CN113433236A - 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 application provides a method for detecting the quality grade of a calyx seu fructus physalis, which adopts a high performance liquid chromatography and a back propagation neural network, reasonably sets functions and parameters of the back propagation neural network by reasonably selecting chromatographic conditions, establishes a method capable of comprehensively evaluating the quality of the calyx seu fructus physalis from the perspective of chemical components, can quickly, accurately, reliably and comprehensively detect the quality grade of the calyx seu fructus physalis, and 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 a calyx seu fructus physalis.
Background
Calyx seu fructus Physalis is dry preserved calyx or preserved calyx with fruit of Solanaceae plant Physalis alkekengi L.var.franchetii (Mast.) Makino, and is recorded in Shennong Ben Cao Jing. The calyx seu fructus physalis is sweet, sour and cold in nature, has the effects of cooling, detumescence, cough relieving, phlegm reducing, diuresis, heart strengthening and fever relieving, and is a common traditional Chinese medicine for clearing heat and removing toxicity. The quality of the calyx seu fructus physalis in different producing areas is uneven, the qualitative identification of the calyx seu fructus physalis in the current Chinese pharmacopoeia only adopts a thin-layer chromatography method, and the quality of the calyx seu fructus physalis is difficult to be integrally evaluated, so that a brand-new method for detecting the quality of the calyx seu fructus physalis is required to be established so as to more reliably and accurately control the quality of the calyx seu fructus physalis.
Disclosure of Invention
The application aims to provide a method for detecting the quality grade of the calyx seu fructus physalis, which can quickly 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 the quality grade of a calyx seu fructus physalis, which comprises the following steps:
(1) taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by using 70-100% methanol by volume as solvent to obtain R parts of calyx seu fructus physalis test solution, wherein R is not less than 30;
(2) detecting the test solution by adopting a high performance liquid chromatography to obtain a chromatogram of R parts of calyx seu fructus physalis; wherein the chromatographic conditions comprise:
a chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid water solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is carried out by adopting a phase A with the volume fraction of 0-95% and a phase B with the volume fraction of 5-100%; flow rate: 0.8-1.2 mL/min; column temperature: 35-45 ℃; sample introduction volume: 8-12 μ L;
(3) analyzing the chromatogram in the step (2), determining common peaks in the chromatogram of each calyx seu fructus physalis according to retention time of chromatographic peaks, and obtaining peak area and retention time of each common peak in each calyx seu fructus physalis;
(4) obtaining a quality grading detection model of the calyx seu fructus physalis by adopting a back propagation neural network according to the peak area of the common peak;
(5) taking a to-be-detected calyx seu fructus physalis sample, respectively carrying out ultrasonic extraction by taking methanol with volume fraction of 70-100% as a solvent to obtain a to-be-detected sample solution, obtaining a chromatogram of the to-be-detected sample solution under the same chromatographic condition, determining a common peak of the chromatogram of the to-be-detected sample solution according to the retention time of the common peak in the step (3) and obtaining a 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 back propagation neural network are adopted, the chromatographic conditions are reasonably selected, the functions and parameters of the back propagation neural network are reasonably set, the method capable of comprehensively evaluating the quality of the calyx seu fructus physalis from the chemical composition perspective is established, the quality grade of the calyx seu fructus physalis can be quickly, accurately, reliably and comprehensively detected, and therefore 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 drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and it is also obvious for a person skilled in the art to obtain other embodiments according to the drawings.
Fig. 1 is a chromatogram of a test solution of a batch 8 calyx seu fructus physalis.
Fig. 2 is a training process for establishing a quality grading detection model of a calyx seu fructus physalis.
Fig. 3 is a result of verifying the accuracy of the quality grading detection model of the calyx seu fructus physalis.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the description herein are intended to be within the scope of the present disclosure.
The application provides a method for detecting the quality grade of a calyx seu fructus physalis, which comprises the following steps:
(1) taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by using 70-100% methanol by volume as solvent to obtain R parts of calyx seu fructus physalis test solution, wherein R is not less than 30;
(2) detecting the test solution by adopting a high performance liquid chromatography to obtain a chromatogram of R parts of calyx seu fructus physalis; wherein the chromatographic conditions comprise:
a chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid water solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is carried out by adopting a phase A with the volume fraction of 0-95% and a phase B with the volume fraction of 5-100%; flow rate: 0.8-1.2 mL/min; column temperature: 35-45 ℃; sample introduction volume: 8-12 μ L;
(3) analyzing the chromatogram in the step (2), determining common peaks in the chromatogram of each calyx seu fructus physalis according to retention time of chromatographic peaks, and obtaining peak area and retention time of each common peak in each calyx seu fructus physalis;
(4) obtaining a quality grading detection model of the calyx seu fructus physalis by adopting a back propagation neural network according to the peak area of the common peak;
(5) taking a to-be-detected calyx seu fructus physalis sample, respectively carrying out ultrasonic extraction by taking methanol with volume fraction of 70-100% as a solvent to obtain a to-be-detected sample solution, obtaining a chromatogram of the to-be-detected sample solution under the same chromatographic condition, determining a common peak of the chromatogram of the to-be-detected sample solution according to the retention time of the common peak in the step (3) and obtaining a 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 methanol aqueous solution or methanol with the volume fraction of more than or equal to 70%.
In the application, the determination of the common peak in the chromatogram of each calyx seu fructus physalis according to the retention time of the chromatographic peak comprises comparing the retention times of the chromatographic peaks in the chromatogram of each calyx seu fructus physalis, and the chromatographic peaks with the same retention time in all chromatograms are the common peak in the chromatogram of each calyx seu fructus physalis. Wherein, the chromatographic peaks with the same retention time refer to chromatographic peaks with retention time deviation less than or equal to 0.01 min. The chromatogram of each calyx seu fructus physalis can be analyzed by performing superposition contrast analysis on multiple chromatograms, or by adopting software, and exemplarily, traditional Chinese medicine chromatogram fingerprint similarity evaluation software can be adopted for analysis, so that a common peak in the chromatograms of each calyx seu fructus physalis is determined.
By adopting 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 back propagation neural network, the method is used for detecting the quality grade of the calyx seu fructus physalis, and has the advantages of rapidness, accuracy, credibility, comprehensiveness and the like, so that the method 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 physalis1Volume V with solvent1The ratio of (1) to (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-.
In the application, the R parts of the calyx seu fructus physalis in the step (1) can be R batches of calyx seu fructus physalis from different origins and different origins, and 1 part of the calyx seu fructus physalis is taken in each batch; or taking multiple batches of calyx seu fructus physalis from different origins and different origins, wherein multiple batches are taken in each batch, and the total number is R. Preferably, in some embodiments of the present application, taking R parts of calyx seu fructus physalis, taking N batches of calyx seu fructus physalis, taking M parts of calyx seu fructus physalis in each batch, taking 70-100% methanol by volume fraction as a solvent, and performing ultrasonic extraction to obtain a test 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 inventor finds in research that better separation effect of chemical components in calyx seu fructus physalis can be obtained by using the gradient elution of the present application, and 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 test solution, and combining the chromatographic conditions, more common peaks can be obtained, and a quality grading detection model of the calyx seu fructus physalis can be established more comprehensively from the perspective of chemical components, so that the quality grade of the calyx seu fructus physalis can be detected accurately, comprehensively and credibly, and the 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 high performance liquid chromatography include: detection wavelength: 253-255 nm.
In some embodiments of the present application, in step (4), the back propagation neural network comprises an input layer, a hidden layer, and an output layer; inputting peak areas of all common peaks in a sample into an input layer; outputting 3 categories in the output layer; the number of nodes of the hidden layer is 6;
the transfer function from the input layer to the hidden layer is logsig, the transfer function from the hidden layer to the output layer is purelin, the training function of the back propagation neural network is thingdx, 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 frequency is 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 function and the parameter of the back propagation neural network, an accurate quality grading detection model of the calyx seu fructus physalis is established, the quality grade of the calyx seu fructus physalis can be accurately detected, and therefore the method can be used for quality control of the calyx seu fructus physalis.
The following is a description of the instruments, reagents and materials used in the present application.
The instrument comprises the following steps: FA2004A ten thousandth balance: shanghai Jingtian electronics plant; XO-4200DT ultrasonic cleaning machine: nanjing Xiou instruments manufacturing Ltd; sartorius BT125D one ten-thousandth balance: sidolisi scientific instruments, Inc.; agilent1260 high performance liquid chromatograph: agilent corporation.
Reagent: chromatographic methanol, acetonitrile: sigma, USA; chromatography of formic acid: tianjin Kemi European Chemicals Co., Ltd; distilled water: drech.
Materials: the information of the producing areas of the 28 batches of calyx seu fructus physalis is shown in table 1, wherein the calyx seu fructus physalis with excellent quality has bright color and almost no worm damage or mildew; the color of the brocade lantern in the texture is light brown, and part of the brocade lantern is mildewed and damaged by worms; the color of the inferior brocade lantern is dark brown or black, and the brocade lantern is seriously damaged by worms and mildewed.
Table 128 pieces of information of producing area of the brocade lantern in batches
| Batches of | Producing area | Quality of | Batches of | Producing area | Quality of | Batches of | Producing area | Quality of | Batches of | Producing area | Quality of |
| 1 | Shaanxi province | In the middle of the body | 8 | (Jilin) | High quality | 15 | (Anhui) | Quality and inferiority | 22 | (Jilin) | High quality |
| 2 | (Jilin) | In the middle of the body | 9 | Heilongjiang | High quality | 16 | (Jilin) | Quality and inferiority | 23 | (Jilin) | |
| 3 | Hebei river | In the middle of the |
10 | Heilongjiang | High quality | 17 | Shaanxi province | In the middle of the body | 24 | (Jilin) | High quality |
| 4 | (Anhui) | In the middle of the body | 11 | Heilongjiang | Quality and inferiority | 18 | (Anhui) | In the middle of the body | 25 | (Jilin) | Quality and inferiority |
| 5 | (Jilin) | In the middle of the |
12 | Shaanxi province | Quality and inferiority | 19 | Heilongjiang | Quality and inferiority | 26 | (Jilin) | |
| 6 | Shaanxi province | In the middle of the body | 13 | (Jilin) | Quality and inferiority | 20 | (Jilin) | High quality | 27 | (Jilin) | High quality |
| 7 | (Anhui) | |
14 | Hebei river | In the middle of the body | 21 | Liaoning medicine | High quality | 28 | (Jilin) | High quality |
The reagents and medicinal materials mentioned in the following examples can 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 in a conical flask with a plug, precisely adding 5mL methanol, ultrasonically extracting for 30min at 400W, 40kHz frequency and 25 deg.C, shaking up after ultrasonic treatment, centrifuging at 8000r/min for 5min, taking supernatant, filtering with 0.22 μm microporous membrane, and taking subsequent filtrate to obtain sample solution. 3 parts are prepared in parallel.
Preparing the test sample solutions of the batches of the calyx seu fructus physalis of 2-28 in the same method from the batches of the calyx seu fructus physalis of 2-28 in the table 1 respectively. Wherein, the chromatogram of the calyx seu fructus physalis of batch 8 is shown in figure 1.
Chromatographic conditions are as follows:
c18 (4.6X 150mm, 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: 1.0 mL/min; column temperature: 40 ℃; sample introduction volume: 10 mu L of the solution; detection wavelength: 254 nm.
Detecting each sample solution by adopting the chromatographic conditions to respectively obtain chromatograms of 84 parts of calyx seu fructus physalis in batches of 1-28.
Analyzing the obtained chromatograms of 84 parts of calyx seu fructus physalis samples, adopting traditional Chinese medicine chromatogram fingerprint similarity evaluation software to analyze, determining that 31 common peaks exist in the chromatograms of the calyx seu fructus physalis according to the retention time of chromatographic peaks, and obtaining the peak area and the retention time of each common peak in each calyx seu fructus physalis.
Example 2 precision test
Taking the calyx seu fructus physalis powder of batch 8 in table 1, preparing a test solution according to the method of example 1, detecting the test solution according to the chromatographic conditions of example 1, and continuously sampling for 6 times to respectively obtain chromatograms of the calyx seu fructus physalis with 31 common peaks. The 31 common peaks are numbered in sequence, the 9 common peak with the largest chromatographic peak area is taken as a reference peak, 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 can be seen from Table 2, the Relative Standard Deviation (RSD) of the relative retention time of each common peak was less than 2%, and from Table 3, the RSD of the relative peak area of each common peak was less than 5%, indicating that the precision of the apparatus was good.
TABLE 2 relative retention time of common peaks
TABLE 3 relative peak area of common peaks
EXAMPLE 3 repeatability test
Taking the calyx seu fructus physalis powder of batch 8 in table 1, preparing the test solution according to the method of example 1, preparing 6 parts in parallel, and detecting the test solution according to the chromatographic conditions of example 1 to respectively obtain chromatograms of the calyx seu fructus physalis including 31 common peaks. Of these, 31 common peaks were numbered in this order, and the reference peak was the common peak No. 9, 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. From Table 4, the RSD of the relative retention time of each common peak is < 2%, and from Table 5, the RSD of the relative peak area of each common peak is < 5%, indicating that the method has good reproducibility.
TABLE 4 relative retention time of common peaks
TABLE 5 relative peak area of common peaks
Example 4 stability test
Sample introduction and detection are carried out on the sample solution of the batch 8 in the example 1 for 0, 2, 4, 8, 12 and 24 hours according to the chromatographic conditions in the example 1, and chromatograms of the calyx seu fructus physalis with 31 common peaks are respectively obtained. Of these, 31 common peaks were numbered in this order, and the reference peak was the common peak No. 9, 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. From Table 6, the RSD of the relative retention time of each common peak was < 2%, and from Table 7, the RSD of the relative peak area of each common peak was < 5%, indicating that the test solution was stable within 24 hours.
TABLE 6 relative retention time of common peaks
TABLE 7 relative peak area of common peaks
Example 5 establishing a quality grading detection model of a calyx seu fructus physalis
According to the chromatogram of 84 parts of calyx seu fructus physalis in example 1, the peak area and retention time of each common peak in the chromatogram of 84 parts of calyx seu fructus physalis are obtained. And taking the peak area of 31 common peaks in each calyx seu fructus physalis as 1 sample, totaling 84 samples, and randomly extracting 54 samples as training samples to establish a quality grading detection model of the calyx seu fructus physalis.
And (3) establishing a back propagation neural network (BP neural network) model by adopting a BP-ANN tool box (MathWorks, Natick, MA, USA) in Matlab 2018b to obtain a quality grading detection model of the calyx seu fructus physalis. The back propagation neural network comprises an input layer, a hidden layer and an output layer; inputting 1 training sample (namely the peak area of each common peak in a brocade lantern sample) in an input layer; outputting 3 categories in the output layer; the hidden layer uses one layer, and the number of nodes of the hidden layer is 6; the transfer function from the input layer to the hidden layer is logsig (namely, a 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 thingdx, 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. As can be seen from FIG. 2, the quality grading detection model of the calyx seu fructus physalis is obtained through 373 times of training.
Example 6 verification of the accuracy of the quality grading detection model of the Lantern
And (3) taking 30 samples except the training sample in the embodiment 5 as test samples, and verifying the accuracy of the quality grading detection model of the calyx seu fructus physalis.
By using the quality grading detection model of the calyx seu fructus physalis obtained in example 5, 1 test sample is input into the input layer, the class of the test sample is output from the output layer, and the class test result of each test sample is shown in fig. 3. As can be seen from fig. 3, the test results obtained by the quality classification test model of the calyx seu fructus physalis can be well fitted with the prediction results of the categories corresponding to the qualities of the test samples in table 1, the accuracy is 100%, the Mean Square Error (MSE) is 0.00995, and the correlation coefficient (R) is 0.97765. The method for detecting the quality grading of the calyx seu fructus physalis by the method is high in accuracy.
The method comprises the steps of obtaining a quality grading detection model of the calyx seu fructus physalis by adopting the method, preparing a test solution of the calyx seu fructus physalis with unknown quality according to the method, detecting the test solution according to the chromatographic conditions of the method to obtain a chromatogram of the calyx seu fructus physalis with unknown quality, determining a common peak of the chromatogram and obtaining a peak area of the chromatogram according to the retention time of the chromatogram peak by taking the chromatogram peak with the retention time of 18.08 +/-0.01 min and the peak area of more than 2800 as a reference peak and marking the reference peak as peak 9, and obtaining the relative retention time of the rest chromatogram peaks as shown in table 8 by adopting the quality grading detection model of the calyx seu fructus physalis, thereby obtaining the quality grade of the calyx seu fructus physalis with unknown quality. Furthermore, the calyx seu fructus physalis is usually taken as the medicine in the form of powder, when the calyx seu fructus physalis powder is taken as the medicine, the quality of the used calyx seu fructus physalis medicine cannot be judged according to the powder, and the quality of the calyx seu fructus physalis powder can be accurately determined by adopting the method, so that the calyx seu fructus physalis powder can be used for quality control of the calyx seu fructus physalis powder.
TABLE 8 relative retention time 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 back propagation neural network are adopted, the chromatographic conditions are reasonably selected, the functions and parameters of the back propagation neural network are reasonably set, the method capable of comprehensively evaluating the quality of the calyx seu fructus physalis from the chemical composition perspective is established, the quality grade of the calyx seu fructus physalis can be quickly, accurately, reliably and comprehensively detected, and therefore the method can be used for quality control of the calyx seu fructus physalis.
The above description is only for the preferred embodiment of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (8)
1. A method for detecting the quality grade of a calyx seu fructus physalis comprises the following steps:
(1) taking R parts of calyx seu fructus physalis, respectively performing ultrasonic extraction by using 70-100% methanol by volume as solvent to obtain R parts of calyx seu fructus physalis test solution, wherein R is not less than 30;
(2) detecting the test solution by adopting a high performance liquid chromatography to obtain a chromatogram of R parts of calyx seu fructus physalis; wherein the chromatographic conditions comprise:
a chromatographic column: octadecylsilane chemically bonded silica chromatographic column;
mobile phase: the phase A is formic acid water solution with the volume fraction of 0.1-0.5%, and the phase B is acetonitrile; gradient elution is carried out by adopting a phase A with the volume fraction of 0-95% and a phase B with the volume fraction of 5-100%; flow rate: 0.8-1.2 mL/min; column temperature: 35-45 ℃; sample introduction volume: 8-12 μ L;
(3) analyzing the chromatogram in the step (2), determining common peaks in the chromatogram of each calyx seu fructus physalis according to retention time of chromatographic peaks, and obtaining peak area and retention time of each common peak in each calyx seu fructus physalis;
(4) obtaining a quality grading detection model of the calyx seu fructus physalis by adopting a back propagation neural network according to the peak area of the common peak;
(5) taking a to-be-detected calyx seu fructus physalis sample, respectively carrying out ultrasonic extraction by taking methanol with volume fraction of 70-100% as a solvent to obtain a to-be-detected sample solution, obtaining a chromatogram of the to-be-detected sample solution under the same chromatographic condition, determining a common peak of the chromatogram of the to-be-detected sample solution according to the retention time of the common peak in the step (3) and obtaining a 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 step (1), the mass M of the calyx seu fructus physalis is1Volume V with solvent1The ratio of (1) to (20-30) g/mL.
3. The method as claimed in claim 1, wherein in the step (1), the ultrasonic extraction time is 20-40min, the extraction power is 300-500W, and the extraction temperature is 20-30 ℃.
4. The method as claimed in claim 1, wherein in the step (1), the step of taking R parts of calyx seu fructus physalis is to take N batches of calyx seu fructus physalis, take M parts of calyx seu fructus physalis in each batch, take 70-100% methanol by volume fraction as solvent, and respectively perform ultrasonic extraction to obtain a test solution of R-NxM parts of calyx seu fructus physalis, wherein N is more than or equal to 10, and M is more than or equal to 3.
5. The method according to claim 1, wherein in step (2), the gradient elution is in particular: 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.
6. The method according to claim 1, wherein in the step (2), the detection conditions of the high performance liquid chromatography include: detection wavelength: 253-255 nm.
7. The method of claim 1, wherein in step (4), the back propagation neural network comprises an input layer, a hidden layer, and an output layer; inputting peak areas of all common peaks in a sample into an input layer; outputting 3 categories in the output layer; the number of nodes of the hidden layer is 6;
the transfer function from the input layer to the hidden layer is logsig, the transfer function from the hidden layer to the output layer is purelin, the training function of the back propagation neural network is thingdx, and the performance function is mse.
8. The method of claim 1, wherein in step (4), the parameters of the back propagation neural network comprise: the maximum training frequency is 400-500, the learning rate is 0.005-0.015, and the training precision is less than or equal to 0.01.
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| CN109376805A (en) * | 2018-12-21 | 2019-02-22 | 四川理工学院 | A kind of classification method based on white wine base liquor Fingerprints |
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| US5424959A (en) * | 1993-07-19 | 1995-06-13 | Texaco Inc. | Interpretation of fluorescence fingerprints of crude oils and other hydrocarbon mixtures using neural networks |
| CN103288846A (en) * | 2013-05-15 | 2013-09-11 | 浙江大学 | Method for extracting and purifying total physalin from physalis plants |
| CN109376805A (en) * | 2018-12-21 | 2019-02-22 | 四川理工学院 | A kind of classification method based on white wine base liquor Fingerprints |
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| YUNLIANG ZHENG 等: "Characterization of physalins and fingerprint analysis for the quality evaluation of Physalis alkekengi L. var. franchetii by ultra-performance liquid chromatography combined with diode array detection and electrospray ionization tandem mass spectrometry", 《JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS》 * |
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