KR100905414B1 - Origin discrimination method of herbal medicine - Google Patents

Abstract

A discrimination method of growing district of herbal medicine is provided to analysis the metabolite mixture without DNA analysis or standard material and discriminate the growing district of the herbal medicine. A method for discriminating the growing district of Scutellaria baicalensis Georgi. Comprises: a step of extracting metabolite from a plurality of groups of Scutellaria baicalensis Georgi. Sample; a step of analyzing the metabolite with 1H NMR(Nuclear magnetic resonance) spectroscopy; a step of converting the analyzed result of 1H NMR; a step of analyzing the converted value with multivariate analysis; a step of detecting the difference among the groups and establishing standard model; a step of discriminating the growing district; and a step of analyzing the metabolite from the Scutellaria baicalensis Georgi. and verifying the growing district.

Description

Oriental medicine herb production method {ORIGIN DISCRIMINATION METHOD OF HERBAL MEDICINE}

The present invention relates to a method for determining the production of herbal medicines, and more particularly, to extract a metabolite from the herbal medicine, and to establish a standard model detected by clustering the significant difference according to the mountain region through statistical processing to accurately determine the production site. It relates to a method of determining the origin of herbal medicines.

80% of the herbal medicines distributed in Korea are imported from China. Since illegally smuggled Chinese herbal medicines are packaged in Korean and distributed in large quantities, there is a fear of serious turmoil in distribution. In addition, the difference in quality and active ingredients of herbal medicines distributed according to the origin or year is very large. Therefore, effective mountain classification of medicinal herbs is essential for the protection of domestic medicinal herb farmers, healthy development of the pharmaceutical industry, and public health.

Globalization of the global trade economy, such as the FTA, requires the development of test methods to check and manage the origin and quality labeling when a number of herbs are imported from foreign countries. In particular, in order to prepare for international harmonization of the criteria for determining the origin of trade goods between countries under the WTO agreement of origin, and to improve the transparency of the domestic market through prevention of bypass imports, scientific herbal medicines' origin determination method is urgently needed.

Traditionally, morphological, cytological, anatomical and physiological methods have been used to classify herbal medicines.

Morphological evaluation is divided into external morphological evaluation by five senses such as shape, size, color, surface characteristics, smell, and taste of herbal medicine, that is, sensory test based on experience and internal morphological evaluation by microscopic differentiation. In many cases, however, not only relying on the subjective perspective of the discriminator, most herbal medicines are not present as crude drugs, but are not easily distinguished because they are processed into powder or cut into slices.

The physicochemical evaluation is a standard test of the Korean Pharmacopoeia or Korean Pharmacopoeia (Herbal Medicine) standard collection, and selectively tests drying loss, ash test, acid insolubility test, X content and essential oil content test according to the characteristics of herbal medicines. Check the components contained by qualitative reaction or thin layer chromatography. In some cases, the content test for the drug or indicator component is tested by absorbance measurement, liquid chromatography, or gas chromatography. However, since herbal medicines exist in many different species and varieties, depending on the plant and origin of origin, it is convinced that it is high accuracy unless it is clear that simply evaluating the active ingredient with relatively high content may exclude other coexistent ingredients. Can not. Therefore, there is a need for a method of measuring most or all components of the herbal medicine or considering the chemical characteristics of the herbal medicine of origin.

On the other hand, recently, when the domestic medicinal herb seeds are grown and imported in China, it is not possible to distinguish them by genetic methods as well as morphological evaluation and physicochemical evaluation. Therefore, there is a need for an integrated metabolic assay for pharmacological agents that are important for the activity of herbal medicines.

Metabolomics is one of the fields of biology that analyzes and analyzes metabolites and metabolic circuits in cells. It does not separate the sample by component and extracts and analyzes all metabolites present in the sample. Therefore, the pretreatment time is very short, and there is an advantage of rapid analysis and whole water analysis compared to the chromatogram.

Therefore, it is urgently needed to establish an objective method that can distinguish the mountain scientifically accurately using metabolic science.

SUMMARY OF THE INVENTION An object of the present invention is to provide a method for determining the herbaceous acid production site that accurately distinguishes the mountain region through NMR spectroscopy and statistical processing using metabolites extracted from the herbal medicine.

In order to achieve the above object, the present invention

S1) extracting a metabolite from a plurality of sample herbal medicines of known origin;

S2) analyzing the extracted metabolites by NMR spectroscopy;

S3) converting the analyzed result into a numerical value capable of statistical processing;

S4) analyzing the converted numerical value by multivariate analysis to obtain data;

S5) detecting the difference between the groups from the data, and establishing a standard model according to the mountain according to the detected difference; And

S6) obtaining the analyzed data by performing the steps S1) to S4) using a sample herbal medicine, and substituting the standard model to determine the place of origin.

It provides a herbal medicine mountain determination method comprising a.

In addition, the present invention

Provided is a golden mountain determination method using the above method.

According to the present invention, it is possible to accurately determine the place of origin by analyzing only the metabolite mixture of the herbal medicine without DNA analysis or standards.

Figure 1 is a flow chart showing a method for determining the herbal medicine producing region according to the present invention.

Referring to Figure 1, to determine the herbal medicine origin

S1) extracting a metabolite from a plurality of sample herbal medicines of known origin;

S2) analyzing the extracted metabolites by NMR spectroscopy;

S3) converting the analyzed result into a numerical value capable of statistical processing;

S4) analyzing the converted numerical value by multivariate analysis to obtain data;

S5) detecting the difference between the groups from the data, and establishing a standard model according to the mountain according to the detected difference; And

S6) obtaining the analyzed data by performing the steps S1) to S4) using a sample herbal medicine, and substituting the standard model to determine the place of origin.

Perform

In this case, the herbal medicines referred to throughout the present specification are used in oriental medicine for the treatment, prevention, and maintenance of health, and mean natural products including plants, animals, minerals, and microorganisms obtained from nature.

Each step will be described in more detail below.

S1) Metabolite Extraction Step

First, in step S1), metabolites are extracted from a plurality of groups of herbal medicines of known origin.

Extract a metabolite mixture comprising the main ingredient and organic compound directly related to the efficacy of the herbal medicine. Using such a metabolite mixture to determine the place of origin, it is possible to distinguish the place of origin even when the Korean seed is planted in China and then imported back.

At this time, the extraction is not particularly limited in the method of the present invention, typically solvent extraction.

The solvent may be any one that can dissolve the metabolite of the herbal medicine. For example, the solvent may include water; Lower alcohols such as methanol, ethanol, propanol and butanol; Acetone; Trifluoroethanol; Tetrahydrofuran; Dichloromethane; And mixed solvents thereof. Preferably 20 to 70% methanol is used.

Specifically, 100 mg of finely pulverized sample herbal medicine is extracted with a mixture of deuterium water and methanol 1: 1. The solvent is then buffered with 100 mM potassium phosphate. Then, 0.025% of trimethylsilane propionic acid (TPS) is added thereto, followed by extraction for about 20 minutes using an ultrasonic cleaning grinder. The supernatant is then centrifuged at 10,000 g for 15 minutes.

Referring to preferred embodiment 1 of the present invention, in order to identify the difference between the domestic and Chinese gold metabolites, it was identified through 2D NMR. As a result, it was confirmed that Korean and Chinese gold are distinguished by citric acid and arginine.

S2) Analysis by NMR Spectroscopy

Subsequently, in step S2), the extracted metabolite mixture is analyzed by NMR spectroscopy.

At this time, due to the nature of the solvent, the signal caused by water is large and overlaps with the annomeric proton signal of sugar contained in the herbal medicine, so weak continous-wave water presaturation is applied to prevent this.

Also, if the 1D NMR spectrum is significantly complex, the 2D-J spectrum can be used to reduce the overlap between the signals.

Specifically, a total of 8000 points of complex data are obtained and 500 MHz NMR data is obtained using a 1 second repetition time. In this case, NMR spectroscopy is preferably ¹ H NMR.

S3) Conversion into numbers that can be processed statistically

Next, in step S3), the NMR analysis result is converted into a numerical value capable of statistical processing.

In order to correct microscopic experimental differences existing between herbal medicines and reduce the number of variables used in statistical analysis, the variables of the NMR data are integrated at regular intervals, and the observations are integrated in units of 0.04 ppm to convert them into numerical values. do.

Specifically, Fourier transform is performed on the NMR data and phases are adjusted to obtain spectra and perform baseline correction again. The strength of the signal in the full spectrum is normalized for the 0.025% TSP signal and the signal strength is converted to an ASCII file. The methanol peak (3.0 ~ 3.7 ppm) and the water peak (4.6 ~ 5.6 ppm) are excluded.

S4) Data analyzed by multivariate analysis

Subsequently, in step S4), the converted numerical value is analyzed by multivariate analysis to obtain data.

In this case, the multivariate analysis method is not particularly limited in the present invention. Typically, Principal Component Analysis (PCA), Partial Least Analysis (PLS) and Discriminant Analysis (DA) (PLS-DA), Orthogonal Partial Least Analysis (OPLS) And discriminant analysis (OPLS-DA). Preferably, orthogonal least squares analysis and discriminant analysis (OPLS-DA) are used.

Specifically, orthogonal least squares analysis and discriminant analysis (OPLS-DA) are performed using a SIMCA-P program to obtain a score plot and an S plot. In this case, OPLS-DA score plots are preferably used as data analyzed by multivariate analysis.

S5) Establish a standard model according to differentiation

Next, in step S5), the difference between the mountainous groups is detected from the data obtained in step S4), and a standard model according to the mountain range is established according to the detected difference.

According to preferred embodiments 1 to 3 of the present invention, in the score plots obtained by performing orthogonal least-squares analysis and discriminant analysis (OPLS-DA), each group is divided according to mountain regions, and the score plot is used as a standard model. .

S6) mountain determination

Finally, the step S1) to S4) is performed using the sample herbal medicine to determine the production site, and the analyzed data is obtained and substituted into the standard model to determine the production site.

Specifically, steps S1) to S4) are performed on a sample for determining a mountain area to obtain points on a score plot according to an orthogonal least-squares analysis and discriminant analysis (OPLS-DA), and substitute these into an established standard model. Determine the place of origin.

According to the method of the present invention, it is possible to accurately determine the origin by extracting the metabolite of the herbal medicine sample and statistically processing it.

Hereinafter, preferred examples are provided to aid in understanding the present invention. However, the following examples are merely provided to more easily understand the present invention, and the contents of the present invention are not limited by the examples.

Example 1: determination of origin of gold

1-1. Metabolite Extract from Domestic and Chinese Gold

The fine root was pulverized and 100 mg of the powder was extracted with a mixture of deuterium water and methanol 1: 1. These solvents were buffered with 10 mM potassium phosphate and 0.025% trimethylsilane propionic acid (TSP) was added. Then, after extracting for about 20 minutes using an ultrasonic cleaning mill, the extract was centrifuged at 10,000 g for 15 minutes to use the supernatant as an NMR sample.

1-2. Analysis by NMR Spectroscopy

A total of 8000 points of complex data were obtained and 500 MHz NMR data were obtained using a 1 second repetition time.

2A to 2N show 1D ¹ H NMR spectra of 14 domestic gold, and FIGS. 3A to 3X show 1D ¹ H NMR spectra of 24 Chinese gold.

2A-2N and 3A-3X, each spectrum shows from -0.1 ppm to 8 ppm. The 0 ppm peak of the spectrum represents the reference TSP. The spectrum is largely divided into aromatics (about 9 ppm to 5 ppm), sugars (5 ppm to 3 ppm), and aliphatic moieties (3 ppm to 0 ppm). The 1D ¹ H NMR spectra of Korean and Chinese gold did not show significant differences except for a slight difference in aliphatic moieties.

1-3. Convert statistics into numbers that can be processed

NMR data were Fourier transformed and phase adjusted to obtain spectra and again baseline correction. The intensity of the signal in the full spectrum was normalized to the 0.025% TSP signal and the intensity of this signal was converted to an ASCII file. The intensity of the signal obtained from NMR was changed numerically and integrated every 0.04 ppm, except for the methanol peak (3.0-3.7 ppm) and the water equivalent peak (4.6-5.6 ppm).

1-4. Analyze with Multivariate Method

1-4-1. Principal Component Analysis (PCA)

Principal component analysis (PCA) was used as a statistical processing method for the overall chemical shift data obtained through the NMR.

Figure 4 shows a PCA score plot of domestic and Chinese gold.

Referring to FIG. 4, the domestic and Chinese products were distinguished to some extent except that two domestic samples were mixed in China as shown in the PCA score plot.

Figure 5 shows the PCA loading plot of domestic and Chinese gold.

Referring to Figure 5, each number shown in the spectrum represents the chemical shift of the peaks shown in the NMR spectrum. Unlike the score plot of FIG. 4, in the loading plot, it is not easy to distinguish the mountain region because the chemical shifts of the domestic and Chinese characteristics are mixed.

1-4-2. Least Squares Analysis and Discriminant Analysis (PLS-DA)

The least squares analysis and the discriminant analysis (PLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

Figure 6 (a) shows a PLS-DA 2D score plot of domestic and Chinese gold, (b) shows a PLS-DA 3D score plot.

Referring to FIG. 6, although the two domestic samples mixed in the PCA analysis were slightly biased toward the Chinese side, the domestic and Chinese samples were clearly distinguished.

Figure 7 shows the PLS-DA loading plot of domestic and Chinese gold.

Referring to FIG. 7, as shown in FIG. 6, the domestic and Chinese samples were separated, and in the case of domestic production, chemical shift values such as 1.89 ppm and 1.93 ppm were distinguished from Chinese gold.

1-4-3. Orthogonal Least Squares Analysis and Discriminant Analysis (OPLS-DA)

Orthogonal least-squares analysis and discriminant analysis (OPLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

Figure 8 shows the OPLS-DA score plot of domestic and Chinese gold.

Referring to FIG. 8, the domestic and Chinese gold were clearly separated into two groups around the Y axis.

Figure 9 (a) is a full chemical shift OPLS-DA S plot of domestic and Chinese gold, (b) 1.89 ppm chemical shift OPLS-DA S plot, (c) 4.58 pm chemical shift OPLS-DA S plot will be.

Referring to FIG. 9, the S plot provides information on which signals are determined by which signals in the OPLS-DA analysis method. Among the components of Korean gold, an aliphatic component that emits 1.7 to 2 ppm of NMR signal was specifically found in Korean gold.

In order to confirm this difference, the difference of ¹ H NMR spectrum was compared.

10 shows representative ¹ H NMR spectra of domestic and Chinese gold.

Referring to FIG. 10, the ¹ H NMR spectrum of domestic gold specifically has a peak around 1.7 to 2.8 ppm, whereas the ¹ H NMR spectrum of Chinese gold does not have a peak unlike domestic.

11 shows marker signal identification results using domestic 2D NMR.

Referring to FIG. 11, it was confirmed that Korean gold and Chinese gold are distinguished by markers citric acid (2.5-2.7 ppm) and arginine (1.7-1.9 ppm, 3.2 ppm).

1-5. Establish a standard model

Figure 12 shows the standard model of gold according to the mountain.

Referring to FIG. 12, the test sample was analyzed by the Leave one-out method through the established standard model, and the lower part was domestically made and the upper part was made in China based on 0.5 of the Y axis. In addition, seven domestic and Chinese gold were randomly selected from the standard model plots and inserted into the standard model as a test sample. As a result, 100% sensitivity and 100% specificity were shown.

1-6. Accuracy measurement

FIG. 13 shows the result of determining the place of origin by performing the above step using five domestic gold samples of which the origin was strictly identified as an additional sample, and then substituting the same into a standard model. As a result, all five were determined to be domestic. That is, as a result of judging the origin of the domestic gold sample through the standard model, it can be seen that the standard model established above has a relatively high accuracy in distinguishing the gold of the domestic and Chinese.

Example 2: determination of origin of peony

2-1. Extraction of Metabolites from Domestic and Chinese Peony

The peony was carried out in the same manner as in Example 1 to extract the metabolite.

2-2. Analysis by NMR Spectroscopy

The metabolites were analyzed by NMR analysis in the same manner as in Example 1.

14A to 14J show 1D NMR spectra of 10 domestic peony, and FIGS. 15A to 15Q show 1D NMR spectra of 17 Chinese peony.

14A-14J and FIGS. 15A-15Q, each spectrum shows up to -0.1 ppm to 8.5 ppm. The spectrum is largely divided into aromatics (about 9 ppm to 5 ppm), sugars (5 ppm to 3 ppm), and aliphatic moieties (3 ppm to 0 ppm). The 1D NMR spectra of domestic and Chinese peony did not show a big difference except for a slight difference in aliphatic moieties.

2-3. Convert to statistical numbers

NMR data were converted into numerical values capable of being statistically processed in the same manner as in Example 1.

2-4. Analyze with Multivariate Method

2-4-1. Principal Component Analysis (PCA)

Principal component analysis (PCA) was used as a statistical method for the overall chemical shift data obtained through the NMR.

Figure 16 shows the PCA score plot of domestic and Chinese peony.

Referring to FIG. 16, domestic peony samples generally exist in a cluster, while Chinese peony samples are spread and some Chinese are mixed with domestic products.

Figure 17 shows the PCA loading plot of domestic and Chinese peony.

Referring to FIG. 17, as in the score plot of FIG. 16, it was not easy to distinguish between domestic and Chinese products, and in the loading plot of FIG. 17, chemical shifts indicating characteristics of domestic and Chinese products were mixed.

2-4-2. Least Squares Analysis and Discriminant Analysis (PLS-DA)

The least squares analysis and the discriminant analysis (PLS-DA) were used as statistical processing methods for the entire chemical shift data obtained through the NMR.

Figure 18 (a) shows a PLS-DA 2D score plot of domestic and Chinese peony, (b) shows a PLS-DA 3D score plot.

Referring to FIG. 18, domestic and Chinese samples, which were not easy to distinguish in PCA analysis, were distinguished to some extent.

19 shows a PLS-DA loading plot of domestic and Chinese peony.

Referring to FIG. 19, as in the PLS-DA score plot of FIG. 18, domestic and Chinese samples are separated to some extent, in the case of domestic and Chinese separations, chemical shift values such as 1.48 ppm and 1.91 ppm are made in China. For, it seems that the chemical shift value such as 4.09 ppm affects.

2-4-3. Orthogonal Least Squares Analysis and Discriminant Analysis (OPLS-DA)

Orthogonal least-squares analysis and discriminant analysis (OPLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

20 shows OPLS-DA score plots of domestic and Chinese peony.

Referring to FIG. 20, the domestic and Chinese peony were clearly separated into two groups around the Y axis.

Figure 21 (a) shows the overall chemical shift OPLS-DA S plot of domestic and Chinese peony, (b) 1.48 ppm chemical shift OPLS-DA S plot, (c) shows 4.09 ppm chemical shift OPLS-DA S plot will be.

As can be seen from (b) and (c) of FIG. 21, although the chemical shift of 1.89 ppm does not characterize the Chinese sample, the chemical shift of 4.09 ppm, which is characteristic of the Chinese sample, is generally higher than that of the domestic samples. intensity).

2-5. Establish a standard model

Figure 22 shows the standard model of peony according to the mountain.

Referring to FIG. 22, the test sample was analyzed by the Leave one-out method through the established standard model. The upper part was determined to be domestic and the lower part was made in China based on 0.5 of the Y-axis. In addition, 12 Chinese peony and Chinese peony were randomly selected from the standard model plot, and the test sample was inserted into the standard model. As a result, two Chinese peony were identified as domestic and showed 83% accuracy.

2-6. Accuracy measurement

Fig. 23 shows the result of substituting the standard model with two domestic peony samples whose origins were strictly identified as an additional sample to determine the place of origin. As a result, both were determined to be domestic. That is, as a result of judging the origin of the domestic peony sample through the standard model, it can be seen that the standard model established above has a relatively high accuracy in distinguishing between the domestic and Chinese peony.

Example 3: Production origin determination

3-1. Extract metabolites from domestic and Chinese production

Generation was performed in the same manner as in Example 1, and the metabolite was extracted.

3-2. Analysis by NMR Spectroscopy

The metabolites were analyzed by NMR analysis in the same manner as in Example 1.

24A to 24J show 1D NMR spectra of 10 domestically generated points, and FIGS. 25A to 25J show 1D NMR spectra of 10 domestically generated points.

24A to 24J and 25A to 25J, each spectrum shows -0.1 ppm to 8 ppm. The spectrum is largely divided into aromatics (about 9 ppm to 5 ppm), sugars (5 ppm to 3 ppm), and aliphatic moieties (3 ppm to 0 ppm). The 1D NMR spectra of domestic and Chinese productions looked very similar except for a slight difference in aliphatic moieties.

3-3. Convert to statistical numbers

NMR data were converted into numerical values capable of being statistically processed in the same manner as in Example 1.

3-4. Analyze with Multivariate Method

3-4-1. Principal Component Analysis (PCA)

Principal component analysis (PCA) was used as a statistical method for the overall chemical shift data obtained through the NMR.

Figure 26 shows the PCA score plot of domestic and Chinese production.

Referring to FIG. 26, domestic and Chinese generated samples were separated based on the Y axis (PC2).

Figure 27 shows the PCA loading plot of domestic and Chinese production.

Referring to FIG. 27, 0.95 ppm, 0.99 ppm, 1.15 ppm, 1.19 ppm, and the like were farthest from PC2. These chemical shifts seem to distinguish between domestic and Chinese products.

3-4-2. Least Squares Analysis and Discriminant Analysis (PLS-DA)

The least squares analysis and the discriminant analysis (PLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

Figure 28 (a) shows a PLS-DA 2D score plot of domestic and Chinese generation, (b) shows a PLS-DA 3D score plot.

Referring to FIG. 28, the PLS-DA analysis is also separated based on the X-axis and the Y-axis, similar to the PCA analysis, and it can be seen that the samples produced in Korea and China are separated on the Y-axis.

29 shows a PLS-DA loading plot of domestic and Chinese production.

Referring to FIG. 29, in the PLS-DA score plot of FIG. 28, as the domestic and Chinese samples were separated based on the Y axis, the chemical shift value of 0.95 ppm in the case of domestic production and the chemical shift value of 1.19 ppm in the case of Chinese production were Characterized in each group, this chemical shift seemed to make a distinction between domestic and Chinese.

3-4-3. Orthogonal Least Squares Analysis and Discriminant Analysis (OPLS-DA)

Orthogonal least-squares analysis and discriminant analysis (OPLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

Figure 30 shows the OPLS-DA score plot of domestic and Chinese production.

Referring to FIG. 30, domestic and Chinese creations were divided into two groups around the Y axis.

Figure 31 (a) shows the overall chemical shift OPLS-DA S plot of domestic and Chinese production, (b) 0.95 ppm chemical shift OPLS-DA S plot, (c) 1.19 ppm chemical shift OPLS-DA S plot will be.

As can be seen from (b) and (c) of FIG. 31, in the chemical shift of 0.95 ppm, 10 domestic samples denoted by K have higher values than the Chinese sample denoted by C, and in 1.19 ppm, Chinese samples are domestically produced. It has a high value compared to the sample.

3-5. Establish a standard model and measure accuracy

32 shows a standard model of creation by mountain region.

Referring to FIG. 32, the test sample was analyzed through the established standard model, and as a result, it was determined that the upper part was domestic and the lower part was Chinese based on 0.5 of the Y axis. In addition, eight domestic and Chinese genera- tions were randomly selected from the standard model plots and inserted into the standard model as a test sample, indicating 100% accuracy.

Example 4 Determination of Origin of Taxi

4-1. Extract metabolites from domestic and Chinese taxis

The metabolites were extracted in the same manner as in Example 1 at all times.

4-2. Analysis by NMR Spectroscopy

The metabolites were analyzed by NMR analysis in the same manner as in Example 1.

33A to 33H show 1D NMR spectra of 10 domestic taxis, and FIGS. 34A to 34Q show 1D NMR spectra of 10 Chinese taxis.

Referring to FIGS. 33A-33H and 34A-34Q, the spectrum is largely divided into aromatics (about 9 ppm to 5 ppm), sugars (5 ppm to 3 ppm), and aliphatic portions (3 ppm to 0 ppm). ¹ D NMR spectrum of domestic and Chinese Alismataceae, and is generally similar, showed to be a domestic sample having a high strength (intensity) and the aromatic per part than in China.

4-3. Convert to statistical numbers

NMR data were converted into numerical values capable of being statistically processed in the same manner as in Example 1.

4-4. Analyze with Multivariate Method

4-4-1. Principal Component Analysis (PCA)

Principal component analysis (PCA) was used as a statistical processing method for the overall chemical shift data obtained through the NMR.

FIG. 35 shows a PCA score plot of domestic and Chinese taxis, and FIG. 36 shows a PCA loading plot of domestic and Chinese generation.

Referring to FIG. 35, domestic and Chinese taxa samples were separated based on the X-axis (PC1), and each sample was separated from the left and right farther from the PC1.

Referring to FIG. 36, the peaks having the chemical shift corresponding to 3.8 ppm on the right side and the chemical shift value near 1 ppm on the upper left side were the farthest from PC1. As such, it seems that the domestic and Chinese products are distinguished by this chemical shift.

4-4-2. Least Squares Analysis and Discriminant Analysis (PLS-DA)

The least squares analysis and the discriminant analysis (PLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

(A) of FIG. 37 shows a PLS-DA 2D score plot of domestic and Chinese taxis, and (b) shows a PLS-DA 3D score plot.

Referring to FIG. 37, the domestic and Chinese taxis are clearly distinguished based on the X axis.

38 shows a PLS-DA loading plot of domestic and Chinese taxis.

Referring to FIG. 38, as in the PLS-DA score plot of FIG. 37, domestic and Chinese samples were separated, the chemical shift value of 3.8 ppm in the case of domestic production, the chemical shift value of 1.19 ppm in the case of Chinese production, etc., were characterized in each group. This chemical shift seemed to make a distinction between domestic and Chinese.

4-4-3. Orthogonal Least Squares Analysis and Discriminant Analysis (OPLS-DA)

Orthogonal least-squares analysis and discriminant analysis (OPLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

39 shows an OPLS-DA score plot of domestic and Chinese taxis.

Referring to FIG. 39, domestic and Chinese taxis are divided into two groups around the Y axis.

Figure 40 (a) is a total chemical shift OPLS-DA S plot of domestic and Chinese taxi, (b) a 3.8 ppm chemical shift OPLS-DA S plot, (c) shows a 1.19 ppm chemical shift OPLS-DA S plot will be.

As can be seen from (b) and (c) of FIG. 40, in the chemical shift of 3.8 ppm, 8 domestic samples denoted K have higher values than the Chinese sample denoted C, and at 1.19 ppm, the Chinese samples are localized. Higher values compared to the sample.

4-5. Establish a standard model and measure accuracy

Fig. 41 shows a standard model of taxis according to mountain regions.

Referring to FIG. 41, the test sample was analyzed by the Leave one-out method through the established standard model. As a result, it was determined that the upper part was domestic and the lower part was Chinese based on 0.5 of the Y axis. In addition, 10 domestic and Chinese taxis were randomly selected from the standard model plots, and the test samples were inserted into the standard model. As a result, 100% accuracy was shown.

Example 5: determination of the origin of the root

5-1. Metabolite Extraction from Domestic and Chinese Roots

Brown roots were carried out in the same manner as in Example 1 to extract metabolites.

5-2. Analysis by NMR Spectroscopy

The metabolites were analyzed by NMR analysis in the same manner as in Example 1.

FIG. 42 shows representative 1D NMR spectra of one of 14 domestic roots and 19 Chinese roots.

42 and 43, the spectrum is largely divided into aromatic (about 9 ppm to 5 ppm), sugar (5 ppm to 3 ppm), and aliphatic moiety (3 ppm to 0 ppm). The 1D NMR spectra of Korean and Chinese roots seemed to be similar.

5-3. Convert to statistical numbers

NMR data were converted into numerical values capable of being statistically processed in the same manner as in Example 1.

5-4. Analyze with Multivariate Method

5-4-1. Principal Component Analysis (PCA)

Principal component analysis (PCA) was used as a statistical processing method for the overall chemical shift data obtained through the NMR.

Figure 44 shows the PCA score plot of domestic and Chinese roots.

Referring to FIG. 44, some Chinese root roots were mixed with domestically and were not separated well.

45 shows a PCA loading plot of domestic and Chinese roots.

Referring to FIG. 45, although some Chinese products are mixed with domestic products, 1.42 ppm, etc., based on PC2, shows characteristics of chemical migration of Chinese products.

5-4-2. Least Squares Analysis and Discriminant Analysis (PLS-DA)

The least squares analysis and the discriminant analysis (PLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

46 (a) shows a PLS-DA 2D score plot of domestic and Chinese roots, and (b) shows a PLS-DA 3D score plot.

Referring to FIG. 46, in the 2D score plot, it appears that the domestic and Chinese roots are mixed, but in the case of 3D, the Chinese and the domestic are well separated.

47 shows a PLS-DA loading plot of domestic and Chinese roots.

Referring to FIG. 47, as in the (b) PLS-DA 3D score plot of FIG. 46, the domestic and Chinese samples were separated, the chemical shift value of 3.8 ppm and the like was 1.42 ppm, respectively, in the case of domestic production. Values, etc., are characteristic in each group, and this chemical shift appeared to make a distinction between domestic and Chinese.

5-4-3. Orthogonal Least Squares Analysis and Discriminant Analysis (OPLS-DA)

Orthogonal least-squares analysis and discriminant analysis (OPLS-DA) were used as statistical processing methods for the overall chemical shift data obtained through the NMR.

Figure 48 shows the OPLS-DA score plot of domestic and Chinese roots.

Referring to Figure 48, it shows that the domestic and Chinese roots are divided into two groups around the Y axis.

Figure 49 (a) is the overall chemical shift OPLS-DA S plot of domestic and Chinese roots, (b) 1.42 ppm chemical shift OPLS-DA S plot, (c) shows the 3.8 ppm chemical shift OPLS-DA S plot will be.

Referring to FIG. 49, (b) shows a plot of 1.42 ppm, which is more characteristic in Chinese rooted sample, and (c) shows a plot of 3.8 ppm, which is characteristic of domestic rooted root sample. Although some samples have different intensities, these chemical shifts can distinguish domestic and Chinese roots.

5-5. Establish a standard model and measure accuracy

Figure 49 shows a standard model of reeds according to the mountain.

Referring to FIG. 49, the test sample was analyzed by the Leave one-out method through the established standard model. As a result, it was determined that the upper part was made in China and the lower part was made in Korea based on 0.5 of the Y axis. In addition, 13 domestic and Chinese roots were randomly selected from the standard model plots and inserted into the standard model as a test sample. As a result, only one Chinese sample was misidentified as domestic and showed 92% accuracy.

1 is a flow chart showing a method for determining the herbal medicine production in accordance with the present invention.

2a to 2n show 1D NMR spectra of 14 domestic gold.

3a to 3x show 1D NMR spectra of 24 Chinese-made gold.

Figure 4 shows a PCA score plot of domestic and Chinese gold.

Figure 5 shows the PCA loading plot of domestic and Chinese gold.

Figure 6 (a) shows a PLS-DA 2D score plot of domestic and Chinese gold and (b) shows a PLS-DA 3D score plot.

Figure 7 shows the PLS-DA loading plot of domestic and Chinese gold.

Figure 8 shows the OPLS-DA score plot of domestic and Chinese gold.

9 (a) shows OPLS-DA S plots of domestic and Chinese gold, and (b) shows PLS-DA 3D score plots.

10 shows representative ¹ H NMR spectra of domestic and Chinese gold.

11 shows marker signal identification results using domestic 2D NMR.

Figure 12 shows the standard model of gold according to the mountain.

Fig. 13 shows the results of the determination of the place of origin by substituting the standard model with five domestic gold samples whose origins were strictly identified as additional samples.

14A to 14J show 1D NMR spectra of 10 domestic peony flowers.

15a to 15q show 1D NMR spectra of Chinese peony peony.

Figure 16 shows the PCA score plot of domestic and Chinese peony.

Figure 17 shows the PCA loading plot of domestic and Chinese peony.

Figure 18 (a) shows a PLS-DA 2D score plot of domestic and Chinese peony and (b) shows a PLS-DA 3D score plot.

19 shows a PLS-DA loading plot of domestic and Chinese peony.

20 shows OPLS-DA score plots of domestic and Chinese peony.

Figure 21 (a) shows the OPLS-DA S plot of the domestic and Chinese peony, (b) shows the PLS-DA 3D score plot.

Figure 22 shows the standard model of peony according to the mountain.

Fig. 23 shows results obtained by substituting five standard Chinese peony samples of which the origin was strictly identified as an additional sample and substituting the standard model.

24A to 24J illustrate 1D NMR spectra of 10 domestically generated points.

25A to 25J illustrate 1D NMR spectra of 10 Chinese-produced spots.

Figure 26 shows the PCA score plot of domestic and Chinese production.

Figure 27 shows the PCA loading plot of domestic and Chinese production.

Figure 28 (a) shows a PLS-DA 2D score plot of domestic and Chinese production, and (b) shows a PLS-DA 3D score plot.

29 shows a PLS-DA loading plot of domestic and Chinese production.

Figure 30 shows the OPLS-DA score plot of domestic and Chinese production.

Figure 31 (a) shows the OPLS-DA S plot of domestic and Chinese generation, (b) shows a PLS-DA 3D score plot.

32 shows a standard model of creation by mountain region.

33A to 33H show 1D NMR spectra of 8 domestic taxis.

34A to 34Q illustrate 1D NMR spectra of 17 Chinese taxis.

35 shows a PCA score plot of domestic and Chinese taxis.

36 shows a PCA loading plot of domestic and Chinese taxis.

(A) of FIG. 37 shows a PLS-DA 2D score plot of domestic and Chinese taxis, and (b) shows a PLS-DA 3D score plot.

38 shows a PLS-DA loading plot of domestic and Chinese taxis.

39 shows an OPLS-DA score plot of domestic and Chinese taxis.

(A) of FIG. 40 shows OPLS-DA S plots of domestic and Chinese taxis, and (b) shows PLS-DA 3D score plots.

Fig. 41 shows a standard model of taxis according to mountain regions.

FIG. 42 shows representative 1D NMR spectra of one of 14 domestic roots and 19 Chinese roots.

Figure 43 shows the PCA score plot of domestic and Chinese roots.

Figure 44 shows the PCA loading plot of domestic and Chinese roots.

Figure 45 (a) shows a PLS-DA 2D score plot of domestic and Chinese roots, (b) shows a PLS-DA 3D score plot.

Figure 46 shows the PLS-DA loading plot of domestic and Chinese roots.

Figure 47 shows the OPLS-DA score plot of domestic and Chinese roots.

(A) of FIG. 48 shows OPLS-DA S plots of domestic and Chinese roots, and (b) shows PLS-DA 3D score plots.

Figure 49 shows a standard model of reeds according to the mountain.

Claims (11)

delete delete delete delete delete S1) extracting a metabolite from a plurality of groups of sample gold with known origin; S2) analyzing the extracted metabolites by ¹ H NMR spectroscopy; S3) converting the ¹ H NMR analysis result into a numerical value capable of statistical processing; S4) analyzing the converted numerical value by multivariate analysis to obtain data; S5) Detecting the difference between the groups from the data, according to the detected difference Establishing a standard model according to the mountains; S6) Day analyzed by performing the steps S1) to S4) using the sample gold Obtaining a site and substituting the standard model to determine a mountain area; And S7) Analyze the metabolites extracted from the sample gold by ¹ H NMR spectroscopy to check the citrate peak of 2.5 to 2.7 ppm and the arginine peak of 1.7 to 1.9 ppm and 3.2 ppm, which are domestic gold markers, to confirm the determined acidity. step Golden mountain determination method comprising a. The method of claim 6, The extraction is golden mountain determination method using 20 to 70% methanol as the extraction solvent. delete The method of claim 6, The multivariate analysis method is orthogonal least square analysis (ORTHOGONAL PARTIAL LEAST SQUARES ANALYSIS: OPLS) and discriminant analysis (DISCRIMINANT ANALYSIS: DA). The method of claim 6, The data obtained by analyzing the multivariate analysis method is a gold mountain determination method. delete

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