CN113433236B - Method for detecting quality grade of calyx seu fructus physalis - Google Patents
Method for detecting quality grade of calyx seu fructus physalis Download PDFInfo
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Abstract
The method for detecting the quality grade of the calyx seu fructus physalis is characterized in that a high performance liquid chromatography and a counter-propagation neural network are adopted, the function and the parameter of the counter-propagation neural network are reasonably set through reasonably selecting chromatographic conditions, and a method capable of comprehensively evaluating the quality of the calyx seu fructus physalis from the angle of chemical components is established, so that the quality grade of the calyx seu fructus physalis can be rapidly, accurately, reliably and comprehensively detected, and the method can be used for quality control of the calyx seu fructus physalis.
Description
Technical Field
The application relates to the technical field of traditional Chinese medicine analysis, in particular to a method for detecting quality grade of calyx seu fructus physalis.
Background
Calyx seu fructus Physalis is dried calyx of Physalis alkekengi (Physalis alkekengi L. Var. Franketii (Mast.) Makino) of Solanaceae, or fruit-bearing calyx, originally in Shennong Ben Cao Jing. Calyx seu fructus physalis is sweet, sour and cold in nature, has the effects of cooling, detumescence, relieving cough, resolving phlegm, promoting urination, strengthening heart and relieving fever, and is a common traditional Chinese medicine for clearing heat and detoxicating. The calyx seu fructus physalis is widely distributed and the quality of the calyx seu fructus physalis in different production places is uneven, and the qualitative identification of the calyx seu fructus physalis in China only adopts a thin layer chromatography method at present, so that the quality of the calyx seu fructus physalis is difficult to carry out integral evaluation, and therefore, a brand new method for detecting the quality of the calyx seu fructus physalis needs to be established so as to control the quality of the calyx seu fructus physalis more reliably and accurately.
Disclosure of Invention
The purpose of the application is to provide a method for detecting the quality grade of the calyx seu fructus physalis, which can rapidly and accurately obtain the quality grade of the calyx seu fructus physalis and can be used for quality control of the calyx seu fructus physalis.
The application provides a method for detecting quality grade of calyx seu fructus physalis, which comprises the following steps:
(1) Taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by taking 70-100% methanol as solvent to obtain R parts of calyx seu fructus physalis test solution, wherein R is more than or equal to 30;
(2) Detecting the sample solution by adopting a high performance liquid chromatography to obtain a chromatogram of the R parts of calyx seu fructus physalis; wherein the chromatographic conditions include:
chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid aqueous solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is carried out by adopting 0-95% of phase A and 5-100% of phase B in volume fraction; flow rate: 0.8-1.2mL/min; column temperature: 35-45 ℃; sample injection volume: 8-12 mu L;
(3) Analyzing the chromatograms of the step (2), and determining the common peaks in the chromatograms of all the calyx seu fructus physalis according to the retention time of the chromatographic peaks to obtain the peak area and the retention time of the common peaks in all the calyx seu fructus physalis;
(4) According to the peak area of the common peak, a counter propagation neural network is adopted to obtain a quality grading detection model of the calyx seu fructus physalis;
(5) Taking a calyx seu fructus physalis sample to be detected, respectively carrying out ultrasonic extraction by taking methanol with the volume fraction of 70-100% as a solvent to obtain a sample solution to be detected, obtaining a chromatogram of the sample solution to be detected under the same chromatographic condition, determining a common peak of the chromatogram of the sample solution to be detected according to the retention time of the common peak in the step (3) and obtaining the peak area of the common peak, and obtaining the quality grade of the calyx seu fructus physalis by adopting the quality grading detection model.
According to the method for detecting the quality grade of the calyx seu fructus physalis, the high performance liquid chromatography and the counter-propagation neural network are adopted, the function and the parameter of the counter-propagation neural network are reasonably set through reasonably selecting the chromatographic conditions, the method for evaluating the quality of the calyx seu fructus physalis from the angle of chemical components is established, the quality grade of the calyx seu fructus physalis can be rapidly, accurately, reliably and comprehensively detected, and accordingly the method can be used for quality control of the calyx seu fructus physalis.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other embodiments may also be obtained according to these drawings to those skilled in the art.
FIG. 1 is a chromatogram of a test solution of a batch 8 of calyx seu fructus physalis.
Fig. 2 is a training process for establishing a quality grading detection model of calyx seu fructus physalis.
Fig. 3 is a verification result of the accuracy of the quality grading detection model of calyx seu fructus physalis.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. Based on the embodiments herein, a person of ordinary skill in the art would be able to obtain all other embodiments based on the disclosure herein, which are within the scope of the disclosure herein.
The application provides a method for detecting quality grade of calyx seu fructus physalis, which comprises the following steps:
(1) Taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by taking 70-100% methanol as solvent to obtain R parts of calyx seu fructus physalis test solution, wherein R is more than or equal to 30;
(2) Detecting the sample solution by adopting a high performance liquid chromatography to obtain a chromatogram of the R parts of calyx seu fructus physalis; wherein the chromatographic conditions include:
chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid aqueous solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is carried out by adopting 0-95% of phase A and 5-100% of phase B in volume fraction; flow rate: 0.8-1.2mL/min; column temperature: 35-45 ℃; sample injection volume: 8-12 mu L;
(3) Analyzing the chromatograms of the step (2), and determining the common peaks in the chromatograms of all the calyx seu fructus physalis according to the retention time of the chromatographic peaks to obtain the peak area and the retention time of the common peaks in all the calyx seu fructus physalis;
(4) According to the peak area of the common peak, a counter propagation neural network is adopted to obtain a quality grading detection model of the calyx seu fructus physalis;
(5) Taking a calyx seu fructus physalis sample to be detected, respectively carrying out ultrasonic extraction by taking methanol with the volume fraction of 70-100% as a solvent to obtain a sample solution to be detected, obtaining a chromatogram of the sample solution to be detected under the same chromatographic condition, determining a common peak of the chromatogram of the sample solution to be detected according to the retention time of the common peak in the step (3) and obtaining the peak area of the common peak, and obtaining the quality grade of the calyx seu fructus physalis by adopting the quality grading detection model.
In the application, the methanol with the volume fraction of 70-100% refers to a methanol aqueous solution or methanol with the volume fraction of more than or equal to 70%.
In the application, the method comprises the steps of determining the common peak in the chromatograms of all calyx seu fructus physalis according to the retention time of the chromatographic peak, and comparing the retention time of the chromatographic peak in the chromatograms of all calyx seu fructus physalis, wherein the chromatographic peak with the same retention time exists in all chromatograms, namely the common peak in the chromatograms of all calyx seu fructus physalis. Wherein, the chromatographic peaks with the same retention time refer to chromatographic peaks with the deviation of the retention time less than or equal to 0.01 minute. The analysis of the chromatograms of the calyx seu fructus physalis can be performed by overlapping, comparing and analyzing a plurality of chromatograms, and can also be performed by adopting software, and the analysis can be performed by adopting traditional Chinese medicine chromatographic fingerprint similarity evaluation software, so that the common peak in the chromatograms of the calyx seu fructus physalis is determined.
By the method, the establishment of the quality grading detection model of the calyx seu fructus physalis is realized by adopting the high performance liquid chromatography and the counter-propagation neural network, and the quality grading detection model is used for detecting the quality grade of the calyx seu fructus physalis and has the advantages of rapidness, accuracy, reliability, comprehensiveness and the like, so that the quality grading detection model can be used for quality control of the calyx seu fructus physalis.
In some embodiments of the present application, in step (1), the mass M of the calyx seu fructus physalis 1 Volume with solvent V 1 The ratio of (C) is 1 (20-30) g/mL.
In some embodiments of the present application, in step (1), the time of ultrasonic extraction is 20-40min, the extraction power is 300-500W, and the extraction temperature is 20-30 ℃.
In the application, in the step (1), the R parts of calyx seu fructus physalis can be taken from different sources in different production places in R batches, and 1 part of calyx seu fructus physalis is taken from each batch; or a plurality of batches of calyx seu fructus physalis with different origins in different places of production can be taken, and each batch is taken with a total of R parts. Preferably, in some embodiments of the present application, the taking of R parts of calyx seu fructus physalis is taking N batches of calyx seu fructus physalis, taking M parts of each batch, and respectively performing ultrasonic extraction with 70-100% methanol as solvent to obtain a sample solution of r=n×m parts of calyx seu fructus physalis, where N is greater than or equal to 10, and M is greater than or equal to 3.
The inventors found in the study that the gradient elution of the present application can achieve better separation effect of each chemical component in calyx seu fructus physalis, preferably, in some embodiments of the present application, the gradient elution is specifically: 0-3min,5-10% B;3-10min,10-12% B;10-13min,12-15% B;13-20min,15-30% B;20-25min,30-40% B;25-30min,40-45% B;30-40min,45-80% B;40-45min,80-100% B.
By adopting the preparation method of the sample solution, the chromatographic conditions are combined, so that more common peaks can be obtained, a quality grading detection model of the calyx seu fructus physalis can be built more comprehensively from the angle of chemical components, the quality grade of the calyx seu fructus physalis can be accurately, comprehensively and reliably detected, and the quality control method is further used for quality control of the calyx seu fructus physalis.
In some embodiments of the present application, in step (2), the detection conditions of the high performance liquid chromatography include: detection wavelength: 253-255nm.
In some embodiments of the present application, in step (4), the back propagation neural network includes an input layer, an hidden layer, and an output layer; inputting the peak area of each common peak in one sample into the input layer; outputting 3 categories in an output layer; the node number of the hidden layer is 6;
the transfer function from the input layer to the hidden layer is log sig, the transfer function from the hidden layer to the output layer is purelin, the training function of the back propagation neural network is traingdx, and the performance function is mse.
In some embodiments of the present application, in step (4), the parameters of the back propagation neural network include: the maximum training times are 400-500, the learning rate is 0.005-0.015, and the training precision is less than or equal to 0.01.
By adopting the functions and parameters of the back propagation neural network, an accurate quality grading detection model of the calyx seu fructus physalis is established, and the quality grade of the calyx seu fructus physalis can be accurately detected, so that the method can be used for quality control of the calyx seu fructus physalis.
The following describes the instruments, reagents and materials used in the present application.
Instrument: FA2004A ten-thousandth balance: shanghai smart electronic instrumentation works; XO-4200DT ultrasonic cleaner: nanjing first Europe instruments Co., ltd; sartorius BT125D one ten thousandth balance: sidoriscom instruments Inc.; agilent1260 high performance liquid chromatograph: agilent Inc.
Reagent: chromatographic methanol, acetonitrile: sigma Co., USA; chromatographic formic acid: tianjin chemical reagent Co., ltd; distilled water: and (3) dropsy.
Materials: the information of the producing areas of the 28 batches of calyx seu fructus physalis is shown in table 1, wherein the excellent calyx seu fructus physalis has bright color and almost no worm damage and mildew spots; the color of the calyx seu fructus physalis in the cytoplasm is light brown, and the calyx seu fructus physalis has partial mildew and worm-eating; inferior calyx seu fructus physalis is dark brown or black in color, and is seriously damaged by worm.
Table 1 production place information of 28 batch calyx seu fructus physalis
| Batch of | Production area | Quality of | Batch of | Production area | Quality of | Batch of | Production area | Quality of | Batch of | Production area | Quality of |
| 1 | Shanxi province | Middle of the mass | 8 | (Jilin) | High quality | 15 | (Anhui) | Inferior quality | 22 | (Jilin) | High quality |
| 2 | (Jilin) | Middle of the mass | 9 | Heilongjiang river | High quality | 16 | (Jilin) | Inferior quality | 23 | (Jilin) | |
| 3 | Hebei river | Middle of the mass | 10 | Heilongjiang river | High quality | 17 | Shanxi province | Middle of the mass | 24 | (Jilin) | High quality |
| 4 | (Anhui) | Middle of the mass | 11 | Heilongjiang river | Inferior quality | 18 | (Anhui) | Middle of the mass | 25 | (Jilin) | Inferior quality |
| 5 | (Jilin) | Middle of the mass | 12 | Shanxi province | Inferior quality | 19 | Heilongjiang river | Inferior quality | 26 | (Jilin) | |
| 6 | Shanxi province | Middle of the mass | 13 | (Jilin) | Inferior quality | 20 | (Jilin) | High quality | 27 | (Jilin) | High quality |
| 7 | (Anhui) | |
14 | Hebei river | Middle of the mass | 21 | Liaoning (Liaoning) | High quality | 28 | (Jilin) | High quality |
The reagents and materials referred to in the examples below may be obtained commercially or according to methods known in the art unless otherwise specified.
Example 1
Taking calyx seu fructus physalis powder of batch 1 in Table 1, sieving with No. 3 sieve, precisely weighing 0.2g, placing into conical flask with plug, precisely adding 5mL of methanol, ultrasonic extracting for 30min at 40kHz with extraction power of 400W and temperature of 25deg.C, shaking uniformly after ultrasonic treatment, centrifuging at 8000r/min for 5min, collecting supernatant, filtering with 0.22 μm microporous membrane, and collecting subsequent filtrate to obtain sample solution. 3 parts were prepared in parallel.
Respectively taking the calyx seu fructus physalis powder of batches 2-28 in table 1, and preparing test sample solutions of the calyx seu fructus physalis of batches 2-28 by the same method. Wherein, the chromatogram of the batch 8 calyx seu fructus physalis is shown in figure 1.
Chromatographic conditions:
c18 (4.6X105 mm,5 μm); mobile phase: the phase A is formic acid aqueous solution with the volume fraction of 0.3 percent, and the phase B is acetonitrile; elution gradient: 0-3min,5-10% B;3-10min,10-12% B;10-13min,12-15% B;13-20min,15-30% B;20-25min,30-40% B;25-30min,40-45% B;30-40min,45-80% B;40-45min,80-100% B; flow rate: 1.0mL/min; column temperature: 40 ℃; sample injection volume: 10. Mu.L; detection wavelength: 254nm.
And detecting each sample solution by adopting the chromatographic conditions to obtain the chromatograms of 84 parts of calyx seu fructus physalis in batches 1-28 respectively.
Analyzing chromatograms of 84 total calyx seu fructus physalis samples, analyzing by using traditional Chinese medicine chromatographic fingerprint similarity evaluation software, determining 31 total peaks in chromatograms of all calyx seu fructus physalis according to retention time of chromatographic peaks, and obtaining peak areas and retention time of all total peaks in all calyx seu fructus physalis.
EXAMPLE 2 precision test
Test solutions were prepared as in example 1 from batch 8 of calyx seu fructus physalis powder in table 1, and the test solutions were tested under the chromatographic conditions of example 1 and continuously sampled 6 times, to obtain chromatograms of calyx seu fructus physalis containing 31 common peaks, respectively. Wherein 31 common peaks are numbered in sequence, 9 common peaks with the largest chromatographic peak area are taken as reference peaks, the relative retention time of each common peak is shown in table 2, and the relative peak area of each common peak is shown in table 3. As shown in Table 2, the Relative Standard Deviation (RSD) of the relative retention time of each common peak was less than 2%, and as shown in Table 3, the RSD of the relative peak area of each common peak was less than 5%, indicating that the instrument precision was good.
TABLE 2 relative retention times of common peaks
TABLE 3 relative peak areas of common peaks
Example 3 repeatability test
Test solutions were prepared in parallel by the method of example 1 from batch 8 of calyx seu fructus physalis powder in Table 1, and the test solutions were tested under the chromatographic conditions of example 1 to obtain chromatograms of calyx seu fructus physalis containing 31 common peaks, respectively. Wherein 31 common peaks are numbered in sequence, 9 common peaks are used as reference peaks, the relative retention time of each common peak is shown in table 4, and the relative peak area of each common peak is shown in table 5. As shown in Table 4, the relative retention time of each common peak was less than 2% and the relative peak area of each common peak was less than 5% as shown in Table 5, indicating that the reproducibility of the method was good.
TABLE 4 relative retention times of common peaks
TABLE 5 relative peak areas of common peaks
Example 4 stability test
Sample solutions of the sample solution of the batch 8 in the example 1 are taken and detected at 0, 2, 4, 8, 12 and 24 hours according to the chromatographic conditions of the example 1, so as to obtain chromatograms of the calyx seu fructus physalis with 31 common peaks. Wherein 31 common peaks are numbered in sequence, 9 common peaks are used as reference peaks, the relative retention time of each common peak is shown in table 6, and the relative peak area of each common peak is shown in table 7. As shown in Table 6, the relative retention time of each common peak was less than 2%, and as shown in Table 7, the relative peak area of each common peak was less than 5%, indicating that the sample solution was stable within 24 hours.
TABLE 6 relative retention times of common peaks
TABLE 7 relative peak areas of common peaks
Example 5 establishment of quality classification detection model for calyx seu fructus physalis
According to the chromatogram of 84 parts total calyx seu fructus Physalis of example 1, the peak area and retention time of each common peak in 84 parts total calyx seu fructus Physalis chromatogram were obtained. The peak area of 31 common peaks in each calyx seu fructus physalis is 1 sample, 84 samples are taken in total, 54 samples are randomly extracted as training samples, and a quality grading detection model of the calyx seu fructus physalis is built.
And establishing a back propagation neural network (BP neural network) model by adopting a BP-ANN toolbox (MathWorks, natick, MA, USA) in Matlab 2018b so as to obtain a quality grading detection model of the calyx seu fructus physalis. The back propagation neural network comprises an input layer, an implicit layer and an output layer; inputting 1 training sample (namely the peak area of each common peak in one calyx seu fructus physalis sample) into an input layer; outputting 3 categories in an output layer; the hidden layer uses one layer, and the node number of the hidden layer is 6; the transfer function from the input layer to the hidden layer is log (i.e. unipolar S function), the transfer function from the hidden layer to the output layer is purelin, the training function of the back propagation neural network is traingdx, and the performance function is mse; parameters of the BP neural network include: the maximum training frequency is 500, the training precision is 0.01, and the learning rate is 0.01; the training process is shown in fig. 2. According to fig. 2, a quality grading detection model of the calyx seu fructus physalis is obtained through 373 times of training.
Example 6 verification of accuracy of quality-graded detection model of calyx seu fructus physalis
30 samples except the training samples in example 5 were taken as test samples, and the accuracy of the quality classification detection model of calyx seu fructus physalis was verified.
By adopting the quality grading detection model of the calyx seu fructus physalis obtained in the embodiment 5, 1 test sample is input into the input layer, the output layer outputs the category of the test sample, and the category test result of each test sample is shown in figure 3. As can be seen from fig. 3, the quality classification detection model of the calyx seu fructus physalis detects to obtain a test result, and the test result can be better fitted with the prediction result of the category corresponding to the quality of each test sample in table 1, the accuracy is 100%, the Mean Square Error (MSE) is 0.00995, and the correlation coefficient (R) = 0.97765. The quality classification detection model of the calyx seu fructus physalis obtained by the method is higher in accuracy.
The method is adopted to obtain a quality grading detection model of the calyx seu fructus physalis, a sample solution of the calyx seu fructus physalis with unknown quality is prepared according to the method, the sample solution is detected according to chromatographic conditions of the method to obtain a chromatogram of the calyx seu fructus physalis with unknown quality, the chromatogram peak with the retention time of 18.08+/-0.01 min and the peak area of more than 2800 is taken as a reference peak and marked as a peak 9, the relative retention time of the rest chromatogram peaks is shown in a table 8, so that the common peak of the chromatogram is determined, the peak area of the chromatogram is obtained, and the quality grading detection model of the calyx seu fructus physalis with unknown quality is adopted to obtain the quality grade of the calyx seu fructus physalis with unknown quality. Furthermore, calyx seu fructus physalis is often used as a medicine with powder, when the calyx seu fructus physalis powder is used as a medicine, the medicine quality of the used calyx seu fructus physalis cannot be judged according to the powder, and the quality of the used calyx seu fructus physalis powder can be accurately measured by adopting the method of the application, so that the method is used for controlling the quality of the calyx seu fructus physalis powder.
TABLE 8 relative retention times of common peaks
According to the method for detecting the quality grade of the calyx seu fructus physalis, the high performance liquid chromatography and the counter-propagation neural network are adopted, the function and the parameter of the counter-propagation neural network are reasonably set through reasonably selecting the chromatographic conditions, the method for evaluating the quality of the calyx seu fructus physalis from the angle of chemical components is established, the quality grade of the calyx seu fructus physalis can be rapidly, accurately, reliably and comprehensively detected, and accordingly the method can be used for quality control of the calyx seu fructus physalis.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application. Any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (3)
1. A method of detecting a quality grade of a calyx seu fructus physalis, comprising the steps of:
(1) Taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by taking 70-100% methanol as solvent to obtain test solution of R parts of calyx seu fructus physalis, wherein R is more than or equal to 30, and the mass M of calyx seu fructus physalis 1 Volume with solvent V 1 The ratio of (3) is 1 (20-30) g/mL;
(2) Detecting the sample solution by adopting a high performance liquid chromatography to obtain a chromatogram of the R parts of calyx seu fructus physalis; wherein the chromatographic conditions include:
chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid aqueous solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is adopted: 0-3min,5-10% B;3-10min,10-12% B;10-13min,12-15% B;13-20min,15-30% B;20-25min,30-40% B;25-30min,40-45% B;30-40min,45-80% B;40-45min,80-100% B; flow rate: 0.8-1.2mL/min; column temperature: 35-45 ℃; sample injection volume: 8-12 mu L;
the detection conditions include: detection wavelength: 253-255nm;
(3) Analyzing the chromatograms of the step (2), and determining the common peaks in the chromatograms of all the calyx seu fructus physalis according to the retention time of the chromatographic peaks to obtain the peak area and the retention time of the common peaks in all the calyx seu fructus physalis;
(4) According to the peak area of the common peak, a counter propagation neural network is adopted to obtain a quality grading detection model of the calyx seu fructus physalis; the back propagation neural network comprises an input layer, an implicit layer and an output layer; inputting the peak area of each common peak in one sample into the input layer; outputting 3 categories in an output layer; the node number of the hidden layer is 6;
the transfer function from the input layer to the hidden layer is log sig, the transfer function from the hidden layer to the output layer is purelin, the training function of the back propagation neural network is traingdx, and the performance function is mse;
the parameters of the back propagation neural network include: the maximum training times are 400-500, the learning rate is 0.005-0.015, and the training precision is less than or equal to 0.01;
(5) Taking a calyx seu fructus physalis sample to be detected, respectively carrying out ultrasonic extraction by taking methanol with the volume fraction of 70-100% as a solvent to obtain a sample solution to be detected, obtaining a chromatogram of the sample solution to be detected under the same chromatographic condition, determining a common peak of the chromatogram of the sample solution to be detected according to the retention time of the common peak in the step (3) and obtaining the peak area of the common peak, and obtaining the quality grade of the calyx seu fructus physalis by adopting the quality grading detection model.
2. The method according to claim 1, wherein in the step (1), the ultrasonic extraction is performed for 20-40min at a power of 300-500W and at a temperature of 20-30 ℃.
3. The method of claim 1, wherein in the step (1), the R parts of calyx seu fructus physalis are prepared by taking N batches of calyx seu fructus physalis, taking M parts of each batch, and respectively performing ultrasonic extraction by taking 70-100% methanol as a solvent to obtain a sample solution of r=n×m parts of calyx seu fructus physalis, wherein N is greater than or equal to 10, and M is greater than or equal to 3.
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