CN117390234A - Quality detection method of oral liquid - Google Patents
Quality detection method of oral liquid Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 168
- 239000007788 liquid Substances 0.000 title claims abstract description 56
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- 239000012074 organic phase Substances 0.000 description 3
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Abstract
The invention relates to the field of detection, in particular to a quality detection method of oral liquid, which comprises the steps of completing detection of detection samples through a high performance liquid chromatograph, acquiring multi-dimensional detection parameters of the detection samples and forming data in a vector format, and ensuring that one vector corresponds to one detection sample; the data in the vector format are arranged to form a space vector database, each detection sample multidimensional detection parameter is characterized by a space vector, the space vector database is a database for storing detection samples, each sample in the space vector database is characterized by a space vector, and the circulation is carried out for a plurality of times to expand the data in the space vector database; marking effect parameters of the detection samples in the space vector database by collecting actual drug effects of the detection samples, wherein the effect parameters represent the quality of the corresponding detection samples; and filtering the detection samples in the spatial vector database leaves only typical samples.
Description
Technical Field
The invention belongs to the field of detection, and particularly relates to a quality detection method of oral liquid.
Background
In the prior art, the quality detection of the oral liquid generally only detects the content of important components in the oral liquid, and some techniques improve the reference and the accuracy of detection by setting a control group, but the fundamental existing measurement technique only can detect the content of a certain component in the sample detection of the oral liquid. In practical application, the oral liquid is generally a traditional Chinese medicine oral liquid, and the quality of the oral liquid is related to various factors, such as ambient temperature, humidity, measurement conditions and the like.
The quality of the oral liquid cannot be objectively determined by taking the content of a certain component as an evaluation standard in the prior art.
Disclosure of Invention
The invention aims to provide a quality detection method of oral liquid, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the quality detection method of the oral liquid comprises the steps of,
step 1, setting multi-dimensional detection parameters and manufacturing a detection sample;
step 2, detecting a detection sample through a high performance liquid chromatograph, detecting the content of a detection target object in the multi-dimensional detection parameters through the high performance liquid chromatograph, directly recording and acquiring the environmental temperature, the environmental humidity and the detection equipment model in the multi-dimensional detection parameters in real time, acquiring the multi-dimensional detection parameters of the detection sample, forming data in a vector format, and ensuring that one vector corresponds to one detection sample;
step 3, the data in the vector format in the process of step 2 are arranged to form a space vector database, each detection sample multidimensional detection parameter is characterized by a space vector, the space vector database is a database for storing detection samples, each sample in the space vector database is characterized by a space vector, and the step 2 is circularly carried out for a plurality of times to expand the data in the space vector database;
step 4, marking effect parameters of the detection samples in the space vector database by collecting actual drug effects of the detection samples, wherein the effect parameters represent the quality of the corresponding detection samples; and filtering the detection samples in the space vector database to leave only typical samples;
and 5, finishing detection of the sample to be detected through a high performance liquid chromatograph in actual detection, acquiring multi-dimensional detection parameters of the sample to be detected and forming vector format data of the sample to be detected, then adopting an algorithm for calculating a vector distance to determine a typical sample closest to the sample to be detected in a space vector database, and taking the effect parameter of the typical sample as the effect parameter of the sample to be detected.
Preferably, the detection of the detection sample by the high performance liquid chromatograph comprises the filtration and the ultrasound of a mobile phase, the starting-up of the instrument and the operation of software, the setting of sampling parameters, the sampling operation and the data analysis processing.
Preferably, the filtration and sonication of the mobile phase comprises carefully washing the filter device with distilled water, then assembling the filter device, placing the filter membrane, securing with clamps after assembly, connecting the vacuum pump conduit, opening the vacuum pump, pouring distilled water, pulling the conduit off, closing the vacuum pump, removing the filter device, removing the filter membrane, pouring into a reagent bottle, and sonicating for three minutes.
Preferably, the operation of starting up and software of the instrument comprises sequentially opening two pumps, detectors and column incubators of the high performance liquid chromatograph, opening a computer when a red lamp above the instrument turns green, opening LC program software on a desktop, selecting a program, clicking to determine, hearing a dripping sound to indicate that the instrument and the software are successfully connected, entering an LC data acquisition window, flushing a pipeline for 30min before the beginning of an experiment, selecting an equal concentration elution program, and setting the maximum pressure difference of the flow rate and the pumps.
Preferably, setting the sampling parameters comprises turning on the data acquisition, changing the LC stop time into 10min, the detector end time into 10min, still selecting equal concentration elution, checking the maximum pump pressure without changing the pump flow rate, turning on the detector, and setting the detection wavelength.
Preferably, the sample injection operation comprises the steps of firstly cleaning the micro-injector, injecting waste liquid into a waste liquid bottle after cleaning, wiping off surface liquid by using filter paper, removing standard substances with the volume ratio of 0.05%, enabling the redundant standard substances to flow into samples on the side of the injector, ensuring that no bubbles exist at the front section of the injector, injecting the standard substances into the injector, pushing the standard substances into the injector, quickly pressing a valve, and automatically starting the program.
Preferably, the data analysis processing includes opening an LC program on a desktop, selecting to re-analyze, entering a re-analyze interface, clicking a file, opening a data file, finding a folder of an experiment to be performed at this time, selecting data to be analyzed, namely, a corresponding peak map can appear, and in order to remove some tiny miscellaneous peaks, performing optimization operation, namely, clicking a method and data analysis parameters, changing a slope, clicking a certain point, leaving a main peak, clicking a view again, and selecting a peak table, namely, specific parameters of each peak can appear.
Preferably, the multi-dimensional detection parameters comprise the ambient temperature, the ambient humidity, the type of the detection equipment and the content of the detection target object during detection.
Preferably, an algorithm for calculating the vector distance is adopted to determine a typical sample closest to the sample to be measured in the space vector database, and specifically, the method comprises the step of calculating and determining a typical sample closest to the sample to be measured in the space vector database, and the distance calculation adopts Manhattan distance.
Advantageous effects
According to the method, the multidimensional detection parameters are set, detection of the detection samples is completed through the high-performance liquid chromatograph, data in a vector format are acquired to form a space vector database, effect parameters are marked on the detection samples in the space vector database through actual drug effects of the detection samples, the effect parameters represent the quality of the corresponding detection samples, the multidimensional detection parameters of the samples to be detected are acquired, data in the vector format of the samples to be detected are formed, then a typical sample closest to the samples to be detected is determined in the space vector database through an algorithm for calculating the vector distance, the effect parameters of the typical sample serve as effect parameters of the samples to be detected, so that quality detection of the samples to be detected can be completed, and the quality of the samples to be detected is that of the effect parameters of the typical samples.
Drawings
Fig. 1 is a flow chart of a quality detection method of an oral liquid.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, the quality of the oral liquid cannot be objectively determined by taking the content of a certain component as an evaluation standard in the quality detection of the oral liquid, and for this purpose, the application discloses a quality detection method of the oral liquid, which comprises the steps of (refer to figure 1),
step 1, setting multi-dimensional detection parameters, wherein the multi-dimensional detection parameters comprise the environment temperature, the environment humidity, the detection equipment model and the content of a detection target object during detection, quantifying the detection equipment model in implementation, measuring and obtaining other environment temperature, environment humidity and the content of the detection target object, manufacturing a detection sample, obtaining an oral liquid, and manufacturing the detection sample according to the oral liquid;
step 2, the detection of the detection sample is completed through a high performance liquid chromatograph, and the principle and structure of the high performance liquid chromatograph are introduced: the high performance liquid chromatography is a chromatography method which uses liquid as mobile phase, solid particles as stationary phase, the components to be tested are distributed and separated at high speed between two phases of the chromatographic column, mainly comprising a transfusion system, a high pressure pump, a detector, a sample injection valve, the chromatographic column, a data processing system and the like, wherein the mobile phase in a liquid storage bottle enters the high performance liquid chromatography system through the transfusion pipeline by the high pressure pump, the solution to be tested is loaded into the chromatographic column by the mobile phase after being mixed with the mobile phase by the high pressure pump after being input by the sample injection system, and as each component of the solution to be tested has different distribution coefficients in the two phases of the chromatographic column, when the components are relatively moved in the two phases, each component is separated into single components and flows out of the column in sequence, and when the components pass through the detector, the content of each component is converted into an electric signal and is transmitted to the data processing system for recording, analysis and calculation.
The detection of the detection sample by the high performance liquid chromatograph comprises the filtration and the ultrasound of a mobile phase, the starting-up of the instrument and the operation of software, the setting of sampling parameters, the sampling operation and the data analysis and treatment, and the detection comprises the following specific steps:
filtering and ultrasonic treatment of mobile phase includes carefully cleaning the filter device with distilled water, assembling the filter device, putting a filter membrane, separating the filter membrane into two kinds of membrane and organic membrane, respectively filtering the water phase and the organic phase, fixing the membrane and the organic phase by a clamp after assembling, connecting a vacuum pump conduit, opening the vacuum pump, pouring distilled water, pulling the conduit off, closing the vacuum pump, removing the filter device, removing the filter membrane, pouring the filter membrane into a reagent bottle, filtering the organic phase by the same method, and ultrasonic treating for three minutes;
the method comprises the steps of starting up an instrument and operating software, namely sequentially starting two pumps, detectors and a column temperature box of a high-performance liquid chromatograph, when a red lamp above the instrument turns green, starting up normally, starting a computer, starting LC program software on a desktop, selecting a program, clicking to confirm, hearing a dripping sound to indicate that the instrument and the software are successfully connected, entering an LC data acquisition window, flushing a pipeline for 30min before the beginning of an experiment, selecting an equal-concentration elution program, setting the flow rate and the maximum pressure difference of the pumps, selecting the detector lamp, then clicking a file, saving a method file for saving on the desktop, naming date and flushing, saving the point, clicking right side for downloading, and starting flushing;
setting sampling parameters, namely, starting data acquisition, changing LC stop time into 10min, setting detector end time into 10min, still selecting equal concentration elution, keeping pump flow rate unchanged, checking pump maximum pressure, starting detector, setting detection wavelength, setting a column temperature box and a controller without setting, setting point files, setting new folders on a desktop, naming time, naming method files according to time and sampling volume ratio, storing, clicking single sampling, popping up a dialog box, checking whether the method files are stored correctly, naming the data files as time + volume ratio, determining the point, and paying attention to not to start or exit;
the sample injection operation comprises the steps of firstly cleaning a microinjector for three times, injecting waste liquid into a waste liquid bottle after cleaning, wiping off surface liquid by filter paper, removing standard substances with the volume ratio of 0.05%, enabling the redundant standard substances to flow into samples on the side of the injector, ensuring that no bubbles exist at the front section of the injector, injecting the standard substances into the injector, pushing the standard substances into the injector, quickly pressing a valve, and automatically starting a program;
the data analysis processing comprises the steps of opening an LC program on a desktop, selecting and re-analyzing, entering a re-analyzing interface, clicking a file, opening a data file, finding a folder of an experiment to be performed at this time, selecting data to be analyzed, namely, generating a corresponding peak map, performing optimization operation, namely, clicking a method and data analysis parameters, changing a slope, clicking a certain point, leaving a main peak, generating peaks in different polarities of components in a time-sharing manner, clicking a view again, selecting a peak table, namely, generating specific parameters of each peak, such as retention time, peak area and the like, selecting and copying the data, creating a folder on the desktop, naming, pasting the data into the table, and noting the volume ratio;
the content of the detection target object in the multi-dimensional detection parameters is detected by a high performance liquid chromatograph, the environmental temperature, the environmental humidity and the detection equipment model in the multi-dimensional detection parameters are all obtained by direct real-time recording, the multi-dimensional detection parameters of the detection samples are obtained, data in a vector format are formed, and one vector corresponds to one detection sample;
step 3, the data in the vector format in the process of step 2 are arranged to form a space vector database, each detection sample multi-dimensional detection parameter is represented by a space vector, each component of the space vector corresponds to one dimension detection parameter), the space vector database is a database for storing detection samples, each sample of the space vector database is represented by one space vector, the step 2 is circularly carried out for a plurality of times to expand the data of the space vector database, and the cycle times can be fixed times such as 100 times or other conditions;
step 4, marking effect parameters of the detection samples in the space vector database by collecting actual drug effects of the detection samples, wherein the effect parameters represent the quality of the corresponding detection samples; the actual efficacy is the corresponding efficacy parameters of the statistical detection sample in the use process of the patient, and the efficacy parameters comprise cure rate; filtering the detection samples in the space vector database to leave only typical samples, filtering the detection samples in the space vector database to remove some sample groups with particularly close vector distances, for example, if more than 2 samples are particularly close to each other, only one sample is reserved as a typical sample, and other samples are removed;
in the step 5, the detection of the sample to be detected is completed through the high performance liquid chromatograph, the detection of the sample to be detected is consistent with the detection step of the original sample to be detected, but the detection objects are different, the multi-dimensional detection parameters of the sample to be detected are obtained, the data of the vector format of the sample to be detected are formed (the multi-dimensional detection parameters of the sample to be detected are required to be emphasized, the data of the vector format of the sample to be detected are not completed through the high performance liquid chromatograph in the application), then a representative sample closest to the sample to be detected is determined in the space vector database through an algorithm for calculating the vector distance, and particularly, the representative sample closest to the sample to be detected in the space vector database is determined through calculation, because each sample in the space vector database is characterized by a space vector, the representative sample in the space vector database is characterized by a space vector, and the representative sample to be detected is the data of the vector format, namely the actual vector, and the representative effect of the sample to be detected can be directly calculated, and the representative effect of the sample closest to the sample to be detected is taken as the representative effect parameter of the sample.
The method comprises the steps of setting multi-dimensional detection parameters, finishing detection of a detection sample through a high performance liquid chromatograph, acquiring data in a vector format, sorting the data to form a space vector database, marking effect parameters of the detection sample in the space vector database through actual drug effects of the detection sample, representing the quality of the corresponding detection sample by the effect parameters, acquiring the multi-dimensional detection parameters of the detection sample, forming data in the vector format of the detection sample, determining a typical sample closest to the detection sample in the space vector database by adopting an algorithm for calculating a vector distance, taking the effect parameters of the typical sample as the effect parameters of the detection sample, and then finishing quality detection of the detection sample, wherein the quality of the detection sample is the quality of the effect parameter representation of the typical sample.
As shown in fig. 1, the embodiment to be protected in the present application includes:
the quality detection method of the oral liquid comprises the steps of,
step 1, setting multi-dimensional detection parameters and manufacturing a detection sample;
step 2, detecting a detection sample through a high performance liquid chromatograph, detecting the content of a detection target object in the multi-dimensional detection parameters through the high performance liquid chromatograph, directly recording and acquiring the environmental temperature, the environmental humidity and the detection equipment model in the multi-dimensional detection parameters in real time, acquiring the multi-dimensional detection parameters of the detection sample, forming data in a vector format, and ensuring that one vector corresponds to one detection sample;
step 3, the data in the vector format in the process of step 2 are arranged to form a space vector database, each detection sample multidimensional detection parameter is characterized by a space vector, the space vector database is a database for storing detection samples, each sample in the space vector database is characterized by a space vector, and the step 2 is circularly carried out for a plurality of times to expand the data in the space vector database;
step 4, marking effect parameters of the detection samples in the space vector database by collecting actual drug effects of the detection samples, wherein the effect parameters represent the quality of the corresponding detection samples; and filtering the detection samples in the space vector database to leave only typical samples;
and 5, finishing detection of the sample to be detected through a high performance liquid chromatograph in actual detection, acquiring multi-dimensional detection parameters of the sample to be detected and forming vector format data of the sample to be detected, then adopting an algorithm for calculating a vector distance to determine a typical sample closest to the sample to be detected in a space vector database, and taking the effect parameter of the typical sample as the effect parameter of the sample to be detected.
Preferably, the detection of the detection sample by the high performance liquid chromatograph comprises the filtration and the ultrasound of a mobile phase, the starting-up of the instrument and the operation of software, the setting of sampling parameters, the sampling operation and the data analysis processing.
Preferably, the filtration and sonication of the mobile phase comprises carefully washing the filter device with distilled water, then assembling the filter device, placing the filter membrane, securing with clamps after assembly, connecting the vacuum pump conduit, opening the vacuum pump, pouring distilled water, pulling the conduit off, closing the vacuum pump, removing the filter device, removing the filter membrane, pouring into a reagent bottle, and sonicating for three minutes.
Preferably, the operation of starting up and software of the instrument comprises sequentially opening two pumps, detectors and column incubators of the high performance liquid chromatograph, opening a computer when a red lamp above the instrument turns green, opening LC program software on a desktop, selecting a program, clicking to determine, hearing a dripping sound to indicate that the instrument and the software are successfully connected, entering an LC data acquisition window, flushing a pipeline for 30min before the beginning of an experiment, selecting an equal concentration elution program, and setting the maximum pressure difference of the flow rate and the pumps.
Preferably, setting the sampling parameters comprises turning on the data acquisition, changing the LC stop time into 10min, the detector end time into 10min, still selecting equal concentration elution, checking the maximum pump pressure without changing the pump flow rate, turning on the detector, and setting the detection wavelength.
Preferably, the sample injection operation comprises the steps of firstly cleaning the micro-injector, injecting waste liquid into a waste liquid bottle after cleaning, wiping off surface liquid by using filter paper, removing standard substances with the volume ratio of 0.05%, enabling the redundant standard substances to flow into samples on the side of the injector, ensuring that no bubbles exist at the front section of the injector, injecting the standard substances into the injector, pushing the standard substances into the injector, quickly pressing a valve, and automatically starting the program.
Preferably, the data analysis processing includes opening an LC program on a desktop, selecting to re-analyze, entering a re-analyze interface, clicking a file, opening a data file, finding a folder of an experiment to be performed at this time, selecting data to be analyzed, namely, a corresponding peak map can appear, and in order to remove some tiny miscellaneous peaks, performing optimization operation, namely, clicking a method and data analysis parameters, changing a slope, clicking a certain point, leaving a main peak, clicking a view again, and selecting a peak table, namely, specific parameters of each peak can appear.
Preferably, the multi-dimensional detection parameters comprise the ambient temperature, the ambient humidity, the type of the detection equipment and the content of the detection target object during detection.
Preferably, an algorithm for calculating the vector distance is adopted to determine a typical sample closest to the sample to be measured in the space vector database, and specifically, the method comprises the step of calculating and determining a typical sample closest to the sample to be measured in the space vector database, and the distance calculation adopts Manhattan distance.
It will be appreciated that the exemplary sample functions described herein using the algorithm for calculating the vector distance in the spatial vector database to determine a closest sample to the sample to be measured may also be implemented in program code, the corresponding program code being stored on a machine readable medium, which may be a tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. To provide for interaction with a user, the algorithm described herein that uses calculated vector distances may be implemented on a computer that determines a representative sample cell function in a spatial vector database that is closest to the sample to be measured, the computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The above examples only represent some embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of the invention should be assessed as that of the appended claims.
Claims (2)
1. The quality detection method of the oral liquid is characterized by comprising the following steps of,
step 1, setting multi-dimensional detection parameters, namely, manufacturing a detection sample, wherein the multi-dimensional detection parameters comprise the environment temperature, the environment humidity, the detection equipment model and the content of a detection target object during detection, and quantifying the detection equipment model;
step 2, detecting a detection sample through a high performance liquid chromatograph, detecting the content of a detection target object in the multi-dimensional detection parameters through the high performance liquid chromatograph, directly recording and acquiring the environmental temperature, the environmental humidity and the detection equipment model in the multi-dimensional detection parameters in real time, acquiring the multi-dimensional detection parameters of the detection sample, forming data in a vector format, and ensuring that one vector corresponds to one detection sample;
step 3, the data in the vector format in the step 2 are arranged to form a space vector database, each detection sample multidimensional detection parameter is characterized by a space vector, the space vector database is a database for storing detection samples, and the step 2 is circularly carried out for a plurality of times to expand the data of the space vector database;
step 4, marking effect parameters of the detection samples in the space vector database by collecting actual drug effects of the detection samples, wherein the effect parameters represent the quality of the corresponding detection samples; the actual efficacy is the corresponding efficacy parameter of the statistical detection sample in the use process of the patient, and the efficacy parameter is the cure rate; and filtering the detection samples in the space vector database to leave only typical samples;
and 5, finishing detection of the sample to be detected through a high performance liquid chromatograph in actual detection, acquiring multi-dimensional detection parameters of the sample to be detected and forming vector format data of the sample to be detected, and then determining a typical sample closest to the sample to be detected in a space vector database by adopting an algorithm for calculating a vector distance, wherein the method specifically comprises the steps of calculating and determining a typical sample closest to the vector of the sample to be detected in the space vector database, and taking the effect parameter of the typical sample as the effect parameter of the sample to be detected.
2. The method for detecting the quality of oral liquid according to claim 1, wherein the vector distance calculation uses manhattan distance.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040002968A1 (en) * | 1999-04-02 | 2004-01-01 | Bull S.A. | Method for preconditioning and encoding a data table, and method for the implementation of table requests on a vectoral processor |
| CN103823004A (en) * | 2013-12-18 | 2014-05-28 | 广西科技大学 | Method for quickly measuring pigment and preservative in jelly |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20040002968A1 (en) * | 1999-04-02 | 2004-01-01 | Bull S.A. | Method for preconditioning and encoding a data table, and method for the implementation of table requests on a vectoral processor |
| CN103823004A (en) * | 2013-12-18 | 2014-05-28 | 广西科技大学 | Method for quickly measuring pigment and preservative in jelly |
Non-Patent Citations (1)
| Title |
|---|
| 李涵等: "多源信息融合技术在中药质量评价中的应用研究进展", 药学学报, vol. 58, no. 10, 18 September 2023 (2023-09-18), pages 2835 - 2852 * |
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