Comprehensive detection method for quality detection of traditional Chinese medicine capsules

Abstract

The invention discloses a comprehensive detection method for detecting the quality of a traditional Chinese medicine capsule, and relates to the technical field of medicine quality detection.According to the method, the quality fluctuation of a shell can be effectively recognized by monitoring the elasticity modulus of the shell of the capsule in real time and constructing a variable coefficient; and secondly, further monitoring is carried out aiming at capsule filling types and density uniformity, so that the uniformity of medicine filling is ensured, the medicine effect fluctuation caused by non-uniform filling is avoided, and the release consistency of the medicine is improved. And then, based on dissolution rate gradient monitoring under different temperature and pH value conditions, the dissolution characteristics of the capsule in various environments can be comprehensively evaluated, so that the dissolution performance stability of the capsule is guaranteed. And finally, accurately calculating a dissolution stable factor by combining the detection of trace dissolution substances with a dissolution characteristic analysis model, and further optimizing the quality evaluation of the capsules by combining a dissolution rate change coefficient, thereby reducing unqualified batches caused by uneven dissolution.

Description

Technical Field

[0001] The invention relates to the technical field of drug quality detection, in particular to a comprehensive detection method for quality detection of traditional Chinese medicine capsules. Background Art

[0002] As an important dosage form of traditional Chinese medicine, Chinese medicine capsules have been widely used in clinical treatment due to their convenience, ease of administration, and accurate dosage. With the advancement of the modernization of Chinese medicine, the quality requirements for Chinese medicine capsules are also increasing. The quality of Chinese medicine capsules is not only related to the efficacy of the drug, but also directly affects the safety of patients. Therefore, how to efficiently and accurately detect the quality of Chinese medicine capsules has become an important issue in current drug research and development and production.

[0003] In the quality inspection of traditional Chinese medicine capsules, traditional methods usually focus on monitoring the physical properties of the capsule shell, the uniformity of the filling, the appearance and its stability. However, most of the existing detection methods are difficult to fully evaluate the dissolution characteristics of the drug, especially the monitoring of the key indicator of dissolution rate is relatively lagging. The dissolution rate refers to the rate at which the drug is released in the in vivo or in vitro environment. It is an important parameter for evaluating the bioavailability and efficacy of the drug. The dissolution rate of the drug directly affects its absorption rate and degree in the body, thereby affecting the therapeutic effect of the drug. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a comprehensive detection method for quality detection of traditional Chinese medicine capsules to solve the problems mentioned in the background technology.

[0005] To achieve the above purpose, the present invention is implemented by the following technical scheme: a comprehensive detection method for quality detection of traditional Chinese medicine capsules, comprising the following steps: S1. Obtain an electronic distribution map of the detection area of ​​the traditional Chinese medicine capsule in advance, and monitor the elastic modulus of the capsule shell in real time to obtain an elastic modulus data set, and construct a coefficient of variation Byxs based on the elastic modulus data set, and pre-set a fluctuation threshold M. If the coefficient of variation Byxs is lower than the fluctuation threshold M, generate a first qualified label; S2. After generating the first qualified label, collect the capsule filling type and density uniformity data, establish a filling monitoring data set, and calculate the filling uniformity coefficient Fyjxs based on the preprocessed filling monitoring data set, and preset the uniformity threshold K. If the filling uniformity coefficient Fyjxs reaches the uniformity threshold K, generate a second qualified label; S3. After the second qualified label is generated, data on the capsule dissolution rate under different temperature and pH conditions are collected based on the dissolution rate gradient monitoring, the dissolution rate variation coefficient Sdjxs is obtained by curve fitting, and the dissolution rate gradient threshold Gd is pre-set. If the dissolution rate variation coefficient Sdjxs does not exceed the dissolution rate gradient threshold Gd, a third qualified label is generated; S4. After generating the third qualified label, collect trace dissolved matter and perform component analysis, build a trace dissolved matter detection model to extract characteristic data corresponding to the trace dissolved matter, obtain the initial release amount Shjl, release rate Ll and trace dissolved component amount Fbz respectively, and calculate the dissolution stability factor Czwdz according to the pre-trained dissolution characteristic analysis model, associate the dissolution stability factor Czwdz with the dissolution rate variation coefficient Sdjxs, and obtain the comprehensive quality evaluation index Qpz by fitting; pre-set the evaluation threshold Z, compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z, so as to comprehensively evaluate the overall quality status of the current Chinese medicine capsules, and formulate corresponding detection plans.

[0006] Preferably, S1 comprises: S11, according to the spatial layout of the capsule detection area, using three-dimensional scanning technology to obtain three-dimensional structural data of the area; S12, converting the three-dimensional structure data into an electronic distribution map, dividing the detection area through a hierarchical algorithm, marking the capsule distribution position and the detection equipment layout; S13. Mark the elastic modulus monitoring area in the electronic distribution map, and number the elastic modulus monitoring area accordingly.

[0007] Preferably, S1 further comprises: S14. Using a nanoindenter or micromechanical testing equipment, randomly select sample capsules in the elastic modulus monitoring area to perform real-time elastic modulus testing; S15. For each capsule, the deformation of the capsule shell under different mechanical conditions is obtained by using a pressure sensor, an elastic modulus data set is constructed, and the coefficient of variation Byxs is calculated based on the elastic modulus data set: The method for obtaining the coefficient of variation Byxs is specifically as follows: S151. Collect elastic modulus values ​​of each capsule in real time from the capsule detection area. , and record the corresponding sampling time and ambient humidity ; S152, according to the elastic modulus value , the mean elastic modulus is calculated by the following formula : ; Where N represents the total number of sampling capsules; represents the time weight; Time Weight The calculation formula is: ; In the formula, represents the corresponding sampling time, Indicates the benchmark sampling time point, specifically set to the first batch monitoring time, It represents the time attenuation coefficient, which is used to reduce the impact of data far away from the reference sampling time point. The initial value is set to 0.05; S153, according to the ambient humidity Calculate the ambient humidity correction factor : ; In the formula, Represents the humidity sensitivity coefficient, with an initial value of 0.1. Represents the average humidity value, and the calculation formula is: ; S154, elastic modulus value calculated based on S151-S153 , mean elastic modulus , time weight and humidity correction factor , calculate the corrected standard deviation of the elastic modulus : ; S155, based on the standard deviation of elastic modulus and the mean elastic modulus , the coefficient of variation Byxs is calculated using the following formula: ; In the formula, k represents the correction coefficient, and its initial value is: 0.1≤k≤0.5, which is used to adjust the standard deviation of the elastic modulus. Mean elastic modulus contribution.

[0008] Preferably, S1 further comprises: S16. Preset a fluctuation threshold M, and compare the coefficient of variation Byxs with the fluctuation threshold M to analyze the degree of dispersion of the elastic modulus between samples, including: When the coefficient of variation Byxs ≥ the fluctuation threshold M, it indicates that the elastic modulus of the capsule shell of this batch fluctuates abnormally and there is a quality problem. A first warning instruction is generated, and it is necessary to conduct key inspections on the capsule shell of this batch or to investigate the production process problems. When the coefficient of variation Byxs is less than the fluctuation threshold M, it indicates that the elastic modulus fluctuation of the capsule shell of this batch is within the qualified range of the fluctuation threshold M, the capsule instruction of this batch is stable, and the first qualified label is generated.

[0009] Preferably, S2 comprises: S21. After generating the first qualified label, collect the filling data of each capsule in the production process, including the capsule filling type and density uniformity data, and construct a filling monitoring data set; the filling monitoring data set includes: the filling weight of the i-th capsule , Standard deviation of electrostatic field strength on the surface of capsule fillings , the friction coefficient of the actual particle surface , the instantaneous change of filling rate and the time it takes for the capsule shell to fully recover from compression ; S22. After data cleaning, noise elimination and normalization processing of the filling monitoring data set, feature analysis is performed to construct the filling uniformity coefficient Fyjxs: The method for obtaining the filling uniformity coefficient Fyjxs includes: S221. The accumulation of static electricity will cause the filling material to be unevenly distributed inside the capsule. The static electricity distribution index Esx is calculated by the following formula: ; In the formula, represents the standard deviation of the electrostatic field strength on the surface of the capsule filling, It represents the mean value of the overall electrostatic field strength of the filler; S222. The lubricity of the particle filling material will affect the fluidity of the particles in the capsule, thereby affecting the density distribution. The lubricity index Ls is calculated by the following formula: ; In the formula, represents the friction coefficient of the actual particle surface, is the ideal friction coefficient threshold of particles; S223. During the filling process, the equipment will experience rate fluctuations, resulting in reduced uniformity of capsule density. The filling rate fluctuation index Frv is calculated using the following formula: ; In the formula, Indicates the instantaneous change value of the filling rate, represents the average value of the filling rate; S224. The elastic recovery time of the capsule shell will lead to the filling density distribution, and the recovery coefficient Rt is calculated by the following formula: ; In the formula, Indicates the time it takes for the capsule shell to fully recover from being compressed. Indicates the filling cycle time; S225, combining the electrostatic distribution index Esx, lubrication index Ls, filling rate fluctuation index Frv and recovery coefficient Rt obtained in S221-S224, and the filling weight of the i-th capsule in the filling monitoring data set , after dimensionless processing, the filling uniformity coefficient Fyjxs is calculated by the following formula: ; ; ' In the formula, represents the standard deviation of the fill weight, represents the mean of the filling weight, N represents the total number of sampled capsules, , , , and is the weight, , , , , ,and .

[0010] Preferably, S2 further includes: S23, presetting a uniformity threshold K, and comparing and analyzing the filling uniformity coefficient Fyjxs with the uniformity threshold K to obtain a uniformity evaluation result, including: When the filling uniformity coefficient Fyjxs ≥ the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is qualified, and a second qualified label is generated; When the filling uniformity coefficient Fyjxs is less than the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is unqualified, triggering the filling unevenness prompt instruction, including prompting the production line to reduce the current filling rate by 20% and activating the vibration damper and electrostatic shielding device around the filling equipment.

[0011] Preferably, S3 includes: S31. Setting experimental conditions and collecting instantaneous dissolution rate data of the capsule in the dissolution medium, specifically including: Set different temperature conditions, including: 25℃, 37℃, 45℃; Set different pH conditions, including: pH 1.5, pH 4.5, pH 7.0; Under different temperature and pH conditions, the instantaneous dissolution rate data of the capsules in the dissolution medium were collected; S32. Record the instantaneous dissolution rate data per unit time, expressed as mass, in units of mg / min; use an optical sensor or a conductivity sensor to collect data in the dissolution process in real time to form an original data set: the expression is: , represents the i-th temperature, represents the jth pH value, represents the instantaneous dissolution rate data under the conditions of the i-th temperature and the j-th pH value; S33, after normalizing the original data set formed in S32, the dissolution rate variation coefficient Sdjxs is calculated by the following formula: ; In the formula, Indicates that under the conditions of the i-th temperature and the j-th pH value, the time The instantaneous dissolution rate data at the time, represents the average instantaneous dissolution rate of the capsules under the conditions of the i-th temperature and the j-th pH value, N represents the total number of sampled capsules; u is the dissolution rate weight coefficient, which is used to balance the influence of different conditions on the results and is defined as: ; In the formula, 37 represents 37°C, and 7 represents pH 7, which is the physiological condition of the human body.

[0012] Preferably, S3 further includes: S34, presetting the dissolution rate gradient threshold Gd, and comparing and analyzing the dissolution rate variation coefficient Sdjxs with the dissolution rate gradient threshold Gd to evaluate the dissolution performance of the capsule batch, including: When the dissolution rate variation coefficient Sdjxs> the dissolution rate gradient threshold Gd, it means that the dissolution performance of the batch of capsules is abnormal, and the dissolution performance abnormality prompt instruction is triggered. It is recommended to check the capsule formula, dissolution medium, production temperature or pH value; When the dissolution rate variation coefficient Sdjxss ≤ the dissolution rate gradient threshold Gd, it indicates that the dissolution performance of the batch of capsules is normal, and a third qualified label is generated.

[0013] Preferably, S4 includes: S41. Use modern analytical instruments, including liquid chromatography or mass spectrometry technology HPLC-MS, to detect trace dissolutions, extract samples from the dissolutions, collect time series data of the dissolutions, use neural network technology CNN to establish and train a trace dissolution detection model, and extract the initial release amount, release rate and component distribution characteristics of the trace dissolutions during the dissolution process from the time series data to obtain: initial release amount Shjl, release rate Ll and trace dissolution component amount Fbz; S42, after dimensionless processing of the initial release amount Shjl, the release rate Ll and the amount of trace dissolved components Fbz, the dissolution stability factor Czwdz is calculated by the following formula; ; In the formula, , and is the weight, , , ,and ; The dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs are related by the following formula to obtain the comprehensive quality evaluation index Qpz: ; In the formula, and They respectively represent the preset proportional coefficients of the dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs.

[0014] Preferably, S4 further includes: S43, preset an evaluation threshold Z, and compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z to comprehensively evaluate the overall quality status of the current Chinese medicine capsule, including: When the comprehensive quality evaluation index Qp ≥ the evaluation threshold Z, it means that the overall quality of the dissolution of the batch of capsules is qualified, a quality qualification report is generated, and the batch of capsule products is released to the next production stage or packaging link; When the comprehensive quality evaluation index Qp is less than the evaluation threshold Z, it means that the overall quality of the dissolution of the capsules in this batch is unqualified, and a corresponding testing plan is formulated, including: S431. Check the environmental conditions of the production line, including whether the temperature and humidity control are up to standard and whether the equipment status is normal; S432. Check the operating status of the dissolution test equipment, including whether the rotation speed and stirring uniformity meet the standards; S433. Repeat the dissolution rate of the capsules under different temperature and pH conditions, focusing on the coefficient of variation of the dissolution rate Sdjxs, to check whether there are abnormal fluctuations or unstable dissolution patterns.

[0015] The present invention provides a comprehensive detection method for quality detection of traditional Chinese medicine capsules. It has the following beneficial effects: (1) This comprehensive detection method for the quality inspection of traditional Chinese medicine capsules can timely identify the quality fluctuations of the capsule shell and ensure the stability of the shell quality by real-time monitoring of the elastic modulus of the capsule shell and calculating the coefficient of variation Byxs.

[0016] (2) This is a comprehensive testing method for the quality inspection of traditional Chinese medicine capsules. By monitoring the uniformity of capsule filling, it ensures the uniform distribution of drugs in the capsules and improves the consistency and accuracy of drug release.

[0017] (3) This comprehensive detection method for the quality inspection of traditional Chinese medicine capsules is based on the monitoring of dissolution rate changes under different temperature and pH conditions. It can effectively evaluate the dissolution performance of capsules and ensure their stable release under different environments.

[0018] (4) This is a comprehensive testing method for the quality inspection of traditional Chinese medicine capsules. Through the analysis and comprehensive evaluation of the components of trace dissolutions, it accurately monitors the dissolution characteristics of capsules, achieves quality control throughout the entire process, and ensures that the overall quality of the capsules is qualified. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a schematic diagram of the steps of a comprehensive detection method for quality detection of traditional Chinese medicine capsules. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0021] See also Figure 1 The present invention provides a comprehensive detection method for quality detection of traditional Chinese medicine capsules, comprising the following steps: S1. Obtain an electronic distribution map of the detection area of ​​the traditional Chinese medicine capsule in advance, and monitor the elastic modulus of the capsule shell in real time to obtain an elastic modulus data set, and construct a coefficient of variation Byxs based on the elastic modulus data set, and pre-set a fluctuation threshold M. If the coefficient of variation Byxs is lower than the fluctuation threshold M, generate a first qualified label; S2. After generating the first qualified label, collect the capsule filling type and density uniformity data, establish a filling monitoring data set, and calculate the filling uniformity coefficient Fyjxs based on the preprocessed filling monitoring data set, and preset the uniformity threshold K. If the filling uniformity coefficient Fyjxs reaches the uniformity threshold K, generate a second qualified label; S3. After the second qualified label is generated, data on the capsule dissolution rate under different temperature and pH conditions are collected based on the dissolution rate gradient monitoring, the dissolution rate variation coefficient Sdjxs is obtained by curve fitting, and the dissolution rate gradient threshold Gd is pre-set. If the dissolution rate variation coefficient Sdjxs does not exceed the dissolution rate gradient threshold Gd, a third qualified label is generated; S4. After generating the third qualified label, collect trace dissolved matter and perform component analysis, build a trace dissolved matter detection model to extract characteristic data corresponding to the trace dissolved matter, obtain the initial release amount Shjl, release rate Ll and trace dissolved component amount Fbz respectively, and calculate the dissolution stability factor Czwdz according to the pre-trained dissolution characteristic analysis model, associate the dissolution stability factor Czwdz with the dissolution rate variation coefficient Sdjxs, and obtain the comprehensive quality evaluation index Qpz by fitting; pre-set the evaluation threshold Z, compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z, so as to comprehensively evaluate the overall quality status of the current Chinese medicine capsules, and formulate corresponding detection plans.

[0022] In the present embodiment, the traditional Chinese medicine capsule quality detection method focuses on the aspects of appearance and physical properties, and cannot fully evaluate the dissolution characteristics of the drug, especially the monitoring of the dissolution rate. The present invention can accurately obtain the dissolution rate change of the capsule under different temperature and pH conditions through a monitoring method based on the dissolution rate gradient, thereby comprehensively reflecting the release characteristics of the drug. This provides a more reliable scientific basis for the bioavailability assessment of Chinese medicine capsules. By real-time monitoring of the elastic modulus of the capsule shell, combined with the uniformity data of the filler, the variation factors that may affect the quality can be found in time, avoiding the problem of lagging quality control in the traditional method. The real-time collected data provides accurate feedback for dynamically adjusting the quality control during the production process, which helps to ensure the quality stability of Chinese medicine capsules in production. The present invention not only introduces the variation coefficient Sdjxs in the monitoring of the drug dissolution rate, but also combines the analysis of the trace dissolved components to construct a comprehensive quality assessment model. Through the combination of the dissolution stability factor Czwdz and the dissolution rate variation coefficient, the present invention can achieve a comprehensive assessment of the overall quality of the capsule, providing a scientific and systematic means for quality control. By generating multi-level qualified labels and conducting phased assessments according to the qualified standards for key parameters such as elastic modulus, filling uniformity, dissolution rate, and dissolution stability, potential quality problems can be discovered in advance during the production process, avoiding the risk of unqualified batches of products and improving the quality controllability of Chinese medicine capsules.

[0023] The present invention introduces multi-dimensional testing standards into the quality control of capsules, integrating factors such as physical properties, drug release characteristics, dissolution rate, etc. By comparing and analyzing with the evaluation threshold Z, the quality status of traditional Chinese medicine capsules can be quantitatively evaluated, providing a decision-making basis for production process optimization, quality improvement and testing planning. Example 2

[0024] This embodiment is an explanation of the embodiment 1. Specifically, S1 includes: S11. Based on the spatial layout of the capsule inspection area, the three-dimensional structural data of the area is obtained using three-dimensional scanning technology; this provides a clear and comprehensive view for subsequent inspection area division, equipment layout optimization, and data collection, ensuring that the inspection process has higher accuracy and efficiency.

[0025] S12. Convert the three-dimensional structure data into an electronic distribution map, divide the detection area through a hierarchical algorithm, mark the capsule distribution position and the detection equipment layout; through the combination of the electronic distribution map and the hierarchical algorithm, the detection area and equipment layout can be planned more carefully. This precise area division can help to reasonably configure the detection equipment, improve space utilization, and ensure that each capsule is in the best monitoring position, thereby improving the comprehensiveness and flexibility of the detection.

[0026] S13. Mark the elastic modulus monitoring area in the electronic distribution map and number the elastic modulus monitoring area accordingly. Clearly marking and numbering the elastic modulus monitoring area in the electronic distribution map can facilitate the subsequent rapid positioning of the area to be monitored and make each monitoring area have a unique identifier. This helps to improve the accuracy and effectiveness of data collection, and in actual production, can achieve efficient real-time detection.

[0027] S14. Use a nano-indenter or micro-mechanical testing device to randomly select sample capsules in the elastic modulus monitoring area and conduct real-time elastic modulus testing; the nano-indenter or micro-mechanical testing device has high-precision testing capabilities and can conduct efficient and accurate real-time testing of capsules in the elastic modulus monitoring area. By randomly selecting samples and conducting tests, data distortion caused by sampling bias can be effectively avoided, thereby ensuring that the test results are widely representative and reliable.

[0028] S15. For each capsule, the deformation of the capsule shell under different mechanical conditions is obtained by using a pressure sensor, an elastic modulus data set is constructed, and the coefficient of variation Byxs is calculated based on the elastic modulus data set: The use of pressure sensors can monitor the deformation of the capsule shell under different mechanical conditions in multiple dimensions, ensuring that the elastic modulus data obtained has higher accuracy. Constructing a data set and calculating the coefficient of variation Byxs helps to quantify the mechanical consistency of the capsule and make the assessment of capsule quality fluctuations more accurate, thereby improving the level of batch quality control.

[0029] The method for obtaining the coefficient of variation Byxs is specifically as follows: S151. Collect elastic modulus values ​​of each capsule in real time from the capsule detection area. , and record the corresponding sampling time and ambient humidity ; S152, according to the elastic modulus value , the mean elastic modulus is calculated by the following formula : ; Where N represents the total number of sampling capsules; represents the time weight; Time Weight The calculation formula is: ; In the formula, represents the corresponding sampling time, Indicates the benchmark sampling time point, specifically set to the first batch monitoring time, It represents the time attenuation coefficient, which is used to reduce the impact of data far away from the benchmark sampling time point. The initial value is set to 0.05. The mean value of the calculated elastic modulus can effectively reflect the general mechanical properties of the capsule shell and provide basic data for subsequent quality control. Combined with the time weight, the data can be further corrected to reduce the impact of time delay on the data and ensure more accurate data.

[0030] S153, according to the ambient humidity Calculate the ambient humidity correction factor : ; In the formula, Represents the humidity sensitivity coefficient, with an initial value of 0.1. Represents the average humidity value, and the calculation formula is: ; Ambient humidity has a certain influence on the physical properties of the capsule shell. The introduction of the ambient humidity correction factor helps to eliminate the interference of ambient humidity and ensure that the elastic modulus data is more real and accurate. By accurately calculating the ambient humidity correction factor, the environmental adaptability of the data can be improved, making the test results more reliable.

[0031] S154, elastic modulus value calculated based on S151-S153 , mean elastic modulus , time weight and humidity correction factor , calculate the corrected standard deviation of the elastic modulus : ; S155, based on the standard deviation of elastic modulus and the mean elastic modulus , the coefficient of variation Byxs is calculated using the following formula: ; In the formula, k represents the correction coefficient, and its initial value is: 0.1≤k≤0.5, which is used to adjust the standard deviation of the elastic modulus. Mean elastic modulus The contribution of the modified elastic modulus standard deviation can be more accurately reflected by comprehensively calculating the modified elastic modulus standard deviation. The modified standard deviation can effectively eliminate errors caused by environmental factors or time delays, enhance the accuracy of data analysis, and ensure the true reflection of elastic modulus fluctuations.

[0032] S16. Preset the fluctuation threshold M, and compare the coefficient of variation Byxs with the fluctuation threshold M to analyze the dispersion of the elastic modulus between samples and evaluate the consistency of the batch of products, including: When the coefficient of variation Byxs ≥ the fluctuation threshold M, it indicates that the elastic modulus of the capsule shell of this batch fluctuates abnormally and there is a quality problem. A first warning instruction is generated, and it is necessary to conduct key inspections on the capsule shell of this batch or to investigate the production process problems. When the coefficient of variation Byxs is less than the fluctuation threshold M, it indicates that the elastic modulus fluctuation of the capsule shell of this batch is within the qualified range of the fluctuation threshold M, the capsule instruction of this batch is stable, and the first qualified label is generated.

[0033] In this embodiment, by calculating the coefficient of variation Byxs, the volatility of the elastic modulus of the capsule can be quantified, thereby effectively evaluating the consistency of the mechanical properties of the capsule product. As an indicator of product consistency, the coefficient of variation helps to detect whether there are problems with unstable production processes or abnormal quality, and provides data support for subsequent quality control. By presetting the fluctuation threshold M and comparing it with the coefficient of variation Byxs, the consistency of the capsule batch can be clearly judged. If the coefficient of variation exceeds the fluctuation threshold M, an early warning can be issued in time to prevent unqualified products caused by quality fluctuations from entering the market; if the coefficient of variation is lower than the fluctuation threshold M, it means that the quality of the batch of products is stable, which helps to ensure the efficiency and stability of the production process. This can not only effectively ensure the qualified rate of the product, but also improve the efficiency of the production process, reduce unnecessary quality inspection links, thereby saving costs and improving production efficiency. Example 3

[0034] This embodiment is explained in Embodiment 2. Specifically, S2 includes: S21. After generating the first qualified label, collect the filling data of each capsule in the production process, including the capsule filling type and density uniformity data, and construct a filling monitoring data set; the filling monitoring data set includes: the filling weight of the i-th capsule , Standard deviation of electrostatic field strength on the surface of capsule fillings , the friction coefficient of the actual particle surface , the instantaneous change of filling rate and the time it takes for the capsule shell to fully recover from compression ; S22. After data cleaning, noise elimination and normalization processing of the filling monitoring data set, feature analysis is performed to construct the filling uniformity coefficient Fyjxs: The method for obtaining the filling uniformity coefficient Fyjxs includes: S221. The accumulation of static electricity will cause the filling material to be unevenly distributed inside the capsule. The static electricity distribution index Esx is calculated by the following formula: ; In the formula, represents the standard deviation of the electrostatic field strength on the surface of the capsule filling, It indicates the average value of the overall electrostatic field strength of the filling material; therefore, by calculating the electrostatic distribution index Esx, we can better identify the impact of static electricity on filling uniformity. This helps to take targeted measures to reduce the interference of static electricity during the production process.

[0035] S222. The lubricity of the particle filling material will affect the fluidity of the particles in the capsule, thereby affecting the density distribution. The lubricity index Ls is calculated by the following formula: ; In the formula, represents the friction coefficient of the actual particle surface, is the ideal friction coefficient threshold of the particles; by calculating the lubrication index Ls, the influence of lubrication on the filling quality can be judged, providing guidance for optimizing the selection of particle materials and production processes.

[0036] S223. During the filling process, the equipment will experience rate fluctuations, resulting in reduced uniformity of capsule density. The filling rate fluctuation index Frv is calculated using the following formula: ; In the formula, Indicates the instantaneous change value of the filling rate, Indicates the average value of the filling rate; by calculating the filling rate fluctuation index Frv, the equipment operation status can be monitored and the production rate can be adjusted in time to ensure filling uniformity, thereby improving product consistency.

[0037] S224. The elastic recovery time of the capsule shell will lead to the filling density distribution, and the recovery coefficient Rt is calculated by the following formula: ; In the formula, Indicates the time it takes for the capsule shell to fully recover from being compressed. Represents the filling cycle time; by calculating the recovery coefficient Rt, the elastic recovery characteristics of the capsule shell can be monitored and the production parameters can be adjusted in time to ensure stable capsule filling quality.

[0038] S225, combining the electrostatic distribution index Esx, lubrication index Ls, filling rate fluctuation index Frv and recovery coefficient Rt obtained in S221-S224, and the filling weight of the i-th capsule in the filling monitoring data set , after dimensionless processing, the filling uniformity coefficient Fyjxs is calculated by the following formula: ; ; ; In the formula, represents the standard deviation of the fill weight, represents the mean of the filling weight, N represents the total number of sampled capsules, , , , and is the weight, , , , , ,and .

[0039] S23, preset a uniformity threshold K, and compare and analyze the filling uniformity coefficient Fyjxs with the uniformity threshold K to obtain a uniformity evaluation result, including: When the filling uniformity coefficient Fyjxs ≥ the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is qualified, and a second qualified label is generated; When the filling uniformity coefficient Fyjxs is less than the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is unqualified, triggering the filling unevenness prompt instruction, including prompting the production line to reduce the current filling rate by 20% and activating the vibration damper and electrostatic shielding device around the filling equipment.

[0040] In this embodiment, the filling uniformity coefficient Fyjxs is calculated by combining the electrostatic distribution index, lubrication index, filling rate fluctuation index and recovery coefficient with the filling weight. By integrating multiple influencing factors, a more comprehensive quality assessment is provided to provide accurate data for quality control in the production process. Based on the comparative analysis of the filling uniformity coefficient and the uniformity threshold K, a second qualified label is generated or an alarm is triggered. If the filling uniformity is unqualified, an uneven filling prompt instruction will be triggered, and measures such as reducing the filling rate, activating the damper and the electrostatic shielding device will be recommended. This process ensures that problems are discovered in a timely manner and can be quickly adjusted on the production line to avoid the production of unqualified products. Example 4

[0041] At present, the detection method of dissolution rate mainly relies on a single dissolution experiment. Although this method can provide drug dissolution data at a certain moment, it often cannot reflect the dynamic changes of drug release during the actual use of capsules, especially under different environmental conditions. Traditional dissolution test methods have problems such as long experimental time, single data, and difficulty in comprehensive monitoring. In addition, it is impossible to conduct a comprehensive evaluation in combination with other quality indicators of capsules, resulting in insufficient comprehensive monitoring of drug release characteristics. Especially when conditions such as temperature and pH value change, traditional methods are difficult to accurately capture the fluctuations and changing trends of dissolution rate.

[0042] This embodiment is explained in Embodiment 3. Specifically, S3 includes: S31. Setting experimental conditions and collecting instantaneous dissolution rate data of the capsule in the dissolution medium, specifically including: Set different temperature conditions, including: 25℃, 37℃, 45℃; Set different pH conditions, including: pH 1.5, pH 4.5, pH 7.0; Under different temperature and pH conditions, the instantaneous dissolution rate data of the capsules in the dissolution medium were collected; S32. Record the instantaneous dissolution rate data per unit time, expressed as mass, in units of mg / min; use an optical sensor or a conductivity sensor to collect data in the dissolution process in real time to form an original data set: the expression is: , represents the i-th temperature, represents the jth pH value, represents the instantaneous dissolution rate data under the conditions of the i-th temperature and the j-th pH value; S33, after normalizing the original data set formed in S32, the dissolution rate variation coefficient Sdjxs is calculated by the following formula: ; In the formula, Indicates that under the conditions of the i-th temperature and the j-th pH value, the time The instantaneous dissolution rate data at the time, represents the average instantaneous dissolution rate of the capsules under the conditions of the i-th temperature and the j-th pH value, N represents the total number of sampled capsules; u is the dissolution rate weight coefficient, which is used to balance the influence of different conditions on the results and is defined as: ; In the formula, 37 represents 37°C, and 7 represents pH 7, which is the physiological condition of the human body.

[0043] S34, presetting the dissolution rate gradient threshold Gd, and comparing and analyzing the dissolution rate variation coefficient Sdjxs with the dissolution rate gradient threshold Gd to evaluate the dissolution performance of the capsule batch, including: When the dissolution rate variation coefficient Sdjxs> the dissolution rate gradient threshold Gd, it means that the dissolution performance of the batch of capsules is abnormal, and the dissolution performance abnormality prompt instruction is triggered. It is recommended to check the capsule formula, dissolution medium, production temperature or pH value; When the dissolution rate variation coefficient Sdjxss ≤ the dissolution rate gradient threshold Gd, it indicates that the dissolution performance of the batch of capsules is normal, and a third qualified label is generated.

[0044] In this embodiment, by setting different temperature conditions (25°C, 37°C, 45°C) and pH conditions (1.5, 4.5, 7.0), the dissolution rate data of the capsule under these conditions are collected. In this way, the dissolution behavior of the capsule in the actual use process, especially under different physiological environments in the body, can be simulated. Through diversified environmental simulations, the dynamic changes of the dissolution rate can be fully captured. Compared with the traditional dissolution test method, real-time monitoring can capture the changes in the drug dissolution rate more timely, especially the dynamic response under changes in conditions such as temperature and pH, which helps to more accurately evaluate the dissolution performance of the capsule. Through the comprehensive analysis of multiple environmental conditions, the dissolution performance of the drug in the actual use environment can be more comprehensively understood, thereby improving the accuracy of the dissolution rate prediction. Comparing the dissolution rate variation coefficient with the threshold value can not only detect the abnormal dissolution performance in time, but also provide data support for the adjustment of the production process, which helps to ensure the consistency and stability of product quality. Once the dissolution performance is abnormal, the system will automatically prompt the possible causes and recommend solutions, such as checking the capsule formula or adjusting the production conditions, so as to optimize the production process and capsule formula. Example 5

[0045] This embodiment is explained in Embodiment 4. Specifically, S4 includes: S41. Use modern analytical instruments, including liquid chromatography or mass spectrometry technology HPLC-MS, to detect trace dissolutions, extract samples from dissolutions, collect time series data of dissolutions, use neural network technology CNN to establish and train a trace dissolution detection model, and extract the initial release amount, release rate and component distribution characteristics of trace dissolutions during the dissolution process from the time series data to obtain: initial release amount Shjl, release rate Ll and trace dissolution component amount Fbz; use modern analytical instruments (liquid chromatography-mass spectrometry technology HPLC-MS) to extract samples from the dissolution solution and detect trace dissolutions. This highly sensitive technology can accurately capture trace components of dissolutions and is particularly suitable for monitoring the dissolution process of fine drugs.

[0046] S42, after dimensionless processing of the initial release amount Shjl, the release rate Ll and the amount of trace dissolved components Fbz, the dissolution stability factor Czwdz is calculated by the following formula; ; In the formula, , and is the weight, , , ,and ; The initial release amount, release rate and trace dissolved component amount after dimensionless treatment are used to calculate the dissolution stability factor (Czwdz) using the preset weight coefficient (the specific formula parameters are not fully expanded). This factor can reflect the stability of the drug during the dissolution process and is a key indicator for quality assessment.

[0047] The dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs are related by the following formula to obtain the comprehensive quality evaluation index Qpz: ; In the formula, and Respectively represent the preset proportional coefficients of the dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs. The dissolution stability factor Czwdz is associated with the dissolution rate variation coefficient Sdjxs, and the quality comprehensive evaluation index Qpz is obtained by fitting the set proportional coefficients. This comprehensive evaluation index combines multiple factors to provide a basis for the overall quality evaluation of the dissolution process.

[0048] S43, preset an evaluation threshold Z, and compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z to comprehensively evaluate the overall quality status of the current Chinese medicine capsule, including: When the comprehensive quality evaluation index Qp ≥ the evaluation threshold Z, it means that the overall quality of the dissolution of the batch of capsules is qualified, a quality qualification report is generated, and the batch of capsule products is released to the next production stage or packaging link; When the comprehensive quality evaluation index Qp is less than the evaluation threshold Z, it means that the overall quality of the dissolution of the capsules in this batch is unqualified, and a corresponding testing plan is formulated, including: S431. Check the environmental conditions of the production line, including whether the temperature and humidity control are up to standard and whether the equipment status is normal; S432. Check the operating status of the dissolution test equipment, including whether the rotation speed and stirring uniformity meet the standards; S433. Repeat the dissolution rate of the capsules under different temperature and pH conditions, focusing on the coefficient of variation of the dissolution rate Sdjxs, to check whether there are abnormal fluctuations or unstable dissolution patterns.

[0049] In this embodiment, by HPLC-MS and neural network technology, the trace dissolution in the drug dissolution process can be detected in real time and accurately, and the initial release amount, release rate and component distribution characteristics can be obtained, so as to more comprehensively understand the performance of the capsule in the dissolution process. By introducing the dissolution stability factor and the comprehensive quality evaluation index, combined with the analysis of the dissolution rate variation coefficient, the dissolution performance of the capsule can be evaluated more comprehensively and accurately. This method is more sensitive and reliable than the traditional dissolution test, and quality problems can be discovered in time. When the quality evaluation index is unqualified, effective measures can be taken in time to diagnose the problem, including checking the production environment, equipment operation status and retesting the dissolution rate, so as to ensure the stability of the production process and the quality of the capsule product. By combining the data-driven neural network model with the traditional dissolution test, the intelligentization of quality monitoring in the production process is promoted. This technology can improve production efficiency while reducing human intervention and errors.

[0050] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0051] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A comprehensive detection method for quality detection of traditional Chinese medicine capsules, characterized in that: The following steps are involved: S1. Obtain an electronic distribution map of the detection area of ​​the traditional Chinese medicine capsule in advance, and monitor the elastic modulus of the capsule shell in real time to obtain an elastic modulus data set, and construct a coefficient of variation Byxs based on the elastic modulus data set, and pre-set a fluctuation threshold M. If the coefficient of variation Byxs is lower than the fluctuation threshold M, generate a first qualified label; S2. After generating the first qualified label, collect the capsule filling type and density uniformity data, establish a filling monitoring data set, and calculate the filling uniformity coefficient Fyjxs based on the preprocessed filling monitoring data set, and preset the uniformity threshold K. If the filling uniformity coefficient Fyjxs reaches the uniformity threshold K, generate a second qualified label; S3. After the second qualified label is generated, data on the capsule dissolution rate under different temperature and pH conditions are collected based on the dissolution rate gradient monitoring, the dissolution rate variation coefficient Sdjxs is obtained by curve fitting, and the dissolution rate gradient threshold Gd is pre-set. If the dissolution rate variation coefficient Sdjxs does not exceed the dissolution rate gradient threshold Gd, a third qualified label is generated; S4. After generating the third qualified label, collect trace dissolved matter and perform component analysis, build a trace dissolved matter detection model to extract characteristic data corresponding to the trace dissolved matter, obtain the initial release amount Shjl, release rate Ll and trace dissolved component amount Fbz respectively, and calculate the dissolution stability factor Czwdz according to the pre-trained dissolution characteristic analysis model, associate the dissolution stability factor Czwdz with the dissolution rate variation coefficient Sdjxs, and obtain the comprehensive quality evaluation index Qpz by fitting; pre-set the evaluation threshold Z, compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z, so as to comprehensively evaluate the overall quality status of the current Chinese medicine capsules, and formulate corresponding detection plans.

2. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 1, characterized in that: S1 includes: S11, according to the spatial layout of the capsule detection area, using three-dimensional scanning technology to obtain three-dimensional structural data of the area; S12, converting the three-dimensional structure data into an electronic distribution map, dividing the detection area through a hierarchical algorithm, marking the capsule distribution position and the detection equipment layout; S13. Mark the elastic modulus monitoring area in the electronic distribution map, and number the elastic modulus monitoring area accordingly.

3. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 2, characterized in that: S1 also includes: S14. Using a nanoindenter or micromechanical testing equipment, randomly select sample capsules in the elastic modulus monitoring area to perform real-time elastic modulus testing; S15. For each capsule, the deformation of the capsule shell under different mechanical conditions is obtained by using a pressure sensor, an elastic modulus data set is constructed, and the coefficient of variation Byxs is calculated based on the elastic modulus data set: The method for obtaining the coefficient of variation Byxs is specifically as follows: S151. Collect elastic modulus values ​​of each capsule in real time from the capsule detection area. , and record the corresponding sampling time and ambient humidity ; S152, according to the elastic modulus value , the mean elastic modulus is calculated by the following formula : ； Where N represents the total number of sampling capsules; represents the time weight; Time Weight The calculation formula is: ； In the formula, represents the corresponding sampling time, Indicates the benchmark sampling time point, specifically set to the first batch monitoring time, It represents the time attenuation coefficient, which is used to reduce the impact of data far away from the reference sampling time point. The initial value is set to 0.05; S153, according to the ambient humidity Calculate the ambient humidity correction factor : ； In the formula, Represents the humidity sensitivity coefficient, with an initial value of 0.

1. Represents the average humidity value, and the calculation formula is: ; S154, elastic modulus value calculated based on S151-S153 , mean elastic modulus , time weight and humidity correction factor , calculate the corrected standard deviation of the elastic modulus : ； S155, based on the standard deviation of elastic modulus and the mean elastic modulus , the coefficient of variation Byxs is calculated using the following formula: ； In the formula, k represents the correction coefficient, and its initial value is: 0.1≤k≤0.5, which is used to adjust the standard deviation of the elastic modulus. Mean elastic modulus contribution.

4. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 3, characterized in that: S1 also includes: S16. Preset a fluctuation threshold M, and compare the coefficient of variation Byxs with the fluctuation threshold M to analyze the degree of dispersion of the elastic modulus between samples, including: When the coefficient of variation Byxs ≥ the fluctuation threshold M, it indicates that the elastic modulus of the capsule shell of this batch fluctuates abnormally and there is a quality problem. A first warning instruction is generated, and it is necessary to conduct key inspections on the capsule shell of this batch or to investigate the production process problems. When the coefficient of variation Byxs is less than the fluctuation threshold M, it indicates that the elastic modulus fluctuation of the capsule shell of this batch is within the qualified range of the fluctuation threshold M, the capsule instruction of this batch is stable, and the first qualified label is generated.

5. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 1, characterized in that S2 include: S21. After generating the first qualified label, collect the filling data of each capsule in the production process, including the capsule filling type and density uniformity data, and construct a filling monitoring data set; the filling monitoring data set includes: the filling weight of the i-th capsule , Standard deviation of electrostatic field strength on the surface of capsule fillings , the friction coefficient of the actual particle surface , the instantaneous change of filling rate and the time it takes for the capsule shell to fully recover from compression ; S22. After data cleaning, noise elimination and normalization processing of the filling monitoring data set, feature analysis is performed to construct the filling uniformity coefficient Fyjxs: The method for obtaining the filling uniformity coefficient Fyjxs includes: S221. The accumulation of static electricity will cause the filling material to be unevenly distributed inside the capsule. The static electricity distribution index Esx is calculated by the following formula: ； In the formula, represents the standard deviation of the electrostatic field strength on the surface of the capsule filling, It represents the mean value of the overall electrostatic field strength of the filler; S222. The lubricity of the particle filling material will affect the fluidity of the particles in the capsule, thereby affecting the density distribution. The lubricity index Ls is calculated by the following formula: ； In the formula, represents the friction coefficient of the actual particle surface, is the ideal friction coefficient threshold of particles; S223. During the filling process, the equipment will experience rate fluctuations, resulting in reduced uniformity of capsule density. The filling rate fluctuation index Frv is calculated using the following formula: ； In the formula, Indicates the instantaneous change value of the filling rate, represents the average value of the filling rate; S224. The elastic recovery time of the capsule shell will lead to the filling density distribution, and the recovery coefficient Rt is calculated by the following formula: ； In the formula, Indicates the time it takes for the capsule shell to fully recover from being compressed. Indicates the filling cycle time; S225, combining the electrostatic distribution index Esx, lubrication index Ls, filling rate fluctuation index Frv and recovery coefficient Rt obtained in S221-S224, and the filling weight of the i-th capsule in the filling monitoring data set , after dimensionless processing, the filling uniformity coefficient Fyjxs is calculated by the following formula: ； ； ； In the formula, represents the standard deviation of the fill weight, represents the mean of the filling weight, N represents the total number of sampled capsules, , , , and is the weight.

6. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 5, characterized in that: S2 also includes: S23, presetting a uniformity threshold K, and comparing and analyzing the filling uniformity coefficient Fyjxs with the uniformity threshold K to obtain a uniformity evaluation result, including: When the filling uniformity coefficient Fyjxs ≥ the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is qualified, and a second qualified label is generated; When the filling uniformity coefficient Fyjxs is less than the uniformity threshold K, it indicates that the filling uniformity of the batch of capsules is unqualified, triggering the filling unevenness prompt instruction, including prompting the production line to reduce the current filling rate by 20% and activating the vibration damper and electrostatic shielding device around the filling equipment.

7. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 1, characterized in that: S3 includes: S31. Setting experimental conditions and collecting instantaneous dissolution rate data of the capsule in the dissolution medium, specifically including: Set different temperature conditions, including: 25℃, 37℃, 45℃; Set different pH conditions, including: pH 1.5, pH 4.5, pH 7.0; Under different temperature and pH conditions, the instantaneous dissolution rate data of the capsules in the dissolution medium were collected; S32. Record the instantaneous dissolution rate data per unit time, expressed as mass, in units of mg / min; use an optical sensor or a conductivity sensor to collect data in the dissolution process in real time to form an original data set: the expression is: , represents the i-th temperature, represents the jth pH value, represents the instantaneous dissolution rate data under the conditions of the i-th temperature and the j-th pH value; S33, after normalizing the original data set formed in S32, the dissolution rate variation coefficient Sdjxs is calculated by the following formula: ； In the formula, Indicates that under the conditions of the i-th temperature and the j-th pH value, the time The instantaneous dissolution rate data at the time, represents the average instantaneous dissolution rate of capsules under the conditions of the i-th temperature and the j-th pH value, N represents the total number of sampled capsules; u is the dissolution rate weight coefficient, which is used to balance the influence of different conditions on the results and is defined as: ； In the formula, 37 represents 37°C, and 7 represents pH 7, which is the physiological condition of the human body.

8. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 7, characterized in that: S3 also includes: S34, presetting the dissolution rate gradient threshold Gd, and comparing and analyzing the dissolution rate variation coefficient Sdjxs with the dissolution rate gradient threshold Gd to evaluate the dissolution performance of the capsule batch, including: When the dissolution rate variation coefficient Sdjxs> the dissolution rate gradient threshold Gd, it means that the dissolution performance of the batch of capsules is abnormal, and the dissolution performance abnormality prompt instruction is triggered. It is recommended to check the capsule formula, dissolution medium, production temperature or pH value; When the dissolution rate variation coefficient Sdjxs ≤ the dissolution rate gradient threshold Gd, it indicates that the dissolution performance of the batch of capsules is normal, and a third qualified label is generated.

9. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 1, characterized in that S4 include: S41. Use modern analytical instruments, including liquid chromatography or mass spectrometry technology HPLC-MS, to detect trace dissolutions, extract samples from the dissolutions, collect time series data of the dissolutions, use neural network technology CNN to establish and train a trace dissolution detection model, and extract the initial release amount, release rate and component distribution characteristics of the trace dissolutions during the dissolution process from the time series data to obtain: initial release amount Shjl, release rate Ll and trace dissolution component amount Fbz; S42, after dimensionless processing of the initial release amount Shjl, the release rate Ll and the amount of trace dissolved components Fbz, the dissolution stability factor Czwdz is calculated by the following formula; ； In the formula, , and is the weight; The dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs are related by the following formula to obtain the comprehensive quality evaluation index Qpz: ； In the formula, and They respectively represent the preset proportional coefficients of the dissolution stability factor Czwdz and the dissolution rate variation coefficient Sdjxs.

10. A comprehensive detection method for quality detection of traditional Chinese medicine capsules according to claim 9, characterized in that: S4 also includes: S43, preset an evaluation threshold Z, and compare and analyze the comprehensive quality evaluation index Qpz with the evaluation threshold Z to comprehensively evaluate the overall quality status of the current Chinese medicine capsule, including: When the comprehensive quality evaluation index Qp ≥ the evaluation threshold Z, it means that the overall quality of the dissolution of the batch of capsules is qualified, a quality qualification report is generated, and the batch of capsule products is released to enter the next production stage or packaging link; When the comprehensive quality evaluation index Qp is less than the evaluation threshold Z, it means that the overall quality of the dissolution of the capsules in this batch is unqualified, and a corresponding testing plan is formulated, including: S431. Check the environmental conditions of the production line, including whether the temperature and humidity control are up to standard and whether the equipment status is normal; S432. Check the operating status of the dissolution test equipment, including whether the rotation speed and stirring uniformity meet the standards; S433. Repeat the dissolution rate of the capsules under different temperature and pH conditions, focusing on the coefficient of variation of the dissolution rate Sdjxs, to check whether there are abnormal fluctuations or unstable dissolution patterns.

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