Method for extracting total saponins from Jinping ginseng

Abstract

The invention provides a method for extracting total saponins in Jinping ginseng, which comprises the following steps of: 1, constructing a Jinping ginseng extraction process prediction model by taking ethanol concentration, extraction times and ultrasonic temperature as input of a model and taking total saponin extraction rate as output; 2, selecting an extraction process corresponding to the maximum total saponin extraction rate by using the constructed Jinping ginseng extraction process prediction model; and 3, extracting the total saponins of the Jinping ginseng by using an extraction process corresponding to the maximum total saponin extraction rate. According to the method provided by the invention, the extraction rate of the total saponins in the Jinping ginseng can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine, and particularly to a method for extracting total saponins from Panax vietnamensis Ha et Grushv.. Background Art

[0002] Panax vietnamensis Ha et Grushv. is a plant of the genus Panax in the family Araliaceae, also known as Vietnamese ginseng and Yuling ginseng. Research shows that Panax vietnamensis is similar to other ginsengs in the same genus and contains rich saponin components, mainly ocotillol-type saponins. It also has functions such as anti-inflammatory, anti-tumor, anti-nociception, inhibition of melanin formation, sedation and hypnosis. Ginseng saponins, as important active components of the genus Panax in the family Araliaceae, belong to triterpenoid saponins and are further divided into protopanaxadiol-type, protopanaxatriol-type, ocotillol-type and oleanolic acid-type saponins.

[0003] The currently common method for the total saponin extraction process from Panax vietnamensis is the method combined with response surface analysis. However, the extraction rate of the total saponins extracted by the extraction process determined by the method combined with response surface analysis is not high enough. Summary of the Invention

[0004] The purpose of the present invention is to solve the defects existing in the above-mentioned prior art and provide a method for extracting total saponins from Panax vietnamensis.

[0005] A method for extracting total saponins from Panax vietnamensis includes:

[0006] Step 1: Taking ethanol concentration, extraction times, and ultrasonic temperature as the inputs of the model and the total saponin extraction rate as the output, construct a prediction model for the extraction process of Panax vietnamensis.

[0007] Step 2: Using the constructed prediction model for the extraction process of Panax vietnamensis, select the extraction process corresponding to the maximum total saponin extraction rate.

[0008] Step 3: Use the extraction process corresponding to the maximum total saponin extraction rate to extract the total saponins of Panax vietnamensis.

[0009] Further, for the method for extracting total saponins from Panax vietnamensis as described above, Step 1 includes:

[0010] Use the genetic algorithm to optimize the weights and thresholds of the BP neural network, and bring the optimized weights and thresholds into the BP neural network to construct the prediction model for the extraction process of Panax vietnamensis.

[0011] Further, for the method for extracting total saponins from Panax vietnamensis as described above, the prediction model for the extraction process of Panax vietnamensis in Step 1 includes: 3 input layers, 30 hidden layers, and 1 output layer.

[0012] The 3-layer input layer is: ethanol concentration, extraction times, and ultrasonic temperature; the 1-layer output layer is the total saponin extraction rate.

[0013] Furthermore, in the method for extracting total saponins from Jinping ginseng as described above, the node transfer function of the 30-layer hidden layer uses the S-shaped tangent function tansig; the node transfer function of the output layer uses the linear function purelin.

[0014] Furthermore, in the method for extracting total saponins from Jinping ginseng as described above, step two includes: using the genetic algorithm to optimize the constructed prediction model of the Jinping ginseng extraction process again to determine the extraction process corresponding to the maximum total saponin extraction rate.

[0015] Beneficial effects:

[0016] The method provided by the present invention optimizes the weights and thresholds of the BP neural network through the genetic algorithm, constructs a prediction model of the Jinping ginseng extraction process, and then uses the genetic algorithm to optimize the constructed prediction model of the Jinping ginseng extraction process again, so as to select the extraction process corresponding to the maximum total saponin extraction rate to extract the total saponins of Jinping ginseng. This method can effectively improve the extraction rate of total saponins in Jinping ginseng. Description of the drawings

[0017] Figure 1 is the flow chart of the method for extracting total saponins from Jinping ginseng of the present invention;

[0018] Figure 2 is the error iteration diagram after optimization of the GA-BP neural network;

[0019] Figure 3 is the curve fitting diagram of the GA-BP neural network model;

[0020] Figure 4 is the comparison curve diagram of the model prediction value and the true value. Specific embodiments

[0021] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Figure 1 is the flow chart of the method for extracting total saponins from Jinping ginseng of the present invention, as Figure 1 shown, this method includes:

[0023] Step 1: Taking ethanol concentration, extraction times, and ultrasonic temperature as the inputs of the model and the total saponin extraction rate as the output, construct a prediction model for the extraction process of Jinping ginseng.

[0024] Specifically, this application aims at the total saponin extraction rate of Jinping ginseng, screens ethanol concentration, extraction times, and ultrasonic temperature as the response surface optimization factors, and establishes a prediction model for the extraction process of Jinping ginseng. The construction method is as follows:

[0025] First, construct a BP neural network. This application constructs a BP neural network by building a three-layer structure. The three-layer structure is: an input layer with 3 layers (A ethanol concentration, B extraction times, C ultrasonic temperature), a hidden layer with 30 layers (the number of nodes in the hidden layer will affect the prediction and generalization ability of the BP neural network), and an output layer with 1 layer (total saponin extraction rate). Among them, the transfer function of the nodes in the hidden layer of the neural network uses the S-shaped tangent function tansig, and the transfer function of the nodes in the output layer uses the linear function purelin. Divide the BP neural network training data (Table 1) according to 12:5, where 12 groups are used as the training set and 5 groups are used as the validation set. The neural network parameters are set as follows: the number of training times is 1000, the training target is 0.0001, the learning rate is 0.1, and the rest of the parameters are default values.

[0026] Table 1 Neural network training data

[0027]

[0028] Then, use the genetic algorithm to optimize the BP neural network to obtain a prediction model for the extraction process of Jinping ginseng.

[0029] Specifically, this application uses the genetic algorithm and selects appropriate parameters to optimize the weights and thresholds of the BP neural network. To avoid the BP neural network falling into a local optimum during the training process and improve the generalization ability of the model, the present invention uses the GA genetic algorithm to optimize the thresholds and weights of the BP network, obtains the optimized thresholds and weights, and applies the optimized thresholds and weights to the BP neural network, thereby obtaining the prediction model for the extraction process of Jinping ginseng.

[0030] Finally, apply the optimized threshold and weight values to the established prediction model for the extraction process of Jinping ginseng, thereby obtaining the established prediction model for the extraction process of Jinping ginseng: The number of variables N to be optimized = the number of neurons in the input layer * the number of neurons in the hidden layer + the number of neurons in the output layer * the number of neurons in the hidden layer + the number of neurons in the hidden layer + the number of neurons in the output layer = 151. Set the genetic algorithm parameters: population size 20, number of iterations 100, individual length 10, generation gap 0.95, crossover probability 0.4, mutation probability 0.2. Set the initial value of the optimization result, create an arbitrary discrete random population for optimization, and through iteration, obtain the optimized threshold and weight values bestX. Then, use the optimized threshold and weight values to train the BP neural network and obtain the threshold and weight values of the optimized neural network. And output the error of the optimized threshold and weight values of the GA-BP neural network (see Table 2) and the error iteration Figure 2 . Since the neural network itself has randomness, there are difference and error iteration graphs. To obtain a network close to the true value, it is necessary to continuously iterate the conditions. As the program is set, the iteration makes the error gradually decrease and stabilize. When the iteration reaches a stable curve, it means that the relative randomness of the threshold and weight values of the neural network is reduced, and it can better conform to the real conditions, thereby obtaining the extraction process corresponding to the maximum total saponin extraction rate.

[0031] Table 2 Error table of optimized threshold and weight values of GA-BP neural network

[0032]

[0033] In summary, this application uses a GA-BP neural network model, uses the genetic algorithm for optimization, predicts the best process conditions, and detects the components of the best process samples.

[0034] Step 2: Using the established prediction model for the extraction process of Jinping ginseng, select the extraction process corresponding to the maximum total saponin extraction rate;

[0035] Specifically, the purpose of this application is to improve the extraction rate of total saponin. Therefore, it is necessary to determine the optimal extraction process. To determine the optimal extraction process in this application, the genetic algorithm is used again to optimize the established GA-BP model above: Call the optimized threshold and weight values and the neural network model, set the upper limit of the conditions as: ethanol concentration 70%, extraction times 4 times, ultrasonic temperature 50°C, and the lower limit as: ethanol concentration 60%, extraction times 2 times, ultrasonic temperature 30°C. The neural network parameters are set as: number of training times 1000, training target 0.000001, learning rate 0.01, and the rest of the parameters are default values. Through continuous training, a GA-BP neural network model with the best fitting degree R = 0.99826 is obtained (see Figure 3) and use the genetic algorithm toolbox for objective optimization. The optimal extraction conditions for total ginsenosides of Jinping ginseng obtained in this way are: ethanol concentration 70.0%, extraction times 3.8 times, ultrasonic temperature 41.0°C, and the predicted total ginsenoside extraction rate is 4.4548%. And compare the model prediction value with the real value (see Figure 4 ).

[0036] Step 3: Use the extraction process corresponding to the maximum total ginsenoside extraction rate to extract the total ginsenosides of Jinping ginseng.

[0037] Based on single-factor experiments, this application adopts ultrasonic-assisted extraction method. Taking the total ginsenoside extraction rate of Jinping ginseng as the target, the ethanol concentration, extraction times, and ultrasonic temperature are selected as the response surface optimization factors. A GA-BP neural network model is established, the genetic algorithm is used for optimization, the optimal process conditions are predicted, and the components of the optimal process samples are detected. The results show that: the optimal process obtained by response surface optimization is ethanol concentration 65.8%, extraction times 4.0 times, ultrasonic temperature 40.7°C, and the extraction rate is 4.4103%. The optimal process optimized by the GA-BP neural network model is ethanol concentration 70.0%, extraction times 4.0 times, ultrasonic temperature 41.0°C, and the extraction rate is 4.5492%. By comparison, it is found that the total ginsenoside extraction rate of the process obtained by using the GA-BP neural network model is higher than that of the response surface optimization process. Therefore, the extraction process obtained by the GA-BP neural network selected by the present invention is more ideal and has high stability. Therefore, the GA-BP neural network extraction process is finally determined as the optimal process for extracting total ginsenosides of Jinping ginseng.

[0038] Verification experiment

[0039] Weigh 2 portions of 1g Jinping ginseng powder samples and conduct verification experiments using the optimal process optimized by the GA-BP model. For the convenience of the experiment, the extraction times are adjusted to 4 times, and the total ginsenoside extraction rate is 4.5492%, with a difference of 2.12% from the predicted value (see Table 3). This shows that it is highly feasible to use the GA-BP neural network to optimize the total ginsenoside extraction process in Jinping ginseng, and the optimized process is more ideal compared with the response surface design. The optimal process optimized by the GA-BP neural network is determined as ethanol concentration 70.0%, extraction times 4.0 times, and ultrasonic temperature 41.0°C.

[0040] Table 3

[0041]

[0042] Component detection

[0043] Detection method

[0044] The combination of chromatography and mass spectrometry realizes the entire process from separating substances using chromatography to identifying substances using mass spectrometry. Among them, ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS / MS) can accurately identify and quantify.

[0045] Sample preparation

[0046] (1) Concentrate the saponin extract into an extract and store it in the refrigerator.

[0047] (2) Take out the sample from the -80 °C refrigerator and thaw it.

[0048] (3) Vortex for 1 min. If it cannot be vortexed evenly, stir manually with a weighing spoon for 30 s.

[0049] (4) Add the -20 °C pre-cooled 70% methanol-water internal standard extract in proportion (add 600 μL of extractant to every 50 mg of sample), and vortex for 15 min.

[0050] (5) Centrifuge (12000 r / min, 4 °C) for 3 min, take the supernatant and filter it through a microporous filter membrane (0.22 μm pore size), and store it in an injection vial for UPLC-MS / MS testing.

[0051] Chromatography and mass spectrometry acquisition conditions

[0052] The data acquisition instrument system mainly includes ultra-high performance liquid chromatography (Ultra Performance Liquid Chromatography, UPLC) and tandem mass spectrometry (Tandem mass spectrometry, MS / MS).

[0053] The liquid phase conditions mainly include:

[0054] 1) Chromatographic column: Agilent SB-C18 1.8 μm, 2.1 mm * 100 mm;

[0055] 2) Mobile phase: Phase A is ultrapure water (added with 0.1% formic acid), and phase B is acetonitrile (added with 0.1% formic acid);

[0056] 3) Elution gradient: The proportion of phase B is 5% at 0.00 min, linearly increases to 95% within 9.00 min, and is maintained at 95% for 1 min. From 10.00 - 11.10 min, the proportion of phase B drops to 5% and is balanced at 5% until 14 min;

[0057] 4) Flow rate 0.35 mL / min; column temperature 40 °C; injection volume 2 μL.

[0058] The mass spectrometry conditions mainly include:

[0059] The temperature of the electrospray ionization (ESI) source was 550 °C; the ion spray voltage (IS) was 5500 V (positive ion mode) / -4500 V (negative ion mode); the ion source gases I (GSI), II (GSII), and curtain gas (CUR) were set at 50, 60, and 25 psi, respectively, and the collision-induced ionization parameters were set to high. QQQ scans were performed using the MRM mode, and the collision gas (nitrogen) was set to medium. Through further optimization of the declustering potential (DP) and collision energy (CE), the DP and CE of each MRM ion pair were completed. According to the substances eluted in each period, a specific set of MRM ion pairs was monitored in each period.

[0060] Principle of substance qualitative and quantitative analysis

[0061] Based on the self-built MWDB (Metware Database), substances were qualitatively analyzed according to the secondary spectrum information. During the analysis, isotope signals, repeated signals containing K+, Na+, and NH4+ ions, and repeated signals of fragment ions that were themselves fragments of other larger molecular weight substances were removed.

[0062] Quantification of component substances was completed using the multiple reaction monitoring (MRM) mode of triple quadrupole mass spectrometry. In the MRM mode, the quadrupole first selects the precursor ions (parent ions) of the target substance, excluding ions corresponding to other molecular weight substances to preliminarily eliminate interference; the precursor ions are fragmented by collision-induced ionization in the collision cell to form many fragment ions, and then a characteristic fragment ion required is selected through filtering by the triple quadrupole, excluding non-target ion interference, making the quantification more accurate and the repeatability better. After obtaining the mass spectrometry analysis data of different samples, the peak areas of all substance chromatographic peaks were integrated, and the mass spectrometry peaks of the same substance in different samples were corrected for integration (Fraga et al. 2010).

[0063] Data preprocessing

[0064] Based on the mass spectrometry data, the k-nearest neighbors (KNN) algorithm was first used to fill in the missing values, and then the CV value of the QC samples was calculated. Substances with a CV value less than 0.5 were retained to obtain the final data of the relative content of substance components.

[0065] Data analysis

[0066] A total of 255 substances were detected based on the UPLC-MS / MS detection platform and the self-built database. Further analysis of the classification showed that 100% of the detected substances were terpenoids, mainly triterpenoids. Among them, 183 species were detected, accounting for 84.56%. Monoterpenoids, sesquiterpenoids, and diterpenoids accounted for 9.03%, 4.71%, and 1.70% respectively.

[0067] The triterpenoids were further classified. Among them, OA, OT, PPD, and PPT accounted for 5.11%, 6.39%, 18.60%, and 49.69% respectively, and other types of saponins accounted for 20.21%.

[0068] The use of the GA-BP neural network to optimize the total saponin extraction process from Jinping ginseng in this invention has high feasibility, and the optimized process is more ideal compared with the response surface design. 255 terpenoid substances were detected by ultra-high performance liquid chromatography tandem mass spectrometry. Among them, 84.56% were triterpenoids. Among the triterpenoid substances, OA, OT, PPD, and PPT accounted for 5.11%, 6.39%, 18.60%, and 49.69% respectively, and other types of saponins accounted for 20.21%. This indicates that Jinping ginseng contains rich saponin components. The method provided by this invention provides a theoretical basis for improving the utilization of total saponin resources in Jinping ginseng and lays a foundation for the rational development and utilization of Jinping ginseng plant resources.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting total saponins from Jinping ginseng, characterized in that, Including: Step 1: Using ethanol concentration, extraction times, and ultrasonic temperature as the inputs of the model and the total saponin extraction rate as the output, construct a prediction model for the extraction process of Jinping ginseng; Step 2: Using the constructed prediction model for the extraction process of Jinping ginseng, select the extraction process corresponding to the maximum total saponin extraction rate; Step 3: Use the extraction process corresponding to the maximum total saponin extraction rate to extract the total saponin of Jinping ginseng.

2. The extraction method of total saponins in Jinping ginseng according to claim 1, characterized in that, The said Step 1 includes: Use the genetic algorithm to optimize the weights and thresholds of the BP neural network, and bring the optimized weights and thresholds into the BP neural network to construct the prediction model for the extraction process of Jinping ginseng.

3. The extraction method of total saponins in Jinping ginseng according to claim 1, characterized in that The prediction model for the extraction process of Jinping ginseng in the said Step 1 includes: 3 input layers, 30 hidden layers, and 1 output layer; The said 3 input layers are: ethanol concentration, extraction times, and ultrasonic temperature; the said 1 output layer is the total saponin extraction rate.

4. The extraction method of total saponins in Jinping ginseng according to claim 3, characterized in that, The node transfer function of the said 30 hidden layers adopts the S-shaped tangent function tansig; the node transfer function of the output layer adopts the linear function purelin.

5. The extraction method of total saponins in Jinping ginseng according to claim 1, characterized in that, The said Step 2 includes: Use the genetic algorithm to optimize the constructed prediction model for the extraction process of Jinping ginseng again, so as to determine the extraction process corresponding to the maximum total saponin extraction rate.

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