CN116908138A - Four-gas-flow characterization method of plant traditional Chinese medicine - Google Patents
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Abstract
The application discloses a four-gas-flow characterization method of plant traditional Chinese medicines, which comprises the following steps: preparing a traditional Chinese medicine sample to be tested; collecting near infrared spectrum holographic chemical fingerprint of a traditional Chinese medicine sample to be tested; performing spectrum pretreatment correction on the near infrared spectrum holographic chemical fingerprint spectrum; determining black aconite root as traditional Chinese medicine with a heat reference mode by quantifying and representing relative cold-heat index of traditional Chinese medicine; constructing a traditional Chinese medicine four-gas characterization prediction model based on a near infrared spectrum holographic chemical fingerprint of black aconite; calculating the relative cold-heat index of the traditional Chinese medicine based on a traditional Chinese medicine four-gas-flow characterization prediction model; calculating and dividing four-qi grade standard of traditional Chinese medicine based on relative cold and heat indexes of the traditional Chinese medicine; four-qi characterization of unknown traditional Chinese medicine samples is performed based on traditional Chinese medicine four-qi grade standards. The four-qi quantitative characterization method of the plant traditional Chinese medicine adopts the relative four-qi index quantitative characterization method of the traditional Chinese medicine to accurately quantitatively characterize the four-qi of the plant traditional Chinese medicine, and provides scientific basis for guiding the compatibility of the traditional Chinese medicine and clinical medication.
Description
Technical Field
The application belongs to the field of traditional Chinese medicine quality control, and particularly relates to a four-gas-flow characterization method of plant traditional Chinese medicines.
Background
The earliest four qi is recorded in Shen nong Ben Cao Jing-Xue Lu (Shen nong's herbal meridian and sequence record), which means that the medicine has four qi of cold, heat, warm and cool. The four qi of Chinese herbs refers to the four different properties of the Chinese herbs (also called four properties). It reflects the action trend of the medicine on yin and yang and the change of cold and heat, is an important component of the theory of traditional Chinese medicine property, and is one of the main theoretical basis for elucidating the action of the medicine.
How to specifically quantify the cold, cool, warm and hot of four Chinese herbs by different numerical values becomes one of the most important contents of the quantitative study of four qi (nature) of Chinese herbs. Quantification of the drug properties of traditional Chinese medicines is always a difficult and hot problem of basic research of traditional Chinese medicines. The current quantitative research of the traditional Chinese medicine property mainly develops modern quantitative research and exploration of the traditional Chinese medicine property theory by adopting theoretical achievements and technical means in the fields of physics, chemistry, computer technology, mathematical modeling and the like. Different researchers are from physical electromigration, biological thermodynamics, biological photon imaging, biological infrared imaging, chemical macroscopic chemical components, element oxidation potential distribution, element intermediate spectrum distribution, artificial neural networks of computer technology, decision trees and databases; the related research is carried out at various angles such as the research of a prescription drug property expression method and the research of a traditional Chinese medicine drug property theoretical model characterization method, the research of a traditional Chinese medicine compound multidimensional drug property quantification index, and the like, a large number of staged research results are obtained, and precious experience and foundation are provided for further deep research of traditional Chinese medicine property quantification. Cheng Binbin et al put forward the concept of quantification of "four qi index", which was to set warm water at 24℃as a reference scale to be flat and 0, and to feed rats with warm and cold drugs, respectively, and to measure their body temperature. The difference of body temperature is taken as four qi index, the four qi index is taken as index of cold, heat, warm and cool, the index is positive, the property of the medicine belongs to warm type, the property of the medicine belongs to cold and cool type, and the four qi index approaches 0 and is flat.
Zhang Yan et al propose that the quantitative research of the traditional Chinese medicine property should follow the principle of 'from clinic, returning to clinic', and design a quantitative scale of the traditional Chinese medicine property according to the principle, and carry out sectional assignment (flatness is 0, cold direction is "-", heat direction is "+", and each stage is 10 carry) according to the cognition degree experience of cold, heat, warm and cool of the medicine by expert individuals. The traditional Chinese medicine clinical specialist quantitatively evaluates the traditional Chinese medicine property according to own clinical experience, and the subjective error is larger in the unavoidable medicine property intensity judgment.
In summary, the quantitative research of four qi (nature) of traditional Chinese medicine is basically in the exploring stage. Most of the researches are based on the intensity of human assigned medicine property, the fuzzy quantification of the medicine property of the traditional Chinese medicine is carried out, and a related mathematical model is established after the traditional Chinese medicine property is converted into a numerical value. Although the four-gas-flow research is an important step, the method has dominant subjective experience, lacks scientific, objective, reasonable and effective analytical instrument detection means, cannot reflect the real four-gas intensity of the traditional Chinese medicine, cannot correct the whole fuzzy and partial error content of the four-gas expression of the traditional Chinese medicine, and seriously affects the clinical treatment effect of the traditional Chinese medicine.
Disclosure of Invention
In order to solve the technical problems, the application provides a four-gas-flow characterization method of plant traditional Chinese medicines, which aims to solve the problems that the prior art has dominant artificial subjective experience, lacks scientific, objective, reasonable and effective analytical instrument detection means and cannot reflect the real four-gas intensity of the traditional Chinese medicines.
In order to achieve the aim, the application provides a four-gas-flow characterization method of plant traditional Chinese medicines, which comprises the following steps:
preparing a traditional Chinese medicine sample, and collecting a near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample;
correcting and preprocessing the near infrared spectrum holographic chemical fingerprint spectrum to obtain a preprocessed spectrum;
determining traditional Chinese medicine of a heat reference mode by quantitatively representing relative cold and heat indexes of the traditional Chinese medicine;
constructing a traditional Chinese medicine four-gas characterization prediction model based on the pretreatment map and traditional Chinese medicine relative cold-heat index quantification characterization heat reference model traditional Chinese medicine;
and calculating relative cold and heat indexes of the traditional Chinese medicine based on the traditional Chinese medicine four-gas characterization prediction model, and dividing the traditional Chinese medicine four-gas according to the calculated relative cold and heat indexes to obtain the traditional Chinese medicine four-gas characterization.
Preferably, the method for preparing a traditional Chinese medicine sample comprises the following steps:
vacuum drying the Chinese medicine sample at 60 deg.c for 24 hr, crushing in a Chinese medicine crusher, sieving with 100-200 mesh sieve or grinding in a glass mortar, sieving with 100-200 mesh sieve to obtain powder Chinese medicine sample.
Preferably, the method for collecting the near infrared spectrum holographic chemical fingerprint spectrum of the traditional Chinese medicine sample comprises the following steps:
and placing the traditional Chinese medicine sample powder into a near-infrared sample test cup, and performing near-infrared spectrum full-band scanning by adopting an integrating sphere diffuse reflection accessory of a near-infrared spectrometer and taking gold foil as a reference to obtain a near-infrared spectrum holographic chemical fingerprint.
Preferably, the method of correction preprocessing includes:
and performing multi-element scattering correction and Savitzky-Golay smoothing filter treatment on the near infrared spectrum holographic chemical fingerprint.
Preferably, the method for constructing the traditional Chinese medicine four-gas characterization prediction model comprises the following steps:
measuring near infrared spectrum holographic chemical fingerprints of the traditional Chinese medicine in a reference mode by a near infrared spectrometer powder diffuse reflection technology, performing information processing and data mining on the near infrared spectrum holographic chemical fingerprints of the traditional Chinese medicine in the reference mode and a traditional Chinese medicine sample by a machine learning method to obtain fingerprint information, performing correlation analysis on the fingerprint information and the traditional Chinese medicine four-gas to obtain a correlation analysis result, and establishing a quantitative characterization prediction model of the traditional Chinese medicine four-gas according to the correlation analysis result.
Preferably, the machine learning method is an euclidean distance similarity analysis method.
Preferably, the method for calculating the relative cold-heat index of the traditional Chinese medicine comprises the following steps:
and calculating Euclidean distance index of the traditional Chinese medicine sample by using an Euclidean distance similarity analysis method, taking an average value, and calculating the traditional Chinese medicine relative cold and heat index of the traditional Chinese medicine sample based on the average value.
Preferably, the method for dividing four qi of the traditional Chinese medicine based on the relative cold-heat index of the traditional Chinese medicine comprises the following steps:
constructing index thresholds of different medicine flavor grades based on the relative cold-heat indexes of the traditional Chinese medicines; judging the medicine taste grade of the traditional Chinese medicine sample based on the index threshold value, wherein the judging method comprises the following steps: the relative cold-heat index of the traditional Chinese medicine is below-200, which is severe cold; -200-100 cold property, 100-25 cool property, 25-25 flat property; 25< the relative cold-heat index of traditional Chinese medicine is less than or equal to 100 and is warm, 100< the relative cold-heat index of traditional Chinese medicine is less than or equal to 200 and is hot, and the relative cold-heat index of traditional Chinese medicine is more than 200 and is big.
Compared with the prior art, the application has the following advantages and technical effects:
the four-gas quantitative characterization method of the plant traditional Chinese medicine adopts the quantitative characterization method of the relative cold and heat indexes of the traditional Chinese medicine to accurately quantitatively characterize the four-gas of the plant traditional Chinese medicine, reveals the scientific connotation of the four-gas theory of the traditional Chinese medicine, and provides scientific basis for guiding the compatibility of the traditional Chinese medicine and clinical medication.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a four-gas flow characterization method of a plant traditional Chinese medicine in an embodiment of the application;
fig. 2 is a graph of a thermal traditional Chinese medicine common mode constructed by taking near infrared diffuse reflection spectrum of black aconite (Beijing 302 hospital) as a reference in the embodiment of the application;
FIG. 3 is a graph showing Euclidean distance similarity analysis of a traditional Chinese medicine reference substance according to a second embodiment of the present application;
fig. 4 is a graph showing euclidean distance similarity analysis of decoction pieces of a third embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 1
As shown in fig. 1, the application provides a four-gas-flow characterization method of plant traditional Chinese medicines, which acquires holographic near-infrared chemical fingerprint information of a plant traditional Chinese medicine sample by using a near-infrared spectrometer powder diffuse reflection technology under the condition of standard and unified analysis. A machine learning method is used for establishing a traditional Chinese medicine four-gas-flow prediction model, and a scientific quantitative characterization method of relative four-gas indexes of plant traditional Chinese medicines is provided, so that quantitative characterization of relative cold and heat indexes of all traditional Chinese medicines containing plant traditional Chinese medicine samples is realized.
The application is realized by the following technical scheme:
the four-gas-flow characterization method of the plant traditional Chinese medicine sample comprises the following steps:
1. preparing a traditional Chinese medicine sample test sample;
2. collecting and testing conditions of a near infrared spectrum holographic chemical fingerprint of a traditional Chinese medicine sample;
3. selecting a spectrum pretreatment correction method of a near infrared spectrum holographic chemical fingerprint of a traditional Chinese medicine sample;
4. screening traditional Chinese medicines in a reference mode by quantitatively characterizing relative cold and heat indexes of the traditional Chinese medicines;
5. the method for constructing the predictive model by quantitatively characterizing four kinds of qi (sex) of the traditional Chinese medicine;
6. a method for calculating the relative cold-heat index of traditional Chinese medicine;
7. traditional Chinese medicine four-qi division standard based on relative cold-heat index of traditional Chinese medicine;
8. four-qi (sex) quantitative characterization prediction model of traditional Chinese medicine is used for four-qi characterization of unknown traditional Chinese medicine samples.
The preparation of the traditional Chinese medicine sample test article comprises the following steps: the sample is dried in vacuum at 60 ℃ for 24 hours, if the sample amount is large, the sample is crushed by a traditional Chinese medicine crusher and then is sieved by a 100-200 mesh sieve (a small amount of sample is ground and sieved by a glass mortar). And (3) placing the sieved fine powder into a dryer for storage and cooling to room temperature, wherein the traditional Chinese medicine sample powder is used for testing by a near infrared spectrometer. The method directly tests the crushed traditional Chinese medicine decoction pieces without any chemical treatment and without any chemical component loss.
The acquisition test conditions of the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample in the application are as follows: placing sample powder of the sample into a near infrared sample test cup, adopting an integrating sphere diffuse reflection accessory of a near infrared spectrometer, and carrying out near infrared spectrum full-wave band (4000-10000 cm) by taking gold foil as a reference -1 ) Scanning. Resolution ratio: 8cm -1 Number of scans: 32 times. Temperature: 25+ -1deg.C, humidity: 45 plus or minus 2 percent. 10 spectra were collected in parallel for each test sample. The method is used for directly testing the crushed traditional Chinese medicine samples without any chemical treatment and without losing any chemical components. The near infrared spectrometer can collect all chemical components containing H groups such as C-H, O-H, N-H, S-H, and thus obtain a fingerprint actually containing information of all chemical components of the traditional Chinese medicine, namely a holographic chemical fingerprint of the traditional Chinese medicine near infrared spectrum (English: holographic Chemical Fingerprint of Traditional Chinese Medicine using Near-infrared Spectroscopy, abbreviation HCFP of TCM using NIRS).
The spectrum pretreatment correction method of the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample comprises the following steps: the original near infrared spectrum is subjected to multi-component scattering correction (MSC) or Savitzky-Golay smoothing filter treatment, so that the difference caused by spectrum scattering due to physical properties such as the size and refractive index of sample particles is eliminated, and the purposes of solving baseline drift and filtering noise are achieved.
Mode traditional Chinese medicine screening in the application: the decoction pieces of radix Aconiti lateralis (Beijing 302 Hospital) are used as heat reference mode traditional Chinese medicine, and Glycyrrhrizae radix decoction pieces (wild in inner Mongolia) are used as flat reference mode traditional Chinese medicine.
The method for constructing the traditional Chinese medicine four-qi (sex) quantitative characterization prediction model comprises the following steps: under the standard and unified analysis condition, the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine and the sample is measured by using the near infrared spectrometer powder diffuse reflection technology. And performing information processing and data mining on the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine by using a machine learning method, performing correlation research on fingerprint information and four gases of the traditional Chinese medicine, and establishing a quantitative characterization prediction model (The Quantitative Characterization and Prediction Model for the Four Qi of Traditional Chinese Medicine, abbreviated as QCPM-FQ of TCM) of the four gases of the traditional Chinese medicine. The method comprises the steps of processing Near infrared chemical fingerprint information of traditional Chinese medicines through various linear models (partial least square method, multiple linear regression and regularized regression), distance models (nearest neighbor distance classification, euclidean distance, ma distance and the like) and integrated models, comparing the advantages and disadvantages of each model on traditional Chinese medicine four-gas discrimination through cross verification, establishing an optimal traditional Chinese medicine four-gas characterization prediction model construction method to be Euclidean distance (Euclidean distance) similarity analysis modeling, constructing a thermal traditional Chinese medicine common mode by taking black aconite decoction pieces (Beijing 302 hospital) as a thermal traditional Chinese medicine mode, taking Near infrared diffuse reflection spectrum (Near-infrared diffuse reflectance spectroscopy, abbreviated NIRDRS) as a benchmark, carrying out Euclidean distance similarity analysis (observation homogenization processing spectral data) on other traditional Chinese medicine samples and traditional Chinese medicines (inner Mongolian wild) in the mode, and obtaining Euclidean distance index (Euclidean distance index, EDI) EDI (X) of other traditional Chinese medicine samples X and Euclidean distance index EDI (0) of liquorice.
The method for calculating the relative cold and heat index of traditional Chinese medicine (English name: relative index of cold-heat of TCM, english abbreviation: RICH of TCM) comprises the steps of averaging Euclidean distance indexes of each sample obtained by analysis of Euclidean distance similarity of four-gas-displacement characterization prediction models of traditional Chinese medicine, and calculating the relative cold and heat index of traditional Chinese medicine sample X:
wherein:is the Euclidean distance index average value of the traditional Chinese medicine sample; />Is the Euclidean distance index average value of liquorice.
In the application, when RICH (X) is positive, the test sample is warm and hot, and the larger the value is, the hotter the drug property is; when RICH (X) is negative, it indicates that the test sample is cool and cold, and the smaller the value, the colder the property is. The application divides the four-qi grade standard of traditional Chinese medicine according to RICH (X): RICH (X) < -200 is severe cold; -200.ltoreq.RICH (X) < -100 is cold, -100.ltoreq.RICH (X) < -25 is cold, -25.ltoreq.RICH (X) < 25 is flat; 25< RICH (X) is less than or equal to 100, 100< RICH (X) is less than or equal to 200, and RICH (X) is greater than 200.
The four-qi quantitative characterization prediction model of the traditional Chinese medicine in the application characterizes four-qi of unknown traditional Chinese medicine samples: and (3) obtaining the relative cold and heat index RICH (X) of the traditional Chinese medicine of the unknown sample by the same operation as the steps 1 to 6. And finally, determining the attribution of four qi of the traditional Chinese medicine sample according to the step 7.
Example two
The four-gas characterization experiment of the traditional Chinese medicine reference substance is as follows:
the main instrument is as follows:
an Antaris II Fourier transform near infrared spectrometer (Thermo Nicolet Co., U.S.A.);
DZF-6021 vacuum oven (Shanghai-constant technology instruments Co., ltd.).
Data processing software:
chemattern 2017 chemometric software (Chemmind Technologies co.ltd.) for spectral processing and model construction.
Sample information of traditional Chinese medicine reference substance:
all traditional Chinese medicine reference substances are purchased from Chinese food and drug institute and Chinese medicine biological product institute, and specific information is shown in table 1.
TABLE 1
The attribution of four qi in table 1 is the attribution of four qi noted in the "chinese pharmacopoeia".
1. Preparing a traditional Chinese medicine sample test article:
vacuum drying the traditional Chinese medicine reference sample powder in table 1 for 24 hours at 60 ℃, grinding the sample powder by a glass mortar, sieving the ground sample powder by a 100-200-mesh sieve, and placing the sieved fine powder in a dryer for storage and cooling to room temperature for testing by a near infrared spectrometer.
2. Acquisition test conditions of near infrared spectrum holographic chemical fingerprint of traditional Chinese medicine sample:
placing sample powder of the sample into a near infrared sample test cup, adopting an integrating sphere diffuse reflection accessory of a near infrared spectrometer, and carrying out near infrared spectrum full-wave band (4000-10000 cm) by taking gold foil as a reference -1 ) Scanning. Resolution ratio: 8cm -1 Number of scans: 32 times. Temperature: 25+ -1deg.C, humidity: 45 plus or minus 2 percent. 10 spectra (original near infrared diffuse reflectance spectra, NIRDRS) were collected in parallel for each test sample.
3. The spectrum pretreatment correction method of the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample comprises the following steps:
the original near infrared diffuse reflectance spectrum of the sample was processed with chemattern 2017 chemometric software Multivariate Scattering Correction (MSC).
4. Screening mode traditional Chinese medicines:
the decoction pieces of radix Aconiti lateralis (Beijing 302 Hospital) are used as heat reference mode traditional Chinese medicine, and Glycyrrhrizae radix decoction pieces (wild in inner Mongolia) are used as flat reference mode traditional Chinese medicine.
5. Constructing a traditional Chinese medicine four-qi (sex) quantitative characterization prediction model:
using chemattern 2017 chemometric software, carrying out observation homogenization on all spectrum data before modeling, constructing a thermal traditional Chinese medicine sharing mode (shown in fig. 2) by taking a near infrared diffuse reflection spectrum of black aconite (Beijing 302 hospital) as a reference, carrying out Euclidean distance similarity analysis (shown in fig. 3) on other traditional Chinese medicine reference samples and traditional Chinese medicine licorice (inner Mongolian wild) in the plain mode, and obtaining Euclidean distance index EDI (X) of other traditional Chinese medicine samples X and Euclidean distance index EDI (0) of licorice. The euclidean distance similarity analysis results of the traditional Chinese medicine reference substances are shown in table 2.
TABLE 2
The average value of 10 times of measurement of Euclidean distance of a reference substance;
(average of 10 times of Euclidean distance of Glycyrrhrizae radix in model);
the calculation of RICH of the relative cold and heat index of the traditional Chinese medicine is carried out according to the following step 6.
6. Calculating relative cold and heat index RICH (X) of traditional Chinese medicine, namely averaging Euclidean distance indexes of each sample obtained by Euclidean distance similarity analysis of a traditional Chinese medicine four-gas-volume characterization prediction model, and calculating relative cold and heat index of traditional Chinese medicine in traditional Chinese medicine sample X (the result is shown in table 2):
7. when RICH (X) is positive, the test sample is warm and hot, and the higher the value is, the hotter the drug property is; when RICH (X) is negative, it indicates that the test sample is cool and cold, and the smaller the value, the colder the property is. Dividing four-qi grade standard of traditional Chinese medicine according to RICH (X):
RICH (X) < -200 is severe cold; -200.ltoreq.RICH (X) < -100 is cold, -100.ltoreq.RICH (X) < -25 is cold, -25.ltoreq.RICH (X) < 25 is flat; 25< RICH (X) is less than or equal to 100, 100< RICH (X) is less than or equal to 200, and RICH (X) is greater than 200.
8. The four qi assignment of several traditional Chinese medicine reference substances identified according to the four qi division criteria are shown in table 2. Comparing table 1 with table 2 shows that the four qi of the two reference medicinal materials except for senna leaf and selaginella is different from the mark of "chinese pharmacopoeia", and the rest are basically the same.
Example III
The four-gas characterization experiment of the traditional Chinese medicine decoction pieces is as follows:
the main instrument is as follows:
an Antaris II Fourier transform near infrared spectrometer (Thermo Nicolet Co., U.S.A.);
DZF-6021 vacuum oven (Shanghai-constant technology instruments Co., ltd.).
Data processing software:
chempattern 2017 chemometric software (Chemmind Technologies Co.Ltd.)
Sample information of traditional Chinese medicine decoction pieces:
all herbal pieces were purchased from the Kangmei pharmaceutical industry and the specific information is shown in Table 3.
TABLE 3 Table 3
Wherein the four gases are recorded in Chinese pharmacopoeia.
1. Preparing a traditional Chinese medicine sample test article:
vacuum drying the Chinese medicinal decoction pieces at 60 ℃ for 24 hours, pulverizing the Chinese medicinal decoction pieces by a Chinese medicinal pulverizer, sieving the powder by a 100-200-mesh sieve, and placing the sieved fine powder into a dryer for storage and cooling to room temperature, wherein the Chinese medicinal sample powder is used for testing by a near infrared spectrometer.
2. Acquisition test conditions of near infrared spectrum holographic chemical fingerprint of traditional Chinese medicine sample:
placing sample powder of the sample into a near infrared sample test cup, adopting an integrating sphere diffuse reflection accessory of a Fourier transform near infrared spectrometer, and carrying out near infrared spectrum full-wave band (4000-10000 cm) by taking gold foil as a reference -1 ) Scanning. Resolution ofThe rate is as follows: 8cm -1 Number of scans: 32 times. Temperature: 25+ -1deg.C, humidity: 45 plus or minus 2 percent. 10 spectra (original near infrared diffuse reflectance spectra, NIRDRS) were collected in parallel for each test sample.
3. The spectrum pretreatment correction method of the near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample comprises the following steps:
the raw near infrared diffuse reflectance spectra were processed with chemattern 2017 chemometric software Multivariate Scattering Correction (MSC).
4. Screening mode traditional Chinese medicines:
the decoction pieces of radix Aconiti lateralis (Beijing 302 Hospital) are used as heat reference mode traditional Chinese medicine, and Glycyrrhrizae radix decoction pieces (wild in inner Mongolia) are used as flat reference mode traditional Chinese medicine.
5. Constructing a traditional Chinese medicine four-qi (sex) quantitative characterization prediction model:
using chemattern 2017 chemometric software, carrying out observation homogenization on all spectrum data before modeling, constructing a thermal traditional Chinese medicine sharing mode (shown in fig. 2) by taking a near infrared diffuse reflection spectrum of a black aconite root as a reference, carrying out Euclidean distance similarity analysis (shown in fig. 4) on other traditional Chinese medicine decoction piece samples and traditional Chinese medicine licorice (inner Mongolian wild) in a plain mode, and obtaining Euclidean distance index EDI (X) values of other traditional Chinese medicine samples X and Euclidean distance index EDI (0) values of the licorice. The euclidean distance similarity analysis results of the decoction pieces of the traditional Chinese medicine are shown in table 4.
TABLE 4 Table 4
Is the average value of 10 times of measurement of Euclidean distance of the traditional Chinese medicine decoction pieces;
(average of 10 times of Euclidean distance of Glycyrrhrizae radix in model);
the calculation of RICH of the relative cold and heat index of the traditional Chinese medicine is carried out according to the following step 6.
6. The calculation method of the traditional Chinese medicine relative cold and heat index RICH (X) comprises the steps of averaging Euclidean distance indexes of each sample obtained by analysis of Euclidean distance similarity of a traditional Chinese medicine four-gas characterization prediction model, and calculating the traditional Chinese medicine relative cold and heat index of the traditional Chinese medicine sample X (the result is shown in Table 4):
7. when RICH (X) is positive, the test sample is warm and hot, and the higher the value is, the hotter the drug property is; when RICH (X) is negative, it indicates that the test sample is cool and cold, and the smaller the value, the colder the property is. Dividing four-qi grade standard of traditional Chinese medicine according to RICH (X):
RICH (X) < -200 is severe cold; -200.ltoreq.RICH (X) < -100 is cold, -100.ltoreq.RICH (X) < -25 is cold, -25.ltoreq.RICH (X) < 25 is flat; 25< RICH (X) is less than or equal to 100, 100< RICH (X) is less than or equal to 200, and RICH (X) is greater than 200.
8. The four-qi distribution criteria are shown in Table 4. Comparing Table 3 with Table 4, it was found that the four qi of the rest of the herbal pieces, except for cinnamon and epimedium, was substantially identical to the records of the Chinese pharmacopoeia.
The four-gas characterization prediction model of the traditional Chinese medicine in the application characterizes four-gas of unknown traditional Chinese medicine samples: and (3) performing the same operation as in the steps (1) to (6), and obtaining the relative four-qi index RICH (Y) of the traditional Chinese medicine of the unknown sample. And finally, determining the attribution of four qi of the traditional Chinese medicine sample according to the step (7). The application adopts the quantitative characterization method of the relative four-qi index of the traditional Chinese medicine to accurately quantitatively characterize the four-qi of the traditional Chinese medicine, reveals the scientific connotation of the four-qi theory of the traditional Chinese medicine, and provides scientific basis for guiding the compatibility of the traditional Chinese medicine and clinical medication.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (8)
1. The four-gas-flow characterization method of the plant traditional Chinese medicine is characterized by comprising the following steps of:
preparing a traditional Chinese medicine sample, and collecting a near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample;
correcting and preprocessing the near infrared spectrum holographic chemical fingerprint spectrum to obtain a preprocessed spectrum;
determining the relative cold and heat index quantification characterization reference mode traditional Chinese medicine;
constructing a traditional Chinese medicine four-gas characterization prediction model based on the pretreatment map and traditional Chinese medicine relative cold and heat index quantification characterization reference mode traditional Chinese medicine;
and calculating relative cold and heat indexes of the traditional Chinese medicine based on the traditional Chinese medicine four-gas characterization prediction model, and dividing the traditional Chinese medicine four-gas according to the calculated relative cold and heat indexes of the traditional Chinese medicine to obtain the traditional Chinese medicine four-gas characterization.
2. The method for four-gas characterization of plant-based traditional Chinese medicine according to claim 1, wherein the method for preparing the traditional Chinese medicine sample comprises the following steps:
vacuum drying the Chinese medicine sample at 60 deg.c for 24 hr, crushing in a Chinese medicine crusher, sieving with 100-200 mesh sieve or grinding in a glass mortar, sieving with 100-200 mesh sieve to obtain powder Chinese medicine sample.
3. The method for four-gas characterization of plant traditional Chinese medicine according to claim 1, wherein the method for collecting near infrared spectrum holographic chemical fingerprint of the traditional Chinese medicine sample comprises the following steps:
and placing the traditional Chinese medicine sample powder into a near-infrared sample test cup, and performing near-infrared spectrum full-band scanning by adopting an integrating sphere diffuse reflection accessory of a near-infrared spectrometer and taking gold foil as a reference to obtain a near-infrared spectrum holographic chemical fingerprint.
4. The method for characterizing the four-gas flow of a plant-based traditional Chinese medicine according to claim 1, wherein the method for correcting pretreatment comprises the following steps:
and performing multi-element scattering correction and Savitzky-Golay smoothing filter treatment on the near infrared spectrum holographic chemical fingerprint.
5. The method for characterizing the four-gas-flow rate of a plant traditional Chinese medicine according to claim 1, wherein the method for constructing a traditional Chinese medicine four-gas-flow rate characterization prediction model comprises the following steps:
measuring near infrared spectrum holographic chemical fingerprints of the traditional Chinese medicine in a reference mode by a near infrared spectrometer powder diffuse reflection technology, performing information processing and data mining on the near infrared spectrum holographic chemical fingerprints of the traditional Chinese medicine in the reference mode and a traditional Chinese medicine sample by a machine learning method to obtain fingerprint information, performing correlation analysis on the fingerprint information and the traditional Chinese medicine four-gas to obtain a correlation analysis result, and establishing a quantitative characterization prediction model of the traditional Chinese medicine four-gas according to the correlation analysis result.
6. The method for characterizing the four-gas flow of a plant traditional Chinese medicine according to claim 5, wherein,
the machine learning method is an Euclidean distance similarity analysis method.
7. The method for characterizing four-gas-flow rate of a plant-based traditional Chinese medicine according to claim 6, wherein the method for calculating the relative cold-heat index of the traditional Chinese medicine comprises the following steps:
and calculating Euclidean distance index of the traditional Chinese medicine sample by using an Euclidean distance similarity analysis method, taking an average value, and calculating the traditional Chinese medicine relative cold and heat index of the traditional Chinese medicine sample based on the average value.
8. The method for four-gas characterization of plant traditional Chinese medicines according to claim 1, wherein the method for four-gas classification of the traditional Chinese medicines comprises the following steps:
constructing index thresholds of different medicine flavor grades based on the relative cold-heat indexes of the traditional Chinese medicines; judging the medicine taste grade of the traditional Chinese medicine sample based on the index threshold value, wherein the judging method comprises the following steps: the relative cold-heat index of the traditional Chinese medicine is below-200, which is severe cold; -200-100 cold property, 100-25 cool property, 25-25 flat property; 25< the relative cold-heat index of traditional Chinese medicine is less than or equal to 100 and is warm, 100< the relative cold-heat index of traditional Chinese medicine is less than or equal to 200 and is hot, and the relative cold-heat index of traditional Chinese medicine is more than 200 and is big.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101485805A (en) * | 2009-02-27 | 2009-07-22 | 中南民族大学 | Quality control method of near-infrared holographic fingerprint for pills of six ingredients with rehmannia |
| CN102288572A (en) * | 2011-05-09 | 2011-12-21 | 河南中医学院 | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique |
| CN103076300A (en) * | 2012-12-31 | 2013-05-01 | 武汉鑫方生物科技有限公司 | Method for judging and analyzing traditional Chinese medicine resource fingerprint information by specific mode identification model |
| CN109668850A (en) * | 2019-02-28 | 2019-04-23 | 山东中医药大学 | Herbal nature recognition methods and system based on ultraviolet fingerprint |
| CN110838343A (en) * | 2019-11-15 | 2020-02-25 | 山东中医药大学 | Traditional Chinese medicine property identification method and system based on multi-modal fingerprint spectrum |
| CN114911977A (en) * | 2022-04-01 | 2022-08-16 | 王耘 | Traditional Chinese medicine property identification method and system, computer equipment and storage medium |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101485805A (en) * | 2009-02-27 | 2009-07-22 | 中南民族大学 | Quality control method of near-infrared holographic fingerprint for pills of six ingredients with rehmannia |
| CN102288572A (en) * | 2011-05-09 | 2011-12-21 | 河南中医学院 | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique |
| CN103076300A (en) * | 2012-12-31 | 2013-05-01 | 武汉鑫方生物科技有限公司 | Method for judging and analyzing traditional Chinese medicine resource fingerprint information by specific mode identification model |
| CN109668850A (en) * | 2019-02-28 | 2019-04-23 | 山东中医药大学 | Herbal nature recognition methods and system based on ultraviolet fingerprint |
| CN110838343A (en) * | 2019-11-15 | 2020-02-25 | 山东中医药大学 | Traditional Chinese medicine property identification method and system based on multi-modal fingerprint spectrum |
| CN114911977A (en) * | 2022-04-01 | 2022-08-16 | 王耘 | Traditional Chinese medicine property identification method and system, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 魏国辉: "《中药寒热药性智能评价》", 中国中医药出版社, pages: 76 - 79 * |
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