Cassia seed digital thin-layer chromatography detection analysis model and establishment method and application thereof

Abstract

The invention relates to a semen cassiae digital thin-layer chromatography detection analysis model as well as an establishment method and application thereof, and belongs to the technical field of quality control of traditional Chinese medicines. The semen cassiae digital thin-layer chromatography detection and analysis model is established on the basis of a relative distance value (RPf) theory and can be used in semen cassiae digital thin-layer chromatography analysis without real object control, the relative standard deviation of the RPf value obtained through comparison in different experimental conditions and different experimental varieties is minimum, and the performance superior to that of RPf is displayed. The application advantages of the characteristic spot distance method in the traditional Chinese medicine thin-layer chromatography digital standard are shown.

Description

[0001] This application is a divisional application of Chinese invention patent application No. CN 2024102966284, filed on March 15, 2024, entitled "Digital Thin-Layer Chromatography Detection and Analysis Model and Its Establishment Method and Application". The full text of the aforementioned Chinese patent application is incorporated herein by reference. Technical Field

[0002] This invention relates to the field of traditional Chinese medicine quality control technology, and in particular to a digital thin-layer chromatography detection and analysis model for cassia seeds, its establishment method, and its application. Background Technology

[0003] Thin-layer chromatography (TLC) technology originated in the 1950s and was rapidly adopted in the 1980s. TLC is an important method for evaluating the authenticity of processed Chinese medicinal herbs and the integrity of raw materials used in prepared Chinese medicines. It has advantages such as ease of operation, cost-effectiveness, speed, and wide applicability, and plays an irreplaceable role in the methodology system of Chinese medicine quality standards.

[0004] However, in the last 40 years, theoretical innovation in thin-layer chromatography (TLC) technology has essentially stagnated, failing to achieve further breakthroughs. Currently, the standard for evaluating TLC results specifies that the test sample chromatogram should show spots of the same color at the corresponding positions as the reference herb or standard in its TLC chromatogram. However, in practical applications, due to the vague description of the standard and the lack of objective, quantifiable numerical values, testing personnel rely on visual observation to evaluate key information such as the number, position, color, and size of characteristic spots in the TLC chromatogram. Different testing personnel have different understandings and evaluation criteria of the standard, easily leading to discrepancies in test conclusions. Furthermore, the reference standards or herb used in TLC significantly increase testing costs. The preparation of the reference standards also consumes large amounts of medicinal materials and organic solvents, resulting in the waste of traditional Chinese medicine resources and damage to the ecological environment. Summary of the Invention

[0005] Therefore, it is necessary to provide a digital thin-layer chromatography detection method for Cassia tora seeds without physical control to address the above problems. This method can be developed by establishing objective and quantifiable indicators and constructing a theoretical model for the digital characterization of traditional Chinese medicine through thin-layer chromatography, thereby eliminating the bias of subjective human evaluation results. At the same time, it can also reduce detection costs and protect traditional Chinese medicine resources.

[0006] On the one hand, the present invention provides a method for establishing a digital thin-layer chromatography detection and analysis model, comprising the following steps:

[0007] Establishment of a standard chromatogram library: collect thin-layer chromatograms of qualified Chinese herbal medicines to be tested, including thin-layer chromatograms under differential experimental conditions, to obtain a standard chromatogram library. The differential experimental conditions include at least two humidity conditions, at least two temperature conditions, and at least two batches of Chinese herbal medicines.

[0008] Analytical Model Establishment: Select at least two representative spots from the above thin-layer chromatograms as analytical spots. With or without the origin, form characteristic spots. Group the characteristic spots to obtain characteristic spot groups of three. In each characteristic spot group, use one characteristic spot as the localizing spot. Calculate the distances between the other two characteristic spots and the localizing spot. Calculate the ratio between the minimum and maximum distances, denoted as the spacing ratio RPf. Use the spacing ratio RPf as the evaluation index. Based on the spacing ratio RPf of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library, establish the evaluation standard range for the spacing ratio RPf.

[0009] In thin-layer chromatography (TLC), the retardation factor (Rf) is typically used to represent the relative distance a substance has traveled for compound identification. However, experimental temperature and humidity, silica gel G plates, and the specifications and batches of polyamide films can all affect the Rf value, leading to inaccuracies in Rf comparisons of the same component between different laboratories. The relative retardation factor (RRf) eliminates systematic errors to some extent, exhibiting better repeatability and reliability than the Rf value. However, both Rf and RRf values ​​use the origin as the positioning point and are calculated by measuring the distance between the thin-layer spot and the origin. Since the origin does not change position due to differences in the developing solvent or environment during TLC development, the reproducibility of retardation factors and relative retardation factors is poor.

[0010] The inventors considered that although thin-layer chromatography behavior is affected by a variety of factors, such as the thickness and density of the thin-layer plate, the accuracy of the developing solvent preparation, the temperature and humidity of the experimental environment, the adequacy of the colorimetric agent spraying, and the heating and color development time, the same component is prone to produce large differences in Rf values. However, since the components detected and developed in the same thin-layer system have a certain similarity, the degree of influence they are affected by environmental factors should be balanced.

[0011] Guided by this concept, the inventors proposed the aforementioned digital thin-layer chromatography detection and analysis model, collected thin-layer chromatograms under different conditions, and based on their own long-term practical experience, proposed that the analytical spots can be grouped. According to the characteristics exhibited by each group of characteristic spots, the relative distance value theory was proposed for the first time, and the distance ratio RPf was calculated based on the relative distance value to reduce the influence of different environmental conditions on thin-layer chromatography, thereby realizing digital thin-layer chromatography detection without reference standards or without reference medicinal materials.

[0012] In some schemes, the criterion for grouping feature spots by adding or not adding the origin in the analysis model building step is:

[0013] The first step is to determine whether the origin is irregular in shape or has unclear boundaries. If the origin and the feature spots are regular in shape and have clear boundaries, then proceed to the second step of the determination.

[0014] The second step is to determine whether the number of analyzed spots is two. In this case, the origin is added as a feature spot for grouping.

[0015] When the number of analytical spots is ≥3, along the solvent spreading direction, the origin and the first three analytical spots are sequentially numbered A, B, C, and D from the origin to the solvent front. The distances L-OB and L-CA between OB and CA are calculated respectively. If L-OB ≥ L-CA, the origin is not included as a characteristic spot for grouping; if L-OB ≥ L-CA, the origin is included as a characteristic spot for grouping.

[0016] In some schemes, the analytical spots meet the following requirements:

[0017] (1) The spots were present in both the chromatogram of the sample of this Chinese herbal medicine and the chromatogram of the reference herb or reference substance, and their positions corresponded.

[0018] (2) The spots are analyzed to be regular in shape, have clear boundaries, and exist stably;

[0019] (3) The specific displacement value of the analytical spot is 0.05 to 0.8, preferably 0.1 to 0.8, and more preferably 0.2 to 0.8;

[0020] (4) The analysis of spots is not affected by negative interference and has specificity.

[0021] Understandably, the aforementioned "positional correspondence" refers to the similarity of the ratio shift (Rf) values ​​of the spots in the chromatograms of the sample and the reference medicinal material or reference standard, which would allow a person skilled in the art to determine that the spots are of the same component.

[0022] In some schemes, the localization spots are determined by the following method: Within each group of feature spots, three feature spots are selected as candidate localization spots. The distances between the other two feature spots and the localization spot are measured, the ratio of the minimum distance to the maximum distance is calculated, the relative standard deviation of each ratio is calculated, and the feature spot with the smallest relative standard deviation is selected as the localization spot. That is, different spots are used in turn as candidate localization spots, and their standard deviations are examined through experimental testing. Spots with small standard deviations and high stability are selected as localization spots, which can improve the accuracy and stability of this analysis model.

[0023] In some schemes, the analytical model building step is grouped according to the following rules:

[0024] (1) Number the feature spots along the solvent spreading direction, and divide the first three spots and the last three spots into the first group and the last group, respectively.

[0025] (2) Each group shares common characteristic spots with its adjacent groups;

[0026] (3) Except for the first or last group, the feature spots in the remaining groups are grouped according to the principle of proximity;

[0027] (4) The grouping meets the requirements of (1)-(3) above, and the total number of groups is the smallest.

[0028] The general idea of ​​the above rules is to first group the head and tail spots, and then combine them to obtain the middle group. Each group should be related to each other in order to reflect the overall effect. Therefore, each group should have common feature spots with the adjacent group. However, the selection of 1 or 2 feature spots as common spots can be adjusted according to the number and distance of spots to meet the requirements of (3) and (4).

[0029] In some schemes, the feature spots are numbered along the solvent spread direction and grouped according to the following rules:

[0030] When the total number of spots analyzed is 2, add the origin or thin-layer plate spot as a characteristic spot to form a group;

[0031] When the total number of feature spots is 3, these 3 feature spots are grouped together.

[0032] When the total number of feature spots is 4, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 2, 3, and 4 are divided into the last group.

[0033] When the total number of feature spots is 5, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 3, 4, and 5 are divided into the last group.

[0034] When the total number of feature spots is 6, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 4, 5, and 6 are divided into the last group. The distance between feature spots numbered 2 and 4 and the distance between feature spots numbered 3 and 5 are calculated, which are D-24 and D-35 respectively. When D-24 > D-35, feature spots numbered 3, 4, and 5 are selected as the middle group. When D-24 < D-35, feature spots numbered 2, 3, and 4 are selected as the middle group. When D-24 = D-35, feature spots numbered 3, 4, and 5 or feature spots numbered 2, 3, and 4 can be selected as the middle group.

[0035] When the total number of feature spots is 7, feature spots numbered 1, 2, and 3 are divided into the first group, feature spots numbered 5, 6, and 7 are divided into the last group, and feature spots numbered 3, 4, and 5 are divided into the middle group.

[0036] When the total number of feature spots is 8, feature spots numbered 1, 2, and 3 are divided into the first group, feature spots numbered 6, 7, and 8 are divided into the last group, feature spots numbered 3, 4, and 5 are divided into the first middle group, and feature spots numbered 4, 5, and 6 are divided into the second middle group.

[0037] In some schemes, the above-described establishment method meets at least one of the following conditions:

[0038] (1) The Chinese medicine varieties mentioned include Chinese medicinal decoction pieces and / or Chinese medicine preparations;

[0039] (2) The differential experimental conditions also include at least one of the following conditions: at least two specifications of thin-layer plates, at least two experimental dates, at least two development times, and at least two operators.

[0040] Understandably, the more differential experimental conditions are selected and the more comprehensive the standard chromatograms are collected, the more comprehensive the model can be analyzed and judged for the test samples. However, considering the different humidity, temperature and batch samples, which already include factors that have a significant impact on thin-layer chromatography, the basic judgment requirements can be met.

[0041] On the other hand, the present invention also provides a digital thin-layer chromatography analysis model, which is established using the above-described method for establishing a digital thin-layer chromatography detection and analysis model.

[0042] Understandably, the aforementioned digital thin-layer chromatography analysis model can be based on pre-stored chromatograms and RPf (pigmentation ratio) data, which can be used to make judgments by directly comparing RPf data values. Alternatively, it can be pre-programmed into software that scans the thin-layer chromatogram of the Chinese herbal medicine to be tested, uses image recognition technology to identify spots, automatically judges the results, and outputs the findings. The image recognition and data comparison methods can be designed according to conventional conditions.

[0043] On the other hand, this invention also provides a digital thin-layer chromatography detection and analysis method. A thin-layer chromatogram of the Chinese herbal medicine to be tested is taken, and the corresponding interval ratio RPf is calculated according to the aforementioned method for establishing a digital thin-layer chromatography detection and analysis model. This interval ratio is then compared with the evaluation standard range to obtain the digital thin-layer chromatography detection result. For example, when the interval ratio RPf of the Chinese herbal medicine to be tested falls within the evaluation standard range, it can be considered a qualified product; conversely, if it exceeds the evaluation standard range, it can be considered an unqualified product.

[0044] On the other hand, the present invention also provides a digital thin-layer chromatography detection and analysis method for Cassia tora seeds, comprising the following steps:

[0045] Thin-layer chromatography detection: Take cassia seed sample, soak in methanol, filter and collect the filtrate, evaporate the filtrate to dryness, dissolve the residue in water, add hydrochloric acid and heat in a water bath, cool, extract with ether, evaporate the ether solution to dryness, dissolve the residue in chloroform to obtain the test solution; spot the test solution on a silica gel H thin-layer plate, develop with petroleum ether-acetone at 30-60℃ (v / v) at 1.5-2.5:1, remove, air dry, fumigate in ammonia vapor, and photograph to obtain the thin-layer chromatogram;

[0046] Establishment of a standard chromatographic library: collect thin-layer chromatograms of qualified cassia seed products, including thin-layer chromatograms under differential experimental conditions, to obtain a standard chromatographic library. The differential experimental conditions include at least two humidity conditions, at least two temperature conditions, and at least two batches of cassia seed samples.

[0047] Analytical Model Establishment: Along the solvent development direction, the origin and each spot are numbered sequentially from the origin to the solvent front. When the cassia seed sample is raw cassia seed, spots 1, 3, and 5 are designated as analytical spots, and these three analytical spots are grouped as characteristic spots. Spot 5 is designated as the localizing spot. The distances L-15 and L-35 between spot 5 and spots 1 and 3 are calculated, respectively. The ratio between the minimum distance L-35 and the maximum distance L-15 is calculated and denoted as the spacing ratio RPf6. This spacing ratio RPf6 is used as the evaluation index, based on the spacing ratio R of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library. For Pf6, establish the evaluation standard range for the spacing ratio RPf6; when the cassia seed is roasted cassia seed, the 1st, 2nd, and 5th spots are used as analytical spots, and these three analytical spots are used as characteristic spot groups. The 5th spot is used as the positioning spot. The distances L-15 and L-25 between the 5th spot and the 1st and 2nd spots are calculated respectively. The ratio between the minimum distance L-25 and the maximum distance L-15 is calculated and recorded as the spacing ratio RPf7. Using the spacing ratio RPf7 as the evaluation index, the evaluation standard range for the spacing ratio RPf7 is established based on the spacing ratio RPf7 of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library.

[0048] In some of the thin-layer chromatography detection procedures for the above-mentioned sample being cassia seed, the test solution is extracted by the following method: Take the cassia seed sample, add 10±5 ml of methanol, soak for 1±0.5 hours, filter, evaporate the filtrate to dryness, add 10±5 ml of water to dissolve the residue, add 1±0.2 ml of hydrochloric acid, heat in a water bath for 30±10 minutes, cool immediately, extract with ether 2-3 times, 20±10 ml each time, combine the ether solutions, evaporate to dryness, add 1±0.5 ml of chloroform to dissolve the residue, which is the test solution.

[0049] In some schemes for the thin-layer chromatography detection steps of the above-mentioned sample being cassia seed, the developing solvent is petroleum ether-acetone at 30-60°C with a volume ratio of 2:1.

[0050] In some protocols for the thin-layer chromatography detection steps of the above-mentioned sample being Cassia tora, the humidity conditions include relative humidity of 32±5%, 56±5%, and 88±5%.

[0051] In some protocols for the thin-layer chromatography detection steps of the above-mentioned sample being cassia seed, the temperature conditions include 5±2℃ and 25±5℃.

[0052] In some of the above-mentioned thin-layer chromatography detection procedures for cassia seeds, the differential experimental conditions also include at least two different sizes of thin-layer plates.

[0053] In some of the thin-layer chromatography detection procedures for the above-mentioned sample being cassia seed, the thin-layer plate includes silica gel H thin-layer plates produced by Merck AG & Co., Ltd. and Qingdao Ocean Chemical Co., Ltd.

[0054] In some schemes of the analytical model establishment steps for the above-mentioned sample being cassia seed, when the cassia seed sample is raw cassia seed, the Rf value of the first spot is 0 (i.e., the origin), the Rf value of the third spot is 0.63±0.06, and the Rf value of the fifth spot is 0.75±0.07; when the cassia seed sample is roasted cassia seed, the Rf value of the first spot is 0 (i.e., the origin), the Rf value of the second spot is 0.59±0.06, and the Rf value of the fifth spot is 0.79±0.08.

[0055] On the other hand, the present invention also provides a digital thin-layer chromatography detection and analysis method for cassia seeds, comprising the following steps: taking the thin-layer chromatogram of the cassia seed sample to be tested obtained by the same method in the above-mentioned thin-layer chromatography detection steps, calculating the corresponding spacing ratio RPf6 or RPf7 according to the method in the analysis model establishment step, comparing it with the evaluation standard range, and obtaining the digital thin-layer chromatography detection result of cassia seeds.

[0056] In some of the above-mentioned digital thin-layer chromatography detection and analysis methods for cassia seeds, when the interval ratio RPf6 is 0.59 to 0.84, or the interval ratio RPf7 is 0.65 to 0.78, the raw or roasted cassia seed sample is deemed qualified.

[0057] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0058] The reagents and raw materials used in this invention are all commercially available.

[0059] The positive and progressive effects of this invention are as follows:

[0060] This invention is based on the concepts of Rf and RRf, and proposes for the first time the theory of relative distance value (RPf), which is applied to the digital evaluation of the relative positions of cassia seeds in thin-layer chromatography. This yields a method for establishing a digital thin-layer chromatography detection and analysis model for cassia seeds. The digital thin-layer chromatography detection and analysis model for cassia seeds established using this method can be used in digital thin-layer chromatography analysis of cassia seeds without physical controls. Under different experimental conditions and with different experimental varieties, the relative standard deviation of the RPf value is found to be the smallest, showing superior performance compared to RRf. This demonstrates the advantages of the characteristic spot distance method in the application of digital standards for thin-layer chromatography of traditional Chinese medicine. Attached Figure Description

[0061] Figure 1 The thin-layer chromatogram of raw cassia seeds in Example 2;

[0062] Figure 2 The values ​​and ranges of the spacing ratio RPf6 in different thin-layer chromatograms of Example 2;

[0063] Figure 3 The image shows a thin-layer chromatogram of roasted cassia seeds in Example 3.

[0064] Figure 4 The values ​​and ranges of the spacing ratio RPf7 in different thin-layer chromatograms of Example 3; Detailed Implementation

[0065] The present invention is further illustrated below by way of embodiments, but the invention is not limited to the scope of the embodiments described herein. Experimental methods in the following embodiments that do not specify specific conditions were performed according to conventional methods and conditions, or as selected according to the product instructions.

[0066] Instruments and reagents

[0067] 1.1 Main Instruments

[0068] XS105DU electronic balance (Mettler-Toledo Group); KQ-500DE CNC ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd., Jiangsu Province); AUTOMSTIC TLC Sampler 4 automatic sampler (CAMAG, Switzerland); TLC Visualizer thin-layer digital imaging system (CAMAG, Switzerland); Milli-Q Academic ultrapure water system (Millipore, USA).

[0069] 1.2 Main Reagents and Medicinal Materials

[0070] Cinnamaldehyde reference standard and Ligusticum chuanxiong reference medicinal material were purchased from the National Institutes for Food and Drug Control; petroleum ether, ethanol, ethyl acetate, and diethyl ether were all analytical grade and purchased from Guangzhou Chemical Reagent Factory; dinitrophenylhydrazine was purchased from Beijing Bailingwei Technology Co., Ltd.; Milli-Q water was prepared in the laboratory.

[0071] The commercially available cinnamon medicinal material was identified by the chief pharmacist of the China National Institutes for Food and Drug Control as the dried bark of *Cinnamomum cassia* Presl, a plant in the Lauraceae family. The medicinal material is stored in the Traditional Chinese Medicine Sample Room of the Shenzhen Institute for Drug Control.

[0072] Example 1

[0073] A digital thin-layer chromatography detection and analysis method.

[0074] I. Establishment of Standard Atlas Library

[0075] Collect thin-layer chromatograms of qualified Chinese herbal medicine varieties to be tested. The thin-layer chromatograms of the qualified varieties include thin-layer chromatograms under differential experimental conditions to obtain a standard chromatogram library. The differential experimental conditions include at least two humidity conditions, at least two temperature conditions, and at least two batches of Chinese herbal medicine varieties.

[0076] The thin-layer chromatograms obtained from the above tests on Chinese herbal medicine varieties were obtained under the routine testing conditions for that variety. Alternatively, the testing conditions for thin-layer chromatograms of that variety as recorded in the Chinese Pharmacopoeia, or any other testing conditions that can be used to evaluate that Chinese herbal medicine variety, can be referenced.

[0077] Understandably, the aforementioned standard chromatographic library needs to comprehensively collect thin-layer chromatograms of qualified products under different experimental conditions. The more chromatograms of different categories and conditions included, the more comprehensive the standard chromatographic library will be, and the higher the accuracy of the subsequent analytical model will be. Considering that the possibilities of non-conforming products being non-conforming are diverse and difficult to collect completely through enumeration, this case is based on the standardization of a large number of qualified products. This avoids the impact of non-conforming products on overall effectiveness, and is highly practical with high application value.

[0078] The above-mentioned qualified products refer to traditional Chinese medicines or prepared Chinese medicines that meet the relevant standards of the Chinese Pharmacopoeia. If there are any traditional Chinese medicines or prepared Chinese medicines that do not meet the relevant standards of the Chinese Pharmacopoeia, they are considered unqualified products.

[0079] The different conditions mentioned above include different humidity, temperature, batch, thin-layer plate specifications, experimental date, development time, operators, etc. Understandably, factors that may affect the Rf value in actual work can be added as different conditions.

[0080] As for the selection of Chinese medicine varieties, it can be selected according to the actual object to be analyzed, such as Chinese medicine decoction pieces, preparations, etc., which can be detected by thin-layer chromatography analysis.

[0081] II. Establishment of Analytical Model

[0082] 1. Select and analyze spots

[0083] Select at least two representative spots from the above thin-layer chromatograms as analytical spots, and these analytical spots should meet the following requirements:

[0084] (1) The spots were found in both the chromatogram of the sample of this Chinese medicine and the chromatogram of the reference herb or reference substance, and their positions corresponded.

[0085] It is understandable that although reference standards or reference medicinal materials are not necessarily used in the establishment of the standard spectral library and in subsequent analysis and evaluation, considering the spots of index reference standards or reference medicinal materials in the establishment of the standard spectral library and selecting characteristic spots based on these can better reflect the quality of the Chinese medicine varieties being analyzed and tested.

[0086] (2) The spots are regular in shape, have clear boundaries, and are stable.

[0087] This standard is consistent with the requirements for inspecting spots in the field, which can improve the stability of the method.

[0088] (3) The specific displacement value of the analytical spot is 0.05 to 0.8, preferably 0.1 to 0.8, and more preferably 0.2 to 0.8.

[0089] By limiting the specific shift value of the analytical spots to 0.05 to 0.8, preferably 0.1 to 0.8, and more preferably 0.2 to 0.8, the influence of different conditions on the specific shift value can be further reduced, thereby improving the stability of this method.

[0090] (4) The analysis of spots is not affected by negative interference and has specificity.

[0091] 2. Consider whether to add the origin.

[0092] The criteria for determining whether to include the origin to form feature spots for grouping are as follows:

[0093] The first step is to determine whether the original spot is unclear or difficult to identify. If the original spot is clear and identifiable, then proceed to the second step of the determination.

[0094] The second step is to determine if there are two spots being analyzed. In this case, the origin is added as a feature spot for grouping.

[0095] When the number of analytical spots is ≥3, along the solvent spreading direction, the origin and the first three analytical spots are sequentially numbered A, B, C, and D from the origin to the solvent front. The distances between AC and BD are L-ac and L-bd. If L-ac ≥ L-bd, the origin is not included as a characteristic spot for grouping; if L-ac < L-bd, the origin is included as a characteristic spot for grouping.

[0096] 3. Grouping

[0097] The feature spots are grouped into groups of three, according to the following grouping rules:

[0098] (1) Number the feature spots along the solvent spreading direction, and divide the first three spots and the last three spots into the first group and the last group, respectively.

[0099] (2) Each group shares common characteristic spots with its adjacent groups;

[0100] (3) Except for the first or last group, the feature spots in the remaining groups are grouped according to the principle of proximity;

[0101] (4) The grouping meets the requirements of (1)-(3) above, and the total number of groups is the smallest.

[0102] The following are examples of grouping methods when the number of feature spots is different.

[0103] The characteristic spots are numbered along the solvent spread direction and grouped according to the following rules:

[0104] When the total number of spots analyzed is 2, which is less than three spots, the origin or a thin-layer plate spot must be added as a characteristic spot (if the origin is difficult to identify, it can be manually identified by the spotting location and then added as a thin-layer plate spot) to form a group.

[0105] When the total number of feature spots is 3, these 3 feature spots are grouped together.

[0106] When the total number of feature spots is 4, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 2, 3, and 4 are divided into the last group.

[0107] When the total number of feature spots is 5, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 3, 4, and 5 are divided into the last group.

[0108] When the total number of feature spots is 6, feature spots numbered 1, 2, and 3 are divided into the first group, and feature spots numbered 4, 5, and 6 are divided into the last group. The distance between feature spots numbered 2 and 4 and the distance between feature spots numbered 3 and 5 are calculated, which are D-24 and D-35 respectively. When D-24 > D-35, feature spots numbered 3, 4, and 5 are selected as the middle group. When D-24 < D-35, feature spots numbered 2, 3, and 4 are selected as the middle group. When D-24 = D-35, feature spots numbered 3, 4, and 5 or feature spots numbered 2, 3, and 4 can be selected as the middle group.

[0109] When the total number of feature spots is 7, feature spots numbered 1, 2, and 3 are divided into the first group, feature spots numbered 5, 6, and 7 are divided into the last group, and feature spots numbered 3, 4, and 5 are divided into the middle group.

[0110] When the total number of feature spots is 8, feature spots numbered 1, 2, and 3 are divided into the first group, feature spots numbered 6, 7, and 8 are divided into the last group, feature spots numbered 3, 4, and 5 are divided into the first middle group, and feature spots numbered 4, 5, and 6 are divided into the second middle group.

[0111] 4. Selecting and positioning spots

[0112] After grouping, at least one feature spot group is obtained, and one feature spot in each feature spot group is selected as the localization spot. The method for selecting the localization spot is as follows: within each feature spot group, three feature spots are selected as candidate localization spots, the distances between the other two feature spots and the localization spot are measured, the ratio of the minimum distance to the maximum distance is calculated, the relative standard deviation of each ratio is calculated, and the feature spot with the smallest relative standard deviation value is selected as the localization spot.

[0113] 5. Calculate the spacing ratio RPf

[0114] In each group of feature spots, the distance between the other two feature spots and the localized spot is calculated, and the ratio between the minimum distance and the maximum distance is calculated and denoted as the spacing ratio RPf. The spacing ratio RPf is used as the evaluation index.

[0115] Understandably, each set of feature spots can yield a corresponding spacing ratio RPf. If there are two sets of feature spots, then the corresponding spacing ratios RPf1 and RPf2 can be obtained respectively; if there are three sets of feature spots, then the corresponding spacing ratios RPf1, RPf2 and RPf3 can be obtained respectively; and so on.

[0116] 6. Establish the scope of evaluation standards

[0117] Based on the spacing ratio RPf of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library, an evaluation standard range for the spacing ratio RPf is established, which is then obtained.

[0118] Understandably, the evaluation criteria range for the above-mentioned interval ratio RPf is defined by the values ​​that may be obtained under different experimental conditions of a large sample size of qualified Chinese medicine varieties.

[0119] The specific requirements depend on the testing requirements and the data from different Chinese medicine varieties.

[0120] III. Comparative Analysis

[0121] Take the thin-layer chromatogram of the Chinese herbal medicine to be tested, calculate the corresponding interval ratio RPf according to the above-mentioned method for establishing the digital thin-layer chromatographic detection and analysis model, and compare it with the evaluation standard range. If it falls within the qualified range, it can be judged as qualified, and the digital thin-layer chromatographic detection result is obtained.

[0122] Example 2

[0123] A digital thin-layer chromatography detection and analysis method for cassia seeds, referring to the method in Example 1, is applied to the digital thin-layer chromatography detection and analysis of raw cassia seed medicinal materials.

[0124] I. Thin-layer chromatography detection

[0125] Take a sample of cassia seeds, add 10 ml of methanol, soak for 1 hour, filter, evaporate the filtrate to dryness, dissolve the residue in 10 ml of water, add 1 ml of hydrochloric acid, heat in a water bath for 30 minutes, cool immediately, extract twice with 20 ml of ether each time, combine the ether extracts, evaporate to dryness, dissolve the residue in 1 ml of chloroform, and use this as the test solution. Spot the test solution onto a silica gel H thin-layer plate, develop with petroleum ether (30-60℃)-acetone (2:1) as the developing solvent, remove, and air dry. After fumigating in ammonia vapor, photograph to obtain a thin-layer chromatogram (e.g., Figure 1 ).

[0126] II. Establishment of Standard Atlas Library

[0127] Thin-layer chromatograms of qualified cassia seed products were collected, including thin-layer chromatograms under different experimental conditions, to obtain a standard chromatogram library.

[0128] In this embodiment, the thin-layer chromatograms of Cassia tora seeds were collected from different silica gel thin-layer chromatography plates (Merck Corporation and Qingdao Ocean Chemical Co., Ltd.) at different temperatures (5°C and 25°C) and different relative humidityes (32%, 56%, and 88%) according to the above method.

[0129] The thin-layer chromatograms of Cassia seed were divided into 58 batches of Cassia seed samples for establishing the method and 30 batches of Cassia seed samples for validating the method. The samples for establishing the method uniformly covered various plates, temperatures, and relative humidity levels.

[0130] III. Establishment of Analytical Model

[0131] 1. Select and analyze spots

[0132] Following the principles of Example 1, analytical spots were selected. Along the solvent development direction, the origin and each spot were numbered sequentially from the origin towards the solvent front. Based on the characteristics of the reference herb spots in the cassia seed identification chromatography, the differences in the test sample spots, and combined with experience gained through manual judgment, the relatively stable 3rd and 5th spots were ultimately selected as analytical spots. The Rf values ​​for spots 3 and 5 were 0.63 and 0.75, respectively (e.g., ...). Figure 1 (As shown).

[0133] 2. Consider whether to add the origin.

[0134] In this embodiment, only two spots are analyzed, and the origin is clear. The origin is added as a feature spot for grouping.

[0135] 3. Grouping

[0136] This embodiment has a total of 3 feature spots, which are grouped together.

[0137] 4. Selecting and positioning spots

[0138] Within the aforementioned feature spot group, three feature spots were selected as candidate positioning spots. The distances between the other two feature spots and the positioning spots were measured, the ratio of the minimum distance to the maximum distance was calculated, and the relative standard deviation of each ratio was calculated. The results are shown in the table below.

[0139] Table 1. Analysis results of location data for different locating spots

[0140]

[0141] The results show that the relative standard deviations of the three different positioning points in the thin-layer chromatogram of cassia seed are 0.25, 0.34 and 0.09, respectively. The relative standard deviation obtained by using the 5th spot as the positioning spot is the smallest. Therefore, the 5th spot with the smallest relative standard deviation value is selected as the positioning spot.

[0142] 5. Establishment of calculation rules for feature spot positions

[0143] Referring to the method in Example 2, this example simultaneously compares three calculation methods to evaluate the positions of three spots in cassia seeds. Because the origin is included as the analytical spot, the second calculation method is the same as the first calculation method. X1 and X3 are calculated according to the two different calculation methods described above, and the results are shown in the table below.

[0144] Table 2. Analysis results of location data using different calculation methods

[0145]

[0146] The above results show that the relative standard deviations of the two different calculation methods in the cassia seed sample are 0.07 and 0.23, respectively. The first calculation method has a smaller relative standard deviation, indicating that the first calculation method is least affected by various factors. Therefore, the RPf value is selected as the evaluation method for spot localization in thin-layer chromatography of cassia seed.

[0147] 6. Calculate the spacing ratio RPf

[0148] Following the first method described above, using the 5th spot as the positioning spot, calculate the distances L-35 and L-15 between the 5th spot and the 3rd and 1st spots, respectively. Calculate the ratio between the minimum distance L-35 and the maximum distance L-15, denoted as the spacing ratio RPf6. Using this spacing ratio RPf6 as the evaluation index, based on the spacing ratio RPf6 of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library (58 batches of samples) (e.g., ... Figure 2 (The black circle in the middle) The evaluation criteria for the spacing ratio RPf6 is set in the range of 0.59 to 0.84.

[0149] IV. Comparative Analysis

[0150] Take the thin-layer chromatograms of the above 30 batches of Cassia seed samples, and calculate the corresponding spacing ratio RPf6 according to the first method determined in the analytical model establishment step (e.g., Figure 2 The results of digital thin-layer chromatography detection of Cassia seed (circled in blue) were compared with the above evaluation criteria range to obtain the results of digital thin-layer chromatography detection of Cassia seed.

[0151] The data results show that the RPf6 values ​​of the Cassia tora samples used in the 30 batches of validation methods were all within the intended range, indicating that the RPf6-based digital evaluation method for Cassia tora thin-layer chromatography has good performance.

[0152] Example 3

[0153] A digital thin-layer chromatography detection and analysis method for stir-fried cassia seeds, referring to the method in Example 1, is applied to the digital thin-layer chromatography detection and analysis of stir-fried cassia seed medicinal materials.

[0154] I. Thin-layer chromatography detection

[0155] Take a sample of roasted cassia seeds, add 10 ml of methanol, soak for 1 hour, filter, evaporate the filtrate to dryness, dissolve the residue in 10 ml of water, add 1 ml of hydrochloric acid, heat in a water bath for 30 minutes, cool immediately, extract twice with 20 ml of ether each time, combine the ether extracts, evaporate to dryness, dissolve the residue in 1 ml of chloroform, and use this as the test solution. Spot the test solution onto a silica gel H thin-layer plate, develop with petroleum ether (30-60℃)-acetone (2:1) as the developing solvent, remove, and air dry. After fumigating in ammonia vapor, photograph to obtain a thin-layer chromatogram (e.g., Figure 3 ).

[0156] II. Establishment of Standard Atlas Library

[0157] Thin-layer chromatograms of qualified roasted cassia seeds were collected, including thin-layer chromatograms under different experimental conditions, to obtain a standard chromatogram library.

[0158] In this embodiment, the above method was used to detect and collect the thin-layer chromatograms of roasted cassia seeds on different silica gel thin-layer chromatography plates (Merck Corporation and Qingdao Ocean Chemical Co., Ltd.), at different temperatures (5°C and 25°C), and at different relative humidityes (32%, 56%, and 88%).

[0159] The thin-layer chromatograms of roasted cassia seeds were divided into 40 batches of roasted cassia seed samples for establishing the method and 39 batches of roasted cassia seed samples for validating the method. The samples for establishing the method uniformly covered various silica gel H plates, various temperatures and various relative humidity.

[0160] III. Establishment of Analytical Model

[0161] 1. Select and analyze spots

[0162] Following the principles of Example 1, analytical spots were selected. Along the solvent development direction, the origin and each spot were numbered sequentially from the origin towards the solvent front. Based on the characteristics of the reference herb spots in the identification chromatography of roasted cassia seeds, the differences in the test sample spots, and combined with experience gained through manual judgment, the relatively stable 2nd and 5th spots were ultimately selected as analytical spots. The Rf values ​​for spots 2 and 5 were 0.59 and 0.79, respectively (e.g., ...). Figure 3 (As shown).

[0163] 2. Consider whether to add the origin.

[0164] In this embodiment, only two spots are analyzed, and the origin is clear. The origin is added as a feature spot for grouping.

[0165] 3. Grouping

[0166] This embodiment has a total of 3 feature spots, which are grouped together.

[0167] 4. Selecting and positioning spots

[0168] Within the aforementioned feature spot group, three feature spots were selected as candidate positioning spots. The distances between the other two feature spots and the positioning spots were measured, the ratio of the minimum distance to the maximum distance was calculated, and the relative standard deviation of each ratio was calculated. The results are shown in the table below.

[0169] Table 3. Analysis results of location data for different locating spots

[0170]

[0171] The results show that the relative standard deviations of the three different positioning points in the thin-layer chromatogram of roasted cassia seeds are 0.17, 0.25 and 0.07, respectively. The fifth spot has the smallest relative standard deviation, so the fifth spot with the smallest relative standard deviation is selected as the positioning spot.

[0172] 5. Establishment of calculation rules for feature spot positions

[0173] Referring to the method in Example 2, this example simultaneously compares three calculation methods to evaluate the positions of three spots in cassia seeds. Because the origin is included as the analytical spot, the second calculation method is the same as the first calculation method. X1 and X3 are calculated according to the two different calculation methods described above, and the results are shown in the table below.

[0174] Table 4. Analysis results of location data using different calculation methods

[0175]

[0176] The above results show that the relative standard deviations of the three different calculation methods in the fried cassia seed sample are 0.07 and 0.23, respectively. The first calculation method has the smallest relative standard deviation, indicating that the first calculation method is least affected by various factors. Therefore, the RPf value is selected as the evaluation method for spot localization in thin-layer chromatography of fried cassia seed.

[0177] 6. Calculate the spacing ratio RPf

[0178] Following the first method described above, using the 5th spot as the positioning spot, calculate the distances L-51 and L-52 between the 5th spot and the 1st and 2nd spots, respectively. Calculate the ratio between the minimum distance L-52 and the maximum distance L-51, denoted as the spacing ratio RPf7. Using this spacing ratio RPf7 as the evaluation index, and based on the spacing ratio RPf1 of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library (40 batches of samples) (e.g., ...), Figure 4 (The black circle in the middle) The evaluation criteria for the spacing ratio RPf7 is set in the range of 0.65 to 0.78.

[0179] IV. Comparative Analysis

[0180] Thin-layer chromatograms of the above 39 batches of roasted cassia seed samples were obtained, and the corresponding spacing ratio RPf7 was calculated according to the first method determined in the analytical model establishment steps (e.g., Figure 4 The results of digital thin-layer chromatography detection of roasted cassia seeds (circled in blue) were compared with the above evaluation criteria range to obtain the results of digital thin-layer chromatography detection of roasted cassia seeds.

[0181] The data results show that the RPf7 values ​​of the 39 batches of roasted cassia seed samples used for the validation method were all within the intended range, indicating that the digital evaluation method of roasted cassia seed thin-layer chromatography based on RPf7 value has good performance.

Claims

1. A digital thin-layer chromatography analysis model for Cassia tora seeds, characterized in that, Established using the following method: Thin-layer chromatography detection: Take cassia seed sample, soak in methanol, filter and collect the filtrate, evaporate the filtrate to dryness, dissolve the residue in water, add hydrochloric acid and heat in a water bath, cool, extract with ether, evaporate the ether solution to dryness, dissolve the residue in chloroform to obtain the test solution; spot the test solution on a silica gel H thin-layer plate, develop with petroleum ether-acetone at 30-60℃ (v / v) at 1.5-2.5:1, remove, air dry, fumigate in ammonia vapor, and photograph to obtain the thin-layer chromatogram; Establishment of a standard chromatographic library: collect thin-layer chromatograms of qualified cassia seed products, including thin-layer chromatograms under differential experimental conditions, to obtain a standard chromatographic library. The differential experimental conditions include at least two humidity conditions, at least two temperature conditions, and at least two batches of cassia seed samples. Analytical Model Establishment: Along the solvent development direction, the origin and each spot are numbered sequentially from the origin to the solvent front. When the cassia seed sample is raw cassia seed, spots 1, 3, and 5 are designated as analytical spots, and these three analytical spots are grouped as characteristic spots. Spot 5 is designated as the localizing spot. The distances L-15 and L-35 between spot 5 and spots 1 and 3 are calculated, respectively. The ratio between the minimum distance L-35 and the maximum distance L-15 is calculated and denoted as the spacing ratio RPf6. This spacing ratio RPf6 is used as the evaluation index, based on the spacing ratio R of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library. For Pf6, establish the evaluation standard range for the spacing ratio RPf6; when the cassia seed is roasted cassia seed, the 1st, 2nd, and 5th spots are used as analytical spots, and these three analytical spots are used as characteristic spot groups. The 5th spot is used as the positioning spot. The distances L-15 and L-25 between the 5th spot and the 1st and 2nd spots are calculated respectively. The ratio between the minimum distance L-25 and the maximum distance L-15 is calculated and recorded as the spacing ratio RPf7. Using the spacing ratio RPf7 as the evaluation index, the evaluation standard range for the spacing ratio RPf7 is established based on the spacing ratio RPf7 of each characteristic spot group in different thin-layer chromatograms in the standard chromatogram library.

2. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, The test solution was extracted by the following method: Take a cassia seed sample, add 10±5 ml of methanol, soak for 1±0.5 hours, filter, evaporate the filtrate to dryness, add 10±5 ml of water to dissolve the residue, add 1±0.2 ml of hydrochloric acid, heat in a water bath for 30±10 minutes, cool immediately, extract with ether 2-3 times, 20±10 ml each time, combine the ether extracts, evaporate to dryness, add 1±0.5 ml of chloroform to dissolve the residue, which is the test solution.

3. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, The developing agent is petroleum ether-acetone at 30-60°C with a volume ratio of 2:

1.

4. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, The humidity conditions include relative humidity of 32±5%, 56±5%, and 88±5%.

5. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, The temperature conditions include 5±2℃ and 25±5℃.

6. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, The differential experimental conditions also include at least two different sizes of thin-layer plates.

7. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 6, characterized in that, The thin-layer plate includes silicone H thin-layer plates produced by Merck AG & Co., Ltd. and Qingdao Ocean Chemical Co., Ltd.

8. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, When the cassia seed sample is raw cassia seed, the Rf value of the first spot is 0, the Rf value of the third spot is 0.63±0.06, and the Rf value of the fifth spot is 0.75±0.07; when the cassia seed sample is roasted cassia seed, the Rf value of the first spot is 0, the Rf value of the second spot is 0.59±0.06, and the Rf value of the fifth spot is 0.79±0.

08.

9. A digital thin-layer chromatography detection and analysis method for Cassia tora seeds, characterized in that, The method includes the following steps: taking the thin-layer chromatogram of the Cassia tora sample to be tested obtained by the same method in the thin-layer chromatography detection step according to any one of claims 1-8, calculating the corresponding spacing ratio RPf6 or RPf7 according to the method in the analytical model establishment step, and comparing it with the evaluation standard range to obtain the digital thin-layer chromatography detection result of Cassia tora.

10. The digital thin-layer chromatography analysis model for Cassia tora seeds as described in claim 1, characterized in that, When the spacing ratio RPf6 is 0.59 to 0.84, or the spacing ratio RPf7 is 0.65 to 0.78, the raw or roasted cassia seed sample is deemed qualified.

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