CN116773686B - Method and system for measuring content of ketone compounds in swertia davidiana - Google Patents
Method and system for measuring content of ketone compounds in swertia davidiana Download PDFInfo
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- -1 ketone compounds Chemical class 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 78
- 241001530209 Swertia Species 0.000 title claims description 42
- 241000974657 Swertia pseudochinensis Species 0.000 claims abstract description 37
- 238000005259 measurement Methods 0.000 claims abstract description 27
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 23
- 150000002576 ketones Chemical class 0.000 claims abstract description 14
- 238000004128 high performance liquid chromatography Methods 0.000 claims abstract description 13
- AEDDIBAIWPIIBD-ZJKJAXBQSA-N mangiferin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1C1=C(O)C=C(OC=2C(=CC(O)=C(O)C=2)C2=O)C2=C1O AEDDIBAIWPIIBD-ZJKJAXBQSA-N 0.000 claims description 58
- 238000001228 spectrum Methods 0.000 claims description 44
- 238000000605 extraction Methods 0.000 claims description 42
- YWQSXCGKJDUYTL-UHFFFAOYSA-N Mangiferin Natural products CC(CCC=C(C)C)C1CC(C)C2C3CCC4C(C)(C)CCCC45CC35CCC12C YWQSXCGKJDUYTL-UHFFFAOYSA-N 0.000 claims description 29
- 229940043357 mangiferin Drugs 0.000 claims description 29
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 claims description 24
- 239000000284 extract Substances 0.000 claims description 21
- 150000001875 compounds Chemical class 0.000 claims description 20
- 230000000694 effects Effects 0.000 claims description 19
- 239000000843 powder Substances 0.000 claims description 18
- HEYZWPRKKUGDCR-QBXMEVCASA-N Swertiamarin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1O[C@H]1[C@H](C=C)[C@]2(O)CCOC(=O)C2=CO1 HEYZWPRKKUGDCR-QBXMEVCASA-N 0.000 claims description 15
- HEYZWPRKKUGDCR-WRMJXEAJSA-N Swertiamarin Natural products O([C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1)[C@H]1[C@@H](C=C)[C@@]2(O)C(C(=O)OCC2)=CO1 HEYZWPRKKUGDCR-WRMJXEAJSA-N 0.000 claims description 15
- 238000010521 absorption reaction Methods 0.000 claims description 15
- OZWDYLLMBFLSNH-UHFFFAOYSA-N Swertisin Natural products COc1cc2OC(=CC(=O)c2c(O)c1OC3OC(CO)C(O)C(O)C3O)c4cccc(O)c4 OZWDYLLMBFLSNH-UHFFFAOYSA-N 0.000 claims description 13
- ABRULANJVVJLFI-DGHBBABESA-N swertisin Chemical compound COC1=CC=2OC(C=3C=CC(O)=CC=3)=CC(=O)C=2C(O)=C1[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O ABRULANJVVJLFI-DGHBBABESA-N 0.000 claims description 13
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- BDAGIHXWWSANSR-UHFFFAOYSA-N methanoic acid Natural products OC=O BDAGIHXWWSANSR-UHFFFAOYSA-N 0.000 claims description 6
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- 238000010828 elution Methods 0.000 claims description 4
- 125000000524 functional group Chemical group 0.000 claims description 4
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- OSWFIVFLDKOXQC-UHFFFAOYSA-N 4-(3-methoxyphenyl)aniline Chemical compound COC1=CC=CC(C=2C=CC(N)=CC=2)=C1 OSWFIVFLDKOXQC-UHFFFAOYSA-N 0.000 claims description 3
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 3
- 235000019253 formic acid Nutrition 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
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- MUCRYNWJQNHDJH-OADIDDRXSA-N Ursonic acid Chemical class C1CC(=O)C(C)(C)[C@@H]2CC[C@@]3(C)[C@]4(C)CC[C@@]5(C(O)=O)CC[C@@H](C)[C@H](C)[C@H]5C4=CC[C@@H]3[C@]21C MUCRYNWJQNHDJH-OADIDDRXSA-N 0.000 claims 2
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- 241000723353 Chrysanthemum Species 0.000 description 2
- 235000005633 Chrysanthemum balsamita Nutrition 0.000 description 2
- DUAGQYUORDTXOR-GPQRQXLASA-N Gentiopicrin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1O[C@H]1[C@H](C=C)C2=CCOC(=O)C2=CO1 DUAGQYUORDTXOR-GPQRQXLASA-N 0.000 description 2
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- JDIORNFCMMYMLF-UHFFFAOYSA-N bellidifolin Chemical compound C1=CC(O)=C2C(=O)C3=C(O)C=C(OC)C=C3OC2=C1O JDIORNFCMMYMLF-UHFFFAOYSA-N 0.000 description 2
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- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
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- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- CNMVOPXKKWCJHT-UHFFFAOYSA-N isogentiakochianin Natural products OC1=CC(O)=C2C(=O)C3=C(O)C(OC)=CC=C3OC2=C1 CNMVOPXKKWCJHT-UHFFFAOYSA-N 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
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- 235000020638 mussel Nutrition 0.000 description 1
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- VSJGJMKGNMDJCI-QXSNVGMTSA-N sweroside Natural products OC[C@H]1O[C@H](O[C@@H]2OC=C3[C@@H](CCOC3=O)[C@H]2C=C)[C@H](O)[C@@H](O)[C@@H]1O VSJGJMKGNMDJCI-QXSNVGMTSA-N 0.000 description 1
- 238000004704 ultra performance liquid chromatography Methods 0.000 description 1
- 230000000304 vasodilatating effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
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Abstract
The invention discloses a method and a system for measuring the content of ketone compounds in swertia pseudochinensis, which comprises the following steps: collecting swertia pseudochinensis at different months as a sample; measuring ketone components of the sample by using a high performance liquid chromatography to obtain content measurement results of different ketone compounds; respectively collecting infrared spectrograms of samples of each month, and comparing infrared spectrogram characteristics of samples of different months; establishing quantitative models of different ketone compounds by combining infrared spectrum characteristics, and optimizing the quantitative models by using different modeling methods and/or spectrogram pretreatment methods; using a plurality of indexes to evaluate the quantitative model and combining the content measurement results to verify the quantitative model so as to obtain an optimal quantitative model of different ketone compounds; and determining the content of different ketone compounds in the swertia pseudochinensis by using an optimal quantitative model. The method can rapidly and correctly determine the content of the ketone compounds in the swertia pseudochinensis and provides a basis for rapid quality identification of the swertia pseudochinensis.
Description
Technical Field
The invention relates to the technical field of compound content measurement, in particular to a method for measuring the content of a compound in swertiaMethod and system for measuring the content of ketone compounds.
Background
Swertia mussel is a two-year-old plant of swertia genus of gentiaceae family, and is a representative Tibetan medicine material called "Tibetan capillaris herba". Swertia davidiana is grown in Qinghai, tibet, sichuan and Yunnan, and has an altitude of 1900-3800m.The ketone is also called benzochromone, is yellow or colorless, is a natural product with a tricyclic aromatic hydrocarbon (C 6-C3-C6) structure, has higher occupation in swertia praecox, and is separated and identified from swertia praecox at presentThe number of ketone compounds is 50 or more,/>The ketone compounds have antidepressant, antitumor, diabetes treating, vasodilating, blood pressure lowering, etc. effects, because/>The ketone has important medicinal value and is very critical to the content measurement of the ketone in the swertia davidiana.
The quality detection of the traditional Chinese medicinal materials is commonly used as high performance liquid chromatography, ultraviolet spectrophotometry, UPLC (ultra-violet chromatography) method and the like, the methods need to separate the mixture first and then analyze the pure components or ingredients, so that the time and the labor are wasted, the most original information of the sample cannot be ensured, and the interaction and the integral relation of the chemical components of the medicinal materials cannot be reflected. Therefore, a more convenient, rapid and accurate method for analyzing and identifying the quality of medicinal materials is needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior swertia pseudochinensisThe problem of the ketone compound determination technology is that a method for/>, in swertia pseudochinensis is providedMethod and system for measuring the content of ketone compounds.
The aim of the invention is realized by the following technical scheme:
in a first aspect, there is provided a method of producing a swertia pseudochinensis A method for determining the content of a ketone compound, the method comprising:
Collecting swertia pseudochinensis at different months as a sample;
determination of the sample using high performance liquid chromatography Ketone components, and optimizing component extraction by ultrasonic method to obtain different/>The content measurement result of the ketone compounds;
Respectively collecting infrared spectrograms of samples of each month, and comparing infrared spectrogram characteristics of samples of different months;
Establishing differences in combination with the infrared spectral features A quantitative model of a ketone compound, wherein the quantitative model is optimized using different modeling methods and/or spectrogram preprocessing methods;
using multiple indexes to evaluate the quantitative model and combining the content measurement results to verify the quantitative model to obtain different indexes An optimal quantitative model of the ketone compound;
determining the difference in said swertia pseudochinensis using said optimal quantitative model The content of ketone compounds.
As a preferred option, a swertia pseudochinensisThe method for measuring the content of the ketone compounds comprises the steps of collecting swertia davidiana of different months as a sample, wherein the method comprises the following steps:
and respectively collecting swertia pseudochinensis of 6-9 months, wherein 20 samples are collected each month.
As a preferred option, a swertia pseudochinensisMethod for determining the content of ketone compounds, said differences/>The ketone compounds include mangiferin, swertiamarin and gentiopicrin.
As a preferred option, a swertia pseudochinensisThe content determination method of ketone compounds, the ultrasonic method optimizes component extraction, comprises the following steps:
optimizing extraction time, feed-liquid ratio, extraction concentration, extraction temperature and extraction power, designing an orthogonal experiment, and optimizing the most suitable extraction conditions through extremely poor analysis, variance analysis and comparison of a theoretical optimal combination and an actual optimal combination.
As a preferred option, a swertia pseudochinensisThe method for measuring the content of the ketone compounds comprises the steps of respectively collecting infrared spectrograms of samples of each month, wherein the infrared spectrograms comprise the following steps:
Infrared spectra NIR were collected as powder samples and sample extracts, respectively.
As a preferred option, a swertia pseudochinensisMethod for measuring the content of ketone compounds, wherein different/>'s are established by combining the infrared spectrum characteristicsA quantitative model for ketone compounds, comprising:
According to different situations The ketone compounds select different modeling wave bands.
As a preferred option, a swertia pseudochinensisThe modeling method comprises partial least square and principal component regression.
As a preferred option, a swertia pseudochinensisThe spectrogram preprocessing method comprises multi-element scattering correction, variable standardization, first derivative, second derivative and SG convolution smoothing.
As a preferred option, a swertia pseudochinensisThe indexes comprise modeling set correlation coefficient, verification set correlation coefficient, correction error root mean square, prediction error root mean square and residual error prediction deviation.
In a second aspect, a method of producing a swertia pseudochinensis is providedA system for determining the content of a ketone compound, the system comprising:
The sample collection unit is used for collecting swertia pseudochinensis at different months as a sample;
a chromatography content determination module for determining the sample using high performance liquid chromatography Ketone components, and optimizing component extraction by ultrasonic method to obtain different/>The content measurement result of the ketone compounds;
The infrared spectrum acquisition module is used for respectively acquiring infrared spectrograms of samples of each month and comparing infrared spectrum characteristics of samples of different months;
The quantitative model building module is used for combining the infrared spectrum characteristics to build different types of images A quantitative model of a ketone compound, wherein the quantitative model is optimized using different modeling methods and/or spectrogram preprocessing methods;
The quantitative model optimization module is used for evaluating the quantitative model by using various indexes and verifying the quantitative model by combining the content measurement results to obtain different indexes An optimal quantitative model of the ketone compound;
The content measurement module is used for measuring different swertia pseudochinensis in the optimal quantitative model The content of ketone compounds.
It should be further noted that the technical features corresponding to the above options may be combined with each other or replaced to form a new technical scheme without collision.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention combines the infrared spectrum characteristics to build different Quantitative models of ketone compounds are optimized under different modeling methods, different spectrogram preprocessing methods and multiple index evaluation to obtain different/>The optimal quantitative model of the ketone compounds can realize the different/>The rapid and accurate detection of the ketone compounds provides a basis for the rapid quality identification of the swertia pseudochinensis. The measurement operation does not need expert knowledge, and the required time is greatly shortened compared with the traditional measurement method.
(2) In one example, the swertia pseudochinensis Franch of different months is taken as a sample for analysis, so that the biomass change condition of the swertia pseudochinensis Franch of different growth periods can be measured, and guidance is provided for cultivation of the swertia pseudochinensis Franch.
Drawings
FIG. 1 shows a method of producing a swertia pseudochinensisA flow chart of a method for measuring the content of ketone compounds;
FIG. 2 shows the biomass change of swertia pseudochinensis in different growth periods according to the embodiment of the invention;
FIG. 3 is a chromatographic separation of a standard according to an embodiment of the invention;
fig. 4 is a NIR spectrum of a crude drug and its extract according to an embodiment of the present invention;
FIG. 5 is a near infrared spectrum analysis showing an embodiment of the present invention;
FIG. 6 shows NIR, D1, D2 spectra and 3 species of the present invention Correlation of ketone compound models;
FIG. 7 shows the linear relationship between the predicted and measured values of three compound models according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully understood from the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In an exemplary embodiment, a method of producing a swertia pseudochinensis is providedA method for determining the content of ketone compounds, referring to fig. 1, the method comprises:
Collecting swertia pseudochinensis at different months as a sample;
determination of the sample using high performance liquid chromatography Ketone components, and optimizing component extraction by ultrasonic method to obtain different/>The content measurement result of the ketone compounds;
Respectively collecting infrared spectrograms of samples of each month, and comparing infrared spectrogram characteristics of samples of different months;
Establishing differences in combination with the infrared spectral features A quantitative model of a ketone compound, wherein the quantitative model is optimized using different modeling methods and/or spectrogram preprocessing methods;
using multiple indexes to evaluate the quantitative model and combining the content measurement results to verify the quantitative model to obtain different indexes An optimal quantitative model of the ketone compound;
determining the difference in said swertia pseudochinensis using said optimal quantitative model The content of ketone compounds.
The invention combines the infrared spectrum characteristics to build differentQuantitative models of ketone compounds are optimized under different modeling methods, different spectrogram preprocessing methods and multiple index evaluation to obtain different/>The optimal quantitative model of the ketone compounds can realize the different/>The rapid and accurate detection of the ketone compounds provides basis for rapid quality identification of swertia pseudochinensis
Specifically, the swertia davidi used is two years old, and in one example, the collecting swertia davidi of different months as a sample includes:
And respectively collecting swertia pseudochinensis in the growth period of 6-9 months, wherein 20 samples are collected each month. Cleaning, air drying, pulverizing, and drying in a dryer.
Determination of the sample using high performance liquid chromatographyThe instruments and reagents previously prepared for the ketone component include: agilent 1260 high performance liquid chromatograph (Agilent, usa), nicolette iS fourier transform infrared spectrometer (sampler, usa), electronic balance (mertler), reflux tube, absolute ethanol (AR) and methanol (chromatographic purity, merck).
For the samples obtained during each growth period, the growth and development characteristics thereof were measured, the leaf number, plant height, fresh weight, dry weight thereof were recorded, and the dry rate was calculated. Dry rate = dry weight (g)/wet weight (g) 100%. Specifically, the biomass change conditions of the swertia davidiana in different growth periods are shown in a figure 2, wherein a represents plant height, b represents leaf number, c represents fresh weight, d represents dry weight, e represents drying rate, and plant height is gradually increased in a period of 6-8 months until 9 months reach stability. The leaf number is in a growing trend in the research range, the increase of 9 months is large, and the plant biomass is in an increasing trend. The drying rate of swertia davidiana is also in an upward trend, which shows that the moisture in the plant body gradually decreases along with the growth of the plant.
Further, the chromatographic conditions are: ZORBAX SB-C18 column (4.6X 150mm,5um,Agilent Technologies, USA), sample size: 10 μl, flow rate: 1.0mL/min, column temperature: detection wavelength 254nm at 35 ℃, mobile phase: a-water (0.1% formic acid), B-100% methanol. Elution gradients are shown in table 1:
gradient elution procedure for Compounds of Table 1
In one example, a swertia pseudochinensisMethod for determining the content of ketone compounds, said differences/>The ketone compounds include mangiferin, swertiamarin and gentiopicrin. By using the above chromatographic conditions, compound standard curves were drawn, and each compound standard curve was measured as follows: mangiferin (MANGIFERIN, M) linear regression equation is y= 931.37x-1211.5 (R 2 = 0.9942, linear range: 0.160-1.600 μg), swertisin (Swertioside, ZC) linear regression equation is y=124.52x+4.47 (R 2 =0.9999, linear range: 0.024-0.240 μg), bellflower gentione (Bellidifolin, C) linear regression equation is y=31.01x-26.09 (R 2 = 0.9971, linear range: 0.001-0.012 μg), and each compound standard curve has good linear relationship.
As shown in fig. 3, the standard substance was separated under the above chromatographic conditions, and the standard substance was separated based on the baseline, as can be seen from fig. 3. And then carrying out methodological verification on the conditions, and examining the precision, repeatability, stability within 24 hours and standard adding recovery rate of the method. The results of methodological verification are shown in Table 2, and the chromatographic methods adopted in the research have good repeatability, precision and stability of mangiferin, swertisin and bellytriazole, and good labeling recovery rate of mangiferin and swertisin, and lower labeling recovery rate of bellytriazole, probably due to lower content of the bellytriazole.
Table 2 chromatographic condition methodological validation
Further, in one example, a swertia pseudochinensisThe content determination method of ketone compounds, the ultrasonic method optimizes component extraction, comprises the following steps:
optimizing extraction time, feed-liquid ratio, extraction concentration, extraction temperature and extraction power, designing an orthogonal experiment, and optimizing the most suitable extraction conditions through extremely poor analysis, variance analysis and comparison of a theoretical optimal combination and an actual optimal combination.
Specifically, the extraction of mangiferin, swertiin and belleville gentione in swertia, adopts an ultrasonic extraction method, and an orthogonal experiment of L9 (3 4) is designed, as shown in table 3:
TABLE 3 Swertian Swiss herb Ketone component extraction optimization orthogonal test design
The optimization index is the peak area and the comprehensive score of each compound, and the calculation formula is as follows:
Composite score = (a i/Amax+Bi/Bmax+Ci/Cmax) ×100/3
Wherein a i、Bi、Ci is the measured value of each factor at different levels, and a max、Bmax、Cmax is the maximum value of each factor.
Based on the above scoring scheme, the results of the orthogonal test are shown in table 4.
TABLE 4 results of orthogonal experiments
As is clear from Table 4, the total score was highest under the conditions of combination 9 (extraction time: 50min, feed-liquid ratio: 30g/mL, extraction methanol concentration: 80%, extraction temperature: 50 ℃ C.) and reached 91.75. The results of the orthogonal test were analyzed extremely poorly as shown in table 5.
TABLE 5 Quadrature test error analysis
As can be seen, the results of the A factor are better at the level L 3, the B factor is better at the level L 2, the C factor is better at the level L 1, and the D factor is better at the level L 1, so that the theoretical optimal combination is A 3B2C1D1 (namely, the extraction time is 50min, the feed liquid ratio is 25g/mL, the concentration of extracted methanol is 60%, and the extraction temperature is 50 ℃). The influence of each factor on the extraction result is as follows: temperature > time > feed to liquid ratio > extraction concentration. The theoretical optimal extraction condition is compared with the actual optimal extraction condition, so that the extraction effect is better under the actual optimal extraction condition, therefore, the ultrasonic extraction condition of the swertia davidi champ is that the extraction time is 50min, the feed-liquid ratio is 30g/mL, the concentration of the extracted methanol is 80%, and the extraction temperature is 50 ℃.
Further, three kinds of swertia pseudochinensis in different growth periodsThe results of the ketone content measurement are shown in FIG. 6.
TABLE 6 three of swertia pseudochinensis in different growth phasesResults of measuring the content of ketone component (n=3)
As shown in Table 6, the content of mangiferin is highest (7.99-20.62 mg/g), the content of swertia (0.08-2.26 mg/g) and the content of belleville gentione is lowest (0.07-0.52 mg/g). The content of mangiferin is gradually reduced in 8 and 9 months, the content of swertisin is in an increasing trend, the fluctuation range is large in 7-8 months, no belleville leaf gentione is detected in 6 and 7 months samples, and the compound is accumulated in 8 and 9 months.
In one example, a swertia pseudochinensisThe method for measuring the content of the ketone compounds comprises the steps of respectively collecting infrared spectrograms of samples of each month, wherein the infrared spectrograms comprise the following steps:
Infrared spectra NIR were collected as powder samples and sample extracts, respectively. The preparation process of the sample extracting solution comprises the following steps: 0.1000g (+ -0.0002) of the sample is weighed, 3mL of 80% methanol solution is added, extraction is carried out at 50 ℃ for 50min, the extraction liquid is centrifuged at 4000rpm for 10min, the supernatant is taken out in a 10mL volumetric flask, and then the supernatant is fixed to 10mL by the extraction liquid.
When the near infrared spectrum NIR spectrogram of the powder sample is collected, the powder sample is crushed, sieved by a 100-mesh sieve and placed in a dryer for standby. The collection of the NIR spectra of the powder samples was performed from the envelope, and in order to make the spectra more representative, the spectra were collected at3 points on each side of the package, and then the average spectra were taken for analysis. The spectrogram acquisition condition is that the scanning times are 32 times, the resolution is 4cm -1, the spectrogram acquisition range is 10000-4000cm -1, and air is used as a background during acquisition.
When the near infrared spectrum NIR spectrogram of the sample extracting solution is collected, the optical path is well regulated, the spectrogram collecting condition is that the scanning times are 32 times, the resolution is 4cm -1, the spectrogram collecting range is 10000-4000cm -1, and air is used as the background during collecting (n=3).
Specifically, the NIR spectrogram of the raw medicinal material (powder sample) of swertia davidi and its extract is shown in figure 4, and the figure shows that the absorbance is larger because the refractive index of the extract is low because of 7 ：8245cm-1、6838cm-1、5775cm-1、5665cm-1、5186cm-1、4755cm-1、4323cm-1, absorption peaks of the raw medicinal material and 9 absorption peaks ：8389cm-1、6848cm-1、6350cm-1、5893cm-1、5784cm-1、5163cm-1、4862cm-1、4397cm-1、4277cm-1. of the extract. The functional groups of the raw medicinal materials of swertia davidi and the extracting solution are similar in absorption, but the absorbance ratio among the functional groups is different, and the difference mainly concentrates on upsilon (C-H)/upsilon as (C-H) and (N-H)/O-H), and the difference is possibly caused by a solvent system.
Further, the average spectrum, D1, D2 and the corresponding difference spectrum of the raw material samples of each month of the swertia davidiana in different growth periods are shown in figure 5, wherein a represents the average spectrum, D1, D2 and the corresponding difference spectrum of the raw material in different growth periods, and b represents the average spectrum, D1, D2 and the corresponding difference spectrum of the extracting solution in different growth periods. As can be seen from the graph, the NIR average spectrum, D1 spectrum and D2 spectrum of different months are similar, the absorption peaks are not different, but the corresponding difference spectrum shows that the samples of different months are different, the small month average spectrum is taken as a standard spectrum, the large month average spectrum is taken as a sample spectrum, the positive and negative values of the absorption peaks of the difference spectrum of 6-8 months are consistent in all the difference spectrum calculation results, but the positive and negative values of the absorption peaks of 8-9 months are opposite to the difference spectrum of 6-8 months, which shows that the absorbance of 9 months swertia is changed from the absorbance of the first three months.
The NIR average spectrum, D1 spectrum and D2 spectrum of the sample extract of different months are similar, the absorption peaks are not different, but the corresponding difference spectrum shows that the samples of different months are different, the difference spectrum calculation mode is the same as the difference spectrum of the raw medicinal materials, the difference between 8-9 months is the smallest in all difference spectrum calculation results, the sample extracts of 8-9 months are similar, the extract of 6-8 months has larger change, and the three compound contents in the measured samples are combined to change in different growth periods, so that 3 kinds of the sample extract are knownThe change range of the ketone compound content in 6-8 months is larger, the 8-9 months change tends to be gentle, and the change of the difference spectrum of the sample extracting solution is presumed to be related to the change of the compound content. The infrared information of the powder responds more clearly to biomass, while the infrared information of the extract responds more clearly to changes in the compound.
Analyzing a band with larger difference in the difference spectrum, wherein the difference band of the powder sample is gradually increased along with derivative treatment, the difference band is enlarged from 5800-4500cm -1 (NIR) to 7400-7000cm -1、5500-4500cm-1 (D1), and the final range is enlarged to 7400-7000cm -1、6000-4400cm-1 (D2); and the spectrum of the extracting solution is reduced along with derivative treatment, and the difference wave band is reduced from 7000-4400cm -1 (NIR) to 5400-4400cm -1 (D1) and finally reduced to 5400cm -1、4500-4200cm-1 (D2).
Further, three kinds ofThe establishment of the quantitative model of the ketone compound is respectively carried out by the two spectrograms, the sample is randomly divided into a modeling set and a verification set during modeling, and the model is established by partial least Square (PARTIAL LEAST Square, PLS) and principal component regression (PRINCIPAL COMPONENTS REPRESSION, PCR). During modeling, a correlation coefficient method (Correlation Coefficient, CC) is utilized to select variables, and spectrogram processing modes such as multi-component scattering correction (Multiplicative Scatter Correction, MSC), variable standardization (Standard Normal Variate, SNV), a first derivative spectrogram (FIRST DERIVATIVE, D1), a second derivative spectrogram (Second Derivative, D2), SG convolution smoothing and the like are optimized. The model optimization is performed by taking modeling set correlation coefficients (Coefficient of Calibration, R cal), validation set correlation coefficients (Coefficient of Validation, R val), correction error root mean square (Root Mean Square Errors of Calibration, RMSEC), prediction error root mean square (Root Mean Square Errors of Validation, RMSEP) and residual prediction bias (Residual Prediction Deviation, RPD) as indexes. And then verifying the model obtained by optimization by using an external verification set, predicting the content of the sample by using the model to obtain a predicted value, and comparing the predicted value with the measured value by taking an HPLC (high performance liquid chromatography) measurement result as the measured value, wherein the predicted value is obtained by the method comprises the following steps of:
In the formula, y i is a measured value, f i is a predicted value, and N is the number of samples. RPD = prediction set standard deviation/error root mean square; relative deviation (%) = | measured value-predicted value|/measured value 100%. Wherein the data are plotted using GRAPHPAD PRISM software (GraphPad Software), the data are expressed in mean±sd, and the quantitative model is established using TQ ANALYST (Thermo FISHER SCIENTIFIC) software.
In one example, a swertia pseudochinensisMethod for measuring the content of ketone compounds, wherein different/>'s are established by combining the infrared spectrum characteristicsA quantitative model for ketone compounds, comprising:
According to different situations The ketone compounds select different modeling wave bands. Specifically, as shown in fig. 6, a corresponding modeling wave band is selected, and finally, the wave band of mangiferin modeling by using a specrum is 6135-4300cm -1, the wave band of mangiferin modeling by using a D1 is 8900-4300cm -1, and the wave band of mangiferin modeling by using a D2 is 6590-4300cm -1; the wave band of swertiamarin is 5335-4295cm -1 when modeling with a specrum, 7500-4300cm -1 when modeling with D1, 5588-4380cm -1 when modeling with D2; the wave band of the bellyturf gentian is 5840-5630cm -1 when modeling by using a spectrum, 10000-4400cm -1 when modeling by using D1, 10000-4400cm -1 when modeling by using D2.
Further, as shown in table 7, table 7 shows that the RPD values of the respective models of mangiferin and sweroside are about 1, the R values of the respective models are low, and the model effect is best when the modeling condition of mangiferin is pcr+snv+d2, at this time, RMSEC, RMSEP, R C、RV of the models are 2.19mg/g, 2.09mg/g, 0.5427, 0.5695, respectively; the model effect is best when the modeling condition of the swertiamarin is PLS+SNV+D2, and at the moment, RMSEC, RMSEP, R C、RV of the model is 0.32mg/g, 0.27mg/g, 0.4143 and 0.5362 respectively; when the modeling condition of the belleville gentione is PLS+SNV+D2, the maximum RPD value of the model can reach 3.63, and at the moment, RMSEC, RMSEP, R C、RV of the model is 0.03mg/g, 0.9407 and 0.6377 respectively.
TABLE 7 establishment of NIR spectrogram quantitative model of three compound crude drugs
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As shown in Table 8, table 8 shows that the effect of each model of mangiferin is not much different from that of the model modeling by using the crude drugs, and the model effect is best when the modeling condition is PCR+MSC+D1, and at this time, RMSEC, RMSEP, R C、RV of the model is 2.18mg/g, 2.01mg/g, 0.4789 and 0.5276 respectively; the modeling effect of the swertiamarin extracting solution is reduced, the modeling effect is best when the modeling condition is PLS+constant+specrum, and at the moment, RMSEC, RMSEP, R C、RV of the model is 0.39mg/g, 0.48mg/g, 0.0592 and 0.0336 respectively; the modeling effect of the gentian extract of the daisy leaf is improved, and the maximum RPD value of the model can reach 4.17.
Table 8 establishment of quantitative model of NIR spectrogram of three compound sample extracts
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Further, the linear relation between the predicted values and the measured values of the three compound models is compared, as shown in fig. 7, wherein a represents mangiferin, b represents swertisin, and c represents belleville gentione. Of the three compounds, the model effect of the bellyturf gentione is best no matter the sample spectrogram or the extract spectrogram is used for modeling, the model performance of mangiferin is equivalent to that of swertisin when the sample spectrogram is used for modeling, but the model performance of swertisin is greatly reduced when the extract spectrogram is used for modeling, and the model R value is lower than 0.1000, so that the model prediction performance is insufficient. Specifically, the model predicted value and the measured value of the belleville gentian have the best linear relation, R 2 is 0.8844, the model effect of the swertiamarin is the worst linear relation between the predicted value and the measured value, R 2 is 0.2382, the structural formula analysis of the compound is combined, the belleville gentian has the simplest structure, mangiferin has one glycosyl, the swertiamarin has the most complex structure and has two glycosyls, NIR absorption is generated by the change of the vibration state in the molecule, the more complex structure of the compound is, and the more complex interaction among chemical bonds is, so that one absorption peak contains more information, which is probably the reason that the model effect of the belleville gentian without glycosyl is good, and the model effect of the swertiamarin with the most glycosyl is bad.
Further, the model is used for judging the model prediction capability by taking unknown samples as external verification sets respectively, the external verification sets of mangiferin and swertisin are composed of a plurality of samples selected in each month, and as the number of samples with the content being measured by the bellytalin is small, all the samples are used for modeling, 3 spectrograms can be acquired by the samples, and therefore, the external verification set of the bellytalin model is formed by optionally selecting one of the 3 spectrograms of each sample. The results of the actual measurement and the predicted value of each model external validation set are shown in table 9.
TABLE 9 quantitative model external validation results
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As shown in Table 9, the relative deviation between the measured value and the predicted value of mangiferin is 1.16% -85.23%, the average relative deviation is 18.25%, but only one sample has larger relative deviation, and the relative deviation of the rest samples is below 37.50%; the relative deviation range of the actual measurement value and the predicted value of the swertiamarin is 2.22-65.38%, and the average relative deviation is 31.31%; the range of the relative deviation between the measured value and the predicted value of the gentian of the daisy is 0.00-42.86%, and the average relative deviation is 19.70%. From the above results, the prediction ability of swertisin to unknown samples was the worst, and the prediction ability of mangiferin and bellflower gentione to unknown samples was the best.
In a second aspect, a method of producing a swertia pseudochinensis is providedA system for determining the content of a ketone compound, the system comprising:
The sample collection unit is used for collecting swertia pseudochinensis at different months as a sample;
a chromatography content determination module for determining the sample using high performance liquid chromatography Ketone components, and optimizing component extraction by ultrasonic method to obtain different/>The content measurement result of the ketone compounds;
The infrared spectrum acquisition module is used for respectively acquiring infrared spectrograms of samples of each month and comparing infrared spectrum characteristics of samples of different months;
The quantitative model building module is used for combining the infrared spectrum characteristics to build different types of images A quantitative model of a ketone compound, wherein the quantitative model is optimized using different modeling methods and/or spectrogram preprocessing methods;
The quantitative model optimization module is used for evaluating the quantitative model by using various indexes and verifying the quantitative model by combining the content measurement results to obtain different indexes An optimal quantitative model of the ketone compound; /(I)
The content measurement module is used for measuring different swertia pseudochinensis in the optimal quantitative modelThe content of ketone compounds.
The foregoing detailed description of the invention is provided for illustration, and it is not to be construed that the detailed description of the invention is limited to only those illustration, but that several simple deductions and substitutions can be made by those skilled in the art without departing from the spirit of the invention, and are to be considered as falling within the scope of the invention.
Claims (7)
1. A method for determining the content of ketone compounds in swertia davidiana, which is characterized by comprising the following steps:
collecting swertia pseudochinensis at different months as a sample; the method for collecting swertia davidi from different months as a sample comprises the following steps:
collecting swertia pseudochinensis of 6-9 months respectively, wherein 20 samples are collected each month;
measuring ketone components of the sample by using a high performance liquid chromatography, and optimizing component extraction by using an ultrasonic method to obtain content measurement results of different ketone compounds; the chromatographic conditions of the high performance liquid chromatography include:
ZORBAX SB-C18 chromatographic column, detection wavelength 254nm, mobile phase: an aqueous solution of A-0.1% formic acid, B-100% methanol solution and an elution gradient, the procedure of which is shown in Table 1 below:
,
The different ketone compounds comprise mangiferin, swertiamarin and belleville gentione; the content measurement results of the different ketone compounds comprise:
The contents of the different ketone compounds are mangiferin, swertiamarin and belleville gentione from high to low in sequence; wherein, the content of mangiferin is gradually reduced in 8 and 9 months; the content of the swertiamarin is in an increasing trend, and the fluctuation range is larger in 7-8 months; 6. no belleville gentione was detected in the 7 month sample, which accumulated in 8, 9 months;
Respectively collecting infrared spectrograms of samples of each month, and comparing infrared spectrogram characteristics of samples of different months;
the infrared spectrogram for respectively collecting the samples of each month comprises the following steps:
collecting infrared spectrum NIR in the form of powder sample and sample extract;
comparing infrared spectral features of samples of different months, comprising:
The absorbance of the extracting solution sample is larger than that of the powder sample, and the absorbance ratio among the functional groups is different;
Analyzing difference spectrums of samples of different months under different sample states, wherein absorption peaks of the difference spectrums among the samples of different months are different, wherein in the difference spectrums of the powder samples, positive and negative values of absorption peaks of the difference spectrums among 6-8 months are consistent, but positive and negative values of absorption peaks among 8-9 months are opposite to those among 6-8 months, and the absorbance of swertia of 9 months is changed from that of the first three months;
in the difference spectrum of the extracting solution sample, the difference between 8 and 9 months is the smallest, the extracting solution between 6 and 8 months has larger change, the content of 3 ketone compounds has larger change range between 6 and 8 months, and the change of 8 and 9 months is gradually gentle;
the infrared information of the powder sample has more obvious response to biomass, and the infrared information of the sample extracting solution has more obvious response to the change of the compound;
Establishing quantitative models of different ketone compounds by combining the infrared spectrum characteristics, wherein the quantitative models are optimized by using different modeling methods and/or spectrogram preprocessing methods;
the method for establishing quantitative models of different ketone compounds by combining the infrared spectrum characteristics comprises the following steps:
selecting different modeling wave bands according to different ketone compounds;
Respectively establishing quantitative models of different ketone compounds by using powder samples;
respectively establishing quantitative models of different ketone compounds by using sample extracting solutions;
Comparing the effects of the quantitative model under different sample states; the comparing the effect of the quantitative model under different sample conditions comprises:
The model effect of the bellyturf leaf gentione is best no matter the sample spectrogram or the extract spectrogram is used for modeling, the model performance of mangiferin is equivalent to that of swertisin when the sample spectrogram is used for modeling, but the model performance of swertisin is greatly reduced when the extract spectrogram is used for modeling;
Using a plurality of indexes to evaluate the quantitative model and combining the content measurement results to verify the quantitative model so as to obtain an optimal quantitative model of different ketone compounds;
and determining the content of different ketone compounds in the swertia pseudochinensis by using the optimal quantitative model.
2. The method for determining the content of ketone compounds in swertia pseudochinensis according to claim 1, wherein the ultrasonic method optimizes component extraction, and comprises the following steps:
optimizing extraction time, feed-liquid ratio, extraction concentration, extraction temperature and extraction power, designing an orthogonal experiment, and optimizing the most suitable extraction conditions through extremely poor analysis, variance analysis and comparison of a theoretical optimal combination and an actual optimal combination.
3. The method for determining the content of ketone compounds in swertia pseudochinensis according to claim 1, wherein the establishing a quantitative model of different ketone compounds by combining the infrared spectrum features comprises:
different modeling wave bands are selected according to different ketone compounds.
4. The method for determining the content of ketone compounds in swertia pseudolariciresiana of claim 1, wherein the modeling method comprises partial least squares and principal component regression.
5. The method for determining the content of ketone compounds in swertia pseudochinensis according to claim 1, wherein the spectrogram preprocessing method comprises multi-component scattering correction, variable normalization, first derivative, second derivative and SG convolution smoothing.
6. The method for determining the content of ketone compounds in swertia pseudochinensis according to claim 1, wherein the index comprises modeling set correlation coefficient, verification set correlation coefficient, correction error root mean square, prediction error root mean square and residual prediction deviation.
7. A system for determining the content of ketone compounds in swertia davidiana, comprising:
The sample collection unit is used for collecting swertia pseudochinensis at different months as a sample; the method for collecting swertia davidi from different months as a sample comprises the following steps:
collecting swertia pseudochinensis of 6-9 months respectively, wherein 20 samples are collected each month;
The chromatographic content measurement module is used for measuring ketone components of the sample by using a high performance liquid chromatography, optimizing component extraction by using an ultrasonic method, and obtaining content measurement results of different ketone compounds;
the chromatographic conditions of the high performance liquid chromatography include:
ZORBAX SB-C18 chromatographic column, detection wavelength 254nm, mobile phase: an aqueous solution of A-0.1% formic acid, B-100% methanol solution and an elution gradient, the procedure of which is shown in Table 1 below:
,
The different ketone compounds comprise mangiferin, swertiamarin and belleville gentione; the content measurement results of the different ketone compounds comprise:
The contents of the different ketone compounds are mangiferin, swertiamarin and belleville gentione from high to low in sequence; wherein, the content of mangiferin is gradually reduced in 8 and 9 months; the content of the swertiamarin is in an increasing trend, and the fluctuation range is larger in 7-8 months; 6. no belleville gentione was detected in the 7 month sample, which accumulated in 8, 9 months;
Respectively collecting infrared spectrograms of samples of each month, and comparing infrared spectrogram characteristics of samples of different months;
the infrared spectrogram for respectively collecting the samples of each month comprises the following steps:
collecting infrared spectrum NIR in the form of powder sample and sample extract;
comparing infrared spectral features of samples of different months, comprising:
The absorbance of the extracting solution sample is larger than that of the powder sample, and the absorbance ratio among the functional groups is different;
Analyzing difference spectrums of samples of different months under different sample states, wherein absorption peaks of the difference spectrums among the samples of different months are different, wherein in the difference spectrums of the powder samples, positive and negative values of absorption peaks of the difference spectrums among 6-8 months are consistent, but positive and negative values of absorption peaks among 8-9 months are opposite to those among 6-8 months, and the absorbance of swertia of 9 months is changed from that of the first three months;
in the difference spectrum of the extracting solution sample, the difference between 8 and 9 months is the smallest, the extracting solution between 6 and 8 months has larger change, the content of 3 ketone compounds has larger change range between 6 and 8 months, and the change of 8 and 9 months is gradually gentle;
the infrared information of the powder sample has more obvious response to biomass, and the infrared information of the sample extracting solution has more obvious response to the change of the compound;
Establishing quantitative models of different ketone compounds by combining the infrared spectrum characteristics, wherein the quantitative models are optimized by using different modeling methods and/or spectrogram preprocessing methods;
the method for establishing quantitative models of different ketone compounds by combining the infrared spectrum characteristics comprises the following steps:
selecting different modeling wave bands according to different ketone compounds;
Respectively establishing quantitative models of different ketone compounds by using powder samples;
respectively establishing quantitative models of different ketone compounds by using sample extracting solutions;
Comparing the effects of the quantitative model under different sample states; the comparing the effect of the quantitative model under different sample conditions comprises:
The model effect of the bellyturf leaf gentione is best no matter the sample spectrogram or the extract spectrogram is used for modeling, the model performance of mangiferin is equivalent to that of swertisin when the sample spectrogram is used for modeling, but the model performance of swertisin is greatly reduced when the extract spectrogram is used for modeling;
The quantitative model optimization module is used for evaluating the quantitative model by using various indexes and verifying the quantitative model by combining the content measurement results to obtain an optimal quantitative model of different ketone compounds;
and the content measurement module is used for measuring the content of different ketone compounds in the swertia davidiana by using the optimal quantitative model.
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