphoric acid aqueous solution;

[0010] S3, comparing and analyzing the HPLC chromatographic data to obtain a standard fingerprint of the yellow flower water lily HPLC consisting of its common peaks;

[0011] S4, performing cluster analysis and principal component analysis according to the HPLC chromatographic data, classifying the n batches of water lilies of different varieties, and obtaining m groups of water lilies of different varieties with similarities ranging from 0.77 to 1, wherein m<6.

[0012] According to the above technical solution, the principle of selecting a detection wavelength of 250 nm is that when the HPLC diode array detector collects a spectrum test at a detection wavelength of 250 nm, each peak has a good separation and most peaks have a good response.

[0013] Preferably, in the above technical solution, in step S1, the methanol is 20 mL 75% methanol.

[0014] According to the above technical scheme, the principle of using 75% methanol as the extraction solvent is: methanol, 75% methanol, and 50% methanol are used as the extraction solvents to ultrasonically extract the yellow flower water lotus. The results show that the number of peaks collected by 75% methanol extraction is large, the peak separation is good, and the content of Siberian Polygala sugar A5 and 3,6'-di-sinapoyl sucrose is high; using 75% methanol as the solvent, the heating reflux extraction or 30 / 45 / 60min ultrasonic extraction to prepare the test solution is compared, and the results show that the Siberian Polygala sugar A5 and 3,6'-di-sinapoyl sucrose content measured by the 60min ultrasonic extraction test solution is high, and the peak height of each peak is high. Therefore, this method uses 75% methanol ultrasonic 60min extraction to prepare the test solution.

[0015] Preferably, in the above technical solution, in step S2, the elution gradient of the mobile phase is as follows:

[0016] First elution gradient: from 0 to 16 min, mobile phase A increased from 19% to 23%, mobile phase B decreased from 81% to 77%, flow rate was 0.7 mL / min;

[0017] Second elution gradient: from 16 to 40 min, mobile phase A increased from 23% to 38%, mobile phase B decreased from 77% to 62%, flow rate was 0.7 mL / min;

[0018] The third elution gradient: at 40.01-70 min, mobile phase A was 38%, mobile phase B was 62%, and the flow rate was 0.4 mL / min;

[0019] Fourth elution gradient: at 70.01-100 min, mobile phase A was increased from 38% to 43%, mobile phase B was decreased from 62% to 57%, and the flow rate was 0.7 mL / min;

[0020] Fifth elution gradient: at 100-130 min, mobile phase A was 43%, mobile phase B was 57%, and the flow rate was 0.7 mL / min;

[0021] Sixth elution gradient: from 130 to 169 min, mobile phase A increased from 43% to 80%, mobile phase B decreased from 57% to 20%, and the flow rate was 0.7 mL / min;

[0022] Seventh elution gradient: at 169-170.01 min, mobile phase A was reduced from 80% to 19%, mobile phase B was increased from 20% to 81%, and the flow rate was 0.7 mL / min;

[0023] The eighth elution gradient: at 170.01-175 min, the mobile phase A was 19%, the mobile phase B was 81%, and the flow rate was 0.7 mL / min.

[0024] Preferably, in the above technical solution, in step S2, the chromatographic column is an InfinityLab Poroshell 120EC-C18 chromatographic column with a length of 4.6 mm, an inner diameter of 250 mm and a filler particle diameter of 4 μm.

[0025] According to the above technical solution, the principle of using filler particles with a diameter of 4 μm is obtained through multiple experiments using a C18 chromatographic column for separation. When the column length and diameter are the same, the smaller the C18 particle size, the better the separation, and the more component peaks are obtained.

[0026] Preferably, in the above technical solution, the HPLC standard index spectrum of the yellow flower water lily has 10 common peaks.

[0027] Preferably, in the above technical solution, among the 10 common peaks, Peak 5 is Siberian Polygala A5, the main active ingredient of Herba Polygoni Multiflori, and Peak 7 is 3,6'-dicerinoylsucrose, the main active ingredient of Herba Polygoni Multiflori.

[0028] According to the above technical scheme, among the 10 common peaks of the HPLC standard fingerprint of Herba Lycoris Radiatae, Peak 5 is Siberian Polygala A5, the main active ingredient of Herba Lycoris Radiatae, and Peak 7 is 3,6'-dicerinoylsucrose, the main active ingredient of Herba Lycoris Radiatae. These two ingredients have the effects of improving insomnia, calming the nerves, resisting dementia, and resisting depression, and are important factors in achieving the therapeutic effect of Herba Lycoris Radiatae in clinical diagnosis and treatment.

[0029] Preferably, in the above technical solution, the specific steps of performing cluster analysis and principal component analysis on the HPLC chromatographic data in step S4, classifying the 1-n batches of water lilies, and obtaining m groups of water lilies with similarities in the range of 0.77-1 are:

[0030] (1) The HPLC chromatogram data is imported into a similarity evaluation system to obtain a similarity range of 1-n batches of yellow water lilies. If the similarity is not within the range of 0.77-1, cluster analysis is performed on the 1-n batches of yellow water lilies and the groups are divided into p groups, wherein p≤3;

[0031] (2) The HPLC chromatographic data of each group were respectively imported into a similarity evaluation system until the similarity was within the range of 0.77-1, and m groups of yellow flowered water lilies were obtained. The principal component analysis was performed on the common peaks in each group using statistical analysis software, and the principal components and cumulative variance contributions were screened out with the eigenvalue>1 as the standard, to obtain peaks and characteristic peaks that had a significant impact on the first principal component, wherein m<6.

[0032] A method for detecting medicinal materials using the HPLC standard fingerprint of Hedysarum truncatum constructed by the above method, characterized in that the method is specifically as follows:

[0033] (1) Obtaining HPLC spectrum data of the medicinal material sample to be tested according to the method described in step S1 and step S2 above;

[0034] (2) When the HPLC spectrum data of the medicinal material sample to be tested has the same common peaks as the HPLC standard fingerprint spectrum data of Herba Lycoris Radiatae, it is determined that the medicinal material sample to be tested is Herba Lycoris Radiatae.

[0035] A method for determining the content of Siberian polygala A5 and 3,6'-dicerinoyl sucrose in Polygala lutea, the specific steps of the method are as follows:

[0036] S1, weigh dry water lily powder, add 75% methanol solution, ultrasonically treat, make up the weight loss with 75% methanol solution, filter and take the filtrate to obtain the test solution;

[0037] S2, weigh Siberian Polygala A5 and add it to 75% methanol solution to prepare 861.5814 μg / mL Siberian Polygala A5 solution, weigh 3,6'-dicorinoyl sucrose and add it to 75% methanol solution to prepare 815.1388 μg / mL 3,6'-dicorinoyl sucrose solution, put 0.04, 0.2, 0.4, 1, and 2 mL of the Siberian Polygala A5 solution and 3,6'-dicorinoyl sucrose solution into a 10 mL volumetric flask, add 75% methanol to the scale line of the 10 mL volumetric flask, shake well, and prepare a mixed series of solutions for the standard curve;

[0038] S3, the test solution and the mixed series solution are respectively collected by HPLC chromatographic data using a chromatograph, and the injection volume of the chromatograph is set to 10 μL, the column temperature of the chromatographic column is set to 35° C., the detection wavelength is set to 250 nm, and the elution gradient of the mobile phase is set, wherein the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is 0.05% phosphoric acid-methanol, and the mobile phase B is 0.05% phosphoric acid aqueous solution;

[0039] S4, determine and collect the HPLC chromatographic data of the mixed series of solutions according to the steps S2 and S3, obtain the regression equation of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose according to the injection concentration and peak area, determine and collect the HPLC chromatograms of 31 batches of yellow water lilies according to the steps S1 and S3, and substitute the chromatograms into the regression equation to obtain the contents of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose in the 31 batches of yellow water lilies.

[0040] Preferably, in the above technical solution, in step S2, the chromatographic column is an InfinityLab Poroshell 120EC-C18 chromatographic column with a length of 4.6 mm, an inner diameter of 250 mm and a filler particle diameter of 4 μm.

[0041] Preferably, in the above technical solution, the elution gradient of the mobile phase is as follows:

[0042] First elution gradient: from 0 to 16 min, mobile phase A increased from 19% to 23%, mobile phase B decreased from 81% to 77%, flow rate was 0.7 mL / min;

[0043] Second elution gradient: from 16 to 40 min, mobile phase A increased from 23% to 38%, mobile phase B decreased from 77% to 62%, flow rate was 0.7 mL / min;

[0044] The third elution gradient: at 40.01-70 min, mobile phase A was 38%, mobile phase B was 62%, and the flow rate was 0.4 mL / min;

[0045] Fourth elution gradient: at 70.01-100 min, mobile phase A was increased from 38% to 43%, mobile phase B was decreased from 62% to 57%, and the flow rate was 0.7 mL / min;

[0046] Fifth elution gradient: at 100-130 min, mobile phase A was 43%, mobile phase B was 57%, and the flow rate was 0.7 mL / min;

[0047] Sixth elution gradient: from 130 to 169 min, mobile phase A increased from 43% to 80%, mobile phase B decreased from 57% to 20%, and the flow rate was 0.7 mL / min;

[0048] Seventh elution gradient: at 169-170.01 min, mobile phase A was reduced from 80% to 19%, mobile phase B was increased from 20% to 81%, and the flow rate was 0.7 mL / min;

[0049] The eighth elution gradient: at 170.01-175 min, the mobile phase A was 19%, the mobile phase B was 81%, and the flow rate was 0.7 mL / min.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) Creating a standard fingerprint of the Herba Lycopodii HPLC for distinguishing the authenticity of the Herba Lycopodii HPLC-MS / MS, which has the characteristics of strong objectivity and high accuracy, can effectively control the quality of the Herba Lycopodii HPLC-MS / MS, and ensure the clinical efficacy and application safety of the Herba Lycopodii HPLC-MS / MS;

[0052] (2) The yellow water lily medicinal materials from different sources were subjected to cluster analysis and principal component analysis to obtain five groups of peak numbers related to the principal component 1 and principal component 2 of the yellow water lily, providing a theoretical basis for using HPLC technology to screen the yellow water lily medicinal materials containing the content of principal component 1 and principal component 2;

[0053] (3) Provide a new method for determining the content of Siberian polygala sugar A5 and 3,6'-dicerinoyl sucrose in yellow water lily, thereby effectively detecting the content of Siberian polygala sugar A5 and 3,6'-dicerinoyl sucrose in yellow water lily in the field of traditional Chinese medicine preparations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a diagram of the fingerprint spectrum of 31 batches of yellow flower water lilies in Example 1 of the present invention;

[0055] Figure 2 It is the common pattern diagram of the fingerprint spectrum of 31 batches of yellow flower water lilies in Example 1 of the present invention;

[0056] Figure 3 This is the dendrogram of 31 batches of yellow flower water lilies cluster analysis using Ward connection;

[0057] Figure 4 It is a fingerprint spectrum of 21 batches of yellow water lilies in Group I in Example 1 of the present invention;

[0058] Figure 5 This is the dendrogram of the cluster analysis of 21 batches of yellow-flowered water lilies in group I using Ward connection;

[0059] Figure 6 It is a fingerprint spectrum of 8 batches of yellow flower water lotus of group I-1 in Example 1 of the present invention;

[0060] Figure 7 It is a scree plot of 21 common peaks of 8 batches of yellow flower water lilies in group I-1 of Example 1 of the present invention;

[0061] Figure 8 It is a fingerprint spectrum of 9 batches of yellow flower water lotus of group I-2 in Example 1 of the present invention;

[0062] Fig. 9 It is a scree plot of 20 common peaks of 9 batches of yellow flower water lilies in group I-2 of Example 1;

[0063] Fig.10 It is a fingerprint spectrum of 4 batches of yellow flower water lotus of group I-3 in Example 1 of the present invention;

[0064] Fig.11 It is a scree plot of 32 common peaks of 4 batches of yellow flower water lilies in group I-3 in Example 1;

[0065] Fig.12 It is a fingerprint spectrum of 6 batches of yellow flower water lotus of group II in Example 1 of the present invention;

[0066] Fig.13 It is a scree plot of 33 common peaks of 6 batches of yellow flower water lilies in Group II in Example 1 of the present invention;

[0067] Fig.14 It is a fingerprint spectrum of 4 batches of yellow flower water lotus of group III in Example 1 of the present invention;

[0068] Fig.15 It is a scree plot of 49 common peaks of 4 batches of yellow flower water lilies in Group III in Example 1 of the present invention;

[0069] Fig.16 1 and 2 are HPLC chromatograms of the solvent blank (A), the control solution (B) and the test solution (C) in Example 2 of the present invention. DETAILED DESCRIPTION

[0070] The specific implementation modes of the present invention are described in detail below in conjunction with specific embodiments, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0071] Instruments and reagents

[0072] Instruments: 1260infinityⅡ high performance liquid chromatograph equipped with diode array detector (Agilent Technology); ME155DU 1 / 100000 electronic balance and ME204 1 / 100000 electronic balance (Mettler-Toledo), JJ2000B electronic balance (Changshu Shuangjie Testing Instrument Factory); Master-Q30 ultrapure water system (Shanghai Hetai Instrument Co., Ltd.); KQ-300DB ultrasonic cleaning machine (Kunshan Ultrasonic Instrument Co., Ltd.).

[0073] Reagents: Siberian Polygala sugar A5 (batch number: MUST-21052704, content: 93.91%) and 3,6'-dimercaptosucrose (batch number: MUST-21081304, content: 98.13%) were purchased from Chengdu Munster Biotechnology Co., Ltd. Methanol and acetonitrile were chromatographically pure and purchased from Fisher, and the rest of the reagents were analytically pure. 31 batches of yellow flower water lotus samples were identified by Professor Huang Ruisong of Guangxi International Zhuang Medical Hospital as the dried roots of the Polygalaceae plant yellow flower water lotus. The specific sources of the 31 batches of yellow flower water lotus samples are shown in Table 1 below.

[0074] Table 131 Sources of medicinal materials for batches of yellow flower water lotus samples

[0075]

[0076] Example 1

[0077] A method for constructing an HPLC standard fingerprint of Psoralea corylifolia, the construction method comprising the following steps:

[0078] S1, accurately weigh 31 batches of 1.0g dry water lily powder (passed through No. 2 sieve), add 20mL 75% methanol, ultrasonically treat (power 200W, frequency 40kHz) for 60min, make up the weight loss with 75% methanol, filter and take the filtrate, and obtain 31 batches of test solutions;

[0079] S2, using a high performance liquid chromatograph to collect an HPLC chromatogram of each test solution, the high performance liquid chromatograph uses an InfinityLab Poroshell 120EC-C18 chromatographic column for the experiment, the chromatographic column specifications are length × inner diameter of 4.6mm × 250mm, and the filler particle diameter is 4μm; and the injection volume of the high performance liquid chromatograph is set to 10μL, the column temperature of the chromatographic column is 35°C, the detection wavelength is 250nm, the mobile phase includes mobile phase A and mobile phase B, and a mobile phase elution gradient, wherein the mobile phase A is 0.05% phosphoric acid-methanol, and the mobile phase B is 0.05% phosphoric acid aqueous solution, and elution is performed according to the mobile phase elution gradient specified in Table 2;

[0080] Table 2 Mobile phase elution gradient information

[0081] Time (min) Mobile phase A (%) Mobile phase B (%) Flow rate (mL / min) 0.00-16.00 1923 8177 0.7 16.00-40.00 2338 7762 0.7 40.01-70.00 38 62 0.4 70.01-100.00 3843 6257 0.7 100.00-130.00 43 57 0.7 130.00-169.00 4380 5720 0.7 169.00-170.01 8019 2081 0.7 170.01-175.00 19 81 0.7

[0082] The sample of batch number S14 of the yellow water lily was taken to test the precision, stability and repeatability of the above steps S1 and S2. The specific testing method is as follows:

[0083] (1) Precision test

[0084] Take the same mixed reference solution and inject it 6 times continuously according to step S2, record the retention time and peak area of ​​the collected common peak of the mixed reference solution, and calculate its RSD value.

[0085] The precision test measured 6 mixed reference solutions, and the retention time RSD value of each common peak was less than 0.3%, and the peak area RSD value of each common peak was less than 3.0%, indicating that the HPLC chromatogram obtained by 1260infinityⅡ high performance liquid chromatograph according to step S2 had good precision.

[0086] (2) Stability test

[0087] Take the yellow water lily sample of batch number S14, prepare the test solution according to step S1, and inject the sample at 0, 6, 12, 18, and 24 hours according to step S2, collect the retention time and peak area of ​​the common peak, and calculate its RSD value.

[0088] The stability test showed that the batch S18 water lily sample was prepared according to step S1 for the test solution. The retention time RSD values ​​of the common peaks at 0, 6, 12, 18, and 24 hours were all less than 0.2%, and the peak area RSD values ​​of the common peaks were all less than 2.5%, indicating that the batch S18 water lily sample solution had good stability within 24 hours.

[0089] (3) Repeatability test

[0090] Take the yellow water lily sample of batch number S14, prepare 6 test solutions according to step S1, and sample and collect them according to step S2 respectively, obtain the retention time and peak area of ​​the common peak of the 6 test solutions, and calculate their RSD values.

[0091] The repeatability test showed that the retention time RSD values ​​of the common peaks of the above 6 samples of Eupatorium truncatum were all less than 0.2%, and the peak area RSD values ​​of the common peaks were all less than 3.0%, indicating that the method had good repeatability.

[0092] Through the above precision, stability and repeatability tests, it was verified that the precision, stability and repeatability of step S1 and step S2 were good. 31 batches of yellow flower water lily samples were processed by the above steps S1 and S2 methods and their HPLC chromatograms were collected. The HPLC chromatogram data of the 31 batches of yellow flower water lily samples were imported into the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format.

[0093] S3, comparative analysis of the HPLC chromatograms, specifically, taking the No. 31 yellow water lily sample spectrum as the reference spectrum (S1), adopting the average method, setting the time window width to 0.5min, and processing by multi-point correction to obtain the yellow water lily fingerprint spectrum and the reference fingerprint spectrum, and obtaining the standard fingerprint spectrum of the yellow water lily HPLC composed of its 10 common characteristic peaks, specifically as follows Figure 1 and Figure 2 shown.

[0094] from Figure 1 and Figure 2 It can be seen that the HPLC chromatograms of 31 batches of water lilies samples collected by the present invention were automatically matched to obtain 10 common peaks, among which peaks 5 and 7 in the figure were compared with the reference product, confirming that peak 5 was Siberian polygala sugar A5 peak and peak 7 was 3,6'-dicerinoylsucrose peak.

[0095] Similarity analysis was performed on 31 batches of yellow water lily samples, and the results are shown in Table 3 below.

[0096] Table 3 Similarity of fingerprints of 31 batches of Herba Lycopodii

[0097] Serial number mark Similarity Serial number mark Similarity 1 S31 0.803 17 S15 0.792 2 S30 0.802 18 S14 0.859 3 S29 0.867 19 S13 0.793 4 S28 0.808 20 S12 0.712 5 S27 0.895 21 S11 0.868 6 S26 0.913 22 S10 0.814 7 S25 0.911 23 S9 0.873 8 S24 0.847 24 S8 0.905 9 S23 0.861 25 S7 0.869 10 S22 0.863 26 S6 0.793 11 S21 0.849 27 S5 0.748 12 S20 0.849 28 S4 0.895 13 S19 0.891 29 S3 0.629 14 S18 0.820 30 S2 0.734 15 S17 0.738 31 S1 0.549 16 S16 0.715

[0098] As can be seen from Table 3, the similarity between the fingerprints of S2-S31 yellow water lily samples and the reference fingerprint S1 is 0.549-0.913, indicating that the overall similarity between the 31 batches of yellow water lily samples is quite different.

[0099] S4, performing cluster analysis and principal component analysis according to the HPLC chromatographic data, classifying the n batches of water lilies, and obtaining m groups of water lilies with similarities in the range of 0.77-1, wherein m<6;

[0100] Specifically, based on the low overall similarity of the 31 batches of yellow water lilies, cluster analysis was combined to prepare for the subsequent principal component analysis. The cluster analysis used the Ward connection method as the identification method and the squared Euclidean distance as the classification basis to obtain a dendrogram of the close relationship of the 31 batches of yellow water lilies fingerprint spectra, as shown in the figure below: Figure 3 shown.

[0101] from Figure 3 It can be seen that based on the Euclidean distance of 10, 31 batches of water lily samples can be divided into 3 groups. 21 batches including batch numbers S1, S3, S4, S6, S9, S11, S12, S13, S14, S18, S19, S20, S21, S22, S25, S26, S27, S28, S29, S30 and S31 are divided into group I, 6 batches including S2, S5, S7, S10, S16 and S17 are divided into group II, and 4 batches including S8, S15, S23 and S24 are divided into group III. Cluster analysis and principal component analysis were performed on groups I, II and III respectively.

[0102] The sample data of group I of P. lutea were imported into the “Chinese Herbal Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)” in AIA file format. The chromatogram of P. lutea sample No. 31 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5 min. The fingerprint spectrum of P. lutea and the reference fingerprint spectrum ( Figure 4 ) and performed similarity analysis (Table 4).

[0103] Table 4 Similarity of 21 batches of samples of Herba Lycoris Radiatae in Group I

[0104] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 1 S21 0.821 14 S10 0.874 2 S20 0.826 18 S9 0.884 3 S19 0.915 19 S8 0.769 4 S18 0.881 20 S7 0.726 5 S17 0.925 21 S6 0.901 6 S16 0.958 23 S5 0.854 7 S15 0.944 26 S4 0.829 10 S14 0.876 28 S3 0.931 11 S13 0.878 29 S2 0.701 12 S12 0.853 31 S1 0.621 13 S11 0.854

[0105] from Figure 4 It can be seen that 13 common peaks were matched in Group I; as can be seen from Table 4, the similarity between the fingerprints of S2-S21 Herba Lycoris Radiatae medicinal samples and the control fingerprints is 0.621-0.958, indicating that the overall similarity between the 21 batches of Herba Lycoris Radiatae samples is still quite different.

[0106] The overall similarity between the 21 batches of yellow flower water lotus medicinal materials samples is quite different. The fingerprints of the yellow flower water lotus samples of group I S1-S21 are further clustered. Specifically, the Ward connection method is used as the identification method, and the square Euclidean distance is used as the classification basis. The cluster analysis diagram of the fingerprints of the 21 batches of yellow flower water lotus is obtained, as shown in the figure. Figure 5 shown.

[0107] from Figure 5 It can be seen that based on the Euclidean distance of 10-15, the 21 batches were divided into 3 groups, namely, group I-1, group I-2 and group I-3. Among them, 8 batches, including S2, S4, S5, S10, S11, S16, S18 and S19, were divided into group I-1, 9 batches, including S3, S6, S7, S8, S9, S13, S14, S15 and S17, were divided into group I-2, and 4 batches, including S1, S12, S20 and S21, were divided into group I-3. Cluster analysis and principal component analysis were performed on group I-1, group I-2 and group I-3, respectively.

[0108] The data of the samples of the medicinal materials of group I-1 of the Herba Lycopodii were imported into the "Chinese Herbal Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format. The sample spectrum of Herba Lycopodii No. 29 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5 min. The fingerprint spectrum of Herba Lycopodii No. 29 and the reference fingerprint spectrum (S2) were obtained by multi-point correction. Figure 6 ) and performed similarity analysis (Table 5).

[0109] Table 5 Similarity of 8 batches of yellow flower water lotus medicinal materials samples in group Ⅰ-1

[0110] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 3 S8 0.946 14 S4 0.879 4 S7 0.920 23 S3 0.879 6 S6 0.963 26 S2 0.892 13 S5 0.873 29 S1 0.794

[0111] from Figure 6 It can be seen from the figure that the fingerprints of the yellow flower water lotus medicinal materials samples in group Ⅰ-1 matched 21 common peaks. From Table 5, it can be seen that the similarity between the fingerprints of the yellow flower water lotus medicinal materials samples S2-S28 in group Ⅰ-1 and the control fingerprints is 0.794-0.963. Among them, except for the S1 sample in group Ⅰ-1 with a similarity of 0.794, the similarities of the other samples are all above 0.87. Figure 6 The fingerprints of the samples showed that the samples of Herba Lycopodii with serial numbers 3, 4, 6, 13, 14, 23, 26 and 29 in group Ⅰ-1 had good similarity.

[0112] SPSS 20.0 statistical analysis software was used to perform principal component analysis on the common peaks of group I-1. The scree plot was used to assist in determining the number of factors to be extracted. The principal components were extracted with the eigenvalue > 1 as the standard, and the eigenvalues ​​and variance contribution rates of the principal components were obtained. The scree plot of group I-1 is shown in Figure 7As shown, the eigenvalues ​​and variance contribution rates of the principal components are extracted as shown in Table 6, and the common peak component matrix of the principal components is obtained as shown in Table 7.

[0113] Table 6 Principal component eigenvalues ​​and variance contribution rates of 8 batches of yellow flower water lily drug samples in group Ⅰ-1

[0114] principal component Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%) 1 6.978 33.23 33.230 2 5.003 23.825 57.055 3 2.901 13.815 70.869 4 2.593 12.346 83.215 5 2.114 10.066 93.281

[0115] Table 7. The main component common peak component matrix of group I-1

[0116]

[0117] from Figure 7 It can be seen that 5 principal components were selected in group Ⅰ-1 with eigenvalue>1 as the standard; from Table 6, it can be seen that the cumulative variance contribution rate is 93.281%; from Table 7, it can be seen that principal component 1 of group Ⅰ-1 is negatively correlated with peaks 12 and 15, and positively correlated with peaks 6, 11, and 19; principal component 2 is positively correlated with peaks 9, 17, and 20.

[0118] The sample data of group I-2 of the medicinal material of Herba Lycoris Radiatae were imported into the "Chinese Herbal Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format. The sample spectrum of Herba Lycoris Radiatae No. 28 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5 min. The fingerprint spectrum of Herba Lycoris Radiatae and the reference fingerprint spectrum ( Figure 8 ) and performed similarity analysis (Table 8).

[0119] Table 8 Similarity of 9 batches of yellow flower water lotus medicinal materials samples in group Ⅰ-2

[0120] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 5 S9 0.940 19 S4 0.805 7 S8 0.958 20 S3 0.773 10 S7 0.913 21 S2 0.906 11 S6 0.934 28 S1 0.959 18 S5 0.947

[0121] from Figure 8 It can be seen from the figure that the fingerprints of the yellow flower water lotus medicinal materials samples in group Ⅰ-2 matched 20 common peaks. From Table 8, it can be seen that the similarity between the fingerprints of the yellow flower water lotus medicinal materials samples S2-S28 in group Ⅰ-2 and the control fingerprints is 0.773-0.959. Among them, except for the similarities of S3 and S4 samples in group Ⅰ-2, which are 0.773 and 0.805, the similarities of the other samples are all above 0.90. Figure 8 The fingerprints of the samples showed that the similarity of the samples of Herba Lycopodii with serial numbers 5, 7, 10, 11, 18, 19, 20, 21 and 28 in group Ⅰ-2 was good.

[0122] SPSS 20.0 statistical analysis software was used to perform principal component analysis on the common peaks of group I-2. The scree plot was used to assist in determining the number of factors to be extracted. The principal components were extracted with the eigenvalue > 1 as the standard, and the eigenvalues ​​and variance contribution rates of the principal components were obtained. The scree plot of group I-2 is shown in Fig. 9 As shown, the eigenvalues ​​and variance contribution rates of the principal components are extracted as shown in Table 9, and the common peak component matrix of the principal components is obtained as shown in Table 10.

[0123] Table 9 Principal component eigenvalues ​​and variance contribution rates of 9 batches of yellow flower water lily drug samples in group Ⅰ-2

[0124] principal component Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%) 1 7.441 37.204 37.204 2 4.022 20.111 57.314 3 3.441 17.204 74.518 4 2.967 14.836 89.355

[0125] Table 10 The main component common peak component matrix of group I-2

[0126]

[0127]

[0128] from Fig. 9 It can be seen that 4 principal components were selected in group I-2 with eigenvalue>1 as the standard; from Table 9, it can be seen that the cumulative variance contribution rate is 89.355%; from Table 10, it can be seen that the principal component 1 of group I-2 is negatively correlated with peaks 3, 4, 5, and 7, and positively correlated with peaks 1, 2, 15, 18, and 20; the principal component 2 is negatively correlated with peaks 8 and 14, and positively correlated with peaks 10, 12, and 13.

[0129] The sample data of the medicinal materials of group I-3 of the yellow flower water lotus were imported into the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format. The sample spectrum of the yellow flower water lotus No. 31 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5min. The fingerprint spectrum of the yellow flower water lotus and the reference fingerprint spectrum ( Fig.10 ) and performed similarity analysis (Table 11).

[0130] Table 11 Similarity of 4 batches of yellow flower water lotus medicinal materials samples in group Ⅰ-3

[0131] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 1 S4 0.960 12 S2 0.910 2 S3 0.975 31 S1 0.797

[0132] from Fig.10 It can be seen from the figure that the fingerprints of the yellow flower water lotus medicinal material samples of group I-3 matched 32 common peaks. From Table 11, it can be seen that the similarity between the fingerprints of the yellow flower water lotus medicinal material samples S2-S4 in group I-3 and the control fingerprints is 0.797-0.975. Among them, except for the S1 sample in group I-3 with a similarity of 0.797, the similarities of the other samples are all above 0.91. Fig.10The fingerprints of the samples showed that the samples of Herba Lycopodii with serial numbers 1, 2, 12 and 31 in group Ⅰ-3 had good similarity.

[0133] In the principal component analysis of group I-3, the scree plot was used to assist in determining the number of factors to be extracted, and the principal components were extracted with the eigenvalue>1 as the standard to obtain the eigenvalue and variance contribution rate of the principal components. The scree plot of group I-3 is shown in Fig.11 As shown, the eigenvalues ​​and variance contribution rates of the principal components are extracted as shown in Table 12, and the common peak component matrix of the principal components is obtained as shown in Table 13.

[0134] Table 12 Principal component eigenvalues ​​and variance contribution rates of four batches of yellow flower water lily drug samples in group Ⅰ-3

[0135] principal component Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%) 1 15.960 49.875 49.875 2 11.154 34.857 84.732 3 4.886 15.268 100

[0136] Table 13 Main component common peak component matrix of group I-3

[0137]

[0138] from Fig.11 It can be seen that three principal components were selected from group I-3 with eigenvalue>1 as the standard; from Table 12, it can be seen that the cumulative variance contribution rate is 100.00%; from Table 13, it can be seen that the principal component 1 of group I-3 is negatively correlated with peaks 2, 9, 10, 18, 19, 20, and 22, and is positively correlated with peaks 6, 8, 12, 13, 14, 21, 25, 28, 30, and 32; the principal component 2 is negatively correlated with peaks 3, 4, 7, 11, 24, and 26, and is positively correlated with peaks 1, 5, 15, 16, 17, 27, and 31.

[0139] The data of the samples of the medicinal materials of group II of Rhizoma Coptidis were imported into the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format. The sample spectrum of Rhizoma Coptidis No. 30 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5min. The fingerprint spectrum of Rhizoma Coptidis No. 30 was processed by multi-point correction to obtain the fingerprint spectrum of Rhizoma Coptidis No. 30 and the reference fingerprint spectrum ( Fig.12 ) and performed similarity analysis (Table 14).

[0140] Table 14 Similarity of 6 batches of yellow flower water lotus medicinal materials samples in group II

[0141] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 15 S6 0.931 25 S3 0.881 16 S5 0.823 27 S2 0.959 22 S4 0.953 30 S1 0.880

[0142] from Fig.12It can be seen that 33 common peaks were matched in Group II; as can be seen from Table 14, the similarity between the fingerprints of S2-S21 Herba Lycoris Radiatae medicinal materials samples and the control fingerprints is 0.823-0.959, and the similarity of the samples is above 0.82. Combined with the fingerprints of the superimposed chromatograms of Group II, it can be seen that the samples from different sources in Group II have good overall similarity.

[0143] SPSS 20.0 statistical analysis software was used to perform principal component analysis on the common peaks of group II. The scree plot was used to assist in determining the number of factors to be extracted. The principal components were extracted with the eigenvalue > 1 as the standard to obtain the eigenvalue and variance contribution rate of the principal components. The scree plot of group II is shown in Fig.13 As shown, the eigenvalues ​​and variance contribution rates of the principal components are extracted as shown in Table 15, and the common peak component matrix of the principal components is obtained as shown in Table 16.

[0144] Table 15 Principal component eigenvalues ​​and variance contribution rates of 6 batches of yellow flower water lily drug samples in group II

[0145] principal component Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%) 1 14.72 44.607 44.607 2 7.808 23.662 68.269 3 4.633 14.041 82.309 4 3.484 10.557 92.866 5 2.354 7.134 100

[0146] Table 16 The main component common peak component matrix of group II

[0147]

[0148]

[0149] from Fig.13 It can be seen that 5 principal components were selected in Group II with the eigenvalue>1 as the standard; from Table 15, it can be seen that the cumulative variance contribution rate is 100.00%; from Table 16, it can be seen that the principal component 1 of Group II is negatively correlated with peaks 1, 8, 9, 11, 13, 16, 23, and 26, and positively correlated with peaks 5, 14, 15, 18, 19, 20, 21, 22, 28, 32, and 33; principal component 2 is negatively correlated with peaks 3, 4, and 7, and positively correlated with peaks 6, 17, 24, 29, and 31. As the positive correlation increases, the corresponding principal component increases, and as the negative correlation increases, the corresponding principal component decreases.

[0150] The data of the samples of the medicinal materials of Group III of Rhizoma Coptidis were imported into the "Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (2004 Edition)" in AIA file format. The sample spectrum of Rhizoma Coptidis No. 24 was used as the reference spectrum (S1). The average method was used, and the time window width was set to 0.5min. The fingerprint spectrum of Rhizoma Coptidis No. 24 was processed by multi-point correction to obtain the fingerprint spectrum of Rhizoma Coptidis No. 24 and the reference fingerprint spectrum ( Fig.14 ) and performed similarity analysis (Table 17).

[0151] Table 17 Similarity of 4 batches of yellow flower water lotus medicinal materials samples in group III

[0152] Medicinal material serial number batch number Similarity Medicinal material serial number batch number Similarity 8 S4 0.945 17 S2 0.893 9 S3 0.955 24 S1 0.980

[0153] from Fig.14 It can be seen that 49 common peaks were matched in Group III; from Table 17, it can be seen that the similarity between the fingerprint spectra of S2-S21 Herba Lycoris Radiatae medicinal materials samples and the control fingerprint spectra is 0.893-0.980, and the similarity of the samples is above 0.89. Combined with the fingerprint spectra of the superimposed chromatogram of Group III, it shows that the overall similarity of samples from different sources in Group III is high, and these Herba Lycoris Radiatae medicinal materials have a certain correlation.

[0154] SPSS 20.0 statistical analysis software was used to perform principal component analysis on the common peaks of group III. The scree plot was used to assist in determining the number of factors to be extracted. The principal components were extracted with the eigenvalue > 1 as the standard to obtain the eigenvalue and variance contribution rate of the principal components. The scree plot of group III is shown in Fig.15 As shown, the eigenvalues ​​and variance contribution rates of the principal components are extracted as shown in Table 18, and the common peak component matrix of the principal components is obtained as shown in Table 19.

[0155] Table 18 Principal component eigenvalues ​​and variance contribution rates of four batches of yellow flower water lily drug samples in group III

[0156] principal component Eigenvalue Variance contribution rate (%) Cumulative variance contribution rate (%) 1 25.788 52.628 52.628 2 13.658 27.873 80.501 3 9.554 19.499 100

[0157] Table 19 The main component common peak component matrix of group III

[0158]

[0159] from Fig.15 It can be seen that three principal components were selected in group III with eigenvalue>1 as the standard; from Table 18, it can be seen that the cumulative variance contribution rate is 100.00%; from Table 19, it can be seen that the principal component 1 of group III is negatively correlated with peaks 4, 5, 6, 8, 12, 16, 17, 23, 25, 26, 27, 37, and 40, and positively correlated with peaks 13, 14, 21, 28, 29, 30, 32, 36, 41, 42, 43, 44, 46, 47, 48, and 49. Principal component 2 is negatively correlated with peaks 24 and 45, and positively correlated with peaks 1, 2, 3, 7, 9, 15, 19, 31, 34, and 35. Principal component 3 is negatively correlated with peaks 10, 18, and 20, and positively correlated with peaks 11, 22, and 33.

[0160] Through the above experiments and experimental results, 31 batches of samples were divided into 5 groups, namely Group I-1, Group I-2, Group I-3, Group II and Group III according to cluster analysis. The results are shown in Table 20.

[0161] Table 205 Common peaks and sources of samples

[0162] Group batch Common peak source Group Ⅰ-1 8 21 Xianzhu, Guilin, Baoshan Group I-2 9 20 Xianzhu, Guilin, Baoshan, Honghe Prefecture, Laibin Group I-3 4 32 Xianzhu, Hezhou Group II 6 33 Guilin, Liuzhou, Hechi Group III 4 49 Baise City, Hezhou City, Baoshan City

[0163] As can be seen from Table 21, there is some overlap in the origins of the samples of the medicinal material of Rhizoma Coptidis among each group, indicating that the origin specificity of Rhizoma Coptidis is not obvious, and the authenticity of the medicinal material is not obvious; while the relative peak areas of the common peaks among the groups are quite different, which may be related to the different sources of the medicinal materials in each group, the large geographical space span, the growth environment, altitude, climate, soil, planting method, picking time, storage time and other factors.

[0164] Through principal component analysis and component matrix calculation, 5 groups of principal component data were obtained, which can express more than 89% of the chemical composition information of all samples. The common peaks that have a significant impact on the first principal component were obtained. Combined with the fingerprint spectrum, the characteristic peaks of each group were found. The principal component information of each group is shown in Table 21.

[0165] Table 21 Cumulative variance contribution rate and characteristic peaks of principal components of yellow water lily

[0166]

[0167]

[0168] As can be seen from Table 21, the number of main components, peaks that have a significant impact on the first principal component and characteristic peaks were summarized through 31 batches of yellow water lily medicinal materials samples, and they were used as the basis for identification of yellow water lily grouping. Applying them in the market can provide a more objective and effective solution for more accurate judgment of the authenticity of yellow water lily medicinal materials, and at the same time provide a theoretical and practical basis for the overall evaluation of the quality of yellow water lily.

[0169] Example 2

[0170] A method for determining the content of Siberian polygala A5 and 3,6'-dicerinoyl sucrose in Polygala lutea, the specific steps are as follows:

[0171] S1, the same as step S1 of embodiment 1;

[0172] S2, weigh Siberian Polygala A5 and add methanol to prepare 861.5814 μg / mL Siberian Polygala A5 solution, weigh 3,6'-dicansinoyl sucrose and add methanol to prepare 815.1388 μg / mL 3,6'-dicansinoyl sucrose solution, accurately pipette 0.04, 0.2, 0.4, 1, and 2 mL of the Siberian Polygala A5 solution and 3,6'-dicansinoyl sucrose solution into a 10 mL volumetric flask, add 75% methanol to the mark, shake well, and prepare a mixed series of solutions for the standard curve;

[0173] S3, collecting HPLC chromatograms of each test solution and the mixed series solution using a high performance liquid chromatograph under the conditions and equipment of step S2 of Example 1;

[0174] S4, according to the step S2 and step S3, the HPLC chromatographic data of the mixed series solution are measured and collected, with the injection concentration (ug / mL) as the horizontal coordinate and the peak area as the vertical coordinate, and the regression equation of Siberian Polygala A5 is y=13.6317x+9.0356, r=0.9999, and the regression equation of 3,6'-dicerinoyl sucrose is y=38.1409x+21.8669, r=0.9999; when the injection amount of Siberian Polygala A5 reference substance is between 0.03261 and 1.63028 ug range, when the injection amount of 3,6'-dicorinoylsucrose reference substance is in the range of 0.03446 to 1.72316 ug, the injection amount and the peak area show a good linear relationship; the HPLC chromatographic data of each test solution are collected according to step S2 of Example 1; the injection concentration and peak area in the HPLC chromatographic data of the test solution are substituted into the Siberian Polygala sugar A5 and 3,6'-dicorinoylsucrose regression equation to calculate the Siberian Polygala sugar A5 and 3,6'-dicorinoylsucrose contents in the Huanghua Shuilian medicinal material sample.

[0175] Take the sample No. 18 of the yellow flower water lily and conduct specificity test, precision test, stability test, repeatability test, accuracy test and durability test according to the above method, as follows:

[0176] (1) Specificity test

[0177] Accurately pipette 10 μL of the control solution and the test solution respectively, inject them into the liquid chromatograph for determination, and make a solvent blank at the same time.

[0178] HPLC chromatograms of reagent blank (A), control solution (B) and test solution (C) are shown in Fig.16 As shown, in which 1 is the peak of Siberian Polygala sugar A5, and 2 is the peak of 3,6'-dicerinoyl sucrose. Fig.16 It can be seen that the Siberian Polygala sugar A5 peak and 3,6'-dicorinoyl sucrose peak in the chromatogram of the test solution are well separated from other impurity peaks, with a separation degree greater than 1.5; the purity analysis of the diode array detector shows that the purity of the Siberian Polygala sugar A5 peak and 3,6'-dicorinoyl sucrose peak are both greater than 0.999, and are both greater than their single point thresholds, indicating that the chromatographic peak has a high purity; the reagent blank has no interference. In summary, the method for determining the content of Siberian Polygala sugar A5 and 3,6'-dicorinoyl sucrose in the Huanghua Daoshuilian medicinal material sample has good specificity.

[0179] (2) Precision test

[0180] Take the same mixed reference solution and inject it 6 times continuously according to step S2 of Example 1, record the peak areas of Siberian polygala sugar A5 and 3,6'-dicerinoyl sucrose in the mixed reference solution, and calculate the RSD values.

[0181] The determined Siberian Polygala sugar A5 RSD = 0.3% (n = 6), 3,6'-dicansinoyl sucrose RSD = 0.3% (n = 6). This indicates that the method has good precision in determining the contents of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose in the samples of Huanghua Shuilian medicinal materials.

[0182] (3) Stability test

[0183] Take the Yellow Water Lily sample No. 18, prepare a test solution according to step S1 of Example 1, and inject the sample at 0, 6, 12, 18, and 24 hours according to step S2 of Example 1, record the peak areas of Siberian Polygala sugar A5 and 3,6'-dicerinoyl sucrose of the test solution, and calculate the RSD values.

[0184] The RSD of Siberian Polygala sugar A5 measured within 24 hours was 2.4% (n=5), and the RSD of 3,6'-dicerinoyl sucrose was 0.3% (n=5), indicating that the sample solution of Polygala xanthophylla had good stability within 24 hours.

[0185] (4) Repeatability test

[0186] Take the Yellow Water Lily sample No. 18, prepare 6 test solutions according to step S1, and sample and collect according to step S2 respectively to obtain HPLC chromatograms of the 6 test solutions, and calculate the RSD values ​​of Siberian Polygala sugar A5 and 3,6'-dicerinoyl sucrose of the 6 test solutions.

[0187] The RSD of Siberian Polygala A5 in 6 samples of the yellow water lotus was 2.7% (n=6), and the RSD of 3,6'-dicansinoyl sucrose was 0.9% (n=6). This indicates that the method used to determine the content of Siberian Polygala A5 and 3,6'-dicansinoyl sucrose in the yellow water lotus medicinal material samples has good repeatability.

[0188] (5) Accuracy test

[0189] Take about 0.5 g of powder of sample No. 18 of yellow water lily with known content, a total of 6 portions, accurately weighed respectively, placed in stoppered conical flasks, accurately added with 0.283 mL of the above-mentioned Siberian Polygala A5 reference substance stock solution and 0.126 mL of 3,6'-dicorinoyl sucrose reference substance stock solution, prepare 6 test sample solutions according to step 1 of the example, determine the contents of Siberian Polygala A5 and 3,6'-dicorinoyl sucrose, and calculate the recovery rate.

[0190] The average recovery rate of Siberian Polygala sugar A5 was 100.05%, RSD = 2.0% (n = 6), and the average recovery rate of 3,6'-dicansinoyl sucrose was 98.46%, RSD = 1.1% (n = 6), indicating that the method used to determine the content of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose in yellow flower water lotus medicinal material samples has high accuracy.

[0191] (6) Durability test

[0192] InfinityLab Poroshell 120EC-C18 column (4μm, 4.6mm×250mm) and HPLCcolumn Shim-pack GIS C18 column (4μm, 4.6mm×250mm) were used to determine the contents of sibirica polygala A5 and 3,6'-dicerinoylsucrose in the No. 18 yellow flower water lotus sample, and the RAD value was calculated.

[0193] The average content of Siberian Polygala sugar A5 measured by two chromatographic columns was 0.048%, RAD = 0.9% (n = 2), and the average content of 3,6'-dicansinoyl sucrose was 0.023%, RAD = 1.0% (n = 2). This shows that the method for determining the content of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose in the medicinal material samples of Huanghua Shuilian has good durability for chromatographic columns of different brands, indicating that the method has strong applicability to different chromatographic columns.

[0194] The contents of Siberian polygala sugar A5 and 3,6'-dicerinoylsucrose in 31 batches of Polygala lutea medicinal materials samples were determined by the above method, and the results are shown in Table 22.

[0195] Table 2231 Contents of Polygala sibirica A5 and 3,6'-dicerinoyl sucrose in Batch 1

[0196]

[0197] As can be seen from Table 22, the Siberian Polygala A5 content of the 31 batches of Yellow Flowering Water Lotus medicinal materials samples tested in this experiment ranged from 0.0304% to 0.1376%, and the 3,6'-dicerinoylsucrose content ranged from 0.0001% to 0.1560%.

[0198] A method for determining the contents of Siberian polygala A5 and 3,6'-dicerinoylsucrose in Polygala xanthophylla was established, which provided a basis for the overall evaluation of the quality of Polygala xanthophylla and laid the foundation for its quality control.

[0199] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. A method for constructing a standard HPLC fingerprint of Herba Lycoris Radiatae, characterized in that: The method comprises the following steps: S1, weigh n batches of water lily powder, add methanol, ultrasonically treat, make up the weight loss with methanol, filter and take the filtrate, and obtain n batches of test solutions, where 31≥n>1; S2, using a high performance liquid chromatograph to collect HPLC chromatographic data of each test solution, the high performance liquid chromatograph uses an InfinityLab Poroshell 120EC-C18 chromatographic column with a length of 4.6 mm, an inner diameter of 250 mm, and a filler particle diameter of 4 μm for the experiment, and the injection volume of the high performance liquid chromatograph is set to 10 μL, the column temperature of the chromatographic column is 35° C., the detection wavelength is 250 nm, the mobile phase and the mobile phase elution gradient, wherein the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is 0.05% phosphoric acid-methanol, the mobile phase B is 0.05% phosphoric acid aqueous solution, and the mobile phase elution gradient is as follows: First elution gradient: from 0 to 16 min, mobile phase A increased from 19% to 23%, mobile phase B decreased from 81% to 77%, flow rate was 0.7 mL / min; Second elution gradient: from 16 to 40 min, mobile phase A increased from 23% to 38%, mobile phase B decreased from 77% to 62%, flow rate was 0.7 mL / min; The third elution gradient: at 40.01-70 min, mobile phase A was 38%, mobile phase B was 62%, and the flow rate was 0.4 mL / min; Fourth elution gradient: at 70.01-100 min, mobile phase A was increased from 38% to 43%, mobile phase B was decreased from 62% to 57%, and the flow rate was 0.7 mL / min; Fifth elution gradient: at 100-130 min, mobile phase A was 43%, mobile phase B was 57%, and the flow rate was 0.7 mL / min; Sixth elution gradient: from 130 to 169 min, mobile phase A increased from 43% to 80%, mobile phase B decreased from 57% to 20%, and the flow rate was 0.7 mL / min; Seventh elution gradient: at 169-170.01 min, mobile phase A was reduced from 80% to 19%, mobile phase B was increased from 20% to 81%, and the flow rate was 0.7 mL / min; The eighth elution gradient: at 170.01-175 min, mobile phase A was 19%, mobile phase B was 81%, and the flow rate was 0.7 mL / min; S3, comparing and analyzing the HPLC chromatographic data, obtaining a standard fingerprint of the HPLC of the yellow flower water lotus consisting of 10 common peaks, wherein the peak 5 is the main active ingredient of the yellow flower water lotus, Siberian polygala sugar A5, and the peak 7 is the main active ingredient of the yellow flower water lotus, 3,6'-diceidoyl sucrose; S4, performing cluster analysis and principal component analysis according to the HPLC chromatographic data, classifying the n batches of Pleurotus eryngii to obtain m groups of Pleurotus eryngii with similarities in the range of 0.77-1, wherein m<6.

2. The method for constructing the HPLC standard fingerprint of Herba Lycoris Radiatae according to claim 1, characterized in that: In step S1, the methanol is 20 mL of 75% methanol.

3. The method for constructing the HPLC standard fingerprint of Herba Lycoris Radiatae according to claim 1, characterized in that: In step S4, the specific steps of performing cluster analysis and principal component analysis on the HPLC chromatographic data, classifying the n batches of water lilies, and obtaining m groups of water lilies with similarities in the range of 0.77-1 are: (1) The HPLC chromatogram data is imported into a similarity evaluation system to obtain a similarity range of n batches of yellow water lilies. If the similarity is not within the range of 0.77-1, cluster analysis is performed on the n batches of yellow water lilies and the groups are divided into p groups, wherein p≤3; (2) The HPLC chromatographic data of each group were respectively imported into a similarity evaluation system until the similarity was within the range of 0.77-1, thereby obtaining m groups of water lotus. The statistical analysis software was then used to perform principal component analysis on the common peaks in each group. The principal components and cumulative variance contributions were screened out with the eigenvalue>1 as the standard, and the peaks and characteristic peaks that had a significant impact on the first principal component were obtained, wherein m<6.

4. A method for detecting medicinal materials using the HPLC standard fingerprint of Herba Lycoris constructed by the method described in any one of claims 1 to 3, characterized in that: The method is specifically as follows: (1) Obtaining HPLC spectrum data of the medicinal material sample to be tested according to the method described in claims S1 and S2; (2) When the HPLC spectrum data of the medicinal material sample to be tested has the same common peak as the HPLC standard fingerprint spectrum data of Herba Lycoris Radiatae, it is determined that the medicinal material sample to be tested is Herba Lycoris Radiatae.

5. A method for determining the content of Siberian polygala A5 and 3,6'-dicerinoyl sucrose in Polygala lutea, characterized in that: The specific steps of the method are as follows: S1, weigh dry water lily powder, add 75% methanol solution, ultrasonically treat, make up the weight loss with 75% methanol solution, filter and take the filtrate to obtain the test solution; S2, weigh Siberian Polygala A5 and add it to 75% methanol solution to prepare 861.5814 μg / mL Siberian Polygala A5 solution, weigh 3,6'-dicorinoyl sucrose and add it to 75% methanol solution to prepare 815.1388 μg / mL 3,6'-dicorinoyl sucrose solution, put 0.04, 0.2, 0.4, 1, and 2 mL of the Siberian Polygala A5 solution and 3,6'-dicorinoyl sucrose solution into a 10 mL volumetric flask, add 75% methanol to the scale line of the 10 mL volumetric flask, shake well, and prepare a mixed series of solutions for the standard curve; S3, the test solution and the mixed series solution are respectively collected by HPLC chromatogram using a chromatograph, and an InfinityLab Poroshell 120EC-C18 chromatographic column with a length of 4.6 mm, an inner diameter of 250 mm and a filler particle diameter of 4 μm is used, and the injection volume of the chromatograph is set to 10 μL, the column temperature of the chromatographic column is set to 35° C., the detection wavelength is set to 250 nm and the elution gradient of the mobile phase, wherein the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is 0.05% phosphoric acid-methanol, and the mobile phase B is 0.05% phosphoric acid aqueous solution; the elution gradient of the mobile phase is specifically as follows: First elution gradient: from 0 to 16 min, mobile phase A increased from 19% to 23%, mobile phase B decreased from 81% to 77%, flow rate was 0.7 mL / min; Second elution gradient: from 16 to 40 min, mobile phase A increased from 23% to 38%, mobile phase B decreased from 77% to 62%, flow rate was 0.7 mL / min; The third elution gradient: at 40.01-70 min, mobile phase A was 38%, mobile phase B was 62%, and the flow rate was 0.4 mL / min; Fourth elution gradient: at 70.01-100 min, mobile phase A was increased from 38% to 43%, mobile phase B was decreased from 62% to 57%, and the flow rate was 0.7 mL / min; Fifth elution gradient: at 100-130 min, mobile phase A was 43%, mobile phase B was 57%, and the flow rate was 0.7 mL / min; Sixth elution gradient: from 130 to 169 min, mobile phase A increased from 43% to 80%, mobile phase B decreased from 57% to 20%, and the flow rate was 0.7 mL / min; Seventh elution gradient: at 169-170.01 min, mobile phase A was reduced from 80% to 19%, mobile phase B was increased from 20% to 81%, and the flow rate was 0.7 mL / min; The eighth elution gradient: at 170.01-175 min, mobile phase A was 19%, mobile phase B was 81%, and the flow rate was 0.7 mL / min; S4. According to the steps S2 and S3, the HPLC chromatograms of the mixed series solutions are determined and collected, and the regression equations of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose are obtained by using the injection concentration and peak area. According to the steps S1 and S3, the HPLC chromatographic data of 31 batches of yellow water lilies are determined and collected, and the data are substituted into the regression equations to obtain the contents of Siberian Polygala sugar A5 and 3,6'-dicansinoyl sucrose in the 31 batches of yellow water lilies, wherein the regression equation of Siberian Polygala sugar A5 is y=13.6317x+9.035, and the regression equation of 3,6'-dicansinoyl sucrose is y=38.1409x+21.8669.

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