CN110988153A - Ultrasonic extraction process optimization method for effective components of salvia miltiorrhiza based on LS-SVM model - Google Patents
Ultrasonic extraction process optimization method for effective components of salvia miltiorrhiza based on LS-SVM model Download PDFInfo
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Abstract
An ultrasonic extraction process optimization method of active ingredients of salvia miltiorrhiza based on an LS-SVM model belongs to the technical field of traditional Chinese medicine extraction. According to the method, a regression model of the comprehensive evaluation value about the extraction factors is established in a high-dimensional feature space by using a least square support vector machine, the regression model is converted into an optimization model related to the kernel function, and finally, the predictive comprehensive evaluation value with the minimum error with the real comprehensive evaluation value under the optimal parameters is continuously learned through a data set so as to find out the extracted optimal process factors. According to the invention, a nonlinear model is established, the quantitative relation among data can be revealed, the model error is small, the reliability is high, and a new thought and reference are provided for the ultrasonic extraction process optimization research of the effective components of the salvia miltiorrhiza.
Description
Technical Field
The invention belongs to the technical field of traditional Chinese medicine extraction, and particularly relates to an ultrasonic extraction process optimization method for active ingredients of salvia miltiorrhiza based on an LS-SVM model.
Background
The salvia miltiorrhiza is a traditional Chinese medicine and has the effects of removing blood stasis, stopping bleeding, promoting blood circulation, stimulating the menstrual flow, clearing away the heart-fire, relieving restlessness and the like. The Salvia miltiorrhiza has the pharmacological effects of protecting cardiovascular system, improving blood circulation, resisting oxidation, protecting cerebral ischemia, protecting reperfusion injury, enhancing anoxia resistance, improving renal function, etc. The active ingredients of the salvia miltiorrhiza mainly comprise two types, namely fat-soluble tanshinone compounds and water-soluble phenolic acid compounds, wherein the active ingredients playing a positive role can be dozens of types, and the optimization of the extraction process of the active ingredients is also the research focus.
A Support Vector Machine (SVM) is a Machine learning model for solving a regression problem, and solves a fussy nonlinear problem by introducing a kernel function, thereby avoiding a dimension disaster problem of high-latitude space calculation. A Least square Support Vector Machine (LS-SVM) is a further improvement of the LS-SVM, and the operation speed is increased and the calculation complexity is reduced by converting an objective function and optimizing equation conditions. At present, many researchers often analyze Chinese medicine extraction data by operating a neural network model in a Matlab environment, but researches on optimizing the process conditions for extracting effective substances of Chinese medicines by using a least square support vector machine are rarely reported.
Disclosure of Invention
Aiming at the defect that the prior art for optimizing the process of the effective ingredients of the traditional Chinese medicine is unstable in processing the nonlinear problem at high latitude, the invention establishes a regression model of a comprehensive evaluation value about extraction factors by using a least square support vector machine in a high-dimensional characteristic space, converts the regression model into an optimization model related to a kernel function, and finally continuously learns through a data set to find out a predictive comprehensive evaluation value with the minimum error with a real comprehensive evaluation value under an optimal parameter so as to find out the optimal extracted process factor, and the method specifically comprises the following steps:
(1) carrying out ultrasonic extraction on active ingredients Sal _ B and Tan _ IIA of the salvia miltiorrhiza bunge according to the Box-Benhnken Design principle, carrying out experimental determination on the extraction rate of the active ingredients and the real comprehensive evaluation value of each process to obtain m groups of analysis scheme experimental data sets D { (X)1,Y1),(X2,Y2),…,(Xm,Ym)}，X1,X2…XmIs a combination of m extraction factor processes, Y1,Y2…YmA real comprehensive evaluation value of m extraction rates corresponding to the extraction factor process combination;
(2) establishing a least square support vector machine model LS-SVM, establishing an extraction factor x based on a Matlab language environmentiAnd comprehensively evaluating the value y to obtain an extraction factor x1,x2…xiA quantitative relation with the predictive comprehensive evaluation value y of the extraction rate;
(3) optimizing a nuclear parameter g and a penalty factor C of a model parameter, wherein the nuclear function adopts a radial basis kernel function RBF, editing by Matlab software, performing cross validation on g and C simultaneously by a cross validation method, and checking each pair of parameter effects one by one in a parameter matrix consisting of g and C to obtain optimal parameters g and C;
(4) inputting the optimal parameters g and C in the step (3) into an LS-SVM model to obtain a comprehensive predictive evaluation value of the Box-Benhnken Design analysis scheme in the step (1);
(5) evaluating the simulated performance of the LS-SVM model using the mean square error MSE, wherein
m is the number of data sets, yiPredictive comprehensive evaluation value, Y, for LS-SVMiThe evaluation value is a real comprehensive evaluation value.
The real comprehensive evaluation value in the step (1) is the sum of numerical values obtained by multiplying the process extraction rate by the weight, and the weight is calculated by a standard deviation method.
And (5) when the MSE is more than or equal to 0 and less than or equal to 0.6, the model predicts that the comprehensive evaluation value is close to the real comprehensive evaluation value, and the model is reliable.
The optimal ultrasonic extraction conditions of the effective components Sal _ B and Tan _ IIA of the salvia miltiorrhiza are as follows: the extraction temperature is 85 deg.C, the ultrasonic treatment time is 52min, the liquid-material ratio is 12:1, and the ethanol concentration is 90%.
The invention has the following beneficial effects:
the LS-SVM model establishes a nonlinear model by using a batch of mutually corresponding input and output data of an ultrasonic extraction process optimization experiment of the effective components of the salvia miltiorrhiza, which is obtained by the Box-Benhnken Design principle, reveals the quantitative relation between the data, has small model error and high reliability, and provides a new thought and reference for the optimization research of the extraction process of the effective components of the traditional Chinese medicine.
Drawings
FIG. 1 is an HPLC chart of a mixed control solution (a) and a Salvia miltiorrhiza test solution (B), wherein 1 is Sal _ B and 2 is Tan _ IIA;
FIG. 2 is a graph of optimal parameter results;
FIG. 3 is a graph of the results of error analysis of experimental data and model predictive data;
FIG. 4 is a comparison graph of the real comprehensive evaluation value and the predictive comprehensive evaluation value of the experiment;
Detailed Description
The present invention is further illustrated by the following examples.
Example (b):
1. instruments and reagents
1.1 instruments XS250 electronic balance (Mettler International Inc.), ultrapure water (Mettler Toledo Inc.), Waters 2695 type high performance liquid chromatograph (on-line vacuum degasser, autosampler system, column oven, 2498 type ultraviolet detector, Empower software), 2-16PK centrifuge (Sigma, Germany), SK5210HP ultrasonic cleaning instrument (Shanghai Ke ultrasonic guide Co., Ltd.).
1.2 reagent salvia miltiorrhiza (Zhejiang, batch number: 190201) is purchased from Zhejiang Chinese medicine university herbal pieces Limited, is identified by professor Huang-rope Wu of the institute of medicine and pharmacy of Zhejiang Chinese medicine university, and accords with the related regulation under the term salvia miltiorrhiza in the 2015 edition pharmacopoeia of the people's republic of China (part one). Sal _ B Standard was purchased from Biotech limited of Nanjing Shimeji (batch No. 101996, content ≥ 98%); tan _ IIA standard is purchased from Shanghai Allantin Biotechnology, Inc. (batch No. D1720010, content is more than or equal to 98%); acetonitrile, methanol (chromatographically pure, Tedia corporation); the water is ultrapure water; other reagents were analytically pure.
2. Method and results
2.1 HPLC determination of SalB and Tan IIA
2.1.1 chromatographic conditions: the chromatographic column is Hypersil BDS C18(4.6 mm. times.300 mm, 5 μm); the mobile phase was acetonitrile (a), 0.1% formic acid (B), with gradient elution: 0-12min, 10% -22% A; 12-30min, 22% -30% A; 30-38min, 30% -45% A; 38-50min, 45% -90% A; 50-55min, 90% -90% A; 55-56min, 90-95% A; 56-60min, 95% -10% A; the flow rate is 1 mL/min; the column temperature is 25 ℃; the detection wavelength is 280 nm; the amount of the sample was 10. mu.L.
2.1.2 preparation of control solutions: accurately weighing appropriate amount of Sal _ B and Tan _ IIA reference substances, respectively placing in 10mL volumetric flasks, adding 75% methanol to scale mark, and thoroughly shaking to obtain single component reference substance stock solutions containing Sal _ B1.00 mg/mL and Tan _ IIA 1.00 mg/mL. Precisely sucking 4.00mL and 0.50mL of the 2 stock solutions respectively, placing in the same 10mL measuring flask, adding 75% methanol to dilute to scale, and shaking to obtain mixed reference solution (Sal _ B0.40 mg/mL, Tan _ II A0.05mg/mL).
2.1.3 preparation of test solution: precisely weighing 5g of Saviae Miltiorrhizae radix powder sample, placing in a conical flask, performing ultrasonic extraction at liquid-material ratio of 12:1, 75 deg.C, ethanol concentration of 80% and ultrasonic frequency of 40kHz for 50min, and vacuum filtering to obtain filtrate. Centrifuging the filtrate at 5000r/min for 10min, and filtering the supernatant with 0.22 μm microporous membrane to obtain test solution.
2.1.4 plotting of Standard Curve: accurately sucking appropriate amounts of 1.00mg/mLSal _ B and Tan _ IIA reference substance stock solutions respectively, placing in volumetric flasks of different specifications, adding 75% methanol to scale marks, shaking up, and preparing mixed reference substance solutions of No. 1-5 with different concentrations, wherein the Sal _ B concentrations in the solutions of No. 1-5 are respectively as follows: 0.30, 0.40, 0.70, 0.90, 1.00 mg/mL; the concentrations of Tan _ IIA are: 0.03, 0.04, 0.05, 0.06, 0.08 mg/mL. According to the sample injection detection under the chromatographic condition of '2.1.1', a standard curve is drawn by taking the solution concentration as an abscissa (X) and the absorbance as an ordinate (Y), and the obtained Sal _ B standard curve is as follows: y is 9.30 × 106X+1.60×105(R20.9990) and has good linear relation in the concentration range of 0.30-1.00 mg/mL; the Tan _ iia standard curve is: y2.10 × 107X-6.24×103(R20.9995) in the concentration range of 0.03-0.08 mg/mL, and has good linear relation. When the signal-to-noise ratio is 3:1, the lowest detection limits of Sal _ B and Tan _ IIA are 0.05 and 0.01mg/mL respectively.
2.1.5 specificity test: the mixed reference solution and the salvia miltiorrhiza sample solution with proper amount are precisely absorbed and respectively measured according to the chromatographic conditions under the item of 2.1.1, the Sal _ B and Tan _ IIA peaks are completely separated, the peak shape is better, the separation degree is more than 1.50, and the method is shown in figure 1, which shows that the specificity of the method is good.
2.1.6 precision test: accurately sucking appropriate amount of Sal _ B of 0.50mg/mL and Tan _ IIA reference solution of 0.05mg/mL, respectively, and repeatedly and parallelly determining for 6 times according to the chromatographic conditions under the term of '2.1.1', to obtain peak areas RSD values of 0.69% and 0.76%, which indicates that the instrument has good precision.
2.1.7 stability test: preparing a sample solution according to the method of the item 2.1.3, taking the sample solution to be tested according to the item 2.1.1 and the room temperature condition, respectively injecting samples for 0h, 2 h, 4 h, 6 h and 8h, measuring, recording the chromatographic peak areas of Sal _ B and Tan _ IIA, and calculating RSD values to be 1.24 percent and 1.68 percent respectively, which indicates that the sample solution is stable within 8 h. 2.1.8 repeatability test: precisely weighing 5g of salvia miltiorrhiza powder in the same batch, preparing 6 parts of solution for supply according to the method of the item 2.1.3, and measuring under the chromatographic condition of the item 2.1.1 to obtain the RSD value of the Sal _ B peak area of 0.35 percent and the RSD value of the Tan _ IIA peak area of 0.28 percent, which indicates that the method has good repeatability.
2.1.9 sample recovery test: precisely weighing 5g of salvia miltiorrhiza powder in the same batch, and 6 parts in total, adding 0.50mg of Sal _ B reference substance and 0.10mg of Tan _ IIA reference substance into each part to prepare a test solution, and measuring according to chromatographic conditions, wherein the average recovery rate of Sal _ B is 98.39%, and the RSD is 0.52%; the average recovery of Tan _ IIA was 97.8% and the RSD was 0.57%.
2.2 Single factor experiment
2.2.1 Effect of ethanol concentration on extraction yield: precisely weighing 5 parts of 5g of salvia miltiorrhiza, and performing ultrasonic extraction for 40min under the conditions of liquid-material ratio of 12:1, temperature of 55 ℃, ethanol concentration of 50%, 60%, 70%, 80% and 90% and ultrasonic frequency of 40kHz respectively, wherein experimental results show that the extraction rates of Sal _ B and Tan _ IIA are highest when the ethanol concentration is 80%, so 70%, 80% and 90% are selected as three levels of ethanol concentration in response surface design.
2.2.2 Effect of ultrasound time on extraction yield: precisely weighing 5 parts of 5g of salvia miltiorrhiza, and performing ultrasonic extraction for 20min, 30min, 40min, 50min and 60min respectively under the conditions of liquid-material ratio of 12:1, temperature of 55 ℃ and ethanol concentration of 80% and ultrasonic frequency of 40kHz, wherein experimental results show that the extraction rates of Sal _ B and Tan _ IIA are highest when the ultrasonic time is 50min, so that 40min, 50min and 60min are selected as three levels of ultrasonic time in response surface design.
2.2.3 Effect of ultrasound temperature on extraction yield: precisely weighing 5 parts of 5g of salvia miltiorrhiza, and performing ultrasonic extraction for 50min at 45, 55, 65, 75 and 85 ℃ respectively under the conditions of liquid-material ratio of 12:1, ethanol concentration of 80% and ultrasonic frequency of 40kHz, wherein experimental results show that the extraction rates of Sal _ B and Tan _ IIA are highest when the ultrasonic temperature is 75 ℃, so that 65, 75 and 85 ℃ are selected as three levels of ultrasonic temperature in response surface design.
2.2.4 influence of liquid-to-feed ratio on extraction yield: precisely weighing 5g of Saviae Miltiorrhizae radix, and performing ultrasonic extraction at ethanol concentration of 80%, temperature of 75 deg.C and ultrasonic frequency of 40kHz for 50min at liquid-to-material ratio of 8:1, 10:1, 12:1, 14:1 and 16: 1. The experimental result shows that when the liquid-material ratio is 12: at 1, the extraction rates of Sal _ B and Tan _ IIA are the highest, so 10:1, 12:1 and 14:1 are selected as three levels of liquid-to-material ratio in response surface design.
2.2.5 Effect of extraction times on extraction yield: respectively and precisely weighing 5 parts of 5g of salvia miltiorrhiza, and extracting for 1, 2, 3 and 4 times under the conditions of ethanol concentration of 80%, time of 50min, temperature of 75 ℃ and liquid-material ratio of 12:1, wherein experimental results show that when the extraction frequency is 3, the extraction rate of Sal _ B and Tan _ IIA is relatively high, and the extraction rate tends to be gentle after 3 times, which indicates that the extraction of Sal _ B and Tan _ IIA is finished at the moment.
2.3 optimization technique of response surface method
2.3.1 factor level design: on the basis of a single-factor test, because the extraction frequency has little influence on the extraction rate, the ultrasonic time (A), the liquid-material ratio (B), the ultrasonic temperature (C) and the ethanol concentration (D) are selected as four factors, and finally, the response surface test design of 4-factor 3 level is carried out, so that the optimal extraction process parameters are optimized, and the test factors and specific level values are shown in table 1.
TABLE 1 BBD test factors and codings
2.3.2 test methods: precisely weighing 30 parts of 5g of Salvia miltiorrhiza, selecting factor levels of each experimental group number according to the Box-Benhnken Design (BBD) principle, performing sample injection detection under the chromatographic condition of '2.1.1' term to obtain the peak area values of Sal _ B and Tan _ IIA, respectively calculating the concentrations of Sal _ B and Tan _ IIA according to a regression equation, and combining with an extraction rate formula
Wherein c is Sal _ B, Tan _ IIA concentration (mg/mL) calculated by a calibrated regression equation, v is total volume (mL) of the extract, m is Salvia miltiorrhiza Bunge mass (g) and the extraction rates of Sal _ B and Tan _ IIA are calculated, and the number of experimental groups and results are shown in Table 2.
2.3.3 calculation of comprehensive evaluation value: the weighting values were calculated by the standard dispersion method, and the weighting coefficients of Sal _ B and Tan _ iia were found to be 0.49 and 0.51, respectively, and the total evaluation value was Sal _ B extraction rate × 0.49+ Tan _ iia extraction rate × 0.51, and the calculation results of the total evaluation value are shown in table 2.
TABLE 2 BBD analysis protocol and Experimental results
2.4 least squares support vector machine modeling and analysis
2.4.1 construction of least squares support vector machine model
The least squares support vector machine model can explain several extraction factors (x)1,x2…xi) The relationship between the extraction rate and the comprehensive evaluation index y, the basic idea thereofThink that the ith group of data inputs X for the BBD analysis schemei＝(x1,x2…xn) Representing the corresponding n values of the extraction factor and the ith data output YiRepresenting the overall evaluation value for the extraction yield, given the known m sets of BBD analysis protocol experimental data sets D { (X)1,Y1),(X2,Y2),…,(Xm,Ym) And finding a quantitative relation between the extraction factors and the comprehensive evaluation value. Specifically, a regression model of the comprehensive evaluation value about the extraction factor is established in a high-dimensional feature space by using a least square support vector machine, a Lagrange multiplier is introduced according to a structure risk minimization principle, the regression model is converted into an optimization model related to a kernel function under the condition of the optimization theory Kuhn-Tucker conditions (KKT), and finally the prediction comprehensive evaluation value with the minimum error with the real comprehensive evaluation value under the optimal parameter is continuously learned through a data set so as to find out the extracted optimal process factor. The research is based on Matlab language environment, and sets four extraction process data as xiRespectively, the ultrasonic time (x)1) Liquid to feed ratio (x)2) Ultrasonic temperature (x)3) Ethanol concentration (x)4) And a comprehensive evaluation value Y, wherein Y is a comprehensive evaluation value calculated by linear weighting of extraction rates of Sal _ B and Tan _ IIA in the salvia miltiorrhiza through a standard deviation method.
2.4.2 model parameter optimization
For the LS-SVM model, values of a kernel parameter g and a penalty factor C are closely related to a data sample set, and meanwhile learning and generalization capabilities of the model are influenced. The larger the penalty factor C value is, the smaller the allowable error is, but the overlarge C value can cause overfitting, so that the generalization capability is reduced; the generalization capability of the LS-SVM model can be enhanced as the C value is smaller, and the allowable error becomes larger. If the input range of the experimental data sample is large, the value of the kernel parameter g needs to be increased, otherwise, the value of the kernel parameter g needs to be decreased.
For the small data volume of the experiment, the Radial Basis Function (RBF) has high performance and application range, so when the RBF kernel Function is selected, a proper kernel parameter g and a penalty factor C are required to be simultaneously selected to optimize the LS-SVM model. In the study, 30 groups of experimental result data of the BBD analysis scheme are preprocessed, and an experimental data matrix with 30 rows and 5 columns is divided into an input variable matrix with 30 rows and 4 columns and an output variable matrix with 30 rows and 1 column, which are led into a platform of Matlab 2015b software, wherein input variables are replaced by specific factor level values, and specific values are shown in Table 1. In order to avoid too large difference between input variable dimensions and output variable dimensions, the research simultaneously carries out normalization processing on experimental data, namely, after dividing each extracted index factor level value by the mean value of the extracted index factor level values, programming by using Matlab 2015b software, modeling by using LS-SVM, wherein a kernel function adopts an RBF (radial basis function), finally, carrying out cross verification on a kernel parameter g and a penalty factor C simultaneously by a cross verification method, detecting each pair of parameter effects one by one in a parameter matrix consisting of g and C, and traversing to obtain an optimal kernel parameter g which is 256 and an optimal penalty factor C which is 4.92, wherein the traversing process is shown as the following figure 2.
2.4.3 prediction results and analysis
The optimal parameter result chart of fig. 2 is analyzed and combined with the program result to obtain the optimal kernel parameter g-256 and the penalty factor C-4.92. The comprehensive evaluation prediction values of 30 BBD analysis schemes obtained by returning to the LS-SVM model after obtaining the appropriate nuclear parameter g and penalty factor C are shown in the following table 3.
Table 3 comprehensive evaluation prediction value of LS-SVM model
In the present study, the predicted comprehensive evaluation value is compared with the real comprehensive evaluation value of the experiment, the correlation coefficient R between the predicted value and the experimental value is 0.94, the maximum error is 20.58%, the minimum error is 0.07%, and the error result is shown in fig. 3.
And then using Mean-square Error (MSE) to evaluate the performance of the LS-SVM model, wherein a specific formula is as follows:
wherein m is 30 data sets; y isiFor LS-SVM prediction, YiActual values obtained by experiments.
Finally, in the study, 30 groups of original experimental values and predicted values are substituted into a formula to calculate MSE, and a comparison graph of the real comprehensive evaluation value and the predicted comprehensive evaluation value of the experiment shown in FIG. 4 is drawn.
Fig. 4 shows that the comprehensive evaluation values of 30 sets of actual data and predicted data are very close, which indicates that the trained data is fitted to the actual test data, and then the MSE is calculated to be 0.51, which indicates that the model training and prediction effects are good.
2.4.4 conditional optimization and outcome prediction for the model
Based on BBD experimental design, combining actual reachable conditions of experiments, 68 groups of data sets to be predicted are added in a gradient mode through Matlab 2015b software according to 4 factors (the gradient of three factors including ultrasonic time, ultrasonic temperature and ethanol concentration is 1, and the gradient of liquid-material ratio is 0.5), and then an LS-SVM model is used for predicting optimal combination. The optimal process conditions for the final extraction are as follows: the temperature is 85 ℃, the ultrasonic time is 52min, the liquid-material ratio is 12:1, the ethanol concentration is 90%, and the predictive comprehensive evaluation value under the optimal condition is 15.32.
2.5 validation test
And weighing 4 parts of salvia miltiorrhiza medicinal material according to the optimal extraction process conditions, wherein each part is 20g, and carrying out ultrasonic extraction. The extraction rates of Sal _ B and Tan _ IIA were measured and the overall evaluation value was calculated, and the results are shown in Table 4. As can be seen from table 4, the average comprehensive evaluation value of the ultrasonic experimental method is 15.34, the predicted value is 15.32, and the relative error between the actual experimental value and the predicted model value is 0.13%. The verification tests show that the error is within the allowable range of the tests, so that the LS-SVM model has certain value in optimizing the salvia miltiorrhiza extraction process, namely the best extraction process condition in the research is the result given by the LS-SVM model: the temperature is 85 ℃, the ultrasonic time is 52min, the liquid-material ratio is 12:1, and the ethanol concentration is 90%.
Table 4 validation test under optimum process
3. Analysis and discussion
According to the report of the existing documents and the 2015 edition of Chinese pharmacopoeia, the active ingredients in the salvia miltiorrhiza medicinal material comprise Sal _ B and Tan _ IIA, so the two ingredients are considered, reference is provided for quality control and quality evaluation of the active ingredients in the salvia miltiorrhiza, and reference is also provided for quantitative determination of traditional Chinese medicines.
In the extraction process of the traditional Chinese medicine, the extraction solvent has a great influence on the extraction rate. The commonly used extraction solvent includes inorganic solvent water and organic solvent ethanol, methanol, etc. As the Sal _ B and Tan _ IIA effective components in the salvia miltiorrhiza belong to water-soluble and fat-soluble components respectively, ethanol with different concentrations is selected as an extraction solvent in the experiment.
With the rapid development of computers in the 21 st century, researchers are continuously searching for new traditional Chinese medicine optimized extraction process models. The LS-SVM model is used for predicting the relation between multi-factors and interaction and evaluation indexes of the multi-factors, is successfully applied to the optimization process of multiple fields, and particularly aims at small sample data, a non-linear model is established through analyzing a batch of input data and output data which are provided by experiments and correspond to each other, so that the quantitative relation between the data is revealed. Meanwhile, the LS-SVM model does not need any assumption on experimental data, and the generated result can be judged by a cross-validation method, so that the LS-SVM model is a data mining method or an algorithm model.
The experiment utilizes BBD experimental design and LS-SVM model to optimize the extraction process of Sal _ B and Tan _ IIA in Salvia miltiorrhiza, and the optimal extraction conditions obtained after adjustment according to actual conditions are as follows: the temperature is 85 ℃, the ultrasonic time is 52min, the liquid-material ratio is 12:1, the ethanol concentration is 90%, the comprehensive predictive evaluation value is 15.32, the average verified true value under the condition is 15.34, the relative error is 0.13%, and the error is small, so that the model can reflect the extraction conditions of Sal _ B and Tan _ IIA to a certain extent.
Claims (4)
1. An ultrasonic extraction process optimization method of active ingredients of salvia miltiorrhiza based on an LS-SVM model is characterized by comprising the following steps:
(1) carrying out ultrasonic extraction on active ingredients Sal _ B and Tan _ IIA of the salvia miltiorrhiza bunge according to the Box-Benhnken Design principle, carrying out experimental determination on the extraction rate of the active ingredients and the real comprehensive evaluation value of each process to obtain m groups of analysis scheme experimental data sets D { (X)1,Y1),(X2,Y2),…,(Xm,Ym)}，X1,X2…XmIs a combination of m extraction factor processes, Y1,Y2…YmA real comprehensive evaluation value of m extraction rates corresponding to the extraction factor process combination;
(2) establishing a least square support vector machine model LS-SVM, establishing an extraction factor x based on a Matlab language environmentiAnd comprehensively evaluating the value y to obtain an extraction factor x1,x2…xiA quantitative relation with the predictive comprehensive evaluation value y of the extraction rate;
(3) optimizing a nuclear parameter g and a penalty factor C of a model parameter, wherein the nuclear function adopts a radial basis kernel function RBF, editing by Matlab software, performing cross validation on g and C simultaneously by a cross validation method, and checking each pair of parameter effects one by one in a parameter matrix consisting of g and C to obtain optimal parameters g and C;
(4) inputting the optimal parameters g and C in the step (3) into an LS-SVM model to obtain a comprehensive predictive evaluation value of the Box-BenhnkenDesign analysis scheme in the step (1);
(5) evaluating the simulated performance of the LS-SVM model using the mean square error MSE, wherein
m is the number of data sets, yiPredictive comprehensive evaluation value, Y, for LS-SVMiThe evaluation value is a real comprehensive evaluation value.
2. The ultrasonic extraction process optimization method for effective components of salvia miltiorrhiza based on LS-SVM model as claimed in claim 1, wherein the real comprehensive evaluation value in step (1) is the sum of numerical values obtained by multiplying the process extraction rate by the weight, and the weight is calculated by standard deviation method.
3. The ultrasonic extraction process optimization method for effective components of salvia miltiorrhiza based on LS-SVM model as claimed in claim 1, wherein said step (5) is within the range of 0 MSE ≤ 0.6, the model predicts that the comprehensive evaluation value is close to the true comprehensive evaluation value, and the model is reliable.
4. An ultrasonic extraction process optimization method of active ingredients of salvia miltiorrhiza based on an LS-SVM model is characterized in that the optimal ultrasonic extraction conditions of the active ingredients Sal _ B and Tan _ IIA of salvia miltiorrhiza are as follows: the extraction temperature is 85 deg.C, the ultrasonic treatment time is 52min, the liquid-material ratio is 12:1, and the ethanol concentration is 90%.
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