CN115236212A

CN115236212A - Quality detection method of euphorbia Chinese herbal medicine

Info

Publication number: CN115236212A
Application number: CN202110435518.8A
Authority: CN
Inventors: 吴婉莹; 果德安; 侯晋军; 雷敏; 龙华丽; 张子佳; 倪慧
Original assignee: Shanghai Institute of Materia Medica of CAS
Current assignee: Shanghai Institute of Materia Medica of CAS
Priority date: 2021-04-22
Filing date: 2021-04-22
Publication date: 2022-10-25

Abstract

The invention relates to a quality detection method of euphorbia Chinese herbal medicine, which comprises the following steps: obtaining crude drugs before processing and processed products after processing of euphorbia Chinese herbal medicines, and respectively preparing the crude drugs and the processed products into detection samples; detecting the detection samples of the crude drug and the processed product respectively by adopting a chromatography-mass spectrometry combined method to obtain corresponding detection data; analyzing the detection data by chemometrics method to determine the component difference between the processed product and the crude drug. The quality detection method can clearly indicate the influence of processing on the effects of Euphorbia Chinese herbal medicine substances, so as to realize quality control of each processed product.

Description

Quality detection method of euphorbia Chinese herbal medicine

Technical Field

The invention relates to the field of quality detection of traditional Chinese medicine processing, in particular to a quality detection method of euphorbia Chinese herbal medicines.

Background

The medicinal parts of the traditional Chinese medicine contain active ingredients, but often contain some ingredients which influence the drug effect, so that the medicinal materials can have adverse reactions while playing the therapeutic effect, especially for toxic traditional Chinese medicines. Through the processing technology, the medicine property is adjusted, the benefit is increased, the defect is eliminated, the medicine effect of the traditional Chinese medicine can be better played, and the clinical treatment requirement is met.

Many of the types of euphorbia herbs are toxic medicinal plants, such as: kansui root, one of the toxic Chinese herbs collected in the Chinese pharmacopoeia, needs to be reduced in toxicity in clinical application. Currently, processing methods are commonly used to reduce the toxicity, such as: the traditional processing method of euphorbia kansui comprises the following steps: vinegar processed kansui root, bean curd processed kansui root, licorice processed kansui root, etc. Vinegar euphorbia kansui is collected from the 2010 edition of the Chinese pharmacopoeia; the bean curd-made kansui root is commonly used in some hospitals in Shanghai and has unique clinical application characteristics; kansui root and licorice root are one of the famous eighteen inversions of the traditional Chinese medicine, and this method is also a very characteristic processing method in the processing of traditional Chinese medicine. The three methods comprise three processing methods of frying, boiling and steaming, and relate to three different raw materials (rice vinegar, bean curd and liquorice concentrated decoction), which are very representative. The three different processing methods and the different raw material processing methods are adopted for the euphorbia kansui, so that the toxicity of the euphorbia kansui is reduced, but whether the quality of each processing method meets the clinical medicinal standard cannot be uniformly evaluated, and therefore, a detection method needs to be established for evaluating the quality of the euphorbia Chinese herbal medicine and the processed products thereof.

At present, related researchers have conducted research by using technical means such as a chromatographic fingerprint method and an Evaporative Light Scattering Detector (ELSD) around the problem of difference before and after processing of traditional Chinese medicines; and moreover, the processing technology is optimized by adopting mass spectrum as a detection means. However, there is no method to date that can clearly indicate the influence of the processing on the effective substances of Euphorbia Chinese herbal medicine (such as Euphorbia kansui). Therefore, a quality detection method capable of clearly indicating the influence of processing on the effect substances of euphorbia Chinese herbal medicines is urgently needed so as to realize the quality control of each processed product.

Disclosure of Invention

Therefore, there is a need for a quality detection method for Euphorbia Chinese herbal medicine, which can clearly indicate the influence of processing on the effective substances of Euphorbia Chinese herbal medicine, so as to control the quality of each processed product.

A quality detection method of Euphorbia Chinese herbal medicine comprises the following steps:

obtaining crude drugs before processing and processed products after processing of euphorbia Chinese herbal medicines, and respectively preparing the crude drugs and the processed products into detection samples;

respectively detecting the detection samples of the crude drug and the processed product by adopting a chromatography-mass spectrometry method to obtain corresponding detection data;

and analyzing the detection data by adopting a chemometric method to determine the component difference of the processed product and the crude drug.

In one embodiment, the processed product comprises processed products processed by at least two different methods, and the detection refers to detection by the same method.

In one embodiment, the chemometric method of analysis comprises the steps of:

analyzing the overall data difference of the detection data of each processed product and the raw product;

analyzing local data differences of the detected data of each of the processed products and the raw product.

In one embodiment, the step of analyzing the global data difference and the local data difference is performed by combining Metalign software with SIMCA-P software.

In one embodiment, the step of analyzing the overall data difference of the detected data of each of the processed products and the raw product includes the steps of:

performing principal component analysis on the detection data of each processed product, drawing a score chart of partial principal components, judging the overall data difference according to the point discreteness of the score chart, and performing subsequent steps if the overall data difference is smaller;

respectively establishing a PLS-DA model and an OPLS-DA model, performing data transformation on detection data of each processed product by adopting parN, and judging which model can embody the predication of variables to classification to the greatest extent so as to determine the model to be selected;

processing the detection data of the processed products by adopting the selection model, selecting two and/or three main components, drawing a score map, and judging the component difference degree between the processed products according to the score map;

carrying out difference comparison on the detection data of each processed product and each raw product, carrying out data transformation by using parN, carrying out difference analysis by using the selected model, drawing an S-plot diagram, calculating a variable importance value, and judging the component of each processed product, which changes relative to the raw product;

and analyzing commonly changed components of each processed product relative to the raw product by adopting a Wien diagram, and drawing a chromatographic distribution schematic diagram according to the mass-to-charge ratio and retention time of the commonly changed components to determine a target data range of local data analysis.

In one embodiment, the step of analyzing the local data difference of the detected data of each of the processed products and the raw product includes the steps of:

performing principal component analysis on detection data of a target data range of each processed product, drawing a score map of partial principal components, judging local data difference of the target data range according to the point discreteness of the score map, and performing subsequent steps if the local data difference is smaller;

respectively establishing a PLS-DA model and an OPLS-DA model, performing data transformation on detection data of a target data range of each processed product by using parN, and judging which model can embody the predication of variable pairs to classification to the greatest extent so as to determine the model to be selected;

processing the detection data of the target data range of each processed product by adopting the selection model, selecting two and/or three main components, drawing a score map, and judging the component difference degree between the processed products according to the score map;

carrying out difference comparison on detection data of target data ranges of the various processed products and the raw products, carrying out data transformation by parN, carrying out difference analysis by the selected model, drawing an S-plot graph, calculating a variable importance value, and judging the component of the various processed products, which changes relative to the raw products;

and analyzing the commonly changed components of the processed products relative to the raw products by adopting a Wien diagram, and drawing a chromatographic distribution schematic diagram according to the mass-to-charge ratio and the retention time of the commonly changed components so as to confirm the quality related information of each processed product.

In one embodiment, the step of analyzing the local data difference of the detection data of each of the processed products and the raw product further comprises the following steps:

and verifying and analyzing the whole data difference of the processed product before and after processing by adopting XCMS software.

In one embodiment, the euphorbia is euphorbia kansui, and the processed product is one or more of vinegar euphorbia kansui, liquorice-processed euphorbia kansui and bean curd-processed euphorbia kansui.

In one embodiment, the chromatography-mass spectrometry method is liquid chromatography-mass spectrometry;

wherein, the detection conditions of the liquid chromatogram are as follows: performing gradient elution by using acetonitrile as a mobile phase A and 0.1% formic acid aqueous solution as a mobile phase B; and/or

The mass spectrum is an electrospray ionization source-mass spectrum detector.

obtaining a standard processed product of the euphorbia Chinese herbal medicine, and preparing a detection sample;

detecting the detection sample by adopting a chromatography-mass spectrometry combined method to obtain detection data of a standard processed product;

analyzing the detection data by adopting a chemometrics method, and establishing a discrimination model;

obtaining a processed product to be detected, and preparing a detection sample;

detecting a detection sample of the processed product to be detected by adopting the chromatography-mass spectrometry combined method to obtain detection data of the processed product to be detected;

analyzing the detection data by adopting a chemometrics method, and judging whether the quality of the processed product to be detected meets the requirements or not according to the discrimination model

Has the beneficial effects that:

the quality detection method can be used for determining which ingredients are increased, which ingredients are decreased, the molecular weight ranges and retention time of the increased or decreased ingredients and the like before and after processing, so that the difference of different processed products before and after processing can be visually displayed, researchers can clearly observe the overall influence of the processing on the toxic traditional Chinese medicines, and excellent analysis performance is displayed. The invention provides a valuable research method for the research of the processing of toxic traditional Chinese medicines in the future.

Drawings

FIG. 1 is a graph of principal component analysis scores of overall data (control-quality control data; sample-sample data; SP-raw euphorbia kansui; CP-commercially available vinegar euphorbia kansui; ZP-self-prepared vinegar euphorbia kansui; GP-self-prepared licorice root kansui; DP-self-prepared tofu boiled euphorbia kansui) in the examples of the present invention;

FIG. 2 is an orthogonal partial least squares discriminant analysis score chart of the overall data in the example of the present invention (1-raw kansui root; 2-commercially available vinegar kansui root; 3-self-made vinegar kansui root; 4-self-made licorice root kansui root; 5-self-made tofu boiled kansui root);

fig. 3 is a graph (S-plot) of the overall data difference analysis of different processed euphorbia kansui products and raw euphorbia kansui (black squares indicate the lower components after processing, and red dots indicate the higher components after processing) in the embodiment of the present invention;

FIG. 4 is a graph of Weinn analyzing the common change of processed kansui root in the embodiments of the present invention (left graph: common decrease of processed kansui root; right graph: common increase of processed kansui root);

FIG. 5 is a schematic representation of the chromatographic distribution of the common elevated/lowered components of different preparations of euphorbia kansui in an example of the present invention;

FIG. 6 is a PCA score plot and DmodX diagnostic plot of four sets of data in the range of 430-1000 m/z in an embodiment of the present invention;

FIG. 7 is a two-dimensional (left) and three-dimensional (right) score plot of four sets of data OPLS-DA over a range of 430-1000 m/z in an embodiment of the present invention;

FIG. 8 is a difference between 430-1000 m/z of radix kansui and vinegar kansui analyzed by S-plot in the example of the present invention;

FIG. 9 is a difference component analysis S-plot of 430-1000 m/z between radix kansui and radix kansui made from radix Glycyrrhizae in the present invention;

FIG. 10 is a difference component analysis S-plot of 430-1000 m/z between raw and tofu-made kansui roots in the example of the present invention;

FIG. 11 is a graph of Wien's chart showing the common variation of local data before and after processing of different processed Euphorbiae kansui products in the embodiment of the present invention (left graph: the common decreased component after processing; right graph: the common increased component after processing);

FIG. 12 is a graph of the mean ion intensity of euphorbia kansui (partial data analysis) before and after processing for the common reduced ingredient in the example of the present invention (the abscissa indicates the common reduced ingredient number);

FIG. 13 is a visual chart of the difference between the processed radix kansui of the present invention (prepared by XCMS online software).

Detailed Description

In order that the invention may be more fully understood, a more particular description of the invention will now be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In the description of the present invention, it is to be understood that the terms "first", "second", and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to imply that the number of technical features indicated are in fact significant. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

The invention provides a quality detection method of euphorbia Chinese herbal medicine, which comprises the following steps:

s110: obtaining crude drugs before processing and processed products after processing of euphorbia Chinese herbal medicines, and respectively preparing the crude drugs and the processed products into detection samples;

it is to be understood that the term "processed product" as used herein means a crude drug, and the term "processed product" means a product obtained by processing a crude drug, such as vinegar-processed kansui root, tofu-processed kansui root, and licorice-processed kansui root. In addition, the processed product in step S110 may be processed products prepared by a plurality of different methods, or processed products of different batches by the same processing method.

In one embodiment, the euphorbia herb of the present invention is euphorbia kansui.

In one embodiment, the processed product is one or more of vinegar processed kansui root, licorice processed kansui root and bean curd processed kansui root.

In one embodiment, the processed products comprise processed products processed by at least two different methods, and the processed products are respectively detected and analyzed by the same method to judge the quality difference before and after processing of the processed products and/or the quality difference among the processed products, so that the related parameter range of toxic components is determined, and the establishment of quality standards is promoted.

Further, step S110 includes the steps of:

s111: pulverizing crude drug and processed product of Euphorbia respectively to obtain corresponding pulverized product.

Further, the crushed material passes through a sieve of four in step S111.

S112: mixing the pulverized materials with solvent respectively, dissolving completely, filtering, collecting filtrate, and removing solvent to obtain concentrate.

Further, in step S112, the solvent is ethyl acetate.

Further, in step S112, 4.5 to 5.5mL (preferably 5 mL) of a solvent is added to 0.1g of the pulverized product.

Further, after the crushed material and the solvent are mixed, the ultrasonic treatment step is also included to promote the dissolution of the components; further, the conditions of the ultrasound were: the power is 120W-160W (preferably 140W), the frequency is 40-45KHz (preferably 42 kHz), and the ultrasound is 20min-40min (preferably 30 min).

S113: the concentrate was dissolved in methanol and formulated to a predetermined concentration.

The concentration in step S113 may be selected according to specific equipment used, and should not be construed as limiting the present invention.

S120: and respectively detecting the crude drug and the detection sample of the processed product by adopting a chromatography-mass spectrometry combined method to obtain corresponding detection data.

Further, in step S120, the chromatography-mass spectrometry is liquid chromatography-mass spectrometry.

Further, in step S120, the euphorbia Chinese herbal medicine is euphorbia kansui, and the detection conditions of the liquid chromatography are as follows: acetonitrile is used as a mobile phase A, and 0.1% formic acid aqueous solution is used as a mobile phase B, and gradient elution is carried out.

Further, the gradient elution procedure was:

time/min	Mobile phase A
		0	35% acetonitrile
1	54% acetonitrile
		6	54% acetonitrile
7	70% acetonitrile
		13	92% acetonitrile
15	92% acetonitrile
		17	100% acetonitrile
25	100% acetonitrile
		31	100% methanol
35	Initial mobile phase

Further, the mass spectrometer is an electrospray ionization source-mass spectrometry (ESI-MS) detector.

Further, the mass spectrometry conditions were: in the positive ion mode, the spray pressure was 40psi, the flow rate of the drying gas was 7L/min, and the temperature of the drying gas was 300 ℃.

It is understood that, in step S120, the test samples corresponding to the crude drug and each processed product all adopt substantially the same test conditions, and the selection of the operation and conditions in each test process can be varied within the acceptable error range in the art.

S130: analyzing the detection data by chemometrics method to determine the component difference between the processed product and the crude drug.

The mass spectrum-chemometrics method is innovatively introduced into the quality detection of euphorbia Chinese herbal medicines, the method can be used for analyzing the overall profile difference of processed products before and after processing in detail, and further analyzing the difference among the processed products, so that a new research idea and method are provided for analyzing the difference of Chinese herbal medicine decoction pieces before and after processing, the toxic components of the euphorbia Chinese herbal medicines are determined, the quality standard of the processed products is established, and a new idea is provided for quality monitoring of the processed products.

Further, step S130 includes the steps of:

s131: and analyzing the overall data difference of the detection data of each processed product and each raw product.

In one embodiment, metalign software is used in conjunction with SIMCA-P software for global data difference analysis.

In one embodiment, in step S131, at least one of Principal Component Analysis (PCA), partial least squares discriminant analysis (PLS-DA) or orthogonal partial least squares discriminant analysis (OPLS-DA) is used for the analysis.

In one embodiment, step S131 includes the following steps:

s1311: and (5) analyzing the stability of the quality control sample.

Further, in step S1311, the quality control sample is a mixed reference substance obtained by mixing the processed products.

Further, in step S1311, the mixed control sample is injected into the chromatography and mass spectrometry, and sampling analysis is repeated to determine whether the retention time reproducibility of the chromatographic peak and the peak height deviation are within the allowable range, so as to determine the stability of the detection method of the present invention.

S1312: metalign software data processing and export.

It can be understood that, in step S1312, the detection data obtained by the chromatography-mass spectrometry method is detected, for example: and (3) importing mass-charge ratio-retention time (m/z-Rt) data into Metalign software for data processing, and exporting.

In one embodiment, the parameters of Metalign software are set to: peak slope factor =1; peak threshold factor =2; peak threshold =5000; average peak width at half height =10; minimum number of peaks in the chromatographic peak set =10.

S1313: SIMCA-P software analyzes data quality.

Understandably, in step S1313, the data derived by the Metalign software is analyzed by SIMCA-P software to determine the difference in composition between the preparations and the raw material, and/or the difference in composition between the preparations.

In one embodiment, step S1313 includes:

(1) And (3) performing Principal Component Analysis (PCA) on the detection data of each processed product, drawing a score chart of partial principal components (preferably the first two principal components of the processed product), judging the overall data difference according to the point discreteness of the score chart, and performing subsequent steps if the overall data difference is small.

(2) Selection of PLS-DA model and OPLS-DA model.

Specifically, the method comprises the following steps: respectively establishing a PLS-DA model and an OPLS-DA model, performing data transformation on the detection data of each processed product by adopting parN, and judging which model can embody the predictability of variables to classification to the greatest extent so as to determine the model to be selected.

In one embodiment, an OPLS-DA model is selected, which can reflect the predication of the variable pair classification to the maximum extent, and a score map drawn after being processed by the OPLS-DA model can more intuitively and quickly indicate the difference of the component quality.

(3) Processing the detection data of each processed product by adopting a selection model, selecting two and/or three main components, drawing a score map, and judging the component difference degree between the processed products according to the score map.

Understandably, two principal components are selected in the step (3) to obtain a two-dimensional graph, and three principal components are selected to obtain a three-dimensional graph, so that whether differences exist among the components can be improved more intuitively and rapidly.

(4) And performing difference comparison on the detection data of each processed product and each raw product, performing data transformation by using parN, performing difference analysis by using a selected model, drawing an S-plot graph (S-plot graph), calculating a Variable importance Value (VIP), and judging the changed components of each processed product relative to the raw product.

Understandably, the S-plot plotted in step (4) can reflect the degree of contribution of mass-to-charge ratio-retention time (m/z-Rt) data to the difference, where the data distributed in the lower left corner and the upper right corner are the data that contribute most to the difference.

In the step (4), the Variable importance Value (VIP) can quantitatively reflect the contribution rate of mass-to-charge ratio-retention time (m/z-Rt) data to the classification Variable, and generally greater than 1 is considered to be related to classification, i.e. is considered to be greatly related to processing difference.

(5) And analyzing commonly changed components of each processed product relative to the raw product by adopting a Wien diagram, and drawing a chromatographic distribution schematic diagram according to the mass-to-charge ratio and retention time of the commonly changed components to determine a target data range of local data analysis.

In the step (5), a Wien diagram can be adopted, and online Wien diagram drawing software (http:// bioinfogp. Cnb. Cs. Es/tools/venny/index. Html) can be adopted.

S132: and analyzing local data difference of the detection data of each processed product and each raw product.

In one embodiment, the local data difference analysis is performed using Metalign software in combination with SIMCA-P software.

Note that the names of the software of the present invention are merely examples, and should not be construed as limiting the present invention, and it should be understood that software having the same or similar functions to the software of the present invention may be used as long as it can perform the intended operation of the present invention.

The local data difference analysis method in step S132 is basically the same as the whole data analysis method, and specifically, as described above, the two methods are different in that the analyzed data ranges are different, for example: the local data difference analysis comprises the following steps:

(1) Performing principal component analysis on detection data of a target data range of each processed product, drawing a score chart of partial principal components, judging local data difference of the target data range according to the dispersion of points in the score chart, and performing subsequent steps if the local data difference is smaller;

(2) Respectively establishing a PLS-DA model and an OPLS-DA model, performing data transformation on detection data of a target data range of each processed product by adopting parN, and judging which model can embody the predictability of variable pairs for classification to the greatest extent so as to determine the model to be selected;

(3) Processing detection data of a target data range of each processed product by adopting a selection model, selecting two and/or three main components, drawing a score map, and judging the component difference degree between the processed products according to the score map;

(4) Carrying out difference comparison on detection data of target data ranges of the various artillery products and the raw products, carrying out data transformation by parN, selecting a model for carrying out difference analysis, drawing an S-plot graph (the S-plot difference analysis can increase the synchronous display of mass-to-charge ratio and retention time in the S-plot graph), calculating a variable importance value, and judging the components of the various artillery products, which are changed relative to the raw products;

(5) And analyzing the commonly changed components of the processed products relative to the raw products by adopting a Weinn diagram, and drawing a chromatographic distribution schematic diagram according to the mass-to-charge ratio and the retention time of the commonly changed components to confirm the quality related information of the processed products.

It is understood that the quality-related information in step (5) can be determined according to specific detection targets, and is not particularly limited herein, and should be understood as falling within the scope of the present invention. For example, the detection target is to determine toxic components, and the above method can determine that the toxic components act on a certain part of substances in the chromatographic profile (the components are reduced together) after being attenuated by different processing methods, so that the mass-to-charge ratio and the retention time range of the toxic components can be determined, and the quality standard can be established.

S133: and verifying and analyzing the integral data difference of each processed product and each raw product by using XCMS software.

It is understood that the operation method of XCMS software can be used in step S133 to compare the data with those of steps S131 and S132, so as to verify the accuracy of the method.

In one embodiment, in step S133, the key parameter is linear alignment, BW =5; FWH =5; minfac =0.1.

s210: obtaining a standard processed product of euphorbia Chinese herbal medicines, and preparing a detection sample;

s220: detecting the detection sample by adopting a chromatography-mass spectrometry combined method to obtain the detection data of the standard processed product;

s230: analyzing the detection data by adopting a chemometrics method, and establishing a discrimination model;

s240: obtaining a processed product to be detected, and preparing a detection sample;

s250: detecting a detection sample of the processed product to be detected by adopting a chromatography-mass spectrometry combined method to obtain detection data of the processed product to be detected;

s260: and analyzing the detection data by adopting a chemometrics method, and judging whether the quality of the processed product meets the requirement or not according to the discrimination model.

The steps S210 to S260 are substantially the same as steps S110 to S130, and are not described herein again. The method is characterized in that firstly, a standard preparation is determined, the standard preparation is used as a reference, a discriminant model is determined as a standard (for example, which mass-to-charge ratio and retention time component need to be reduced, the reduction range and the like), and whether the preparation to be tested is qualified or not is judged.

The present invention will be described below with reference to specific examples.

Example 1

Detection conditions and detection sample preparation

The method comprises the following steps: halo C8 (3 mm. Times.100mm, 2.7 μm); mobile phase: acetonitrile is used as a mobile phase A,0.1% formic acid aqueous solution is used as a mobile phase B, and the mobile phase gradient program is as follows: 0/1/6/7/13/15/17/25min;35/54/54/70/92/92/100/100 acetonitrile%; after 25min, changing to 100% methanol for 6min; then the balance is changed into the initial mobile phase balance for 4min; the total operation time is 35min; electrospray ionization source-mass spectrometry (QQQ-ESI-MS) detector detection, positive ion mode; spray pressure 40psi; the drying airflow rate is 7L/min; the drying gas temperature was 300 ℃.

(2) Sample source:

SP-commercially available raw euphorbia kansui: 10 batches, the information is as follows.

CP-commercial Vinegar Euphorbiae Gansui: 10 batches, the information is as follows.

ZP-homemade vinegar euphorbia kansui: processing according to kansui root item carried in Chinese pharmacopoeia (2010 version). Taking 50g of raw euphorbia kansui, adding 15g of brewed table vinegar, stirring uniformly, fully adsorbing and soaking overnight; the next day, heating the porcelain evaporation pan to about 250 deg.C with a sealed electric furnace, adding soaked radix kansui, parching for 20min until the surface is yellow to brown yellow, cooling, and pulverizing. 10 batches of commercially available raw euphorbia kansui are adopted to prepare 10 batches of vinegar euphorbia kansui with the number of ZP 01-ZP 10.

GP-self-made radix kansui with liquorice: according to the method under the item of kansui root carried in the handbook of processing decoction pieces of traditional Chinese medicine in Guangdong province (1977 edition). Taking 50g of euphorbia kansui, adding liquorice soup (each 100g of euphorbia kansui is sliced and decocted into thick soup with 20g of liquorice), uniformly stirring, soaking overnight, uniformly stirring, and soaking overnight until the liquorice soup is basically absorbed completely; placing in stainless steel steamer the next day, steaming for 4 hr until it is transparent, cooling with fire, evaporating to dryness (50 deg.C) under reduced pressure, and pulverizing. 10 batches of commercially available raw euphorbia kansui were used to prepare 10 batches of vinegar euphorbia kansui, numbered GP 01-GP 10.

DP-cooking radix kansui with self-made bean curd: according to the method for processing kansui root under the item carried by the processing Specification for Chinese medicinal decoction pieces in Shanghai City (2008 edition). Adding 50g of radix kansui, bleaching with water for 5 days, changing water for 2 times every day until no dry core exists, cleaning, taking out, putting into a stainless steel pot, adding water and bean curd, decocting (water is higher than the powder), decocting for 1 hr until no white core exists, taking out, removing bean curd, evaporating to dryness under reduced pressure (50 deg.C), and pulverizing. 10 batches of bean curd boiled euphorbia kansui are prepared by adopting 10 batches of commercial raw euphorbia kansui, and the number is DP 01-DP 10.

(3) Preparing a test sample: weighing 0.50g of kansui root powder (sieved by a sieve IV), precisely weighing, placing into a conical flask with a plug, adding 25ml of ethyl acetate, sealing the plug, carrying out ultrasonic treatment (power 140W and frequency 42 kHz) for 30 minutes, carrying out reduced pressure filtration, and washing residues; mixing the filtrates; evaporating the filtrate to dryness, dissolving the residue in methanol, transferring to 5ml measuring flask, adding methanol to the scale, and shaking.

(4) Respectively comparing an ion trap mass spectrum detector with a triple quadrupole detector, an electrospray ionization source (ESI) with an atmospheric pressure chemical ionization source (APCI), and a positive/negative ion detection mode to obtain the mass spectrum detection conditions. Under the above detection method, the response of the kansui global contour map is better.

Example 2

Metalign is combined with SIMCA-P to analyze the difference of the whole data before and after processing

Quality control sample stability analysis

In order to monitor the difference between the mass spectrum and the sample separation and response of the chromatographic column, a mixed reference substance is introduced as a quality control sample, and the quality control sample is analyzed once every 10 times of sample introduction. The repeatability results of the quality control samples in the sample collection process show that the retention time reproducibility and the peak height deviation of chromatographic peaks are good, and the quality of all sample data meets the requirement of difference analysis.

Secondly, metalign software data processing and exporting

Data was exported using Metalign software. The main parameter is set to peak slope factor =1; peak threshold factor =2; peak threshold =5000; average peak width at half height =10; minimum number of peaks in the chromatographic peak set =10.

Quality of SIMCA-P analytical data (outlier detection)

The 6 quality control data and 50 batches of sample data are subjected to Principal Component Analysis (PCA), score plots (score plot) of the first two principal components are drawn, and the distribution of each group of data is observed (see the left figure of figure 1). Where the data is not subject to any processing. The left graph of fig. 1 shows that the quality control sample (control) is concentrated in a narrow area, which indicates that the data deviation of the quality control sample is small, the quality control sample is not significantly affected in the whole analysis time, and also indicates that the operation such as peak alignment, baseline elimination and the like is appropriate and the quality control sample is not affected. Indicating that all the obtained sample data are comparable. The quality control samples are removed, and a principal component analysis score chart (shown in a right chart of figure 1) is drawn only for 50 batches of sample data (five processed products, 10 samples each), wherein the score chart of the right chart of figure 1 shows that five groups of samples have larger discreteness. However, all samples were within the cut-off value, indicating that the overall data was less different and that further analysis was possible.

Fourth PLS-DA/OPLS-DA model selection (Overall data analysis)

For the modeling of liquid phase-mass spectrum data, PLS-DA and OPLS-DA are often selected. For PLS-DA and OPLS-DA, the higher R2Y (reflecting the degree of model interpretation of the variables) and Q2 (reflecting the degree of model fitting) are better, but they cannot differ by more than 0.3.

TABLE 1 model selection in Overall data Difference analysis-comparison of the first two principal Components R2Y and Q2 values

The data in Table 1 show that the prediction of the classification of the variables can be embodied to the greatest extent by using parN for data transformation and using OPLS-DA for model building. Therefore, after the data were processed with OPLS-DA, the scores of the first two and the first three principal components were plotted (see fig. 2).

The left diagram of fig. 2 indicates that different processed products have respective characteristics, even though the commercial vinegar processed product (2 in fig. 2) and the self-made vinegar processed product (3 in fig. 2) have larger differences. Meanwhile, the method also prompts that a certain difference still exists between the self-made processed products and the commercially available processed products. In addition, the tofu product (5 in FIG. 2) and the licorice product (4 in FIG. 2) are not separated on the two-dimensional score chart, indicating that the two are most similar; however, the difference between the three main components can be seen after the three main components are introduced and drawn into a three-dimensional score chart (the right diagram of FIG. 2). The graph analysis result prompts that for the difference analysis among the Chinese medicinal groups, the difference between the groups can be more intuitively and quickly prompted by adopting the OPLS-DA score chart.

Step S-plot difference analysis (integral data analysis)

Selecting each processed product and each raw product to perform difference comparison respectively, performing data transformation by using parN, and performing difference analysis between two groups by using OPLS-DA as a model. The first load factor (P1) of the X matrix is plotted on the abscissa and the correlation coefficient (P (crorr) 1) between the X matrix and the principal component T matrix is plotted on the Y coordinate, also called S-plot. The figure qualitatively reflects the degree of contribution of mass-to-charge ratio-retention time (m/z-Rt) data to the difference, with data distributed in the lower left and upper right corners being the data that contributed most to the difference.

And meanwhile, a Variable importance Value (VIP) is calculated, the value quantitatively reflects the contribution rate of mass-to-charge ratio-retention time (m/z-Rt) data to classification variables, and generally more than 1 is considered to be related to classification, namely more relevant to processing difference.

The results of the study are shown in FIG. 3 and Table 2. The black squares in S-plot represent ingredients that were significantly reduced after processing, while the red dots represent ingredients that were significantly increased after processing. It can be seen visually that S-plot of vinegar-roasted products (CP and ZP) is relatively symmetrical, indicating that the difference between the reduced and increased components after processing is not great; most variables were concentrated on the side of the reduced fraction in S-plot of radix kansui (GP) made from licorice and radix kansui (DP) made from tofu, indicating that the processed products are mainly reduced in the processed fraction. Similar results were obtained from table 2.

Table 2 overall data difference analysis results: (iv) different number of peaks rising/falling (VIP > 1.0)

But we are more concerned about what are the co-reduced components of these different preparations which all have reduced toxicity after processing?

Analysis of common changed components of different prepared products before and after processing (integral data analysis)

The components which are changed together after different processed products are processed can be analyzed by adopting a Wien diagram (venny), so that the material basis of the combined action of different processing methods is discovered.

Wien graph adopts network online Wien graph drawing software (http:// bioinfogp. Cnb. Csic. Es/tools/venny/index. Html).

As a result, it was found (FIG. 4) that the number of the components decreased significantly more than the number of the components increased together after processing the different processed products. After processing, the total amount of ingredients is reduced to 76, and the total amount is increased by only 16. In addition, the difference between the commercially available vinegar euphorbia kansui (CP) and the laboratory homemade vinegar euphorbia kansui (ZP) can be found through a Wein chart, the reduction components of the two are only 86, and the reduction components of the two are nearly half different. The reduction of the components of the bean curd processed kansui root (DP) and the licorice processed kansui root (GP) is the maximum, and reaches 152.

The mass-to-charge ratio and retention time of the co-varying ingredients after processing were plotted in a chromatogram (see FIG. 5) according to Wien diagram analysis. From this figure it can be very intuitively seen that the decrease before 11min is mainly between 250 and 340m/z ions, and thereafter the decrease in ions is seen at 550 to 650m/z ions, which have a retention time mainly between 12 and 18.5min. The rising ions after processing are mainly 13-18 min.

In addition, the common compositional data (tables 3 and 4) show that only 6 of the common reduced components had VIP values greater than 2.0, suggesting that different preparations are inconsistent with respect to the heavy component and that ESI is not very sensitive to reductions of 500-700 m/z.

TABLE 3 common elevated composition data sheet (Overall data analysis)

TABLE 4 common data sheet for reduced ingredients (Overall data analysis)

Example 3: in the invention, metalign is combined with SIMCA-P to analyze local data difference before and after processing

The results of the above global data difference analysis found that the major reducing difference species were species with molecular weights below 300, which is due to the higher response of ESI-MS to such species. Due to the greater interest in diterpene and triterpene species with molecular weights above 430 (euphadienol triterpene with molecular weight 426 was not considered). Therefore, for data derived from Metalign, data with mass-to-charge ratio below 430 is removed for local data variance analysis. 430-1000 m/z ions in four groups of data of SP, CP, GP and DP are selected for differential analysis. The vinegar-roasted product prepared by the laboratory is excluded.

First, SIMCA-P analysis of local data quality (abnormal value detection)

The analysis method is the same as the overall data analysis. Since the data are removed from high-response substances below 430m/z, the abnormal data value needs to be observed again. Plot the PCA score map (select par data transform). From fig. 6, it can be seen that no data in the score plot exceeds the critical value, while SP01 and CP05 show larger difference in the DmodX, but the two plots are combined and no culling is performed.

Selection of the PLS-DA/OPLS-DA model (local data analysis)

R2Y and Q2 values from PLS-DA and OPLS-DA were compared to different data normalization methods. The results (see Table 5) show that the highest R2Y values were obtained using the OPLS-DA model after the par data processing was chosen, while the Q2 values were not as great in the par and ParN data processing, considering that the par transform is more favorable for the S-plot analysis. Therefore, the local data model is determined to be modeled by using OPSD-DA after the data is transformed by using par.

TABLE 5 model selection in local data Difference analysis-comparison of R2Y and Q2 values of the first two principal Components

The OPLS-DA two-dimensional score plot (see FIG. 7, left) also shows the maximum separation obtained for each set of data under this model. The tofu-made kansui root and the licorice-made kansui root are overlapped with the overall data analysis result, and further displayed by a three-dimensional score chart (shown in the right part of fig. 7), the two groups can be greatly separated, which indicates that the difference between the groups can be found to the maximum extent under the model. The graph shows that the 430-1000 m/z local data still differed between the four groups and further analysis could obtain the expected results.

Analysis of local data difference of the third plot

S-plot difference analysis method of same overall data. Except that this S-plot difference analysis increased the simultaneous display of mass-to-charge ratio and retention time in the S-plot. Taking fig. 8 as an example: the first plot, using VIP values, shows which points in the S-plot are strongly correlated with differences between the two groups. Where the red squares represent the points most depressed after vinegar processing and the green circles represent the points most significantly raised after vinegar processing. And the second plot shows the point-to-mass-to-charge ratio relationship on the same s-plot. Wherein in the upper right corner of the first plot, blue square spots of predominantly 500-599 m/z are distributed, indicating that the reduction after vinegar is predominantly of components with mass to charge ratios of 500-599; the similarly raised components are mainly the components with the mass-to-charge ratio of 600-699 m/z. The third figure further answers which regions on the chromatogram these components are predominantly distributed over. The upper right and lower left corners are clearly indicated as blue squares, indicating that the components that change are all material after 12min on the chromatogram. Through the three graphs, the user can intuitively answer which components are obviously increased and which components are obviously decreased; the reduced molecular weight range of these components and the range of retention times on the chromatogram.

Through the analysis of three s-plot diagrams of fig. 8-10, for raw euphorbia kansui and vinegar euphorbia kansui (SP/CP), substances with mass-to-charge ratio of 500-599 as the main reducing components and substances with 600-699m/z as the increasing components can be found intuitively, and peak substances appear after 12 min. A similar situation was observed in the comparison between the other two groups. Different processing methods have influence on substances within the range of 500-599 m/z after 12 min. Then further analysis using a wien chart may find a common reduced and increased component.

Table 6 shows that the vinegar-processed products have the same significantly increased and decreased contents after processing, while the licorice-processed kansui root and the bean curd-processed kansui root have significantly more decreased contents after processing than vinegar-processed kansui root. The ingredients of the euphorbia kansui processed by bean curd are reduced the most. These results are consistent with the overall analysis results. Except that the number of peaks was significantly less than the overall data analysis. The increase and decrease in the number of components after vinegar euphorbia kansui processing are comparable, suggesting that the decreased components may be acetylated to higher molecular weight components, which can also be verified from the S-diagram.

TABLE 6 increase/decrease peak number of different processed products in the range of 430-500 m/z (VIP >1.0, VIP >2.0 in parentheses)

Analysis of common change components of different gun products before and after processing by using Wene diagram (local data analysis)

The significant change components after processing obtained by analyzing the local data difference of the three processed products are analyzed by a Wein diagram, and the result shows that the number of the reduced components after processing is obviously more than the number of the increased components (see fig. 11) as the result of the overall analysis. The licorice root and bean curd were reduced the most. After processing, the total amount of the ingredients is reduced to 40, and the total amount is increased to only 11.

From the data sheet (Table 7) of the local data analysis of the common reducing components, it was found that the common reducing components were mainly ions concentrated in the range of 500 to 600m/z and mainly eluted after 12 min. This class of substances is consistent with the acute toxicity site of euphorbia kansui (results not shown).

From the average ion strength figures (fig. 12) of the common reduced ingredients before and after processing, it can be found that the ion strengths of the common reduced ingredients before and after processing are low, no obvious high-content ingredients exist, and the typical ingredients are difficult to find and control.

TABLE 7 common reduced ingredient data Table (430-1000 m/z local data)

(Note: VIP greater than 2.0 is underlined in bold;)

Example 4: XCMS software visual analysis of whole data difference before and after processing

The analysis of the whole data and the local data before and after the preparation of the euphorbia kansui is carried out by adopting Metalign software and combining SIMCA-P software. The overall data analysis finds that the most obvious reduction of the processed product is the material with the mass-to-charge ratio of about 300; after local 430-1000 m/z analysis, substances at 500-600 m/z after 12min of chromatogram were also reduced. And in order to verify the reliability of the result, overall data analysis is performed again by using XCMS software.

And (3) directly analyzing mass spectrum source data by adopting XCMS software. Using radix kansui (SP) as control before processing, commercially available vinegar processed radix kansui (CP), self-made vinegar processed radix kansui (ZP), radix kansui (GP) processed with Glycyrrhrizae radix and bean curd cooked radix kansui (DP) were compared with radix kansui, respectively.

The key parameter is linear alignment, BW =5; FWH =5; minfac =0.1. In the data processing, in order to ensure that the results are consistent, different parameters are adopted for comparison, and the obtained difference graphs are basically consistent. In FIG. 13, the lower red dots are the ones that decrease or disappear after processing, and the upper green dots are the ones that increase or appear after processing; the size of the dots represents the difference after processing, and the bigger the dots, the bigger the content change of the component after processing; the color of the dots represents the statistical p-value of the difference, and the darker the color, the more significant the difference is as the p-value is smaller after processing. The ordinate is m/z, and the abscissa is the retention time of the chromatogram; the middle profile is an overlay of 10 total ion flux maps for each set of samples.

The XCMS software analysis result is completely consistent with the Metalign software analysis result combined with the SIMCA-P software analysis result; wherein the visual graph provided by XCMS online is more visual; the SIMCA-P software provides more autonomy for users, and is more beneficial to the targeted analysis of the users.

All possible combinations of the technical features of the above embodiments may not be described for the sake of brevity, but should be considered as within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A quality detection method of euphorbia Chinese herbal medicine is characterized by comprising the following steps:

2. The quality inspection method according to claim 1, wherein the processed product comprises processed products processed by at least two different methods, and the inspection is performed by the same method.

3. A mass spectrometry method according to claim 2, wherein the chemometric method analysis method comprises the steps of:

4. The quality inspection method according to claim 3, wherein in the step of analyzing the global data difference and the local data difference, metalign software or approximate function software is used in combination with SIMCA-P software or approximate function software.

5. The quality inspection method according to claim 3, wherein the step of analyzing the overall data difference of the inspection data of each of the processed products and the raw product comprises the steps of:

performing principal component analysis on the detection data of each processed product, drawing a score map of partial principal components, judging the overall data difference according to the point discreteness of the score map, and performing subsequent steps if the overall data difference is small;

respectively establishing a PLS-DA model and an OPLS-DA model, performing data transformation on the detection data of each processed product by adopting parN, and judging which model can embody the predictability of variables to classification to the greatest extent so as to determine the model to be selected;

carrying out difference comparison on detection data of each processed product and each raw product, carrying out data transformation by using parN, carrying out difference analysis by using the selected model, drawing an S-plot diagram, calculating a variable importance value, and judging the changed components of each processed product relative to the raw product;

6. The quality inspection method according to claim 5, wherein the step of analyzing the local data difference of the inspection data of each of the processed products and the raw products comprises the steps of:

and analyzing the commonly changed components of the processed products relative to the raw products by using a Weinn diagram, and drawing a chromatographic distribution schematic diagram according to the mass-to-charge ratio and the retention time of the commonly changed components to confirm the mass related information of the processed products.

7. The quality inspection method according to any one of claims 3 to 6, wherein the step of analyzing local data differences of the inspection data of each of the processed goods and the raw goods further comprises the steps of:

and verifying and analyzing the overall data difference of each processed product and the raw product again by adopting XCMS software.

8. The quality detection method according to any one of claims 1 to 6, wherein the Euphorbia Chinese herbal medicine is Euphorbia kansui, and the processed product is one or more of vinegar-processed Euphorbia kansui, glycyrrhiza glabra-processed Euphorbia kansui and tofu-processed Euphorbia kansui.

9. The method of claim 8, wherein the chromatography-mass spectrometry is liquid chromatography-mass spectrometry;

The mass spectrum is an electrospray ionization source-mass spectrum detector.

10. A quality detection method of euphorbia Chinese herbal medicine is characterized by comprising the following steps:

obtaining a processed product to be detected and preparing a detection sample;

detecting the detection sample of the processed product to be detected by adopting the chromatography-mass spectrometry combination method to obtain the detection data of the processed product to be detected;

and analyzing the detection data by adopting a chemometrics method, and judging whether the quality of the processed product to be detected meets the requirement or not according to the discrimination model.