Method and system for determining contents of multiple key quality attribute indexes of rheum tanguticum medicinal material

Abstract

The invention discloses a method and system for determining the content of multiple key quality attribute indexes of a rheum tanguticum medicinal material, and belongs to the technical field of traditional Chinese medicine detection.The method mainly comprises the steps that the correlation among the key quality attribute indexes in the rheum tanguticum medicinal material is analyzed through a principal component analysis method and a Pearson's correlation coefficient method; and establishing a near infrared spectrum prediction model of each key quality attribute index, and optimizing modeling conditions of the near infrared spectrum prediction model to obtain a quantitative model of each key quality attribute index. According to the method, a plurality of key quality attribute indexes of the rheum tanguticum medicinal material are comprehensively evaluated by utilizing principal component analysis, correlation analysis and the like, and modeling conditions such as a modeling interval and a pretreatment method are optimized and screened by virtue of a near infrared spectrum analysis technology; a near-infrared quantitative detection model of the multiple key quality attribute indexes of the rheum tanguticum medicinal material is constructed, synchronous quantitative analysis of the multiple key quality attribute indexes is achieved, and scientific basis and technical support are provided for standardizing medicinal material production and patent medicine preparation quality control.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine detection, and particularly to a method and system for determining the contents of multiple key quality attribute indexes of Rheum tanguticum Maxim. ex Balf. Background Art

[0002] Traditional Chinese medicines have multiple sources, complex components, and many phenomena such as the same substance with different names, which make the quality evaluation of Chinese medicinal materials more difficult than that of chemical drugs. With the continuous improvement of analytical techniques, the research on the quality evaluation of traditional Chinese medicines has developed rapidly in recent years. The current quality evaluation mode of traditional Chinese medicines in China mainly draws on the quality control of chemical drugs in other countries. The content of the quality standard of raw medicinal materials includes multiple aspects such as name, origin, medicinal part, identification, inspection, extract, content determination, etc. On the basis of conventional detection methods such as thin-layer chromatography and high-performance liquid chromatography, the application of new methods has been further expanded. Liquid chromatography-tandem mass spectrometry, molecular biology detection techniques, etc. are used for the quality evaluation and control of traditional Chinese medicines, strengthening the guarantee of the safety and effectiveness of drug use.

[0003] Traditional conventional detection methods such as chromatography usually have problems such as long analysis and detection time, easy loss of detection samples, and chemical reagent pollution. Due to the characteristics of simple operation, short analysis time, and low cost, near-infrared spectroscopy technology has been widely used in the quality evaluation and control of traditional Chinese medicines in recent years, becoming an important means to obtain information on complex chemical components in traditional Chinese medicines, capable of comprehensively reflecting the inherent chemical characteristics of Chinese medicinal materials and realizing the transformation of grade quality control from single control to relevant control. At present, many research works have effectively evaluated the quality of medicinal materials using near-infrared spectroscopy technology. Near-infrared spectroscopy technology combined with chemometrics has good application prospects in the research on the quality evaluation and control of traditional Chinese medicines.

[0004] Rheum tanguticum Maxim. ex Balf. is a perennial tall herb of the genus Rheum Linn. in the family Polygonaceae. Its dried roots and rhizomes are used as medicine and it is one of the three authentic rhubarb species included in the Pharmacopoeia of the People's Republic of China (2020 Edition). Modern pharmacological research shows that rhubarb has various biological activities such as laxative effect, anti-tumor, antibacterial, antioxidant, anti-inflammatory, etc., and has a long history of medication and extensive clinical applications. It is reported that the main pharmacological components of rhubarb herbs are anthraquinones. Among them, aloe-emodin, chrysophanol, emodin, rhein, and physcion are typical anthraquinone components and also the five specified index active components in the pharmacopoeia. In addition, anthrone components such as sennoside A and sennoside B are recognized as the laxative active components in rhubarb, and their laxative activity is stronger than that of anthraquinones and they are the specified index active components in the Japanese Pharmacopoeia. Previous research work on Rheum tanguticum mainly focused on physiological ecology, separation, extraction, purification of chemical components, and pharmacology and pharmacodynamics, etc., and the quality evaluation work of multiple key quality attribute indicators of Rheum tanguticum based on near-infrared spectroscopy technology has not been carried out. Summary of the Invention

[0005] The purpose of the present invention is to overcome the problems existing in the prior art and provide a method and system for determining the contents of multiple key quality attribute indicators of Rheum tanguticum herbs, so as to realize the synchronous quantitative analysis of multiple key quality attribute indicators.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, a method for determining the contents of multiple key quality attribute indicators of Rheum tanguticum herbs is provided. The method includes:

[0008] Analyze the correlation between each key quality attribute indicator in Rheum tanguticum herbs by using the principal component analysis method and the Pearson correlation coefficient method;

[0009] Establish a near-infrared spectroscopy prediction model for each key quality attribute indicator, and optimize the modeling conditions of the near-infrared spectroscopy prediction model to obtain a quantitative model for each key quality attribute indicator.

[0010] Preferably, the key quality attribute indicators include moisture, total ash, extractives, total anthraquinones, free anthraquinones, and sennosides.

[0011] Preferably, the principal component analysis method analyzes the correlation between each key quality attribute indicator in Rheum tanguticum herbs, including:

[0012] Analyze each quality evaluation indicator of Rheum tanguticum by using the principal component analysis method of the correlation matrix, and extract the first two principal components to judge the overall characteristics of each indicator of Rheum tanguticum.

[0013] Preferably, the overall characteristics of the various indicators of the Tangut rhubarb include:

[0014] Free anthraquinone, sennoside A and sennoside B were distributed in the first quadrant, indicating that the contents of these indicators were highly positively correlated with the quality of Rheum tanguticum; total ash and water content were located on the negative axis of the first principal component, indicating that the two were negatively correlated with the quality of Rheum tanguticum.

[0015] Preferably, the Pearson correlation coefficient method for analyzing the correlation between key quality attribute indicators in Tangut rhubarb medicinal materials includes:

[0016] Sennoside A was extremely significantly positively correlated with sennoside B, free anthraquinone was significantly positively correlated with sennoside A and water content, total anthraquinone was significantly positively correlated with extract, extract was significantly negatively correlated with water content and ash, and total anthraquinone was significantly negatively correlated with ash.

[0017] Preferably, the establishment of a near infrared spectroscopy prediction model for each key quality attribute indicator includes:

[0018] Take an appropriate amount of Tangut rhubarb sample and place it in a sample cup, flatten it, and compact it. During the collection process, the background interference of CO2 and water is deducted in real time. -1 The near infrared spectrum of the sample was collected under the following conditions, with a scanning range of 10000-4000cm -1 Each sample was measured 3 times, and the average spectrum of the 3 times was taken as the standard spectrum.

[0019] Preferably, the optimizing the modeling conditions of the near infrared spectrum prediction model comprises:

[0020] Optimize spectral preprocessing methods, modeling band selection, and sample set division.

[0021] Preferably, the sample set division includes:

[0022] The samples of the modeling set were divided according to the concentration gradient method, and the ratio of the correction set and the prediction set was optimized. The models corresponding to moisture, total ash and sennoside A selected a 3:1 sample set division, the model corresponding to sennoside B selected a 4:1 sample set division, the model corresponding to extract and free anthraquinone selected a 5:1 sample set division, and the model corresponding to total anthraquinone selected a 6:1 sample set division.

[0023] Preferably, the Mahalanobis distance and principal component analysis are used to remove outliers from the collected spectral data.

[0024] In a second aspect, a method for determining the content of multiple key quality attribute indicators of Tangut rhubarb medicinal material is provided, and the system comprises:

[0025] The index correlation analysis module is used to analyze the correlation between the key quality attribute indexes in Rheum tanguticum Maxim. ex Balf. by using the principal component analysis method and the Pearson correlation coefficient method;

[0026] The near-infrared spectrum prediction model establishment module is used to establish the near-infrared spectrum prediction models for the key quality attribute indexes;

[0027] The quantitative model establishment module is used to optimize the modeling conditions of the near-infrared spectrum prediction model to obtain the quantitative models for the key quality attribute indexes.

[0028] It should be further noted that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution without conflict.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] The present invention comprehensively evaluates multiple key quality attribute indexes of Rheum tanguticum Maxim. ex Balf. by using the principal component analysis method and the Pearson correlation coefficient method. On this basis, the near-infrared spectrum technology is combined with the chemometrics method, and by optimizing the modeling conditions such as the modeling interval, the pretreatment method, and the sample set division ratio, a synchronous quantitative detection model for the above evaluation indexes is established. During the model optimization process, the optimal modeling conditions for each detection index are different. After optimization, the Rp values of all models are greater than 0.90. The model evaluation indexes of sennoside A and sennoside B are the best, and their Rc and Rp values are both greater than 0.98, and the RPD values are both greater than 6. After external verification, the prediction rates of all detection indexes have reached more than 75%, and the prediction rates of moisture, extract, free anthraquinone, and total anthraquinone have reached more than 90%. The near-infrared spectrum technology combined with the chemometrics method can realize the rapid and accurate detection of multiple key quality attribute indexes in the quality of Rheum tanguticum Maxim. ex Balf., providing technical support for the quality evaluation and control of medicinal materials. Description of the Drawings

[0031] Figure 1 It is a flow chart of a method for determining the contents of multiple key quality attribute indexes of Rheum tanguticum Maxim. ex Balf. shown in an embodiment of the present invention;

[0032] Figure 2 It is an HPLC chromatographic separation diagram of anthraquinone reference substance shown in an embodiment of the present invention;

[0033] Figure 3 It is an HPLC chromatographic separation diagram of sennoside reference substance shown in an embodiment of the present invention;

[0034] Figure 4 It is the determination result of the contents of multiple indexes of Rheum tanguticum Maxim. ex Balf. shown in an embodiment of the present invention;

[0035] Figure 5 Principal component analysis distribution map and factor loading map of multiple key quality attribute indicators shown in the embodiments of the present invention;

[0036] Figure 6 Correlation analysis diagram of multiple key quality attribute indicators of Rheum tanguticum Maxim. ex Balf. shown in the embodiments of the present invention;

[0037] Figure 7 Average NIR spectrum diagram of Rheum tanguticum Maxim. ex Balf. shown in the embodiments of the present invention;

[0038] Figure 8 Outlier rejection result shown in the embodiments of the present invention;

[0039] Figure 9 VIP score diagram of NIR spectral data shown in the embodiments of the present invention;

[0040] Figure 10 Quantitative evaluation model of NIR spectrum of Rheum tanguticum Maxim. ex Balf. shown in the embodiments of the present invention. Detailed implementation manners

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present invention. The components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] It should be noted that the defects existing in the above prior art solutions are all the results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present application below for the above problems should be the contributions made by the inventors to the present application during the invention creation process, and should not be understood as the technical content known to those skilled in the art.

[0043] In view of the technical problems pointed out in the background art, the embodiments provided by the present invention are as follows:

[0044] Embodiment 1

[0045] Referring to Figure 1 , in an exemplary embodiment, a method for determining the contents of multiple key quality attribute indicators of Rheum tanguticum Maxim. ex Balf. medicinal materials is provided, and the method includes:

[0046] Using the principal component analysis method and the Pearson correlation coefficient method to analyze the correlation between each key quality attribute indicator in the Rheum tanguticum Maxim. ex Balf. medicinal materials;

[0047] Build near-infrared spectroscopy prediction models for each key quality attribute index, and optimize the modeling conditions of the near-infrared spectroscopy prediction models to obtain quantitative models for each key quality attribute index.

[0048] Specifically, the present invention first measures the contents of seven key quality attribute indexes such as moisture, ash, extract, total anthraquinones, free anthraquinones, sennoside A and sennoside B in Rheum tanguticum Maxim. ex Balf. respectively, and then comprehensively evaluates its quality by using methods such as principal component analysis method and Pearson correlation coefficient method. On this basis, a synchronous quantitative detection model for the above evaluation indexes is established by using near-infrared spectroscopy technology combined with chemometric methods and optimizing modeling conditions such as modeling interval, pretreatment method, and sample set division ratio. The rapid and accurate detection of multiple key quality attribute indexes in the quality of Rheum tanguticum Maxim. ex Balf. is realized by near-infrared spectroscopy technology combined with chemometric methods.

[0049] Example 2

[0050] Based on the inventive concept of Example 1, this example gives the specific process for the determination of the contents of multiple key quality attribute indexes, mainly including the following parts:

[0051] 1. Instruments and reagents

[0052] Instruments: Fourier transform infrared spectrometer (iS 50, Thermo Nicolet Corporation, near-infrared integrating sphere module), high performance liquid chromatograph (Infinity 1260, Agilent Technologies, USA), centrifuge (5810R, Eppendorf Corporation, Germany), electronic balance (ME104, 0.0001 g, Mettler Toledo, Switzerland), pure water machine (Molecular, China), Eclipse plus C 18 Chromatographic column (4.6×250 mm, 5 μm, Agilent Technologies, USA), pulverizer (Tianjin Test Co., Ltd., China), enclosed electric furnace (FL-2Y, Shanghai Lichen Bangxi Instrument Technology Co., Ltd., China).

[0053] Reagents: Methanol (chromatographic grade, USA), phosphoric acid (chromatographic grade, Aladdin Reagent (Shanghai) Co., Ltd., China), methanol (analytical grade, Fudun, China), hydrochloric acid (analytical grade, Xilong Science, China), chloroform (analytical grade, Xilong Science, China), sodium bicarbonate (analytical grade, Shanghai Aladdin Biochemical Technology Co., Ltd., China), aloe-emodin reference substance (Henan Standard Substance R & D Center, batch number: II20683, HPLC≥98%), chrysophanol reference substance (Henan Standard Substance R & D Center, batch number: MP00111, HPLC≥98%), rhein reference substance (Henan Standard Substance R & D Center, batch number: MP00560, HPLC≥98%), emodin reference substance (Henan Standard Substance R & D Center, batch number: 09H18Q, HPLC≥98%), physcion reference substance (Henan Standard Substance R & D Center, batch number: SA10924, HPLC≥98%), sennoside A (Sichuan Hengcheng Zhiyuan Biotechnology Co., Ltd., batch number: N2303165401, HPLC≥98%), sennoside B (Sichuan Hengcheng Zhiyuan Biotechnology Co., Ltd., batch number: N2303165508, HPLC≥98%).

[0054] 2. Sample source

[0055] The information of Rheum tanguticum samples used in the experiment is shown in Table 1, and the collection time is when the stems and leaves wither in October-November. After the roots of the collected samples are washed, air-dried, and coarsely crushed, they are passed through an 80-mesh sieve and placed in a desiccator for standby. The original plant samples were identified as Rheum tanguticum Maxim. ex Ralf. of the genus Rheum Linn. (Polygonaceae) by Researcher Li Yulin of the Northwest Institute of Plateau Biology, Chinese Academy of Sciences.

[0056] Table 1 Sample information table of Rh. tanguticum

[0057]

[0058] 3. Moisture determination

[0059] Take 2.0000±0.0001 g of the test sample powder, and refer to the second drying method in General Rule 0832 of the 2020 Pharmacopoeia for moisture determination, and calculate the moisture content (%) of the test sample.

[0060] 4. Total ash determination

[0061] Take 2.0000±0.0001 g of the test sample, and refer to Method 2302 in the 2020 Pharmacopoeia for the determination of total ash, and calculate the content (%) of total ash in the test sample.

[0062] 5. Determination of Extractives

[0063] Take about 2.0000 ± 0.0001 g of the test sample, and determine it by the hot extraction method under the method for the determination of water-soluble extractives (General Rule 2201). Calculate the content (%) of water-soluble extractives in the test sample based on the dried product.

[0064] 6. Determination of Content of Free Anthraquinones

[0065] 6.1 Preparation of Test Solution

[0066] Take 0.5000 ± 0.0001 g of the powder of this product, place it in a stoppered conical flask, accurately add 25 mL of methanol, heat under reflux for 1 h, cool, transfer it to a centrifuge tube, centrifuge at 4000 rpm for 15 min, then make up the volume with a volumetric flask, make up the weight loss with methanol, shake well, and take the subsequent filtrate, that is obtained.

[0067] 6.2 Chromatographic Conditions

[0068] Chromatographic column: Eclipse plus C 18 Column (4.6 × 250 mm, i.d. 5 μm); Mobile phase: A - 0.1% phosphoric acid solution, B - methanol; Isocratic elution: ratio 83:17; Flow rate: 1.0 mL / min; Detection wavelength: 254 nm; Column temperature: 30 °C; Injection volume: 10 μL.

[0069] 6.3 Preparation of Standard Curve

[0070] Dissolve five anthraquinone reference substances in methanol at appropriate concentrations to prepare a stock solution of reference substances, and then dilute it to prepare a mixed standard. The standard solution concentrations of aloe-emodin, rhein, emodin, chrysophanol, and physcion are 22 mg / L, 58 mg / L, 38 mg / L, 56 mg / L, and 14 mg / L respectively. Perform HPLC determination on the mixed standard solution with different injection volumes, and perform linear regression with the injection volume as the abscissa and the peak area as the ordinate. The HPLC separation results of the reference substances are shown in Figure 2 ., and the linear regression equations of the 5 free anthraquinones, aloe-emodin, rhein, emodin, chrysophanol, and physcion, are shown in Table 2. The linear relationship between the peak areas of the five compounds and the injection volumes is good, and R 2 is all higher than 0.9995.

[0071] Table 2 Linear regression equations of five anthraquinones

[0072]

[0073] 6.4, Methodology Tests

[0074] (1) Precision Test

[0075] Inject the same sample continuously for 5 times, and calculate the RSD of the retention time and content percentage of five anthraquinone compounds respectively.

[0076] (2) Repeatability Test

[0077] Extract the same sample 5 times, inject 10 μL of each extract separately, calculate the RSD values of the retention time and content percentage of five anthraquinone compounds, and judge the repeatability of the method.

[0078] (3) Stability Test

[0079] Inject 10 μL of the same sample at 0 h, 2 h, 8 h, 12 h, and 24 h after extraction respectively, calculate the RSD values of the retention time and content percentage of five anthraquinone compounds respectively, and judge the stability of the method.

[0080] (4) Standard Addition Recovery Test

[0081] Take the powder sample of Rheum tanguticum, add an appropriate amount of the reference substance mixture, refer to the method under 6.1, and perform HPLC determination. Calculate the retention time and content of five anthraquinone compounds respectively, and calculate their standard addition recovery rates and RSD values. The formula is as follows:

[0082] Standard addition recovery rate = (measured value of the sample content after standard addition - known sample content) / added amount × 100%

[0083] (5) Detection Limit

[0084] Inject the reference substance solution with 0.1 μL, 0.2 μL, 0.3 μL, 0.4 μL, and 0.5 μL for HPLC analysis, and take the content corresponding to a signal-to-noise ratio of 3:1 as the detection limit.

[0085] Table 3 shows the results of the precision, repeatability, stability, and standard addition recovery tests for aloe-emodin, rhein, emodin, chrysophanol, and physcion. As can be seen from the table, the RSD values of the precision, repeatability, and stability of this test are all less than 5%, and the standard addition recovery rates of the five anthraquinones are between 99% and 109%, indicating that the established analytical method is reliable. After detection, the detection limits of aloe-emodin, rhein, emodin, chrysophanol, and physcion are 1.98 ng, 2.90 ng, 2.28 ng, 2.80 ng, and 2.10 ng (S / N = 3 / 1) respectively.

[0086] Table 3 The results of precision, repeatability, stability and standard addition recovery tests

[0087]

[0088]

[0089] 7. Determination of total anthraquinone content

[0090] The total anthraquinone was extracted with reference to the 2020 edition of the Pharmacopoeia and determined with reference to the high performance liquid chromatography method (General Rule 0512). The chromatographic conditions were the same as those for the determination of free anthraquinone content.

[0091] 8. Determination of sennoside content

[0092] 8.1 Preparation of test solution

[0093] Take 0.5000 ± 0.0001 g of the powder of this product, place it in a conical flask, accurately add 50 mL of 0.1% sodium bicarbonate solution, ultrasonicate for 30 min, transfer it to a centrifuge tube and centrifuge at 4000 rpm for 15 min, then make up the volume with a volumetric flask, make up the weight lost with sodium bicarbonate solution, shake well, and take the subsequent filtrate, that is obtained.

[0094] 8.2 Chromatographic conditions

[0095] Chromatographic column: Eclipse plus C 18 Column (4.6 × 250 mm, i.d. 5 μm); Mobile phase: A - 0.1% phosphoric acid water, B - methanol; Gradient elution: 0 - 25 min, 30% - 55% B; Flow rate: 1.0 mL / min; Detection wavelength: 260 nm; Column temperature: 30 °C; Injection volume: 10 μL.

[0096] 8.3 Preparation of standard curve

[0097] Dissolve the reference substances of sennoside A and sennoside B in methanol at appropriate concentrations to prepare the stock solution of reference substances, and then dilute it to make a mixed standard. The concentrations of sennoside A and sennoside B in the mixed standard solution are 140 mg / L and 80 mg / L respectively. The mixed standard solution was determined by HPLC with different injection volumes, and a linear regression was performed with the injection volume as the abscissa and the peak area as the ordinate. The HPLC separation results of the reference substances are shown in Figure 3 , and the linear regression equations of sennoside A and sennoside B are shown in Table 4. It can be seen that the linear relationship between the peak area and the injection volume of the two compounds is good, and R 2 is higher than 0.9995 for both.

[0098] Table 4 Linear regression equation of sennoside

[0099]

[0100] 8.4, Methodological verification

[0101] The experimental method was the same as described in item 6.4. Table 5 shows the results of the precision, repeatability, stability, and standard addition recovery tests for sennoside A and sennoside B. As can be seen from the table, the RSD values of the precision, repeatability, and stability of this test were all less than 5%, and the standard addition recoveries of the two sennosides were between 98% and 102%, indicating that the established analytical method was reliable. After detection, the detection limits of sennoside A and sennoside B were 7.00 ng and 6.40 ng (S / N = 3 / 1), respectively.

[0102] Table 5 The results of precision, repeatability, stability and standard addition recovery tests

[0103]

[0104] 9. Near-infrared spectrum acquisition

[0105] Take an appropriate amount of Rheum tanguticum Maxim. ex Balf. sample and place it in a sample cup, flatten it, and compact it. During the acquisition process, the background interference of CO2 and water was deducted in real time. Under the conditions of 64 scans and a resolution of 8 cm -1 the near-infrared spectrum of the sample was acquired, and the scanning range was 10000 - 4000 cm -1 , and each sample was measured 3 times. The average spectrum of the 3 times was used as the standard spectrum.

[0106] 10. Model establishment and evaluation

[0107] 10.1. Outlier rejection

[0108] The Mahalanobis distance (MD) and principal component analysis (PCA) were used to reject outliers from the NIR spectra.

[0109] 10.2. Spectral preprocessing and modeling method optimization

[0110] Using scattering correction, derivative processing, and smoothing processing, a three-factor and three-level orthogonal experiment was designed. With the correlation coefficient value of the calibration set as the response value of the model, a model was established to select the optimal spectral pretreatment method. The factor level table of the orthogonal experiment is shown in Table 6. After selecting the optimal pretreatment method, partial least squares regression (PLS) was used to establish a model for comparison.

[0111] Table 6 Factor level table of orthogonal test for spectra pretreatment

[0112]

[0113] 10.3. Selection of modeling wavelength range

[0114] Models were established by selecting the modeling wavelength range using the full wavelength range, variable importance for the projection (VIP), and correlation coefficient method (CC) respectively. The optimal modeling range was selected using the correlation coefficient of prediction (Rp) as the index.

[0115] 10.4. Division of sample set

[0116] Six samples were reserved for external validation for each index, and the remaining samples were used as the modeling set. The samples in the modeling set were divided into the calibration set and the prediction set according to the concentration gradient method at the ratios of 3:1, 4:1, 5:1, and 6:1. The model effects under different sample set division ratios were compared.

[0117] 10.5. Evaluation of the model

[0118] The models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), correlation coefficient of calibration (Rc), correlation coefficient of prediction (Rp), and ratio of prediction to deviation (RPD) of the modeling set.

[0119] 10.6. External validation of the model

[0120] Substitute the NIR spectrum of the external validation sample into the optimal model to obtain the model calculated value, compare the difference between the calculated value given by the model and the actual value, and judge the accuracy of the prediction result of the model for external validation through the model prediction rate. The calculation formula is shown in (1).

[0121] Prediction rate = 1 - |δ| / measured value × 100% (1)

[0122] 10.7. Data processing

[0123] In order to comprehensively evaluate the quality of Rheum tanguticum Maxim. ex Balf. from multiple aspects, combine the contents of moisture, total ash, extract, total anthraquinones, free anthraquinones, sennoside A and sennoside B, and calculate the comprehensive membership function value of each sample. The comprehensive membership function value is calculated according to the following equation:

[0124] X + ij (u) = (X ij - X jmin ) / (X jmax - X jmin )

[0125] X - ij (u) = 1 - X + ij (u)

[0126] X i (u) = (X + i1 (u) + X - i2 (u) + X - i3 (u) + X + i4 (u) + X + i5 (u) + X + i6 (u) + X + i7 (u)) / 7

[0127] Among them, X ij is the measured value of the jth index of the ith sample, X jmin is the minimum value of the jth index of all samples, X jmax is the maximum value of the jth index of all samples, X i (u) is the integral membership function value of the ith sample, X + i1 (u) is the membership function value of the moisture content of the ith sample, X -i2 (u) is the membership function value of the total ash content of the i-th sample, X - i3 (u) is the membership function value of the extract content of the i-th sample, X + i4 (u) is the membership function value of the total anthraquinone content of the i-th sample, X + i5 (u) is the membership function value of the free anthraquinone content of the I-th sample, X + i6 (u) is the membership function value of the sennoside A content of the i-th sample, X + i7 (u) is the membership function value of the sennoside B content of the i-th sample.

[0128] In the comprehensive quality evaluation, principal component analysis and correlation analysis are carried out using Origin software, principal component analysis and variable projection importance analysis in the modeling process are carried out using SIMCA software, and the remaining model optimizations are carried out using TQ Analyst software.

[0129] Furthermore, according to the above experiments, the analysis mainly includes:

[0130] 1. Determination results of multi-index component contents

[0131] The determination results of each detection index of 38 Rheum tanguticum samples are shown in Figure 4 . Among them, the moisture content ranges from 4.73% to 7.28%, with an average value of 6.43%; the total ash content ranges from 2.92% to 9.97%, with an average value of 7.71%; the extract content ranges from 27.95% to 61.11%, with an average value of 38.20%; the free anthraquinone content ranges from 0.203% to 0.696%, with an average value of 0.324%. The average contents of the five free anthraquinones are in the order of rhein > chrysophanol > emodin > aloe-emodin > physcion; the total anthraquinone content ranges from 1.527% to 3.174%, with an average value of 2.222%. The average contents are in the order of rhein > chrysophanol > aloe-emodin > emodin > physcion. The measured values of all detection indexes of all samples meet the standards of the Chinese Pharmacopoeia. The determination results of the two sennosides are as shown in Figure 4 -F. The sennoside A content of Rheum tanguticum ranges from 0.313% to 1.344%, with an average value of 0.743%; the sennoside B content ranges from 0.123% to 0.506%, with an average value of 0.302%. The average content of sennoside A > the average content of sennoside B. The Japanese Pharmacopoeia stipulates that the sennoside A content of Rheum tanguticum shall not be less than 0.250%, and all samples meet the standard requirements.

[0132] Research shows that the higher the water content of Rheum officinale Baill., the more prone it is to mildew, resulting in a reduction in nutritional components and quality. The research results of the present invention indicate that the water content of the samples measured in this study is relatively low and within the control standards specified in the Pharmacopoeia, which is conducive to the storage of the medicinal materials. By comparing with the water content of Rheum palmatum L. in the Gansu production area, the measured results of the two types of rhubarb are comparable and both meet the requirements of the Pharmacopoeia. The contents of sennoside A and sennoside B measured in the present invention are relatively high, indicating that Rheum tanguticum Maxim. ex Balf. has a good material basis for laxative effects. Among the three types of rhubarb included in the Pharmacopoeia, the anti-inflammatory and laxative effects of the drug are better in Rheum tanguticum Maxim. ex Balf., followed by Rheum palmatum L. and Rheum officinale Baill. Therefore, it is very necessary to carry out quality control on the sennosides, the laxative components of Rheum tanguticum Maxim. ex Balf.

[0133] 2. Comprehensive evaluation of multiple key quality attribute indicators

[0134] 2.1 Membership function analysis

[0135] The membership function is mainly used to describe the membership relationship of elements in a fuzzy set. It uses a numerical value between 0 and 1 to represent the true degree of an element belonging to a certain fuzzy set. Using this method, we carried out the quality evaluation of Rheum tanguticum Maxim. ex Balf. The results of the membership function are shown in Table 7. The comprehensive membership function values of all samples range from 0.147 to 0.657, with an average value of 0.379 and a standard deviation of 0.135. Among them, the comprehensive functions of samples No. 1, 18, 19, 25, 35, 37, and 38 are greater than 0.500, indicating that the comprehensive quality of these numbered samples is better, mainly concentrated in Guoluo Prefecture, Qinghai Province and Xining City, Qinghai Province, which is consistent with the traditional understanding that the quality of Rheum tanguticum Maxim. ex Balf. in Guoluo and Xining is the best.

[0136] Table 7 Calculation results of membership function of multi-index key quality attributes

[0137]

[0138]

[0139] 2.2 Principal component analysis

[0140] The principal component analysis method of the correlation matrix was used to analyze each quality evaluation index of Rheum tanguticum Maxim. ex Balf., and the first two principal components (the cumulative variance contribution rate reached 70.55%) were extracted to judge the overall characteristics of each index of Rheum tanguticum Maxim. ex Balf. Figure 5) It can be seen that free anthraquinones, sennoside A, and sennoside B are distributed in the first quadrant, indicating that the contents of these indicators are highly positively correlated with the quality of Rheum tanguticum; total ash and water content are located on the negative axis of the first principal component, indicating that the two are negatively correlated with the quality of Rheum tanguticum. The 2020 edition of the Pharmacopoeia stipulates that the higher the ash content and water content, the poorer the quality of the rhubarb medicinal material. Our research results are consistent with the Pharmacopoeia regulations.

[0141] 2.3, Correlation Analysis

[0142] The Pearson correlation coefficient method was used to analyze the correlation between various indicators ( Figure 6 ). Sennoside A and sennoside B showed a highly significant positive correlation (P<0.001), free anthraquinones were significantly positively correlated with sennoside A and water content, total anthraquinones were significantly positively correlated with extractives (P<0.01), extractives were significantly negatively correlated with water content and ash, total anthraquinones were significantly negatively correlated with ash (P<0.01), free anthraquinones were positively correlated with sennoside B and ash, and total anthraquinones were positively correlated with sennoside B (P<0.05). It can be seen that there is a large correlation between the contents of different active ingredients, while ash and water content are negatively correlated with some active ingredients. Therefore, to ensure the quality of Rheum tanguticum medicinal materials, it is first necessary to control the ranges of water content and ash. This result further verifies the results of the aforementioned principal component analysis.

[0143] This part comprehensively evaluated its quality using membership functions, principal component analysis, and correlation analysis.

[0144] 3. Near-infrared Spectral Feature Analysis

[0145] Figure 7 is the near-infrared spectrum of Rheum tanguticum medicinal materials. It can be seen that there are 6 absorption peaks in this medicinal material, which are 8405 cm -1 , 6828 cm -1 , 6007 cm -1 , 5663 cm -1 , 5174 cm -1 and 4680 cm -1 . Among them, the second overtone absorption peak of CH2 is near 8366 cm -1 , the first overtone absorption peak of O-H is near 6823 cm -1 , the first overtone absorption peak of C-H is near 6007 cm -1 , the first overtone absorption peak of CH2 is near 5671 cm -1 , the combination absorption peak of O-H is near 5173 cm -1 , and the combination absorption peak of N-H is near 4685 cm -1 . Among them, the vibrations at O-H, C-H, and CH2 may be related to the hydroxyl, methyl, and other structures on components such as anthraquinones and sennosides.

[0146] 4. Establishment of Infrared Spectroscopy Evaluation Model

[0147] 4.1. Outlier Rejection

[0148] An abnormal sample refers to a sample with a large error in concentration value or spectrum. When establishing an infrared spectroscopy prediction model, the existence of abnormal samples will affect the prediction accuracy of the model, and it is necessary to remove them from the modeling set. Mahalanobis distance and principal component score are two commonly used methods. Therefore, Mahalanobis distance and principal component analysis are used to analyze the outliers in the spectral data, and the results are shown in Figure 8 . As shown in Figure 8 A, the MD method shows that all spectral MD values are less than 1.8, indicating that there are no outliers under this method. The PCA score diagnostic results ( Figure 8 B) show that all samples are within the confidence interval. Therefore, there are no outliers in all sample spectra under the two methods, and no rejection is required.

[0149] 4.2. Spectral Pretreatment and Optimization of Modeling Method

[0150] During the near-infrared spectroscopy detection process, powerful background information will be obtained, and at the same time, it will also cause noise interference and redundant variables in the collected original spectra, making it easy to cause difficulties in extracting information related to the target. It is necessary to preprocess the spectral data. Commonly used spectral preprocessing methods include standard normal variate transformation, multiplicative scatter correction, derivative, etc. Therefore, on the premise of keeping the modeling band and sample set unchanged, the spectral preprocessing method is optimized. All orthogonal test value results are shown in Table 8. The combination with the highest value is the theoretical highest combination. It can be seen that the theoretical optimal combination for moisture spectral pretreatment is A3B2C2, with the Rp value of the model obtained being lower than that of A3B2C1. Therefore, the optimal pretreatment method for moisture modeling is finally determined as SNV + D1 + no smoothing. The Rp value of the model with the theoretical optimal combination A3B2C3 for total ash is lower than that of the model obtained from the A3B3C4 combination. Thus, the optimal pretreatment method for total ash modeling is determined as SNV + D2 + Norris smoothing. The theoretical optimal combination for extractives is A2B1C1, and the Rp value of the model is not as good as that of the model obtained from A1B1C1. Then, the optimal pretreatment method for extractives modeling is no + no derivative + no smoothing. The theoretical optimal combination for free anthraquinones is A2B3C3, and the Rp value of the model is not as good as that of the model established by A2B3C1. The optimal pretreatment method for free anthraquinones modeling is obtained as MSC + D2 + no smoothing. The theoretical optimal combination for total anthraquinones is A2B3C1, and the Rp value of the model is the best among all combinations. The optimal pretreatment method for total anthraquinones modeling is MSC + D2 + no smoothing. The theoretical optimal combination for sennoside A is A3B2C3, and the Rp value of the model is not as good as that of the model established by A2B1C2. The optimal pretreatment method for sennoside A modeling is determined as MSC + no + SG smoothing. The theoretical optimal combination for sennoside B is A1B2C3, and the Rp value is not as good as that of the model established by A1B3C3. The optimal pretreatment method for sennoside B modeling is no + D2 + Norris smoothing. The theoretical optimal combination for membership function is A3B2C3. According to the Rp value of the model established based on the theoretical best combination, which is 0.7876 and not as good as that of the model established by A3B2C1, the optimal pretreatment method for membership function modeling is finally determined as SNV + D1 + no smoothing.

[0151] Table 8 Spectral pretreatment method L9(3 3 ) Summary of the results of orthogonal test Table 8Summary of the results of L9(3 3 ) orthogonal test table of spectral pretreatment method

[0152]

[0153]

[0154]

[0155] 4.3 Modeling band selection

[0156] The original spectral maps used for model establishment usually include all measured wavelengths. However, the full-spectrum model contains some redundant information, which may have a negative impact on the predictive ability of the model. By selecting wavelengths, the performance of the calibration model can be improved. Currently, there are a large number of methods for selecting band variables, which can be classified into wavelength point selection methods including VIP and other methods including the CC method according to the characteristics of near-infrared spectra. Therefore, in this study, three methods, namely the full band (4000 - 10000 cm -1 ), VIP, and CC methods, were used to select bands. The score map obtained by the VIP method is shown in Figure 9 . Four bands with VIP values greater than 1, namely 4000 - 5230 cm -1 , 5986 - 6044 cm -1 , 6495 - 6985 cm -1 , and 8493 -1 - 10000 cm -1 , were selected for modeling.

[0157] The optimal modeling bands obtained by the CC method are different depending on the target component and the pretreatment method. Under the optimized pretreatment method, the optimal modeling bands for moisture are 5188 - 4883 cm -1 and 7320 - 7274 cm -1 ; the optimal modeling bands for total ash content are 4948 - 4867 cm -1 and 5068 - 5022 cm -1 ; the optimal modeling bands for extract content are 6071 - 5415 cm -1 ; the optimal modeling bands for free anthraquinone content are 5789 - 5434 cm -1 and 7317 - 6044 cm -1 ; the optimal modeling bands for total anthraquinone content are 5342 - 5334 cm -1 ; the optimal modeling bands for sennoside A content are 5932 - 5407 cm -1 and 6094 - 5951 cm -1 ; the optimal modeling bands for sennoside B content are 5643 - 5604 cm -1 and 5890 - 5812 cm -1 ; the optimal modeling bands for membership function value are 7116 - 7093 cm -1 and 7239 - 7158 cm -1 .

[0158] Under the determined preprocessing and modeling methods, the optimization results of wavelength selection are shown in Table 9. It can be seen from the results that moisture, free anthraquinone, total anthraquinone, sennoside A, and membership function have the best effects in the full wavelength range, extractives have the best effects under the correlation coefficient method, and ash and sennoside B have the best effects under the VIP method. It can be seen that due to the differences in target compounds, there are certain differences in the selected modeling wavelength bands.

[0159] 4.4. Sample set division

[0160] During the model construction process, the selection of samples affects the performance of the model to a great extent. The modeling sample set should be representative and contain sufficient information about the sources of variability in unknown samples, and a validation set needs to be selected to effectively evaluate the model quality. Therefore, the samples in the modeling set are divided according to the concentration gradient method, and the ratio of the calibration set and the prediction set is optimized. The results are shown in Table 10. It can be seen from the results that the models of moisture, total ash, sennoside A, and membership function values have the best effects under the condition of 3:1 sample set division, the sennoside B model has the best effects under the condition of 4:1 sample set division, the extractives and free anthraquinone models have the best effects under the condition of 5:1 sample set division, and the total anthraquinone model has the best effects under the condition of 6:1 sample set division. Therefore, the corresponding optimal ratios are used for each model to establish the final model.

[0161] Table 9 Optimization results of sample set partition ratio Table 9 Sample set partition ratio optimization results

[0162]

[0163] 4.5. Establishment of quantitative models

[0164] Based on the above optimization process, the optimal model is established. The specific model parameter results are shown in Table 11, and the model diagram is shown in Figure 10 . It can be seen from the results that the model evaluation indexes of sennoside A and sennoside B are the best, with both Rc and Rp values greater than 0.98 and RPD values greater than 6. The model indexes of moisture and total ash are the second, with both Rp values greater than 0.96 and RPD values greater than 3. The model of extractives is slightly lower, with Rp value greater than 0.93 and RPD value greater than 2. The RPD values of the models of free anthraquinone, total anthraquinone, and membership function values are the lowest, only greater than 1, but the Rp values are all greater than 0.95. It can be seen that the near-infrared spectroscopy model has established good models for multiple indexes. It should be noted that the size of the Rc value may be related to the differences in the richness of the selected indexes, the modeling method, and the number of samples.

[0165] Table 10 Parameter table of NIR optimal model for multi-index key quality attributes of Rh.tanguticum

[0166]

[0167]

[0168] 4.6. External verification of the model

[0169] During the modeling process, when the selected modeling order is too high, the phenomenon of overfitting of the model may occur. Although the overfitted model has a good prediction effect on the samples within the model, there will be a large error when predicting other samples outside the prediction model. Therefore, external verification of the model is required to ensure the accuracy and robustness of the model. Using the established optimal PLS model, 6 samples are reserved for each index for external verification. The predicted values are obtained by substituting the NIR spectra of the external verification samples, and the differences between the predicted values and the actual values are compared and the prediction rates are calculated, as shown in Table 12. It can be seen from the results that the external verification prediction rates of all indexes have reached more than 75%, and the prediction rates of moisture, extract, free anthraquinone, and total anthraquinone have reached more than 90%. It can be seen that the established models can all achieve good prediction of unknown samples.

[0170] Table 11 External verification result table

[0171]

[0172] Finally, corresponding conclusions are drawn based on the above two parts of experiments and analysis, as follows:

[0173] In this invention, multiple key quality attribute indicators of Rheum tanguticum Maxim. ex Balf. were determined according to the pharmacopoeia method. By means of near-infrared spectroscopy technology combined with different chemometric methods, the feasibility of using NIRS for rapid and comprehensive evaluation of the quality of raw materials of Rheum tanguticum Maxim. ex Balf. was explored. All the indicators measured in Rheum tanguticum Maxim. ex Balf. conform to the pharmacopoeia standards. Comprehensive quality evaluation of rhubarb was carried out using membership function, principal component analysis and correlation analysis, indicating that in order to ensure the quality of Rheum tanguticum Maxim. ex Balf. medicinal materials, the ranges of moisture content and ash content need to be controlled. During the model optimization process, outlier rejection showed that all spectra were normal, and different optimal modeling methods for different compounds were obtained through spectral pretreatment, modeling wavebands, and optimization of sample set division. The optimized model parameters showed that the model indicators of sennoside A and sennoside B were the best, with both Rc and Rp values greater than 0.98 and RPD values greater than 6; the model indicators of moisture and total ash were the second best, with both Rp values greater than 0.96 and RPD values greater than 3; the model parameters of extractives, free anthraquinones, total anthraquinones, and membership function values were slightly lower, but both Rp values were greater than 0.93. The prediction rates of all indicator external validations reached more than 75%, and the prediction rates of moisture, extractives, free anthraquinones, and total anthraquinones reached more than 90%, indicating that the established model has strong prediction ability for unknown samples. The synchronous quantitative analysis of the above multiple key quality attribute indicators can lay a good foundation for establishing a rapid quality evaluation method for rhubarb, and provide a useful supplement and improvement for the comprehensive evaluation of the quality of raw materials of rhubarb from the perspective of near-infrared spectroscopy.

[0174] Example 3

[0175] Based on the same inventive concept as the embodiment, a system for determining the contents of multiple key quality attribute indicators of Rheum tanguticum Maxim. ex Balf. medicinal materials is provided. The system includes:

[0176] An index correlation analysis module, which is used to analyze the correlation between the key quality attribute indicators in Rheum tanguticum Maxim. ex Balf. medicinal materials by using the principal component analysis method and the Pearson correlation coefficient method;

[0177] A near-infrared spectrum prediction model establishment module, which is used to establish a near-infrared spectrum prediction model for each key quality attribute indicator;

[0178] A quantitative model establishment module, which is used to optimize the modeling conditions of the near-infrared spectrum prediction model to obtain a quantitative model for each key quality attribute indicator.

[0179] It should be noted that the functions corresponding to the implementation of each module and the steps recorded in the embodiment method are not described in detail here.

[0180] The above specific embodiments are detailed descriptions of the present invention. It cannot be determined that the specific embodiments of the present invention are only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions and substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for determining the content of multiple key quality attribute indicators of Tangut rhubarb medicinal material, characterized in that: The method comprises: The principal component analysis method and Pearson correlation coefficient method were used to analyze the correlation between the key quality attribute indicators of Tangut rhubarb. A near infrared spectroscopy prediction model for each key quality attribute indicator is established, and the modeling conditions of the near infrared spectroscopy prediction model are optimized to obtain a quantitative model for each key quality attribute indicator.

2. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 1, characterized in that: The key quality attribute indicators include moisture, total ash, extract, total anthraquinones, free anthraquinones and sennosides.

3. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 1, characterized in that: The principal component analysis method analyzes the correlation between the key quality attribute indicators in Tangut rhubarb medicinal materials, including: The principal component analysis method of correlation matrix was used to analyze the quality evaluation indicators of Tangut rhubarb, and the first two principal components were extracted to determine the overall characteristics of each indicator of Tangut rhubarb.

4. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 3 is characterized in that: The overall characteristics of the various indicators of Tangut rhubarb include: Free anthraquinone, sennoside A and sennoside B were distributed in the first quadrant, indicating that the contents of these indicators were highly positively correlated with the quality of Rheum tanguticum; total ash and water content were located on the negative axis of the first principal component, indicating that the two were negatively correlated with the quality of Rheum tanguticum.

5. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 1, characterized in that: The Pearson correlation coefficient method analyzes the correlation between the key quality attribute indicators in Tangut rhubarb medicinal materials, including: Sennoside A was extremely significantly positively correlated with sennoside B, free anthraquinone was significantly positively correlated with sennoside A and water content, total anthraquinone was significantly positively correlated with extract, extract was significantly negatively correlated with water content and ash, and total anthraquinone was significantly negatively correlated with ash.

6. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 1, characterized in that: The near infrared spectroscopy prediction model for each key quality attribute index is established, including: Take an appropriate amount of Tangut rhubarb sample and place it in a sample cup, flatten it, and compact it. During the collection process, the background interference of CO2 and water is deducted in real time. -1 The near infrared spectrum of the sample was collected under the following conditions, with a scanning range of 10000-4000cm -1 Each sample was measured 3 times, and the average spectrum of the 3 times was taken as the standard spectrum.

7. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 1, characterized in that: The optimizing of the modeling conditions of the near infrared spectrum prediction model comprises: Optimize spectral preprocessing methods, modeling band selection, and sample set division.

8. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 7, characterized in that: The sample set division includes: The samples of the modeling set were divided according to the concentration gradient method, and the ratio of the correction set and the prediction set was optimized. The models corresponding to moisture, total ash and sennoside A selected a 3:1 sample set division, the model corresponding to sennoside B selected a 4:1 sample set division, the model corresponding to extract and free anthraquinone selected a 5:1 sample set division, and the model corresponding to total anthraquinone selected a 6:1 sample set division.

9. The method for determining the contents of multiple key quality attribute indicators of Tangut rhubarb medicinal material according to claim 6, characterized in that: The Mahalanobis distance and principal component analysis were used to remove outliers from the collected spectral data.

10. A method for determining the content of multiple key quality attribute indicators of Tangut rhubarb medicinal material, characterized in that: The system comprises: The indicator correlation analysis module is used to analyze the correlation between the key quality attribute indicators of Tangut rhubarb medicinal materials using the principal component analysis method and the Pearson correlation coefficient method; Near infrared spectroscopy prediction model building module, used to build near infrared spectroscopy prediction models for key quality attribute indicators; The quantitative model building module is used to optimize the modeling conditions of the near-infrared spectroscopy prediction model to obtain the quantitative model of each key quality attribute indicator.

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