阅读说明：本技术 一种预测用于多糖分离的大孔吸附树脂类型的方法 (Method for predicting type of macroporous adsorption resin for polysaccharide separation ) 是由 邸多隆 孙艳艳 刘建飞 于 2021-01-08 设计创作，主要内容包括：本发明公开了一种预测用于多糖分离的大孔吸附树脂类型的方法,利用不同类型的大孔吸附树脂对不同规格的葡聚糖进行静态吸附实验从而进行数据收集,然后依次进行数据处理、数据分析、方法构建和方法验证。该模型高效节能、环保便捷、运行成本低,可以避免分离纯化多糖时筛选大孔吸附树脂的盲目性。(The invention discloses a method for predicting the type of macroporous adsorption resin for polysaccharide separation, which is characterized in that different types of macroporous adsorption resin are utilized to carry out static adsorption experiments on glucans with different specifications so as to collect data, and then data processing, data analysis, method construction and method verification are carried out in sequence. The model has the advantages of high efficiency, energy conservation, environmental protection, convenience and low operation cost, and can avoid the blindness of screening the macroporous adsorption resin during the separation and purification of the polysaccharide.)

1. A method for predicting the type of macroporous adsorbent resin used for polysaccharide separation, comprising the steps of: carrying out static adsorption experiments on the glucans with different specifications by using macroporous adsorption resins with different types so as to collect data, and then sequentially carrying out data processing, data analysis, model equation construction for prediction and accuracy verification.

2. The method according to claim 1, characterized in that the method comprises the following steps:

(1) data collection: preparing different types of macroporous adsorption resins, wherein N parts of each type of macroporous adsorption resin is prepared, and each part is 0.5 g; preparing polysaccharide solutions of dextran control samples with different molecular weights, preparing M parts of polysaccharide solution of dextran control sample with each molecular weight, 40mL parts, and polysaccharide concentration of 4 mg/mL^-1(ii) a Adding 0.5g macroporous adsorbent resin into 40mL dextran control polysaccharide solution, and heating at 30 deg.C for 100r min^-1Carrying out constant-temperature oscillation adsorption for 24h under the condition, and then measuring the polysaccharide content in the filtrate to obtain the adsorption quantity of different types of macroporous adsorption resins to glucan reference substances with different molecular weights;

(2) data processing: respectively calculating the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of a dextran reference substance with one molecular weight and a type of macroporous adsorption resin; the adsorption capacity of one type of macroporous adsorption resin to a molecular weight glucan reference substance, and the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of the molecular weight glucan reference substance and the type of macroporous adsorption resin form a group of data, each group of data is processed, and the natural logarithm is taken to obtain W group of data; in the W group of data, the adsorption quantity is used as a dependent variable parameter for standby, and the natural logarithm of other parameters are used as an independent variable parameter for standby;

(3) and (3) data analysis: adopting a random non-return sampling mode, sequentially extracting a plurality of groups of data from the W group of data to be used as a training set to establish a model, and using the rest data as a test set to verify; repeating the steps for W times randomly to calculate R of different models²And root mean square error RMSE, taking into account R²And RMSE value, screening out the group number of training set and test set;

(4) constructing a model equation for prediction: establishing a multi-source information model equation by using the training set data screened in the step (3), repeating in sequence, calculating fitting equation parameters of each model, and comprehensively considering the P value and the R value²Value, screening a prediction model equation; verifying the prediction model by using the test set data screened in the step (3), and applying the prediction model equation if the prediction effect is accurate;

(5) and (3) verifying the accuracy: and (3) predicting the adsorption capacity of a certain polysaccharide by using the prediction model equation obtained in the step (4), and verifying by using a static adsorption experiment according to the operation in the step (1), wherein the result of the static adsorption experiment is basically consistent with the predicted value, so that the method is proved to be accurate and reliable.

3. The method of claim 2, wherein: n is an integer which is more than or equal to 2; m is an integer of 2 or more, and W is an integer of 2 or more.

4. The method of claim 2, wherein: in the step (1), the different types of macroporous adsorption resins refer to LX-1180 macroporous adsorption resin, LX-20 macroporous adsorption resin, LX-T81 macroporous adsorption resin, LX-T19 macroporous adsorption resin and D101 macroporous adsorption resin.

5. The method of claim 2, wherein: in the step (1), the dextran reference substances with different specifications refer to dextran reference substances with molecular weights of 1000, 5000, 10000, 40000 and 100000.

6. The method of claim 2, wherein: in the step (1), the polysaccharide content in the filtrate is measured by High Performance Liquid Chromatography (HPLC), and the operation conditions are as follows: TSKgel G3000PWxl chromatography column (7.8mm i.d. × 30cm, 7 μm); mobile phase: 100% water; flow rate: 1 mL. min^-1(ii) a A detector: an evaporative light detector; detecting the temperature: 80 ℃; sample introduction amount: 20 μ L.

7. The method of claim 2, wherein: in step (3), R is comprehensively considered²And the RMSE value, the smaller the RMSE value, the better R²The closer the value is to 1, the better.

8. The method of claim 2, wherein: in the step (4), P value and R are comprehensively considered²In the case of the value, the smaller the P value, the better R²The closer the value is to 1, the better.

9. The method of claim 2, wherein: in the step (5), the polysaccharide refers to a polysaccharide in a Chinese medicinal material or a natural plant.

10. The method of claim 9, wherein: the polysaccharide is lycium barbarum polysaccharide LBP-009.

Technical Field

The invention relates to a method for predicting resin types, in particular to a method for predicting macroporous adsorption resin types for polysaccharide separation, and belongs to the technical field of compound separation and purification.

Background

The polysaccharide is an important active ingredient in traditional Chinese medicines, shows remarkable and unique physiological and pharmacological activities in the aspects of immunoregulation, antioxidation, antivirus, anti-aging, antitumor, blood sugar reduction, blood fat reduction and the like, and becomes one of the important directions of traditional Chinese medicine research. The separation and purification of polysaccharide is an important basis for developing the research and development of polysaccharide, and the chromatographic packing and chromatographic medium for the separation and purification of macromolecules at present mainly comprise natural polysaccharides and high molecular polymers. Macroporous adsorption resin based on adsorption and desorption separation and purification mechanisms is an important high molecular polymer separation material, and the macroporous adsorption resin makes important progress in the aspect of separating and purifying micromolecular active ingredients in traditional Chinese medicines, but is still in the beginning stage in the aspect of separating and purifying traditional Chinese medicine polysaccharide. In recent years, researches and applications of macroporous adsorption resin in polysaccharide separation gradually attract the attention of researchers at home and abroad.

The macroporous adsorption resin is an organic high polymer which does not contain exchange groups and has a pore structure, and has the advantages of insolubility in acid, alkali and various organic solvents, large specific surface area, large adsorption capacity, good selectivity, high adsorption speed, mild desorption conditions, convenient regeneration treatment, long service cycle, cost saving and the like. The macroporous adsorption resin technology is used for separating and purifying polysaccharide components in the pumpkin residue, the adsorption rates of pigment and protein are respectively 84.3% and 75.9% after the pumpkin residue is treated by the macroporous adsorption resin, the recovery rate of the polysaccharide is 84.7%, and theoretical basis is provided for more efficiently utilizing pumpkin polysaccharide resources. The macroporous adsorption resin technology is widely applied to the separation and purification of effective components of traditional Chinese medicines due to unique adsorption property and physicochemical property, but the basic research of the structure-activity relationship between the macroporous adsorption resin and target molecules and the separation rule thereof is weak, so that the precise selection of the macroporous adsorption resin cannot be carried out under the theoretical guidance. In the case of a wide variety of macroporous adsorbent resins, the workload is huge and sieve leakage and sieve error often occur when predicting and screening the macroporous adsorbent resin having the best separation efficiency on polysaccharide.

Therefore, the construction of a method for predicting the type of macroporous adsorption resin used for polysaccharide separation, which is used for separating main raw materials of foods, health foods, foods with special medical application and medicines, is of great significance.

Disclosure of Invention

The technical problem to be solved by the invention is to provide a method for predicting the type of macroporous adsorption resin for polysaccharide separation aiming at the defects in the prior art; the method of the invention has accurate and reliable verification, thereby making up the defects and shortcomings of the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme:

a method of predicting the type of macroporous adsorbent resin used for polysaccharide separation, comprising the steps of: carrying out static adsorption experiments on the glucans with different specifications by using macroporous adsorption resins with different types so as to collect data, and then sequentially carrying out data processing, data analysis, model equation construction for prediction and accuracy verification.

In the above technical solution, the method specifically includes the following steps:

(1) data collection: preparing different types of macroporous adsorption resins, wherein N parts of each type of macroporous adsorption resin is prepared, and each part is 0.5 g; preparing polysaccharide solutions of dextran control samples with different molecular weights, preparing M parts of polysaccharide solution of dextran control sample with each molecular weight, 40mL parts, and polysaccharide concentration of 4 mg/mL^-1(ii) a Adding 0.5g macroporous adsorbent resin into 40mL dextran control polysaccharide solution, and heating at 30 deg.C for 100r min^-1Carrying out constant-temperature oscillation adsorption for 24h under the condition, and then measuring the polysaccharide content in the filtrate to obtain the adsorption quantity of different types of macroporous adsorption resins to glucan reference substances with different molecular weights;

(2) data processing: respectively calculating the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of a dextran reference substance with one molecular weight and a type of macroporous adsorption resin; the adsorption capacity of one type of macroporous adsorption resin to a molecular weight glucan reference substance, and the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of the molecular weight glucan reference substance and the type of macroporous adsorption resin form a group of data, each group of data is processed, and the natural logarithm is taken to obtain W group of data; in the W group of data, the adsorption quantity is used as a dependent variable parameter for standby, and the natural logarithm of other parameters are used as an independent variable parameter for standby;

(3) and (3) data analysis: adopting a random non-return sampling mode, sequentially extracting a plurality of groups of data from the W group of data to be used as a training set to establish a model, and using the rest data as a test set to verify; repeating the steps for W times randomly to calculate R of different models²And root mean square error RMSE, taking into account R²And the value of the RMSE is calculated,screening out the groups of the training set and the test set;

(4) constructing a model equation for prediction: establishing a multi-source information model equation by using the training set data screened in the step (3), repeating in sequence, calculating fitting equation parameters of each model, and comprehensively considering the P value and the R value²Value, screening a prediction model equation; verifying the prediction model by using the test set data screened in the step (3), and applying the prediction model equation if the prediction effect is accurate;

(5) and (3) verifying the accuracy: and (3) predicting the adsorption capacity of a certain polysaccharide by using the prediction model equation obtained in the step (4), and verifying by using a static adsorption experiment according to the operation in the step (1), wherein the result of the static adsorption experiment is basically consistent with the predicted value, so that the method can be applied to the research of separating the polysaccharide by using macroporous adsorption resin.

In the technical scheme, N is an integer which is more than or equal to 2; m is an integer of 2 or more, and W is an integer of 2 or more.

In the above technical scheme, in the step (1), the different types of macroporous adsorption resins refer to LX-1180 macroporous adsorption resin, LX-20 macroporous adsorption resin, LX-T81 macroporous adsorption resin, LX-T19 macroporous adsorption resin and D101 macroporous adsorption resin.

In the above technical solution, in the step (1), the dextran reference substances with different specifications refer to dextran reference substances with molecular weights of 1000, 5000, 10000, 40000 and 100000.

In the above technical scheme, in the step (1), the polysaccharide content in the filtrate is determined by High Performance Liquid Chromatography (HPLC), and the operating conditions are as follows: TSKgel G3000PWxl chromatography column (7.8mm i.d. × 30cm, 7 μm); mobile phase: 100% water; flow rate: 1 mL. min^-1(ii) a A detector: an evaporative light detector; detecting the temperature: 80 ℃; sample introduction amount: 20 μ L.

In the above technical solution, in the step (3), the data analysis specifically includes: the Rstudio software adopts a random non-return sampling mode, sequentially extracts a plurality of data from the W group of data to be used as a training set to establish a model, and verifies the rest data as a test set; repeating for W times randomly, and calculating different modesR of type²And the root mean square error RMSE comprehensively considers the interaction of the dependent variable and different independent variables, utilizes Rstudio software to carry out fitting equation, and comprehensively considers R²And RMSE values, and the number of groups of the training set and the test set are screened.

In the above technical scheme, in the step (3), R is comprehensively considered²And the RMSE value, the smaller the RMSE value, the better R²The closer to 1, the better.

In the above technical solution, in the step (4), the constructing of the multi-source information model specifically operates as follows: establishing a multi-source information model equation for training set data by using Rstudio software, repeating the equation equations in sequence, calculating fitting equation parameters of each model, and comprehensively considering P value and R²Value, screening a prediction model equation; and verifying the test set data to the prediction model by using the Rstudio software, and applying the prediction model equation if the prediction effect is accurate.

In the above technical scheme, in the step (4), the P value and the R value are comprehensively considered²In the case of the value, the smaller the P value, the better R²The closer the value is to 1, the better.

In the above technical solution, in the step (5), the certain polysaccharide refers to a certain polysaccharide in a Chinese medicinal material or a natural plant; preferably lycium barbarum polysaccharide LBP-009.

The technical advantages of the invention are as follows: the traditional Chinese medicine polysaccharide has various biological activities of repairing liver injury, reducing blood sugar, resisting blood coagulation, resisting tumor, resisting oxidation, resisting virus, regulating immunity and the like, and the biological activities of the polysaccharides with different molecular weight sections are different. The model is verified accurately and reliably, and the model is used for predicting the adsorption effect of the MAR on the traditional Chinese medicine polysaccharide, so that the blindness of resin selection in the polysaccharide separation process can be avoided.

Drawings

FIG. 1 is a process flow diagram of the present invention;

FIG. 2 is a diagram showing R in the case of dividing the training set and the test set in embodiment 1 of the present invention²The value is obtained.

FIG. 3 shows the RMSE values for the training set and the test set in the case of the division in example 1 of the present invention.

Detailed Description

The following detailed description of the embodiments of the present invention is provided, but the present invention is not limited to the following descriptions:

the invention will now be illustrated with reference to specific examples:

example 1:

a method for predicting the type of macroporous adsorbent resin used for polysaccharide separation, a flow diagram of which is shown in fig. 1, comprising the steps of:

(1) data collection:

1.1 instruments, reagents and materials

Agilent 1260 liquid chromatograph (Agilent Technologies, usa) comprising: a G1312A constant flow pump, a 2200 evaporation photodetector, a G1328B hand sampler, and an Agilent Chemstation software workstation; TSKgel G3000PWxl chromatography column (7.8mm i.d. × 30cm, 7 μm); KQ-250DE model numerical control ultrasonic cleaning machine (Kunshan ultrasonic Instrument Co., Ltd.); BSA 224S-one in ten thousand electronic analytical balance (Beijing Saedodus Instrument systems, Inc.); DHG-9140A type electric heating constant temperature air-blast drying oven (shanghai essence macro experimental facilities limited); THZ-320 desk-top constant temperature oscillator (Shanghai sperm macroexperimental facilities, Inc.).

Absolute ethanol (analytical grade, linalon bothua pharmaceutical chemistry ltd); purified water (Hangzhou child haha group ltd); dextran series controls (molecular weights 1000, 5000, 10000, 40000, and 100000, respectively, Shanghai Michelin Biotech, Inc.); lycium barbarum polysaccharides (prepared and provided by the northwest special plant resource chemical key laboratory of the institute of chemico-physical research, orchids, department of China); LX-1180, LX-20, LX-T81, LX-T19, and MAR type D101 (New science and technology materials Co., Ltd., Xian blue).

1.2 preparation of control solutions

Respectively weighing dextran reference substances (molecular weight 1000, 5000, 10000, 40000 and 100000)40mg with different specifications, dissolving in water, diluting to 10mL, and shaking to obtain dextran reference substances with mass concentration of 4 mg/mL^-1Series of control stocks ofAnd (5) preparing a solution for later use.

1.3 chromatographic conditions

TSKgel G3000PWxl chromatography column (7.8mm i.d. × 30cm, 7 μm); mobile phase: 100% water; flow rate: 1 mL. min^-1(ii) a A detector: an evaporative light detector; detecting the temperature: 80 ℃; sample introduction amount: 20 μ L.

1.4 methodological considerations

1.4.1 Linear relationship

Respectively weighing dextran reference substances (molecular weight 1000, 5000, 10000, 40000 and 100000) of different specifications 80mg, dissolving in water, and diluting to 10mL to obtain final product with mass concentration of 8 mg/mL^-1The reference stock solutions of (1) were diluted with water to give 0.125, 0.25, 0.5, 1, 2, 4, 8 mg/mL solutions^-1The dextran control solution of (2) was assayed according to the "1.3" chromatographic conditions. Drawing a standard curve by taking the mass concentration of the glucan with different molecular weights as an abscissa (x) and taking a peak area as an ordinate (y), and obtaining that the linear ranges of the 5 kinds of glucan are all 0.125-8 mg/mL^-1. The peak area and the mass concentration of the glucan have a linear relation in a certain concentration range of the glucan with the same molecular weight.

1.4.2 precision experiments

Taking the reference substance solution prepared by the 1.2, determining the content of the polysaccharide according to the 1.3 chromatographic condition, repeatedly injecting samples for 6 times, wherein the Relative Standard Deviation (RSD) of the chromatographic peak area is 0.95-4.1 percent, which indicates that the precision of the instrument is good.

1.4.3 stability test

And (3) taking the reference substance solution prepared by the step (1.2), and respectively carrying out sample injection detection for 0 hour, 3 hours, 6 hours, 9 hours, 12 hours and 24 hours according to the chromatographic condition of the step (1.3), wherein the RSD of the chromatographic peak area is 3.2-4.8%, which shows that the glucan solutions with different molecular weights are stable within 24 hours.

1.4.4 repeatability experiments

Accurately weighing 6 parts of each of 40mg of glucan with different molecular weights, preparing 6 parts of sample solution according to a 1.2 method, measuring the content of polysaccharide according to a 1.3 chromatographic condition, wherein the RSD of the chromatographic peak area is 3.1-4.8%, which indicates that the method has good repeatability.

1.5 adsorption experiments

1.5.1 static adsorption experiment

Weighing 5 MARs (LX-1180, LX-20, LX-T81, LX-T19 and D101) 0.5g each, placing into a conical flask with a plug, adding 4 mg/mL^-140mL of the polysaccharide solution (2), at 30 ℃ for 100 r.min^-1After oscillating and adsorbing for 24h at constant temperature, measuring the polysaccharide content in the filtrate, and calculating the adsorption quantity according to the formula (1).

Wherein Q is the adsorption amount (mg. g)^-1)，C₀The mass concentration of the polysaccharide before adsorption (mg. mL)^-1)，C₁To adsorb the mass concentration (mg. mL) of polysaccharide in the raffinate^-1)，V₁The volume of the extract (mL) and W are the mass of the resin (g). The adsorption capacity of 5 macroporous adsorption resins to dextrans with different molecular weights is calculated according to the formula shown in Table 1.

TABLE 1 static adsorption test results of macroporous adsorbent resins

(2) Data processing:

the influence factors of the polysaccharide adsorption effect mainly comprise molecular weight, porosity, specific surface area, pore volume and pore diameter of MAR, and the influence of molecular weight/porosity, molecular weight/specific surface area, molecular weight/pore volume and molecular weight/pore diameter on the polysaccharide adsorption amount is respectively considered. With the increasing molecular weight/porosity, molecular weight/specific surface area, molecular weight/pore volume and molecular weight/pore diameter, the adsorption amount of glucan by different MARs is shown to increase and then decrease, and the presumed reason is that with the increasing molecular weight, glucan cannot enter the MARs, so that the adsorption amount is reduced.

Respectively calculating the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of a dextran reference substance with a molecular weight and a type of macroporous adsorption resin by using the static adsorption experimental data of '1.5.1'; the adsorption capacity of one type of macroporous adsorption resin to one molecular weight glucan reference substance, and the molecular weight/porosity, the molecular weight/specific surface area, the molecular weight/pore volume and the molecular weight/pore diameter of the molecular weight glucan reference substance and the type of macroporous adsorption resin form a group of data, each group of data is processed, natural logarithm is taken, 25 groups of data are obtained, and the result is shown in table 4; the collected data is subjected to natural logarithm, and the characteristics of the collected data are summarized as follows: the data set contained 25 sets of data, each set of data comprising 4 independent variables and 1 dependent variable, with the dependent variable "adsorption" being affected by the other 4 independent variables (table 2).

TABLE 2 macroporous adsorbent resin data analysis

The traditional Chinese medicine polysaccharide has various biological activities of repairing liver injury, reducing blood sugar, resisting blood coagulation, resisting tumor, resisting oxidation, resisting virus, regulating immunity and the like, and the biological activities of the polysaccharides with different molecular weight sections are different. The research is based on variables in 25 groups of data, and a multi-source information fusion model of MAR polysaccharide adsorption is constructed by observing the interaction of dependent variables and different independent variables. The model is used for predicting the adsorption effect of MAR on the traditional Chinese medicine polysaccharide, so that the blindness of resin selection in the polysaccharide separation process can be avoided.

(3) And (3) data analysis:

3.1 training set and test set partitioning

By using Rstudio software, a random sample-not-put-back mode is adopted, 17, 18, 19, 20, 21, 22, 23 and 24 groups of data are sequentially extracted from 25 groups of data to be used as a training set to establish a model, and the rest areThe data is validated as a test set. Repeating the steps randomly for 25 times, and calculating R of different models²And Root Mean Square Error (RMSE) (see fig. 2 and 3). General formula R²And the RMSE value, 23 groups of data are selected as a training set to establish a model with better fitting degree and prediction capability.

(4) Establishing a predictive model equation using a training set

Table 3 parameters of the model equation

Random non-back sampling is adopted by utilizing Rstudio software, 23 groups of data are extracted from 25 groups of data to be used as a training set to establish a model, the model is repeated for 20 times in sequence, and fitting equation parameters of each model are calculated (see table 3). General formula R²And the magnitude of the P value, the number 6 fitting equation in table 3 was selected as the predictive model equation (2).

Y＝5.94X₁-25.15X₂+6.93X₃+17.91X₄+5.03X₁×X₂-23.13X₁×X₃+25.68X₂×X₃+8.81X₁×X₄-15.55X₂×X₄+11.1X₃×X₄-18.89X₁×X₂×X₃+1.97X₁×X₂×X₄-10.93X₁×X₃×X₄+14.23X₂×X₃×X₄-6.96X₁×X₂×X₃×X₄-8.04

R²＝0.9012

The prediction model was verified using the Rstudio software with 2 sets of test set data, with model predicted adsorption amounts of 36 and 97.3mg g^-1RMSE 19.89, test set adsorption data of 7.85 and 96.85mg g^-1The model has accurate prediction effect.

(5) And (3) verifying the accuracy:

the lycium barbarum polysaccharide LBP-009 is a lycium barbarum polysaccharide part screened by the laboratory and having the effects of repairing and preventing drug-induced liver injury, and the adsorption effect of MAR on the lycium barbarum polysaccharide is predicted by using a multi-source information model according to the molecular weight of the lycium barbarum polysaccharide LBP-009. The results show that the adsorption capacity of LX-T19, LX-T81, LX-20, LX-1180 and D101 type resins on the lycium barbarum polysaccharide is 114.13, 112.03, 117.40, 98.27 and 72.18mg g^-1Wherein MAR with the best adsorption effect is LX-20, and the adsorption quantity is 117.40mg g^-1. The adsorption amount of the LX-20 type MAR to the lycium barbarum polysaccharide LBP-009 is 111.23mg g by verification of MAR static adsorption experiments^-1The result is basically consistent with the predicted value. Therefore, the multi-source information fusion model established by the Rstudio software is accurate and reliable.

The above examples are only for illustrating the technical concept and features of the present invention, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.