Physical fingerprint spectrum-based compound radix ophiopogonis particle intermediate quality evaluation method

Abstract

The invention discloses a method for evaluating the quality of a compound radix ophiopogonis granule intermediate based on a physical fingerprint spectrum. Comprising the following steps: detecting and uniformly quantifying specific second-level physical parameters under five first-level index parameter items, namely stacking property, flowability, uniformity, stability and compressibility of a particle intermediate, constructing a physical fingerprint spectrum, and analyzing the quality consistency of the compound radix ophiopogonis particle intermediate by adopting a multivariate statistical analysis method. The method for evaluating the quality of the compound dwarf lilyturf tuber granule intermediate based on the physical fingerprint spectrum is creatively established, and a related basis is provided for quality control of the compound dwarf lilyturf tuber granule intermediate.

Description

Technical Field

[0001] The invention belongs to the technical field of quality evaluation of traditional Chinese medicines, and particularly relates to a quality evaluation method for a compound ophiopogon granule intermediate based on a physical fingerprint spectrum. Background Art

[0002] Compound Ophiopogon japonicus Granules is a clinical experience prescription with the effects of clearing heat and removing phlegm, promoting lung function and relieving cough. The whole prescription is composed of five Chinese medicinal herbs: Ophiopogon japonicus, Licorice, Houttuynia cordata, Oroxylum indica and Platycodon grandiflorum. After extraction, concentration and freeze-drying, dry paste powder is obtained, and finally compound Ophiopogon japonicus granules are obtained after wet granulation of auxiliary materials and drying.

[0003] The safety and stability of Chinese medicine preparations are closely related to their clinical efficacy, and the two influence each other. Stability is one of the important conditions for evaluating the quality of Chinese medicine preparations. It may cause changes in the appearance of the medicine at the least, and reduce the efficacy of Chinese medicine and increase toxic side effects at the most. In the production process of Chinese patent medicines, the physical properties of material intermediates affect the subsequent granulation, mixing and other related processes. Therefore, evaluating the quality consistency of the production process of compound Ophiopogon japonicus granules is conducive to the quality control of the finished product. However, there has been no relevant report on the quality consistency evaluation of the physical properties of the intermediates of compound Ophiopogon japonicus granules.

[0004] Physical fingerprint is a comprehensive and quantifiable quality evaluation method. By measuring the parameters that reflect the physical properties of Chinese medicine preparations, a physical fingerprint is constructed. It can intuitively display the differences in physical property indicators of samples from different batches, and evaluate the physical quality consistency of samples between different batches through the cosine similarity calculation method. Summary of the invention

[0005] The purpose of the present invention is to apply the quality evaluation method based on physical fingerprint to the quality control of compound ophiopogon granule intermediates for the first time in view of the deficiencies of the prior art. According to the principle of quality by design, based on the physical properties of compound ophiopogon granule intermediates, combined with multivariate statistics and physical fingerprint, a quality evaluation method for compound ophiopogon granule intermediates based on physical fingerprint is creatively provided, which is conducive to improving the quality control level in the production process of compound ophiopogon granules and ensuring uniform, stable and controllable product quality.

[0006] The object of the present invention is achieved through the following technical scheme: The present invention provides a quality evaluation method for compound Ophiopogon japonicus granules intermediate based on physical fingerprint, and the specific steps are as follows:

[0007] Step 1: Select physical indicators related to bulking, fluidity, uniformity, stability, and compressibility to characterize the physical properties of the compound Ophiopogon japonicus granules intermediate sample;

[0008] Step 2: Use multivariate statistical analysis to conduct quality consistency analysis on the physical properties of the intermediate samples of Compound Ophiopogon japonicus granules;

[0009] Step 3: Use the physical fingerprint method to evaluate the similarity of the intermediate samples of compound Ophiopogon japonicus granules;

[0010] Step 4: Combine multivariate statistical analysis with physical fingerprint similarity analysis to determine the physical quality consistency control limit of the compound Ophiopogon japonicus granules intermediate.

[0011] According to some specific implementation cases of the present invention, the physical indicators mainly include: bulk density Da, tap density Dc, outflow velocity FR, angle of repose θ, Hausner ratio HR, loss on drying LOD, hygroscopicity H, interparticle porosity Ie, Carr index IC, particle size D corresponding to the cumulative particle size distribution number reaching 10% 10 , the particle size D corresponding to the cumulative particle size distribution number reaching 50% 50 , the particle size D corresponding to the cumulative particle size distribution number reaching 90% 90 , particle size distribution width Span, particle size distribution range Width, relative uniformity index Iθ, and percentage %pf of particles with a size less than 50 μm.

[0012] According to some specific implementation cases of the present invention, the physical indicators are measured based on powder science evaluation methods.

[0013] According to some specific implementation cases of the present invention, the multivariate statistical analysis method includes principal component analysis, correlation analysis, and class average method combined with squared Euclidean distance to perform system cluster analysis.

[0014] According to some specific implementation cases of the present invention, the principal component analysis is performed using Simca software, the correlation analysis is performed using Origin software, and the system cluster analysis is performed using SPSS software.

[0015] According to some specific implementation cases of the present invention, the similarity evaluation of the intermediate sample of compound Ophiopogon japonicus granules by using the physical fingerprint method comprises the following steps:

[0016] Step 1: Dimensionless processing is performed on each physical index, and each parameter value is converted into a value ranging from 0 to 10 for unified quantification;

[0017] Step 2: Construct physical fingerprint map;

[0018] Step 3: Create a reference physical fingerprint map to calculate the cosine similarity.

[0019] According to some specific implementation cases of the present invention, the unified quantification to 0-10 is to arrange the secondary physical indicators under the primary indicators of stackability, fluidity, uniformity, stability, and compressibility in a clockwise direction, and the maximum value of all indicators is ≤10, and the minimum value is ≥0, to obtain a polygonal area.

[0020] According to some specific implementation cases of the present invention, the physical fingerprint spectrum is constructed by connecting the converted physical indicator values ​​in a radar chart manner.

[0021] According to some specific implementation cases of the present invention, the control physical fingerprint is established based on the average spectrum under various parameter items of the physical fingerprint of compound Ophiopogon japonicus granule samples from different batches.

[0022] According to some specific implementation cases of the present invention, the physical indicator values ​​need to be normalized when calculating the cosine similarity.

[0023] According to some specific implementation cases of the present invention, the calculation of cosine similarity is performed using Excel analysis software.

[0024] According to some specific implementation cases of the present invention, the compound Ophiopogon japonicus granule intermediate is a compound Ophiopogon japonicus granule intermediate and a granule.

[0025] Beneficial effects of the invention: The invention applies the quality evaluation method based on physical fingerprint to the quality control of compound ophiopogon granule intermediates for the first time. According to the principle of quality by design, based on the physical properties of compound ophiopogon granule intermediates, combined with multivariate statistics and physical fingerprint, a quality evaluation method for compound ophiopogon granule intermediates based on physical fingerprint is creatively established, so as to more effectively control the quality of the finished product. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 The principal component analysis diagram of the dry paste powder and granules of the intermediate of Compound Ophiopogon Granules; a is the PCA score diagram of the dry paste powder, b is the loading diagram of the dry paste powder, c is the PCA score diagram of the granules, and d is the loading diagram of the granules;

[0028] Figure 2 This is the system cluster diagram of the intermediate dry paste powder and granules of Compound Ophiopogon Granules;

[0029] Figure 3 This is the correlation heat map of the intermediate dry paste powder and granules of Compound Ophiopogon Granules;

[0030] Figure 4 The physical fingerprints of different batches of compound Ophiopogon japonicus granules intermediate dry paste powder and the control physical fingerprints;

[0031] Figure 5 These are the physical fingerprints of different batches of the intermediate granules of Compound Ophiopogon Granules and the control physical fingerprints. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the embodiments of the specification. In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, so the present invention is not limited to the specific embodiments disclosed below.

[0033] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] Example 1: Quality evaluation method of compound Ophiopogon japonicus granules intermediate based on physical fingerprint

[0035] 1. Instruments and Materials

[0036] MS-3000 laser particle analyzer (Malvern Instruments Ltd., UK) and electric blast drying oven (BPG-9056A, Shanghai Yiheng Scientific Instruments Ltd.) were used to prepare 20 batches of dry paste powder and granules of compound Ophiopogon japonicus granules intermediate. The dry paste powder was denoted by G1-G20, and the granules were denoted by K1-K20.

[0037] 2. Methods and Results

[0038] 2.1 Physical index determination method

[0039] The 12 physical parameters of different batches of compound Ophiopogon japonicus granules intermediate dry paste powder and granules were measured respectively:

[0040] Bulk density Da: Take 10g of sample and slowly add it into a 25mL dry measuring cylinder. Gently smooth the surface and read the sample volume Va. According to the total mass m of the powder added to the measuring cylinder, a And the mass m of the measuring cylinder is calculated, the formula is:

[0041]

[0042] Tap density Dc: The measuring cylinder containing the sample is vibrated on a thick rubber pad until the volume of the powder no longer decreases, and the sample volume Vc is read. The formula is:

[0043]

[0044] Angle of repose θ: Use the fixed funnel method to fix three funnels in series above the horizontally placed coordinate paper, so that the bottom funnel mouth and the coordinate paper maintain a certain height h, and slowly pour the sample along the top funnel wall until the tip of the formed stacked cone touches the funnel mouth. Measure the radius r of the cone bottom, and the formula is:

[0045]

[0046] Outflow rate FR: Using the fixed funnel method, fix three funnels in series on top of the horizontally placed coordinate paper, slowly pour the sample along the top funnel wall until all the sample powder flows out, and calculate the outflow time t, the formula is:

[0047]

[0048] Hausner ratio HR: calculated from Da and Dc, the formula is:

[0049]

[0050] Interparticle porosity Ie: Calculated from Da and Dc, the formula is:

[0051]

[0052] Carr Index IC: Calculated from Da and Dc, the formula is:

[0053]

[0054] Loss on drying LOD: Take the sample to be tested and measure it according to the 0831 Loss on drying method in Part 3 of the 2020 edition of the Chinese Pharmacopoeia.

[0055] Hygroscopicity H: Take a dry stoppered glass weighing bottle, saturate it in a 25℃ constant temperature dryer (with a saturated NaCl solution at the bottom) for 24 hours, and accurately weigh the mass m1. Take an appropriate amount of sample and spread it flat in the above weighing bottle, with a thickness of about 1mm, and accurately weigh the mass m2. Place the open weighing bottle and the bottle cap under the above constant temperature and humidity conditions, accurately weigh the mass m3 after moisture balance, and calculate the hygroscopicity H. The formula is:

[0056]

[0057] Particle size and particle size distribution (D 10 , D 50 , D90 , Span, Width): Take the powder to be tested and place it in the laser particle size analyzer dry method measuring device, use air as the dispersion medium, control the refractive index at 5% to 10% to measure the D 10 (particle size corresponding to the cumulative particle size distribution number reaching 10%), D 50 (particle size corresponding to the cumulative particle size distribution number reaching 50%), D 90 (The particle size corresponding to when the cumulative particle size distribution number reaches 90%).

[0058] Particle size distribution width (Span): D 10 , D 50 , D 90 The calculation formula is:

[0059]

[0060] Particle size distribution range Width: by D 10 , D 90 The calculation formula is:

[0061] Width = D 90 -D 10

[0062] Relative uniformity index (Iθ): Using air as the medium, use a laser particle size distribution analyzer to measure the powder particle size distribution and calculate the percentage of particles in each particle size range. According to the particle size values ​​measured for dry paste powder and granules, 4 particle size nodes of 351.670, 211.044, 98.114, and 51.823 are selected and calculated according to the following formula.

[0063]

[0064] In the above formula, Fm is the mass percentage of powder particle size in the range of 98.114~211.044, F m-1 is the mass percentage of powder particle size in the range of 51.823 to 98.114, F m+1 It is the mass percentage of powder with a particle size of 211.044 to 351.670. m ,d m-1 ,d m+1 is the average particle size of the corresponding particle size range. n is the number of particle size ranges determined.

[0065] Percentage of particle size <50 μm (%Pf): %Pf is measured using a laser particle size distribution analyzer with air as the medium.

[0066] The results of the physical index determination of 20 batches of the intermediate dry paste powder of the compound Ophiopogon japonicus granules are shown in Table 1, and the results of the physical index determination of 20 batches of the compound Ophiopogon japonicus granules are shown in Table 2.

[0067] Table 1: Physical index test results of 20 batches of compound Ophiopogon japonicus granules intermediate dry paste powder

[0068]

[0069]

[0070] Table 2: Physical index test results of 20 batches of compound Ophiopogon japonicus granules

[0071] batch number Da Dc θ FR HR LOD H <![CDATA[D 50 ]]> Span Width Ie K1 0.343 0.399 28.81 2.545 1.163 1.17 9.016 1229.18 1.4652 1800.97 0.409 K2 0.273 0.347 28.91 2.781 1.271 1.24 9.407 1310.26 1.3522 1771.74 0.781 K3 0.348 0.419 29.30 3.232 1.204 1.71 8.021 1178.06 1.4738 1736.2 0.487 K4 0.281 0.364 30.36 3.209 1.296 1.95 8.824 1284.81 1.3534 1738.86 0.812 K5 0.310 0.356 28.66 2.743 1.148 1.54 9.357 1000.46 1.6149 1615.68 0.417 K6 0.316 0.364 30.79 3.551 1.152 1.75 8.612 1179.15 1.4304 1686.64 0.417 K7 0.293 0.348 28.94 2.986 1.188 0.85 8.243 979.733 1.7594 1723.71 0.539 K8 0.301 0.372 29.42 3.050 1.236 0.69 8.394 1019.74 1.6593 1692.09 0.634 K9 0.312 0.374 30.52 3.438 1.199 1.42 10.043 919.928 1.7925 1648.95 0.531 K10 0.379 0.434 27.46 3.438 1.145 0.96 8.222 1277.97 1.3659 1745.52 0.334 K11 0.268 0.345 34.38 4.081 1.287 1.93 8.317 1289.18 1.3385 1725.58 0.833 K12 0.313 0.400 32.66 3.865 1.278 1.46 9.539 1098.42 1.5121 1660.93 0.695 K13 0.331 0.401 28.35 2.956 1.211 0.75 9.8707 1096.35 1.6383 1796.2 0.527 K14 0.345 0.426 28.03 2.712 1.235 0.77 8.4364 833.466 1.7197 1433.33 0.551 K15 0.319 0.397 28.84 2.886 1.245 1.80 10.373 1152.06 1.5395 1773.56 0.616 K16 0.337 0.406 27.16 2.561 1.205 1.17 9.0153 1235.68 1.4198 1754.43 0.504 K17 0.301 0.392 27.85 2.698 1.302 0.82 10.21 1057.63 1.5609 1650.88 0.771 K18 0.380 0.423 28.32 2.746 1.113 0.84 9.8684 1217.75 1.4017 1706.94 0.268 K19 0.321 0.368 30.79 3.603 1.146 0.97 10.041 609.325 1.7702 1078.6 0.398

[0072] 2.2 Multivariate statistical analysis

[0073] Simca14.1 software was used for principal component analysis, and the first two principal components were extracted to establish the principal component model. Origin software was used for correlation analysis, and SPSS26.0 software was used for hierarchical cluster analysis. The square Euclidean distance was calculated using the class average method. Figure 1 The principal component analysis modeling results of the physical properties of 20 batches of compound Ophiopogon japonicus dry paste powder and granules.

[0074] The cumulative contribution rate of the first two principal components in the dry paste powder principal component model is 58.2%, and the score diagram is shown in a in Figure 1, where G10 and G13 are outliers. Figure 1 As shown in b, Ie, IC, HR, Width, Da, Dc, H, D 50 Isocompressibility, fluidity, and stability have large loads on principal component 1, while LOD and Span have large loads on principal component 2. The results show that the first two principal components mainly integrate the information related to the stability, stacking, fluidity, moisture, and particle size of the dry paste powder, and the physical properties of G10 and G13 are quite different from those of the other batches.

[0075] Figure 1 The c in Figure 1 The d in the figure is the analysis diagram of the first two principal components of the granules. The analysis results show that the cumulative contribution rate of the first two principal components is 63.5%, which integrates the particle size, density, fluidity, drying loss and other information of the granules. K11 and K4 are outliers. Compared with other batches, K11 has a larger particle size and less fine powder. K4 has a larger Carr index and repose angle than other batches, which indicates that the fluidity of K4 batch is not very good compared with other batches.

[0076] Figure 2The following is a systematic clustering dendrogram of 20 batches of compound Ophiopogon japonicus granule intermediate dry paste powder and granules. When the classification distance is 2.5, the 20 batches of dry paste powder can be divided into 4 categories. Among them, G1, G7, G18-G20 are clustered into one category, G6, G10, G12 are clustered into one category, G3-G4, G8, G14-G18 are clustered into one category, and G2, G9, G11, G13 are clustered into one category. Figure 2 As shown in Figure b, when the classification cluster is 4.8, the 20 batches of granules can be divided into 3 categories. Among them, K2, K4, K11, K12, K15, and K17 are clustered into one category, K10 and K18 are clustered into one category, and the remaining batches are clustered into another category.

[0077] The results showed that cluster analysis showed the degree of difference in physical properties between batches. The physical properties of G6, G10, G12, G3-G4, G8, G14-G18 in dry paste powder were significantly different from those of other batches. The physical properties of K2, K4, K11, K12, K15, and K17 in granules were significantly different from those of other batches.

[0078] Figure 3 This is the correlation heat map of the intermediate and granules of Compound Ophiopogon Granules. The results show that the density of the granules is significantly correlated with the density of the dry paste powder, and the hygroscopicity of the granules is significantly correlated with the hygroscopicity of the dry paste powder. The correlation heat map results show that the density and hygroscopicity of the dry paste powder are potential key factors affecting the physical properties of the granules. *P<0.05 indicates a difference, and **P<0.01 indicates a significant difference.

[0079] 2.3 Standardization conversion of physical indicators

[0080] The values ​​in Table 1 and Table 2 were standardized to the same scale of 0-10 using the conversion formulas in Table 3 and Table 4, respectively. The results of the standardized physical indices of 20 batches of the intermediate dry paste powder of the compound Ophiopogon japonicus granules are shown in Table 5, and the results of the standardized physical indices of 20 batches of the compound Ophiopogon japonicus granules are shown in Table 6.

[0081] Table 3: Standard conversion formula for physical indexes of dry paste powder

[0082]

[0083]

[0084] Table 4: Standard conversion formulas for physical indicators of granules

[0085] index Conversion formula Da(K) 10×Da Dc(K) 10×Dc θ(K) 10-(θ / 5) FR(K) FR HR(K) 5×(2-HR) LOD(K) 10-(5×LOD) H(K) 10-(H / 2) <![CDATA[D 50 (K)]]> <![CDATA[10-(D 50 / 300)]]> Span(K) 10-(4×Span) Width(K) 10-(Width / 300) Ie(K) 10×Ie IC(K) IC / 5

[0086] Table 5: Results of physical index standardization of 20 batches of compound Ophiopogon japonicus granules intermediate dry paste powder

[0087]

[0088] Table 6: Results of physical index standardization of 20 batches of compound Ophiopogon japonicus granules

[0089] batch number Da(K) Dc(K) θ(K) FR(K) HR(K) LOD(K) H(K) <![CDATA[D 50 (K)]]> Span(K) Width(K) Ie(K) IC(K) K1 3.430 3.990 4.238 2.545 4.184 4.150 5.492 5.903 4.139 3.997 4.092 3.265 K2 2.730 3.470 4.218 2.781 3.645 3.800 5.297 5.632 4.591 4.094 7.812 5.421 K3 3.480 4.190 4.141 3.232 3.980 1.450 5.990 6.073 4.105 4.213 4.869 4.080 K4 2.811 3.643 3.929 3.209 3.520 0.250 5.588 5.717 4.586 4.204 8.125 5.920 K5 3.100 3.560 4.268 2.743 4.258 2.300 5.322 6.665 3.540 4.614 4.168 2.968 K6 3.160 3.640 3.842 3.551 4.241 1.250 5.694 6.069 4.278 4.378 4.173 3.038 K7 2.930 3.480 4.213 2.986 4.061 5.750 5.878 6.734 2.963 4.254 5.394 3.754 K8 3.010 3.720 4.115 3.050 3.821 6.550 5.803 6.601 3.363 4.360 6.341 4.718 K9 3.120 3.740 3.897 3.438 4.006 2.900 4.978 6.934 2.830 4.504 5.313 3.974 K10 3.790 4.340 4.508 3.438 4.274 5.200 5.889 5.740 4.537 4.182 3.344 2.902 K11 2.680 3.450 3.125 4.081 3.563 0.350 5.842 5.703 4.646 4.248 8.328 5.746 K12 3.130 4.000 3.469 3.865 3.610 2.700 5.231 6.339 3.952 4.464 6.949 5.559 K13 3.310 4.010 4.329 2.956 3.943 6.250 5.065 6.346 3.447 4.013 5.274 4.230 K14 3.450 4.260 4.393 2.712 3.826 6.150 5.782 7.222 3.121 5.222 5.511 4.696 K15 3.190 3.970 4.233 2.886 3.777 1.000 4.814 6.160 3.842 4.088 6.159 4.890 K16 3.370 4.060 4.569 2.561 3.976 4.150 5.492 5.881 4.321 4.152 5.043 4.095 K17 3.010 3.920 4.430 2.698 3.488 5.900 4.895 6.475 3.756 4.497 7.712 6.047 K18 3.800 4.230 4.335 2.746 4.434 5.800 5.066 5.941 4.393 4.310 2.675 2.263 K19 3.210 3.680 3.842 3.603 4.268 5.150 4.980 7.969 2.919 6.405 3.979 2.928 K20 3.270 3.270 3.270 3.270 3.270 3.270 3.270 3.270 3.270 3.270 3.270 3.270

[0090] 2.4 Construction of physical fingerprints and physical reference fingerprints of dry powder and granules

[0091] The 12 physical quality indicators of different batches of dry paste powder samples after standardized conversion are connected in the form of radar charts to form an irregular dodecagon, which constitutes the physical fingerprint of the dry paste powder. Figure 4 As shown in a; the average spectrum of each parameter item of the physical fingerprint spectrum of the compound Ophiopogon japonicus granules dry paste powder of different batches is used to establish a control physical fingerprint spectrum of the dry paste powder, such as Figure 4 As shown in b in FIG. 1 , the 12 physical indicators of the granule samples from different batches after standardization and conversion are connected in a radar chart manner to form an irregular dodecagon, which constitutes the physical fingerprint of the granule, as shown in FIG. Figure 5 As shown in a in FIG, the average spectrum of each parameter item of the physical fingerprint spectrum of different batches of Fang Maidong Granules was used to establish the control physical fingerprint spectrum of the dry paste powder, such as Figure 5 As shown in b;

[0092] 2.5 Similarity evaluation of physical fingerprints of dry powder and granules

[0093] The cosine similarity between the physical fingerprints of different batches of dry paste powder and granules and the control fingerprints of dry paste powder and granules was calculated, and the results are shown in Table 7. The similarity between the physical fingerprints of dry paste powder is 0.888-0.944, and the similarity between batches G9-G14 is less than 0.92. The similarity between the physical fingerprints of granules is 0.890-0.984, among which the similarity between batches K6, K18, K5, K1, K3, K15, and K17 is less than 0.92.

[0094] Table 7: Cosine similarity table of fingerprints of dry paste powder, granules and control

[0095] sample Similarity sample Similarity sample Similarity sample Similarity G1 0.941 G11 0.889 K1 0.915 K11 0.951 G2 0.923 G12 0.909 K2 0.949 K12 0.954 G3 0.931 G13 0.888 K3 0.918 K13 0.981 G4 0.923 G14 0.917 K4 0.936 K14 0.919 G5 0.928 G15 0.940 K5 0.903 K15 0.929 G6 0.935 G16 0.934 K6 0.890 K16 0.984 G7 0.937 G17 0.937 K7 0.957 K17 0.902 G8 0.928 G18 0.940 K8 0.977 K18 0.943 G9 0.902 G19 0.935 K9 0.934 K19 0.929 G10 0.917 G20 0.944 K10 0.922 K20 0.951

[0096] The above results show that batches G9 to G14 in the dry paste powder and batches K6, K18, K5, K1, K3, K15, and K17 in the granules are abnormal batches, which is basically consistent with the system clustering results. However, the evaluation quality standard based on the system clustering results alone needs to be improved, and the quality attributes of the compound Ophiopogon japonicus granules should also be evaluated in combination with similarity evaluation and correlation heat map analysis. This shows that the physical fingerprint method for the dry paste powder and granules is basically reliable, and basic quality control is performed with a similarity limit of less than 0.92.

[0097] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modification and change made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A quality evaluation method for compound Ophiopogon japonicus granules intermediate based on physical fingerprint, characterized in that: The method comprises the following steps: Step 1: Select physical indicators related to bulking, fluidity, uniformity, stability, and compressibility to characterize the physical properties of the compound Ophiopogon japonicus granules intermediate sample; Step 2: Use multivariate statistical analysis to conduct quality consistency analysis on the physical properties of the intermediate samples of Compound Ophiopogon japonicus granules; Step 3: Use the physical fingerprint method to evaluate the similarity of the intermediate samples of compound Ophiopogon japonicus granules; Step 4: Combine multivariate statistical analysis with physical fingerprint similarity analysis to determine the physical quality consistency control limit of the compound Ophiopogon japonicus granules intermediate.

2. The quality evaluation method of the intermediate of compound Ophiopogon japonicus granules based on physical fingerprint spectrum according to claim 1 is characterized in that: The physical indicators in step 1 include: bulk density Da, tap density Dc, outflow velocity FR, angle of repose θ, Hausner ratio HR, loss on drying LOD, hygroscopicity H, interparticle porosity Ie, Carr index IC, and particle size D corresponding to the cumulative particle size distribution number reaching 10%. 10 , the particle size D corresponding to the cumulative particle size distribution number reaching 50% 50 , the particle size D corresponding to the cumulative particle size distribution number reaching 90% 90 , particle size distribution width Span, particle size distribution range Width, relative uniformity index Iθ, and percentage %pf of particles with a size less than 50 μm.

3. The quality evaluation method of the intermediate of compound ophiopogon granules based on physical fingerprint spectrum according to claim 2 is characterized in that: The physical indices are measured based on powder science evaluation methods.

4. The quality evaluation method of the intermediate of compound ophiopogon granules based on physical fingerprint according to claim 1 is characterized in that: The multivariate statistics described in step 2 include principal component analysis, correlation analysis, and hierarchical cluster analysis using the cluster mean method combined with squared Euclidean distance.

5. The quality evaluation method of the intermediate of compound Ophiopogon japonicus granules based on physical fingerprint according to claim 1 is characterized in that: In step 3, the similarity evaluation of the intermediate sample of the compound Ophiopogon japonicus granules by using the physical fingerprint method comprises the following steps: Step 1: Dimensionless processing is performed on each physical index, and each parameter value is converted into a value ranging from 0 to 10 for unified quantification; Step 2: Construct physical fingerprint map; Step 3: Create a reference physical fingerprint map to calculate the cosine similarity.

6. The quality evaluation method of the intermediate of compound ophiopogon granules based on physical fingerprint according to claim 5 is characterized in that: The control physical fingerprint is established based on the average spectrum under various parameter items of the physical fingerprint of compound Ophiopogon japonicus granule samples from different batches.

7. The quality evaluation method of the intermediate of compound ophiopogon granules based on physical fingerprint spectrum according to claim 5 is characterized in that: The calculation of the cosine similarity requires normalization of the standardized values ​​of the physical indicators.

8. The quality evaluation method of the intermediate of compound Ophiopogon japonicus granules based on physical fingerprint spectrum according to claim 1, characterized in that: The intermediate of the compound ophiopogon granules is the compound ophiopogon granules dry paste powder and granules.

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