A method for evaluating the quality of scutellaria based on the theory of discerning shape

Abstract

This invention belongs to the field of traditional Chinese medicine quality evaluation technology, and relates to a method for evaluating the quality of Scutellaria baicalensis based on morphological characteristics. The method involves: 1) obtaining root length, root mid-section diameter, root cap diameter, powder color, baicalin content, dry weight, alcohol-soluble extract content, and yield of Scutellaria baicalensis at different harvesting periods; 2) using the obtained data as variable factors for dimensionality reduction-principal component analysis, extracting three principal components F1, F2, and F3 with eigenvalues ​​≥1, and obtaining the corresponding contribution rates P1, P2, and P3; 3) constructing a comprehensive evaluation function F = P1F1 + P2F2 + P3F3; and 4) calculating the F value to evaluate the quality of Scutellaria baicalensis. This invention utilizes principal component analysis to jointly analyze the appearance characteristics, effective components, and yield of Scutellaria baicalensis, constructing an evaluation model; rapidly predicting the effective component content of Scutellaria baicalensis through appearance characteristics, achieving accurate quality evaluation of Scutellaria baicalensis, and guiding the harvesting time.

Description

Technical Field

[0001] This invention belongs to the field of traditional Chinese medicine quality evaluation technology, and relates to a quality evaluation method for Scutellaria baicalensis based on morphological differentiation. Background Technology

[0002] Scutellaria baicalensis, first recorded in the *Shennong Bencao Jing* (Shennong's Classic of Materia Medica), is listed as a superior herb, possessing the effects of clearing heat and drying dampness, purging fire and detoxifying, stopping bleeding, and calming the fetus. It is derived from the dried root of *Scutellaria baicalensis* Georgi, a plant belonging to the genus *Scutellaria* of the Lamiaceae family. Scutellaria baicalensis mainly contains flavonoids, terpenes, and volatile oils, possessing anti-inflammatory, antibacterial, antiviral, antitumor, and antioxidant effects. Clinically, Scutellaria baicalensis is often used in classic prescriptions as a synergistic ingredient with other drugs. It holds a significant position in traditional Chinese medicine, being an important raw material for over 600 kinds of prepared Chinese medicines, including Sanjiu Weitai Capsules, Xiaor Feire Kechuan Granules, and Qingre Jiedu Tablets. Traditional Chinese medicine plays a crucial role in the treatment of COVID-19 infection. Because Scutellaria baicalensis is effective in clearing lung heat and cough, it is frequently used in compound prescriptions for treating COVID-19 infection.

[0003] In recent years, the scale of artificial cultivation of Scutellaria baicalensis has gradually expanded and has shown initial results. However, related surveys indicate that the problem of differences between artificial cultivation and harvesting still exists. The 2020 edition of the Chinese Pharmacopoeia stipulates that the harvesting period of Scutellaria baicalensis is in spring and autumn. The harvesting period is quite large and there are significant regional climate differences, which cannot provide a certain basis for the harvesting period in various planting areas. The different harvesting periods have led to the phenomenon of inconsistent quality of cultivated Scutellaria baicalensis.

[0004] Active ingredients, as the material basis of the efficacy of traditional Chinese medicine (TCM), are an important component of the quality of medicinal materials. Harvesting time is one of the key factors affecting the active substances in TCM. The *Compendium of Materia Medica* records: "Production varies between north and south, seasons vary, roots and seedlings are harvested differently, and processing methods differ." "Harvesting at the right time makes it medicine, harvesting at the wrong time makes it grass," demonstrating the crucial importance of timely harvesting for producing high-quality TCM. Harvesting, as a key link in the production process of TCM, significantly impacts not only the quality but also the yield. Therefore, exploring the scientific laws governing the relationship between the quality, yield, and harvesting of TCM, and selecting and establishing the optimal harvesting period, are important research topics and crucial methods for maintaining or improving the quality of medicinal materials.

[0005] The renowned contemporary pharmacognosy and herbalist Xie Zongwan proposed the concept of "identifying quality by appearance," which involves identifying medicinal materials based on their appearance, such as color, shape, and texture, to preliminarily determine their quality. Historical herbal texts record that yellow-colored Scutellaria baicalensis is the best, while others say that "dark-colored Scutellaria baicalensis is better." Some treatises also state that "thick and long, firm, yellow in color, and with the outer skin completely removed, is the best Scutellaria baicalensis."

[0006] In the study "Correlation Study on the Appearance and Internal Quality of Bupleurum chinense Based on the Theory of 'Distinguishing Appearance and Quality'" (Chinese Journal of Traditional Chinese Medicine, 2023, 48(07): 1815-1823), Wang Wenxue et al. found that within a certain range, the redder, longer, and coarser the surface color of Bupleurum chinense, the higher the content of its volatile oil and saikosaponins. In the study "Correlation Study on the Color of Scutellaria baicalensis Slices after Softening and Cutting by Different Methods and the Content of Five Flavonoids" (Chinese Journal of Traditional Chinese Medicine, 2019, 44(20): 4467-4475), Chai Chongchong et al. found that the brighter and yellower the color of Scutellaria baicalensis powder, the higher the total color value, the higher the content of flavonoids and the lower the content of flavonoid aglycones. However, the existing technology uses human judgment to determine the appearance, which is highly subjective and makes it difficult to guarantee the objectivity and accuracy of the evaluation, resulting in inaccurate evaluation results.

[0007] In conclusion, evaluating the quality of Scutellaria baicalensis from multiple dimensions, including appearance, active ingredients, and yield, and then guiding its harvesting, is a challenge that needs to be addressed at this stage. Summary of the Invention

[0008] To address the technical challenges in existing Scutellaria baicalensis quality evaluation methods, this invention provides a Scutellaria baicalensis quality evaluation method based on morphological differentiation.

[0009] This invention aims to utilize principal component analysis to jointly analyze the appearance, active ingredients, and yield of Scutellaria baicalensis, and construct a multi-dimensional evaluation model; by rapidly predicting the content of active ingredients in Scutellaria baicalensis through its appearance characteristics, it achieves accurate evaluation of Scutellaria baicalensis quality and guides its harvesting.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0011] A method for evaluating the quality of Scutellaria baicalensis includes the following steps:

[0012] 1) Obtain data on eight indicators of Scutellaria baicalensis at different harvesting periods.

[0013] Four appearance characteristics of Scutellaria baicalensis at different harvesting periods were obtained: root length, root mid-section diameter, root cap diameter, and powder color.

[0014] To obtain data on three intrinsic material indicators of Scutellaria baicalensis at different harvesting periods: baicalin content, dry weight, and alcohol-soluble extract content;

[0015] To obtain yield data for Scutellaria baicalensis at different harvesting periods;

[0016] 2) Principal component analysis

[0017] Using the eight indicator data obtained in step 1) as variable factors, we performed dimensionality reduction-principal component analysis to extract three principal components F1, F2 and F3 with eigenvalues ​​≥1, and obtained the contribution rates P1, P2 and P3 corresponding to each principal component.

[0018] 3) Constructing the evaluation function

[0019] Using the three principal components F1, F2 and F3 and the corresponding contribution rates P1, P2 and P3 of each principal component as weights, a comprehensive evaluation function F = P1F1 + P2F2 + P3F3 is constructed.

[0020] 4) Quality Evaluation

[0021] The quality of Scutellaria baicalensis was evaluated based on the calculated F-value.

[0022] Further specifying, in step 1), the content of baicalin is obtained by the following method:

[0023] A) Preparation of Scutellaria baicalensis test solution

[0024] It is prepared by mixing Scutellaria baicalensis powder with methanol solution;

[0025] B) Chromatographic analysis

[0026] The chromatogram of the Scutellaria baicalensis sample solution was determined by high performance liquid chromatography, and the content of the active ingredient baicalin was calculated.

[0027] Further specifying the preparation process of the Scutellaria baicalensis test solution, the steps are as follows: accurately weigh 0.10 g of Scutellaria baicalensis powder, place it in a stoppered conical flask, add 40 mL of 70% ethanol, weigh the solution, sonicate for 40 min, cool, weigh the solution again, replenish the lost weight with methanol, shake well, filter, and collect the filtrate to obtain the test solution.

[0028] Further specifying, the high-performance liquid chromatography (HPLC) determination conditions are:

[0029] The chromatographic column was Thermo C10. 18 The mobile phase was acetonitrile (A) - 0.1% formic acid aqueous solution (B), with gradient elution: 0–10 min, 10%–15% A; 10–20 min, 15%–20% A; 20–30 min, 20%–25% A; 30–60 min, 25%–45% A; 60–70 min, 45%–60% A; 70–75 min, 60%–10% A; detection wavelength was 274 nm, and the flow rate was 1.0 mL·min. -1 Injection volume 10 μL, column temperature 30℃.

[0030] Further specifying, in step 1), the dry weight ratio is calculated as follows:

[0031] After washing and drying the Scutellaria baicalensis root, weigh its fresh weight. After drying at 60℃, weigh its dry weight. Calculate the dry weight percentage of the sample by dividing the dry weight by the fresh weight.

[0032] Furthermore, in step 1), the content of alcohol-soluble extract is determined by hot leaching.

[0033] Further specifying, the method for determining the yield in step 1) is: selecting multiple groups of 1m... 2 Within the specified range, weigh the dry weight of Scutellaria baicalensis, calculate the average value, and then multiply it by 666.7m. 2 get.

[0034] To further define this, the specific process of dimensionality reduction-principal component analysis in step 2) is as follows:

[0035] 2.1) Using Scutellaria baicalensis from different harvesting periods as samples, a sample matrix X was constructed using data from eight indicators;

[0036]

[0037] Where: i represents the number of Scutellaria baicalensis samples, i = 1, 2, 3…8; j represents the indicator data corresponding to the Scutellaria baicalensis samples, j = 1, 2, 3…8; x ij This refers to the j-th indicator data corresponding to the i-th sample in the sample matrix X;

[0038] 2.2) Standardize the sample matrix to obtain the standardized matrix Y;

[0039]

[0040] Where: y ij x represents the j-th index data corresponding to the i-th sample in the standardized matrix Y; ij S is the average value of the j-th indicator data corresponding to the i-th sample. j denoted by , i represents the standard deviation of the j-th column of the standardized matrix Y; i represents the number of Scutellaria baicalensis samples, i = 1, 2, 3…8; j represents the indicator data corresponding to the Scutellaria baicalensis samples, j = 1, 2, 3…8;

[0041] 2.3) Use formula (2) to perform correlation analysis on the standardized matrix Y to obtain the correlation matrix R;

[0042]

[0043] Where: r jj Let y represent the element in the j-th row and j-th column of the correlation matrix R. ik For the k-th index in the j-th row of the standardized matrix Y, y represents the mean of the j-th row in the standardized matrix Y; kj For the k-th index in the j-th column of the standardized matrix Y, Let represent the mean of the j-th column in the standardized matrix Y, where j = 1, 2, 3…8, and n is the sample size, which is 8.

[0044] 2.4) Construct the characteristic equation |R-λ j Given I| = 0, find the j non-negative eigenvalues ​​λ of the correlation matrix R. j Arrange these eigenvalues ​​in descending order as λ1≥λ2≥λ3……≥λ j ≥0; I is the identity matrix;

[0045] 2.5) Then from The eigenvalue λ is calculated. j The corresponding eigenvector matrix A;

[0046] μ jj A vector representing the j-th principal component corresponding to the j-th index data; a jj Let represent the feature vector of the j-th index data corresponding to the j-th principal component; j = 1, 2, 3...8.

[0047] 2.6) Calculate the contribution rate and cumulative contribution rate.

[0048] Contribution rate

[0049] Cumulative contribution rate

[0050] Where: λ k This represents the eigenvalue of the k-th principal component;

[0051] 2.7) Find the factor loading matrix B

[0052] Taking the first three eigenvalues ​​λ1, λ2, and λ3 with λ≥1, the factor loading matrix B is obtained by the following formula;

[0053]

[0054] 2.8) Calculate the principal components F1, F2 and F3 according to formula (7); and calculate the contribution rates P1, P2 and P3 corresponding to the three principal components according to formula (5).

[0055] F C =b C1 y i1 +b C2 y i2 +…b Cj y ij (7)

[0056] Where: c = 1, 2, 3, i = 1, 2, 3...8, j = 1, 2, 3...8;

[0057] Where: bC1 Let b be the element in the c-th row and 1-th column of the factor loading matrix B. C2 The element in the c-th row and 2nd column of the factor loading matrix B; b Cj Let y be the element in the c-th row and j-th column of the factor loading matrix B. ij This represents the j-th index data corresponding to the i-th sample in the standardized matrix Y.

[0058] Further specifying, in step 4), the larger the F value, the higher the quality of the Scutellaria baicalensis sample.

[0059] The beneficial effects of this invention are:

[0060] 1. This invention analyzes the appearance characteristics, content of internal effective components, and yield of Scutellaria baicalensis through principal component analysis, and finally constructs a multi-dimensional evaluation model. By using the appearance characteristics of Scutellaria baicalensis, the content of effective components of Scutellaria baicalensis can be quickly predicted, thereby achieving accurate evaluation of the quality of Scutellaria baicalensis and guiding its harvesting.

[0061] 2. This invention, through the determination of the content of Scutellaria baicalensis at different harvesting periods, found that the content of baicalin in Scutellaria baicalensis with different powder colors, root lengths, and root thicknesses varies. Thus, the content of the intrinsic effective components of Scutellaria baicalensis can be externalized to a certain extent in its appearance. Through the objective evaluation of its appearance, the quality of the medicinal material can be preliminarily determined.

[0062] 3. This invention uses L* (brightness), a* (red-green), b* (yellow-blue) color spaces and vernier calipers to digitally and objectively evaluate the powder color, root length, and root thickness of Scutellaria baicalensis at different harvesting periods, resulting in more accurate evaluation results.

[0063] 4. This invention uses high performance liquid chromatography to determine the content of effective components in Scutellaria baicalensis. The determination results have good linear correlation, good precision, stability and repeatability, and the results are accurate and reliable. Attached Figure Description

[0064] Figure 1 Images of Scutellaria baicalensis powder from different harvesting periods according to this invention;

[0065] Figure 2 Chromatograms of Scutellaria baicalensis reference standard (A) and Scutellaria baicalensis sample (B). Detailed Implementation

[0066] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0067] The medicinal materials, reagents, and instruments used in this embodiment are as follows.

[0068] 1. Scutellaria baicalensis medicinal material

[0069] The dried root of Scutellaria baicalensis Georgi, a plant of the Lamiaceae family, was identified by Wang Jitao, a senior laboratory technician at the Dongzhuang Medicinal Botanical Garden in Chunhua County, Shaanxi Province. The sample information is shown in Table 1. The Scutellaria baicalensis medicinal material was collected at different harvesting periods.

[0070] 2. Reagents

[0071] Baicalin (H-106-180322) was purchased from Chengdu Ruifensi Biotechnology Co., Ltd.; chromatographic methanol and acetonitrile (Fisher Laboratories, USA); formic acid (Tianjin Tianli Chemical Reagent Co., Ltd.); and purified water (Wahaha Co., Ltd.).

[0072] Table 1. Scutellaria baicalensis samples from different harvesting periods.

[0073]

[0074]

[0075] 3. Instruments

[0076] Waters 2695 high performance liquid chromatograph (Waters Corporation, USA).

[0077] BP211D electronic analytical balance with a strength of 0.0001 g / L (Sartorius GmbH, Germany).

[0078] SB-5200DT CNC Ultrasonic Cleaner (Ningbo Xinzhi Biotechnology Co., Ltd.)

[0079] CS-520 spectrophotometer (Hangzhou Caipu Technology Co., Ltd.)

[0080] Model F20190815 Moisture Analyzer (Shanghai Jinghai Instrument Co., Ltd.)

[0081] KSW type electric furnace temperature controller for box-type resistance furnace (Beijing Kewei Yongxing Instrument Co., Ltd.).

[0082] Example

[0083] Taking the Scutellaria baicalensis collected in Table 1 above as an example, the quality evaluation method of Scutellaria baicalensis is explained in detail.

[0084] I. Obtaining index data of Scutellaria baicalensis at different harvesting periods

[0085] 1. Determination of alcohol-soluble extract content of Scutellaria baicalensis

[0086] The hot extraction method was used for determination of alcohol-soluble extracts (General Chapter 2201) under the entry for Scutellaria baicalensis in the 2020 edition of the Chinese Pharmacopoeia. Three samples were tested, and the average value was taken. The results are shown in Table 2. All samples met the requirements of the Chinese Pharmacopoeia and complied with the standards for clinical use.

[0087] 2. Determination of the dry weight of Scutellaria baicalensis.

[0088] After washing and drying the Scutellaria baicalensis root, weigh it while it is still fresh. After drying it at 60℃, weigh it again. Calculate the dry weight percentage of the sample based on (dry weight / fresh weight) × 100%. Three samples were tested and the average value was taken. The results are shown in Table 2.

[0089] 3. Determination of the yield of Scutellaria baicalensis medicinal material

[0090] Multiple 1m samples were selected for each sampling. 2 For samples within the specified range, weigh the dry weight and multiply the average value by 666.7m. 2 Preferably, three groups of 1m are selected. 2 The yield results for the samples within the specified range are shown in Table 2.

[0091] Table 2 Results of relevant index measurements for samples ( n=3)

[0092] Sample number alcohol-soluble extract Dry weight (%) Yield (kg / mu) S1-4 50.01±0.494 33.37±1.07 282.85 S2-4 56.21±0.625 35.05±2.09 390.63 O1-4 51.95±2.677 38.57±6.63 420.34 O2-4 53.86±1.568 39.67±2.97 458.35 O3-4 51.31±1.241 37.05±3.45 409.68 N1-4 50.22±2.646 36.46±2.66 361.0 N2-4 48.27±1.763 36.26±4.62 382.33 N3-4 52.82±2.537 34.36±4.1 381.78

[0093] 4. Determination of the appearance characteristics of Scutellaria baicalensis.

[0094] The appearance characteristics of Scutellaria baicalensis at different harvesting periods were measured, including root length, root crown diameter, and root mid-section diameter. Ten Scutellaria baicalensis samples were tested, and the average value was taken. The results are shown in Table 3.

[0095] Table 3. Appearance characteristics of Scutellaria baicalensis at different harvesting periods ( n=10)

[0096]

[0097]

[0098] 5. Color determination of Scutellaria baicalensis powder

[0099] Take an appropriate amount of Scutellaria baicalensis powder in a cuvette, spread it evenly, repeat 3 times, and record the color brightness (L) after eliminating the background. * ), red-green hue (a * ), yellow-blue saturation (b) * The overall color difference value (ΔE) was calculated, and the average value was taken, as shown in Table 4. The powder color of Scutellaria baicalensis samples from different harvest periods is shown in Table 4. Figure 1 .

[0100] Table 4 Color values ​​of Scutellaria baicalensis powder at different harvesting periods ( n=6)

[0101] Sample number L* a* b* ΔE S1-4 22.26±0.05 4.33±0.01 28.39±0.04 36.33±0.02 S2-4 23.49±0.42 3.90±0.12 28.94±0.17 37.51±0.36 O1-4 24.61±0.36 3.59±0.11 28.91±0.05 38.13±0.19 O2-4 21.78±0.02 4.45±0.02 24.82±0.04 33.31±0.04 O3-4 21.90±0.05 4.71±0.05 26.18±0.19 34.46±0.17 N1-4 24.66±0.01 3.87±0.02 26.60±0.02 36.48±0.01 N2-4 24.07±0.26 4.38±0.03 28.53±0.007 37.58±0.11 N3-4 23.84±0.21 4.15±0.05 27.71±0.17 36.79±0.23

[0102] 6. Determination of effective components in Scutellaria baicalensis

[0103] In this embodiment, high performance liquid chromatography was used to determine the content of active ingredients in Scutellaria baicalensis.

[0104] 6.1 Solution Preparation

[0105] 6.1.1 Preparation of Scutellaria baicalensis test solution

[0106] Accurately weigh 0.10 g of Scutellaria baicalensis powder and place it in a stoppered conical flask. Add 40 mL of 70% ethanol and weigh it. Sonicate the flask (500 W power, 40 kHz frequency) for 40 min, cool it, and weigh it again. Make up the weight loss with methanol, shake well, filter it, and collect the filtrate.

[0107] 6.1.2 Preparation of reference solution

[0108] Accurately weigh baicalin, the reference standard, into volumetric flasks, dissolve and dilute to volume with chromatographic methanol to obtain a mass concentration of 408.00 μg·mL. -1 A reference solution of baicalin.

[0109] 6.2 Chromatographic conditions

[0110] The chromatographic column was Thermo C10. 18 (250 mm × 4.6 μm, 5 μm); the mobile phase was acetonitrile (A) - 0.02% formic acid aqueous solution (B), with gradient elution: (0–10 min, 10%–15% A; 10–20 min, 15%–20% A; 20–30 min, 20%–25% A; 30–60 min, 25%–45% A; 60–70 min, 45%–60% A; 70–75 min, 60%–10% A; detection wavelength 274 nm, flow rate 1.0 mL·min). -1 Injection volume 10 μL, column temperature 30℃.

[0111] The chromatograms of the Scutellaria baicalensis test solution and the baicalin reference solution under these conditions are shown below. Figure 2 .

[0112] 6.3 Examination of Linear Relationships

[0113] Take baicalin reference solution and autoinject 2 μL, 5 μL, 10 μL, 15 μL, and 20 μL respectively under the above chromatographic conditions. Record the peak area values. Plot the peak area as the ordinate (Y) and the injection mass (μg) as the abscissa (X), and perform linear regression to obtain the regression equation for baicalin. The results are shown in Table 5.

[0114] Table 5. Linear Relationship of Baicalin Chemical Components

[0115] Active ingredients Linear equations <![CDATA[Correlation coefficient R 2 > Linear range / μg baicalin Y = 3091.1X + 33.838 0.999 5 0.816～8.160

[0116] 6.4 Precision Test

[0117] Baicalin reference solution was taken and injected continuously under the above chromatographic conditions. The determination was performed 6 times, with 10 μL injected each time. The RSD of baicalin was found to be 0.36%, indicating that the instrument precision was good.

[0118] 6.5 Stability Test

[0119] Accurately weigh 0.10 g of Scutellaria baicalensis powder into an Erlenmeyer flask, and prepare the test solution according to the above method. The results were measured at 0, 2, 4, 8, 12 and 24 h. The RSD of the peak area of ​​baicalin was 0.70%, indicating that the above 12 components in the test solution had good stability within 24 h.

[0120] 6.6 Repeatability Test

[0121] Accurately weigh 0.10 g of Scutellaria baicalensis powder into an Erlenmeyer flask, and prepare 6 test solutions according to the above method. Inject 10 μL of each solution under the above chromatographic conditions. The RSD values ​​of baicalin were 1.12%, indicating that the method has good repeatability.

[0122] 6.7 Recovery Test

[0123] Nine portions of Scutellaria baicalensis powder were accurately weighed and then precisely added to reference solutions of low, medium, and high concentrations (equivalent to 80%, 100%, and 120% of the original Scutellaria baicalensis sample, respectively). Three portions of each concentration were taken and the test solution was prepared according to the above method. The chromatographic conditions were as described above. The recovery rate and RSD of each component were calculated based on the measured amount and the amount added. The results showed that the average recovery rate of baicalin was 99.79% and the RSD was 0.36%.

[0124] The above high-performance liquid chromatography (HPLC) experiments show that HPLC is an accurate, reliable, and reproducible method for quantitative analysis of baicalin content in Scutellaria baicalensis, and can be used for quality evaluation research of Scutellaria baicalensis.

[0125] 6.8 Content Determination

[0126] The Scutellaria baicalensis sample was pulverized using a pulverizer and passed through a No. 4 sieve. The test solution was prepared according to the above method, and the content of baicalin was determined under chromatographic conditions. The results are shown in Table 6.

[0127] Table 6. Changes in the mass fraction of baicalin in samples from different harvesting periods of Scutellaria baicalensis (mg / g) -1 (n=3)

[0128] S1-4 S2-4 O1-4 O2-4 O3-4 N1-4 N2-4 N3-4 baicalin 154.054 168.050 168.513 133.848 162.347 181.222 177.885 158.713

[0129] 6.9 Correlation and Regression Analysis

[0130] 6.9.1 Correlation Analysis

[0131] The morphological values ​​of Scutellaria baicalensis (root length and diameter of Scutellaria baicalensis medicinal material, Scutellaria baicalensis powder L) were determined by SPSS. * a * b * Pearson correlation analysis was performed on the content of ΔE and baicalin, the effective components of the scutellarin.

[0132] Specifically, Pearson correlation analysis was performed on the data in Tables 2, 3, 4 and 6 using SPSS, and the results are shown in Table 7.

[0133] Table 7. Pearson correlation analysis of Scutellaria baicalensis samples from different harvest periods.

[0134]

[0135] Table 7 shows that the content of baicalin in Scutellaria baicalensis is related to L... * ΔE showed a significant positive correlation (p<0.05); baicalin content was significantly positively correlated with a * Root length, root cap diameter, and root mid-section diameter were negatively correlated; baicalin content was negatively correlated with b. * The positive correlation indicates that the shape of the Scutellaria baicalensis root, the color of Scutellaria baicalensis, and the content of baicalin are related.

[0136] 6.9.2 Regression Analysis

[0137] The baicalin content of Scutellaria baicalensis powder is correlated with the appearance of Scutellaria baicalensis. Therefore, regression analysis was performed with the appearance of Scutellaria baicalensis as the independent variable and baicalin as the dependent variable. The results are shown in Tables 8-10.

[0138] Table 8 Summary of linear regression models for samples of Scutellaria baicalensis from different harvest periods

[0139] Element r <![CDATA[r 2 ]]> <![CDATA[Adjust r 2 > Error of standard estimation baicalin 0.974 0.95 0.647 8.86449

[0140] Table 9. Analysis of variance of Scutellaria baicalensis samples at different harvesting periods.

[0141]

[0142] Table 10 Linear regression analysis of Scutellaria baicalensis samples from different harvest periods

[0143]

[0144] Table 8 shows that the content of baicalin is related to the R value of the appearance characteristics of Scutellaria baicalensis. 2 The p-value of 0.95 indicates that the baicalin content can be reflected to some extent by its appearance. Table 9 shows that in the regression analysis of variance between baicalin content and the appearance of Scutellaria baicalensis, the p-value for baicalin was greater than 0.05, indicating no statistical significance. Table 10 shows that in the linear regression analysis of baicalin and the appearance of Scutellaria baicalensis, the p-value was greater than 0.05, indicating that the regression was not statistically significant. Therefore, the baicalin content can be quickly predicted using the appearance characteristics of Scutellaria baicalensis.

[0145] II. Principal Component Analysis

[0146] The above-mentioned data indicators were collected, resulting in a total of 8 indicators.

[0147] The appearance characteristics of Scutellaria baicalensis include: root length (X1), root mid-section diameter (X2), root cap diameter (X3), and powder color ΔE (X4). (Data are shown in Tables 3 and 4)

[0148] The intrinsic active ingredients include baicalin content (X5); dry weight (X6); and alcohol-soluble extract (X7). (See Tables 2 and 6 for data)

[0149] Production (X8). (See Table 2 for data)

[0150] Dimensionality reduction-principal component analysis was performed on eight indicators of Scutellaria baicalensis, including appearance traits (root length (X1), root mid-section diameter (X2), root cap diameter (X3), powder color ΔE (X4)), internal substances (baicalin content (X5), dry weight (X6), alcohol-soluble extract (X7)), and yield (X8). The specific analysis steps are as follows:

[0151] 1. A sample matrix X was constructed using the following parameters: appearance characteristics (root length (X1, root mid-section diameter (X2), root cap diameter (X3), and powder color ΔE (X4)), effective internal components (baicalin content (X5), dry weight (X6), alcohol-soluble extract (X7)), and yield (X8) of Scutellaria baicale samples from different harvest periods.

[0152] Wherein: each row of the sample matrix X represents different indicators of the same sample, and each column represents the same indicator of different samples. The number of samples is not determined by the harvest period.

[0153]

[0154] Where: i represents the number of samples of Scutellaria baicalensis, i = 1, 2, 3...8; j represents the number of indicators of the Scutellaria baicalensis samples, j = 1, 2, 3...8.

[0155] Specifically, the data from Tables 2, 3, 4, and 6 are substituted into the sample matrix X, as shown in Table 11.

[0156] Table 11 Data table of sample matrix X

[0157]

[0158]

[0159] 2. Using formula (1), the sample matrix X is standardized to obtain matrix Y.

[0160]

[0161]

[0162] Specifically, the data in Table 11 is standardized to obtain the standardized matrix Y, as shown in Table 12.

[0163] Table 12 Data table of standardized matrix Y

[0164]

[0165] 3. Use formula (2) to calculate the correlation matrix R of the standardized matrix Y.

[0166] After standardization, the mean of each indicator (each column) is 0, and the variance is 1.

[0167]

[0168] In the above formula: k = 1, 2, 3...8, j = 1, 2, 3...8, n = 8

[0169]

[0170] r jj Let y represent the element in the j-th row and j-th column of the correlation matrix R. ik For the k-th index in the j-th row of the standardized matrix Y, y represents the mean of the j-th row in the standardized matrix Y; kj For the k-th index in the j-th column of the standardized matrix Y, Let represent the mean of the j-th column in the standardized matrix Y, where n is the sample size (8).

[0171] Specifically, the data in Table 12 is processed to obtain the correlation matrix R, as shown in Table 13.

[0172] Table 13 Data table for correlation matrix R

[0173] Correlation X1 X2 X3 X4 X5 X6 X7 X8 X1 1 0.51 0.583 0.083 0.084 -0.395 0.398 0.761 X2 0.51 1 0.842 0.009 0.412 0.145 0.533 0.408 X3 0.583 0.842 1 -0.361 -0.063 -0.037 0.33 0.656 X4 0.083 0.009 -0.361 1 0.844 -0.325 -0.14 -0.349 X5 0.084 0.412 -0.063 0.844 1 0.086 0.16 -0.273 X6 -0.395 0.145 -0.037 -0.325 0.086 1 0.539 -0.058 X7 0.398 0.533 0.33 -0.14 0.16 0.539 1 0.445 X8 0.761 0.408 0.656 -0.349 -0.273 -0.058 0.445 1

[0174] 4. Find the eigenvalues ​​of R and the corresponding unit eigenvectors.

[0175] From the characteristic equation of R |R-λ j Given I| = 0, (j = 1, 2, 3...8)(3), obtain j non-negative eigenvalues ​​and arrange these eigenvalues ​​in descending order as λ1 ≥ λ2 ≥ ... ≥ λ j ≥0; I is the identity matrix.

[0176] Specifically, substituting the data from Table 13 yields the eigenvalue λ. j as follows.

[0177]

[0178] The eigenvalue λ is then calculated using formula (4). j The corresponding eigenvectors A and μ jj This represents an element representing the j-th index data corresponding to the j-th principal component; a jj This represents the feature vector of the j-th index data corresponding to the j-th principal component.

[0179]

[0180]

[0181] Specifically, eigenvalues ​​and eigenvectors are calculated from the data in Table 13 to obtain the eigenvector matrix A with eigenvalues ​​λ≥1, as shown in Table 14.

[0182] Table 14 Data table for eigenvector matrix A

[0183] <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X7]]> <![CDATA[X8]]> 0.657 0.800 0.905 -0.451 -0.737 0.187 0.664 0.766 0.678 0.136 0.140 0.603 0.474 -0.814 -0.216 0.307 0.052 0.297 -0.204 0.591 0.304 0.468 0.607 -0.114

[0184] 5. Calculate the contribution rate P j and cumulative contribution rate P 总j

[0185]

[0186] In formula (5), k = 1, 2, 3...8, j = 1, 2, 3...8, and n = 8.

[0187]

[0188] In formula (6), k = 1, 2, 3...j, j = 1, 2, 3...8, n = 8.

[0189] Where: λ kThis represents the eigenvalue of the k-th principal component.

[0190] The calculated eigenvalue λ j Substituting into formulas (5) and (6), the contribution rate P is calculated. j and cumulative contribution rate P 总j .

[0191] Table 15 Contribution Rate P j and cumulative contribution rate P 总j value

[0192] <![CDATA[Contribution rate P j (%)]]> <![CDATA[Cumulative contribution rate P 总j (%) <!-- 12 -->]]> 46.24 46.24 23.638 69.877 14.678 84.555 10.132 94.687 3.914 98.601 0.853 99.453 0.547 100.00 -4.267E-15 100.00

[0193] 6. Find the factor loading matrix.

[0194] Take the first three eigenvalues ​​λ1, λ2, and λ3 that are λ≥1, and calculate the factor loading matrix B using the following formula.

[0195]

[0196] Where: j = 1, 2, 3...8, a 11 The value of the first pair in the first row of Table 14; a 12 The values ​​are the first row and second column pairs in Table 14, and so on; λ1 is 3.699, λ2 is 1.891, λ3 is 1.174, and finally the data in factor loading matrix B are calculated.

[0197] Specifically, the factor loading matrix B is calculated based on the data in Table 14 as described above, and the factor loading matrix B is obtained as shown in Table 16.

[0198] Table 16 Data table for factor loading matrix B

[0199]

[0200] 7. Obtain the principal component relation based on the factor loading matrix B.

[0201] F C =b C1 y i1 +b C2 y i2 +…b Cj y ij (7)

[0202] In the above formula: c = 1, 2, 3, i = 1, 2, 3...8, j = 1, 2, 3...8; b C1 Let b be the element in the c-th row and 1-th column of the factor loading matrix B. C2 The element in the c-th row and 2nd column of the factor loading matrix B; b Cj Let y be the element in the c-th row and j-th column of the factor loading matrix B. ijThis represents the j-th index data corresponding to the i-th sample in the standardized matrix Y.

[0203] Substitute the data from Table 15 into formula (7) to obtain the principal component calculation formula.

[0204]

[0205] The values ​​corresponding to the three principal components in Table 16 show that root cap diameter (X3), baicalin content (X5), and yield (X8) contribute significantly to principal component F1; root mid-section diameter (X2), powder color ΔE (X4), and baicalin content (X5) contribute significantly to principal component F2; and dry weight (X6) and alcohol-soluble extract (X7) contribute significantly to principal component F3.

[0206] 8. According to the principal component score relationship and the weight coefficient of each principal component (i.e., contribution rate P), j The principal component composite score was calculated.

[0207] F = P1F1 + P2F2 + P3F3 (8)

[0208] Substituting the standardized matrix Y and the contribution rate from Table 15 into formula (8) yields the principal component comprehensive score table, as shown in Table 17.

[0209] Table 17 Principal Component Composite Score Table

[0210] Sample number <![CDATA[F1]]> <![CDATA[F2]]> <![CDATA[F3]]> F Sort S1-4 -1.52 -1.46 -0.23 -1.08 7 S2-4 0.89 -0.63 2.45 0.62 3 O1-4 0.9 2.66 0.2 1.08 2 O2-4 3.81 -0.56 -1.18 1.45 1 O3-4 -0.31 -1.34 -0.57 -0.55 6 N1-4 -2.5 0.5 -0.5 -1.11 8 N2-4 -1.05 1.09 -0.38 -0.28 5 N3-4 -0.22 -0.25 0.21 -0.13 4

[0211] As can be seen from Table 17, the larger the F value, the higher the overall quality of Scutellaria baicalensis at that harvesting period. This invention ranks different Scutellaria baicalensis samples according to their F values, and selects the harvesting period corresponding to the higher quality samples as the optimal harvesting period based on the size of the F value. Specifically, the F value is the largest when the sample is O2-4, therefore the optimal harvesting period is in early October.

[0212] As demonstrated by the above embodiments, based on the theory of "differentiation of appearance and quality," this study investigated the relationship between the changes in the appearance of Scutellaria baicalensis roots, the accumulation of chemical components in the roots, and the yield of Scutellaria baicalensis samples from different harvesting periods. The appearance of Scutellaria baicalensis is related to the content of effective components; Scutellaria baicalensis with a bright yellow color has a relatively high content of baicalin. Furthermore, principal component analysis was used to analyze the indicators affecting the quality of Scutellaria baicalensis, and an evaluation function was constructed to evaluate its quality. When different harvesting periods were used as samples, the autumn harvesting period for Scutellaria baicalensis in Chunhua area was determined to be around the time of the Cold Dew solar term. Thus, a principal component analysis model of "appearance characteristics—components—yield" for Scutellaria baicalensis was established, which is beneficial to improving the accuracy of Scutellaria baicalensis quality evaluation.

[0213] The above description is a preferred embodiment of the present invention and is only for further detailed explanation of the technical solution of the present invention. It should not be considered that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the protection scope of the present invention.

Claims

1. A method for evaluating the quality of Scutellaria baicalensis based on its appearance and properties, characterized in that, Includes the following steps: 1) Obtain data on 8 indicators of Scutellaria baicalensis at different harvesting periods. Four appearance characteristics of Scutellaria baicalensis at different harvesting periods were obtained: root length, root mid-section diameter, root cap diameter, and powder color. To obtain data on three intrinsic material indicators of Scutellaria baicalensis at different harvesting periods: baicalin content, dry weight, and alcohol-soluble extract content; To obtain yield data for Scutellaria baicalensis at different harvesting periods; 2) Principal component analysis Using the eight indicator data obtained in step 1) as variable factors, dimensionality reduction-principal component analysis was performed to extract three principal components with eigenvalues ​​≥1. , and And obtain the contribution rate corresponding to each principal component. , and ; The specific process of the dimensionality reduction-principal component analysis is as follows: 2.1) Using Scutellaria baicalensis from different harvesting periods as samples, a sample matrix X was constructed using data from eight indicators; in: This indicates the number of Scutellaria baicalensis samples. ; This represents the indicator data corresponding to the Scutellaria baicalensis sample. 8; Sample matrix The Middle The corresponding sample of the th sample Individual indicator data; 2.2) Standardize the sample matrix to obtain the standardized matrix. ; in: This refers to the j-th index data corresponding to the i-th sample in the standardized matrix Y; The average value of the j-th indicator data corresponding to the i-th sample is... Represents the normalized matrix No. The standard deviation of the column; This indicates the number of Scutellaria baicalensis samples. ; This represents the indicator data corresponding to the Scutellaria baicalensis sample. ; 2.3) Use formula (2) to standardize the matrix Perform correlation analysis to obtain the correlation matrix R; in: Represents the correlation matrix R as the first... Line number Column elements, For the standardized matrix The Middle The first in the row One indicator, Represents the normalized matrix The Middle The mean of the rows, For the standardized matrix The Middle The first in the column One indicator, Represents the normalized matrix The Middle The mean of the column, n is the sample size, which is 8. 2.4) Constructing the characteristic equation Find the correlation matrix R. nonnegative eigenvalues Arrange these eigenvalues ​​in descending order as follows: 0; It is the identity matrix; 2.5) Then by The eigenvalues ​​are calculated. The corresponding eigenvector matrix A; Indicates the first The principal component corresponds to the first... A vector of indicator data; Indicates the first The first principal component corresponds to the th principal component Feature vectors of individual indicator data; ; 2.6) Calculate the contribution rate and cumulative contribution rate. Contribution rate , Cumulative contribution rate in: Indicates the first Eigenvalues ​​of each principal component; 2.7) Find the factor loading matrix B Pick The first three eigenvalues , , ,Depend on , ( ), thus obtaining the factor loading matrix B; 2.8) Calculate the principal components according to formula (7). , and Simultaneously, the contribution rates of the three principal components are calculated according to formula (5). , and Where: c = 1, 2, 3, ; in: For the factor loading matrix B, the first... The element in the first column of the row, For the factor loading matrix B, the first... The element in the second column of the row; For the factor loading matrix B, the first... Line number Column elements, For the standardized matrix The Middle The corresponding sample of the th sample Individual indicator data; 3) Constructing the evaluation function With three principal components , and and the contribution rates of each principal component. , and Construct a comprehensive evaluation function using the weights. ; 4) Quality Evaluation The quality of Scutellaria baicalensis is evaluated based on the calculated F-value.

2. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 1, characterized in that, In step 1), the content of baicalin was obtained using the following method: A) Preparation of Scutellaria baicalensis test solution It is prepared by mixing Scutellaria baicalensis powder with methanol solution; B) Chromatographic analysis The chromatogram of the Scutellaria baicalensis sample solution was determined by high performance liquid chromatography, and the content of the active ingredient baicalin was calculated.

3. The method for evaluating the quality of Scutellaria baicalensis based on morphological differentiation according to claim 2, characterized in that, The preparation process of the Scutellaria baicalensis test solution is as follows: accurately weigh 0.10 g of Scutellaria baicalensis powder, place it in a stoppered conical flask, add 40 mL of 70% ethanol, weigh it, sonicate for 40 min, cool it, weigh it again, make up the weight loss with methanol, shake well, filter it, and take the filtrate to obtain the test solution.

4. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 2, characterized in that, The high-performance liquid chromatography (HPLC) determination conditions are as follows: The chromatographic column was Thermo C10. 18 The mobile phase was acetonitrile (A)-0.1% formic acid aqueous solution (B), with gradient elution: 0–10 min, 10%–15% A; 10–20 min, 15%–20% A; 20–30 min, 20%–25% A; 30–60 min, 25%–45% A; 60–70 min, 45%–60% A; 70–75 min, 60%–10% A. Detection wavelength: 274 nm; Volumetric flow rate: 1.0 mL·min -1 Injection volume 10 μL, column temperature 30℃.

5. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 1, characterized in that, In step 1), the dry weight ratio is calculated as follows: After washing and drying the Scutellaria baicalensis root, weigh its fresh weight. After drying at 60℃, weigh its dry weight. Calculate the dry weight percentage of the sample by dividing the dry weight by the fresh weight (dry weight / fresh weight × 100%).

6. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 1, characterized in that, In step 1), the content of alcohol-soluble extracts is determined by hot leaching.

7. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 1, characterized in that, The method for determining the yield in step 1) is as follows: select multiple groups of 1m 2 Within the specified range, weigh the dry weight of Scutellaria baicalensis, calculate the average value, and then multiply it by 666.7m. 2 get.

8. The method for evaluating the quality of Scutellaria baicalensis based on the differentiation of symptoms and properties according to claim 1, characterized in that, In step 4), the larger the F value, the higher the quality of Scutellaria baicalensis.

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