CN109297929B - Method for establishing quality grading of salvia miltiorrhiza decoction pieces by utilizing near infrared technology - Google Patents

Method for establishing quality grading of salvia miltiorrhiza decoction pieces by utilizing near infrared technology Download PDF

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CN109297929B
CN109297929B CN201811442253.9A CN201811442253A CN109297929B CN 109297929 B CN109297929 B CN 109297929B CN 201811442253 A CN201811442253 A CN 201811442253A CN 109297929 B CN109297929 B CN 109297929B
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decoction pieces
salvia miltiorrhiza
establishing
miltiorrhiza decoction
spectrum
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CN109297929A (en
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伍庆
周宁
孙宜春
李玮
李崭
庞媛媛
王娇
徐家怡
李佳蔚
张亮
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Guoyaojituan Tongjitang Guizhou Pharmaceutical Co ltd
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
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    • G01N2030/062—Preparation extracting sample from raw material

Abstract

The invention relates to a novel method for establishing salvia miltiorrhiza decoction piece grading by utilizing a near-infrared technology, in particular to a novel method for establishing salvia miltiorrhiza decoction piece grading by combining pharmacodynamic components and pharmacological action of salvia miltiorrhiza decoction pieces and applying a near-infrared spectrum analysis technology on the basis of a traditional quality evaluation method. The method adopted by the invention has the advantages of rapidness, no damage and no pollution to the environment. The near infrared technology is utilized to reveal the correlation of the attributes, the pharmacodynamic components and the modern pharmacology of the traditional Chinese medicine decoction pieces, a simple, convenient and effective method is provided for the classification of the salvia miltiorrhiza decoction pieces, and the quality evaluation of the salvia miltiorrhiza decoction pieces is more scientific and practical.

Description

Method for establishing quality grading of salvia miltiorrhiza decoction pieces by utilizing near infrared technology
Technical Field
The invention relates to a method for grading the quality of traditional Chinese medicine decoction pieces, in particular to a method for establishing the quality grading of salvia miltiorrhiza decoction pieces by utilizing a near infrared technology, belonging to the technical field of traditional Chinese medicine research
Background
The Saviae Miltiorrhizae radix is dried root and rhizome of Salvia miltiorrhiza Bge (Salvia militaria Bge.) belonging to Labiatae, and has effects of removing blood stasis, relieving pain, promoting blood circulation, dredging channels, cooling blood, removing carbuncle, protecting liver, resisting bacteria, and relieving inflammation. The traditional Chinese medicine composition is mainly used for treating coronary heart disease and angina pectoris in clinic, and has a good effect.
The main chemical components of the salvia miltiorrhiza mainly comprise fat-soluble and water-soluble components, most of the fat-soluble components of the salvia miltiorrhiza are conjugated quinone and ketone compounds, the components are orange yellow and orange red, and cryptotanshinone is the main antibacterial component of the salvia miltiorrhiza. The water-soluble components of Saviae Miltiorrhizae radix mainly comprise phenolic acids, including salvianolic acid A, salvianolic acid B, rosmarinic acid, caffeic acid, potassium, rosmarinic acid, methyl rosmarinate, carnosol, salvianolate, danshendiol A, danshendiol B, danshendiol C, and danshenxintong IV. The water soluble salvianolic acids have antioxidant, anticoagulant, antithrombotic, blood lipid regulating and cytoprotective effects.
At present, the research on the quality of the salvia miltiorrhiza decoction pieces mainly relates to authenticity identification, such as the near infrared spectrum identification method of the CN200910069865.2 salvia miltiorrhiza, the near infrared spectrum identification of the salvia miltiorrhiza, the CN 103674996B-a method for identifying salvia miltiorrhiza medicinal materials or derivatives, and the method for establishing the quality grading of the salvia miltiorrhiza decoction pieces by using the near infrared technology is not reported.
The salvia miltiorrhiza is widely applied, the quality of medicinal materials and decoction pieces is the basis of the quality of medicinal preparations, and the quality of the medicinal materials and the decoction pieces in the market is greatly different, so that the quality grade division is very important in identifying the authenticity of the salvia miltiorrhiza decoction pieces; the quality grade of the salvia miltiorrhiza decoction pieces is divided, so that the economic benefit of the salvia miltiorrhiza is improved, the quality of the salvia miltiorrhiza related medicinal preparations is guaranteed, and the establishment of a quality grading method of the decoction pieces is urgent.
At present, the grade division of salvia miltiorrhiza decoction pieces is a traditional grading mode, and is divided into three grades according to the properties (shape, diameter, appearance, texture and the like) of the decoction pieces, however, the appearance and the internal quality of the raw material medicinal materials are obviously changed along with the change of the growth environment of the raw material medicinal materials of the decoction pieces. Thereby causing the appearance change of the decoction pieces and causing difficulty in the application of the traditional decoction piece quality grading method. Therefore, a set of scientific, reasonable, strong-operability and good-practicability grade evaluation standard of salvia miltiorrhiza decoction pieces is needed to be established to objectively judge the quality of the salvia miltiorrhiza decoction pieces.
On the basis of the traditional quality evaluation method, the pharmacodynamic components and the pharmacological action of the salvia miltiorrhiza decoction pieces are combined, and a new method for grading the quality of the salvia miltiorrhiza decoction pieces is established on the basis of the salvianolic acid B and cryptotanshinone which have the same pharmacological action and pharmacodynamic action as the medicinal material salvia miltiorrhiza and on the basis of the near infrared spectrum analysis technology. The method is rapid and nondestructive, and does not cause environmental pollution. Therefore, the near infrared technology is utilized to reveal the correlation of the attributes, the pharmacodynamic ingredients and the modern pharmacology of the traditional Chinese medicine decoction pieces, so that the quality evaluation of the salvia miltiorrhiza decoction pieces has more scientificity and practicability.
The method not only can ensure good curative effect of clinical medication, but also has important significance for reasonable configuration of the salvia miltiorrhiza decoction pieces, realization of high quality and high price, standardization of market and contribution to supervision of related departments.
The near infrared spectrum technology has better identification capability, high analysis speed and high accuracy, and is widely applied to the fields of macromolecular compound content detection, photoacoustic spectroscopy research, Chinese herbal medicine research, food safety, object identification and the like at present. In conclusion, the experiment realizes the rapid, accurate and nondestructive identification of the grade of the salvia miltiorrhiza decoction pieces by a new method based on the classification of the salvia miltiorrhiza decoction pieces by the near infrared spectrum technology.
Disclosure of Invention
The invention aims to overcome the defects that the quality grading of salvia miltiorrhiza decoction pieces is difficult to classify and the quality grading is unscientific and stable due to different growth environments of salvia miltiorrhiza, the quality identification difficulty is high, the subjective factors of operators, the ability experience and the like in the prior art, and provides a method for establishing the quality grading of salvia miltiorrhiza decoction pieces by utilizing a near infrared technology.
The invention aims to provide a quick, accurate and nondestructive quality grading method for salvia miltiorrhiza decoction pieces aiming at the problems.
The method specifically comprises the following steps:
(1) establishing visual quality evaluation of the salvia miltiorrhiza decoction pieces on the basis of the identification of appearance, smell and taste;
(2) establishing a drug effect component system;
(3) introducing a mass constant of the traditional Chinese medicine through the effective components;
(4) collecting a near infrared spectrogram of a salvia miltiorrhiza decoction piece sample with known grade classification, performing spectrogram pretreatment, establishing a principal component-Mahalanobis distance discrimination model, and classifying the quality grade of the salvia miltiorrhiza decoction piece.
The method of the step (1): observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation.
The step (2) of establishing a drug effect component system is as follows: and (2) crushing, sieving and numbering the salvia miltiorrhiza decoction pieces of each grade in the step (1), carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research, and analyzing the obtained data.
The method for introducing the mass constant of the traditional Chinese medicine through the pharmacodynamic ingredients in the step (3) comprises the following steps: the content of salvianolic acid B and tanshinone is determined by HPLC, the quality specification grade of the salvia miltiorrhiza decoction pieces is divided by the traditional Chinese medicine quality constant and the traditional quality evaluation method and the pharmacodynamic ingredients, and the salvia miltiorrhiza decoction piece grading method is established.
The method for dividing the quality grade of the salvia miltiorrhiza decoction pieces in the step (4) specifically comprises the following steps: crushing and sieving salvia miltiorrhiza decoction pieces of known types, collecting a near-infrared spectrogram from obtained sample powder, applying chemometrics software to sequentially perform batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing on the obtained spectrogram, classifying and identifying similar medicinal materials by using a chemical pattern recognition method, and performing Markov distance discrimination analysis on a main component by using a supervised linear classification method.
The classification is identified as: and dividing the sample into a training set and a prediction set, and judging the classification effect by predicting the accuracy of the prediction set.
The acquisition method of the near-infrared spectrogram comprises the following steps: weighing a salvia miltiorrhiza decoction piece sample, crushing, and sieving with a 300-mesh sieve, wherein the obtained sample powder uses an integrating sphere to collect a near-infrared spectrogram, and the parameters of the near-infrared spectrometer are as follows: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8-10 cm < -1 >, the scanning times are 64-67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 20-25 ℃, the relative humidity is 45-50%, each sample is collected for 3 times, and the average spectrum is obtained.
The method specifically comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring contents of salvianolic acid B and tanshinone by HPLC, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining traditional quality evaluation method and effective components with traditional Chinese medicine quality constant;
(4) collecting near infrared spectrogram spectrograms of salvia miltiorrhiza decoction piece samples classified in a known grade for preprocessing, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8-10 cm < -1 >, the scanning times are 64-67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 20-25 ℃, the relative humidity is 45-50%, each sample is collected for 3 times, and the average spectrum is obtained; the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
The mass constant range of the salvia miltiorrhiza decoction pieces is 1.42-13.02, wherein the mass constant range of the first-level decoction pieces is as follows: 6.97-13.02; the mass constant range of the second-level decoction pieces is as follows: 3.22 to 5.93; the range of the mass constant of the third-level decoction pieces is as follows: 1.42 to 2.69.
The quality grading method of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) collecting and classifying samples: collecting multiple samples from different manufacturers by taking Saviae Miltiorrhizae radix decoction pieces of different grades as research objects, pulverizing solid medicinal materials, sieving, and numbering.
(2) Collecting near infrared spectrum data: determining parameters of near infrared spectrum test, and selecting a medicinal material powder sample with a proper mesh number to collect near infrared diffuse reflection spectrum signals.
(3) Spectrum pretreatment: the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software
(4) Establishing a principal component-Mahalanobis distance discrimination model: in the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Preferably, the method for quality grading of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: performing fingerprint spectrum, multi-index component quantitative analysis and active component group content research on the salvia miltiorrhiza decoction pieces of each level in the step one, and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring contents of salvianolic acid B and tanshinone by HPLC, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining traditional quality evaluation method and effective components with traditional Chinese medicine quality constant;
(4) collecting near infrared spectrogram spectrograms of salvia miltiorrhiza decoction piece samples classified in a known grade for preprocessing, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8-10 cm < -1 >, the scanning times are 64-67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 20-25 ℃, the relative humidity is 45-50%, each sample is collected for 3 times, and the average spectrum is obtained; the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
Still more preferably, the quality grading method of salvia miltiorrhiza decoction pieces comprises the following steps:
(1) based on the identification of appearance, smell and taste, the visual quality evaluation is established: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: performing fingerprint spectrum, multi-index component quantitative analysis and active component group content research on the salvia miltiorrhiza decoction pieces of each level in the step one, and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring contents of salvianolic acid B and tanshinone by HPLC, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining traditional quality evaluation method and effective components with traditional Chinese medicine quality constant;
(4) collecting near infrared spectrogram spectrograms of salvia miltiorrhiza decoction piece samples classified in a known grade for preprocessing, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 64-67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, the relative humidity is 45-50%, each sample is collected for 3 times, and the average spectrum is obtained; the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
The invention has the following advantages:
1. on the basis of the traditional quality evaluation method, by combining the pharmacodynamic components and pharmacological actions of the salvia miltiorrhiza decoction pieces, and on the basis of salvianolic acid B and cryptotanshinone which have the same pharmacological action and pharmacodynamic action as the medicinal material salvia miltiorrhiza, a scientific and reasonable method for evaluating the grade of the salvia miltiorrhiza decoction pieces with strong operability and good practicability is established based on a near infrared spectrum analysis technology.
2. The method is rapid and lossless, does not pollute the environment, utilizes the near infrared technology to disclose the correlation of the attributes, the pharmacodynamic components and the modern pharmacology of the traditional Chinese medicinal decoction pieces, ensures that the quality evaluation of the salvia miltiorrhiza decoction pieces has more scientificity and practicability, can ensure the good curative effect of clinical medication, and has important significance for reasonable configuration of the salvia miltiorrhiza decoction pieces, realization of high quality and high price, specification of market and contribution to the supervision of relevant departments.
Description of the drawings:
FIG. 1: sample content HPLC chromatogram: 1. protocatechuic acid; 2. protocatechualdehyde; 3. caffeic acid; 4. rosmarinic acid; 5. salvianolic acid B; 6. dihydrotanshinone I; 7. cryptotanshinone; 8. tanshinone I; 9 tanshinone IIA.
FIG. 2: sample spectrogram
FIG. 3: level distinction drawing of salvia miltiorrhiza decoction pieces, wherein □ represents the first level salvia miltiorrhiza, O
"represents the second level of the Salvia miltiorrhiza, and" Δ "represents the third level of the Salvia miltiorrhiza
The specific implementation mode is as follows:
the present invention will be further described in detail with reference to the following examples for better understanding, but the scope of the present invention as claimed is not limited to the scope shown in the examples.
A novel method for classifying salvia miltiorrhiza decoction pieces is established based on a near infrared technology, firstly, classical quality evaluation of traditional experience is established, and visual and simple classification standards are provided for classification of salvia miltiorrhiza decoction pieces on the basis of establishing traditional experience identification (appearance, smell, taste and the like). Then, qualitative analysis of fingerprint spectrum, quantitative analysis of multi-index components, content of active component groups and the like are carried out to reflect the difference between the decoction pieces in different grades as much as possible. And embodies the pharmacological active components of the salvia miltiorrhiza. The traditional quality evaluation is combined with the pharmacodynamic ingredients and the pharmacological basis by a traditional Chinese medicine quality constant method to construct a new method for grading the salvia miltiorrhiza decoction pieces. And performing spectral scanning collection on salvia miltiorrhiza decoction pieces of different grades by using a near infrared spectrum analysis technology, and finally establishing the classification capability of different mode identification methods on samples.
Example 1
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing intuitive quality evaluation of the salvia miltiorrhiza decoction pieces;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. Therefore, the mass constant is in direct proportion to the size of the decoction pieces and the content of the index components and in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation method based on the content of the component, the higher the grade thereof.
Figure BDA0001884930460000071
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal material mass, m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000072
(n is the number of the study drugs)
After simplification is
Figure BDA0001884930460000073
H is the total thickness of the study medicinal materials, M' is the total mass of index components of the study sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, a sheet coefficient a is introduced into the formula, a is the ratio of the short radius (the width of the decoction pieces) to the long radius (the length of the decoction pieces) of the salvia miltiorrhiza decoction pieces, and then the formula is developed as follows:
Figure BDA0001884930460000074
(r1 is short radius, r2 is long radius) (4) collecting near infrared spectrogram of Saviae Miltiorrhizae radix decoction pieces samples of known grade classification, preprocessing, scanning near infrared spectrum of all Saviae Miltiorrhizae radix decoction pieces with Fourier near infrared spectrometer,
establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 100-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 64 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 20 ℃, the relative humidity is 45 percent, each sample is collected for 3 times, and the average spectrum is obtained;
the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000081
Figure BDA0001884930460000082
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000083
example 2
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing intuitive quality evaluation of the salvia miltiorrhiza decoction pieces;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. Therefore, the mass constant is in direct proportion to the size of the decoction pieces and the content of the index components and in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation method based on the content of the component, the higher the grade thereof.
Figure BDA0001884930460000091
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal material mass, m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000092
(n is a studyNumber of medicinal materials)
After simplification is
Figure BDA0001884930460000093
H is the total thickness of the study medicinal materials, M' is the total mass of index components of the study sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, a sheet coefficient a is introduced into the formula, a is the ratio of the short radius (the width of the decoction pieces) to the long radius (the length of the decoction pieces) of the salvia miltiorrhiza decoction pieces, and then the formula is developed as follows:
Figure BDA0001884930460000094
(r1 is the minor radius and r2 is the major radius)
(4) Collecting near infrared spectrogram spectrums of salvia miltiorrhiza decoction piece samples classified in known grades for preprocessing, scanning the near infrared spectrums of all salvia miltiorrhiza decoction pieces by using a Fourier near infrared spectrometer, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 140-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8-10 cm < -1 >, the scanning times are 65 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 22 ℃, the relative humidity is 47%, each sample is collected for 3 times, and the average spectrum is obtained.
The obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000101
Figure BDA0001884930460000102
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000103
example 3
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. Therefore, the mass constant is in direct proportion to the size of the decoction pieces and the content of the index components and in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation method based on the content of the component, the higher the grade thereof.
Figure BDA0001884930460000111
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal material mass, m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000112
(n is the number of the study drugs)
After simplification is
Figure BDA0001884930460000113
H is the total thickness of the medicinal materials, M' is the total mass of index components of the research sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, the sheet coefficient a is introduced into the formula, and a is the short radius of the salvia miltiorrhiza decoction pieces (the width of the decoction pieces)Degree) to major radius (length of the tablet), the formula evolves:
Figure BDA0001884930460000114
(r1 is the minor radius and r2 is the major radius)
(4) Collecting near infrared spectrogram spectrums of salvia miltiorrhiza decoction piece samples classified in known grades for preprocessing, scanning the near infrared spectrums of all salvia miltiorrhiza decoction pieces by using a Fourier near infrared spectrometer, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 150-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 64 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 23 ℃, the relative humidity is 48 percent, each sample is collected for 3 times, and the average spectrum is obtained;
the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000121
Figure BDA0001884930460000122
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000123
example 4
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) based on the identification of appearance, smell and taste, the visual quality evaluation is established: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. It can be seen that the mass constant is related to the beverageThe size of the tablet is in direct proportion to the content of index components, and the tablet is in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation method based on the content of the component, the higher the grade thereof.
Figure BDA0001884930460000131
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal material mass, m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000132
(n is the number of the study drugs)
After simplification is
Figure BDA0001884930460000133
H is the total thickness of the study medicinal materials, M' is the total mass of index components of the study sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, a sheet coefficient a is introduced into the formula, a is the ratio of the short radius (the width of the decoction pieces) to the long radius (the length of the decoction pieces) of the salvia miltiorrhiza decoction pieces, and then the formula is developed as follows:
Figure BDA0001884930460000134
(r1 is the minor radius and r2 is the major radius)
(4) Collecting near infrared spectrogram spectrums of salvia miltiorrhiza decoction piece samples classified in known grades for preprocessing, scanning the near infrared spectrums of all salvia miltiorrhiza decoction pieces by using a Fourier near infrared spectrometer, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 200-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8, the scanning times are 67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, the relative humidity is 48 percent, each sample is collected for 3 times, and the average spectrum is obtained;
the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000141
Figure BDA0001884930460000142
In the formula of1And mu2Mean matrix of global G1 and G2;s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000143
example 5
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. Therefore, the mass constant is in direct proportion to the size of the decoction pieces and the content of the index components and in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation based on the content of the componentsIn the method, the higher the content of the component, the higher the grade thereof.
Figure BDA0001884930460000151
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal material mass, m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000152
(n is the number of the study drugs)
After simplification is
Figure BDA0001884930460000153
H is the total thickness of the study medicinal materials, M' is the total mass of index components of the study sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, a sheet coefficient a is introduced into the formula, a is the ratio of the short radius (the width of the decoction pieces) to the long radius (the length of the decoction pieces) of the salvia miltiorrhiza decoction pieces, and then the formula is developed as follows:
Figure BDA0001884930460000154
(r1 is the minor radius and r2 is the major radius)
(4) Collecting near infrared spectrogram spectrums of salvia miltiorrhiza decoction piece samples classified in known grades for preprocessing, scanning the near infrared spectrums of all salvia miltiorrhiza decoction pieces by using a Fourier near infrared spectrometer, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 270-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 65 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, the relative humidity is 50 percent, each sample is collected for 3 times, and the average spectrum is obtained;
the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000161
Figure BDA0001884930460000162
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000163
example 6
The method for establishing the quality classification of the salvia miltiorrhiza decoction pieces comprises the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving weighting coefficients (10:1) according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
the mass constant (A) of Chinese medicine is defined as the ratio of mass (M) of component in unit Chinese medicine to square of thickness (h), and A is M/h2. In order to simplify the study, the root-stem type unit medicinal materials are regarded as standard cylinders, and a new form can be deduced. Therefore, the mass constant is in direct proportion to the size of the decoction pieces and the content of the index components and in inverse proportion to the thickness of the decoction pieces. Therefore, the larger the tablet shape is, the higher the content of the index component is, and the thinner the tablet thickness is within the predetermined specification range, the larger the mass constant is. The larger the mass constant, the higher its specification. In the traditional character grade evaluation method, the size of the sheet is the most critical evaluation index. Generally, the larger the tablet, the higher its grade. In the evaluation method based on the content of the component, the higher the grade thereof.
Figure BDA0001884930460000164
V is the volume of the medicinal material, V ═ pi r2h (r is radius, h is thickness)
m is unit medicinal materialMass m-PV-rho-pi r2h (ρ is density)
M is the unit medicinal material mass, and M-cm-c rho pi r2h (c is the content of ingredients)
For the mass constant of salvia miltiorrhiza decoction pieces, the common calculation formula of the round decoction pieces is as follows:
Figure BDA0001884930460000171
(n is the number of the study drugs)
After simplification is
Figure BDA0001884930460000172
H is the total thickness of the study medicinal materials, M' is the total mass of index components of the study sample, the salvia miltiorrhiza decoction pieces are round-like or oval decoction pieces, a sheet coefficient a is introduced into the formula, a is the ratio of the short radius (the width of the decoction pieces) to the long radius (the length of the decoction pieces) of the salvia miltiorrhiza decoction pieces, and then the formula is developed as follows:
Figure BDA0001884930460000173
(r1 is the minor radius and r2 is the major radius)
(4) Collecting near infrared spectrogram spectrums of salvia miltiorrhiza decoction piece samples classified in known grades for preprocessing, scanning the near infrared spectrums of all salvia miltiorrhiza decoction pieces by using a Fourier near infrared spectrometer, and establishing a principal component-Mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrum image of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 64 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, the relative humidity is 50 percent, each sample is collected for 3 times, and the average spectrum is obtained;
the obtained spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discrimination analysis is adopted to divide samples into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the grade of the spectrum is predicted by using the established grade evaluation model.
In the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000174
Figure BDA0001884930460000175
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000181
the near infrared spectrum technology has better identification capability, high analysis speed and high accuracy, and is widely applied to the fields of macromolecular compound content detection, photoacoustic spectroscopy research, Chinese herbal medicine research, food safety, object identification and the like at present. In the application field of traditional Chinese medicines, near infrared has successfully realized the identification of the varieties of medicinal materials and the rapid determination of index components in traditional Chinese medicines in different producing areas.
Experimental example 1
1. Apparatus and materials
1.1 instruments
An electronic balance: mettlerlatido (MS105)
High performance liquid chromatograph: agilent 1260;
a chromatographic column: diamonsil5um C18, 250X 4.6mm (8997474);
an american type siemer fly-zel antaris ii fourier near infrared spectrometer;
a SabIR diffuse reflection fiber optic probe accessory;
software: result software (Sammerfei-Shill Co.) for spectral acquisition, TQ
Analyst6.2 software (Sammerfei-Shill) was used for pre-processing of spectra and calculation of algorithms.
1.2 sample sources
Fluidity: acetonitrile (chromatographically pure), 0.1% formic acid
Comparison products: protocatechuic acid, protocatechuic aldehyde, caffeic acid, rosmarinic acid, salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone I and tanshinone IIA.
And (3) testing the sample: collecting multiple samples from different manufacturers by taking salvia miltiorrhiza decoction pieces with different grades as research objects, crushing and sieving solid medicinal materials, and preparing a test solution: the number is: 1-1, 1-2, 1-3 … … 1-8, 2-1, 2-2, 2-3 … … 2-8, 3-1, 3-2, 3-3 … … 3-8, and collecting about lg.
2. Method of producing a composite material
2.1 collecting and classifying Salvia miltiorrhiza decoction pieces
Collecting Saviae Miltiorrhizae radix decoction pieces of different grades, randomly extracting 100 pieces per batch as measurement objects, respectively measuring morphological parameters (including thickness, length, width and mass) of Saviae Miltiorrhizae radix decoction pieces, and determining salvianolic acid B and tanshinone content by HPLC method.
Chromatographic conditions are as follows: a chromatographic column: diamonsil5um C18,250 x 4.6mm (8997474); mobile phase: acetonitrile (B): 0.1% formic acid (A), gradient elution (0-10 min: 10% -20% B, 10-17 min: 20% B, 17-45 min: 20-33% B, 45-90min: 33-100% B), flow rate: 1 ml/min; column temperature: 35 ℃; wavelength: 280 nm.
Preparation of control solutions: appropriate amounts of salvianolic acid B, protocatechuic acid, protocatechuic aldehyde, caffeic acid, rosmarinic acid, salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone I, and tanshinone IIA are precisely weighed, and methanol is added to obtain a mixed reference solution containing 45ug of each reference per 1 ml.
Preparation of a test solution: the number is: the number is: 1-1, 1-2, 1-3 … … 1-8, 2-1, 2-2, 2-3 … … 2-8, 3-1, 3-2, 3-3 … … 3-8 to obtain a test sample, taking about lg, finely weighing, placing into a stopper-shaped bottle, precisely adding 50ml of 50% methanol, weighing the stopper, carrying out ultrasonic treatment at a rate of 500W, carrying out frequency of 40kHz for 30 minutes, cooling, weighing again, supplementing the weight lost by 50% methanol, shaking uniformly, filtering, and continuing filtering to obtain the product. Precisely sucking 10ul of each of the reference substance and the sample solution, injecting into a phase chromatograph, measuring, recording chromatogram, showing the chromatogram of the sample in figure 1, and calculating according to an external standard method, wherein the content results of salvianolic acid B and tanshinone are shown in table 1.
TABLE 1 content results of salvianolic acid B and tanshinone
Figure BDA0001884930460000191
Figure BDA0001884930460000201
The mass constant range of the salvia miltiorrhiza decoction pieces based on the weighted content through the parameters in the table is 1.42-13.02, wherein the mass constant range of the first-level decoction pieces is as follows: 6.97-13.02; the mass constant range of the second-level decoction pieces is as follows: 3.22 to 5.93; the range of the mass constant of the third-level decoction pieces is as follows:
1.42～2.69。
2.2 acquisition of spectra
Adopting an American Saimerfi-Deler Antaris II type Fourier near infrared spectrometer, crushing by a crusher, sieving by a 300-mesh sieve, collecting a near infrared spectrogram of the obtained sample powder by using an integrating sphere, and setting parameters of the near infrared spectrometer: the spectrum acquisition range is 10000-4000cm < -1 >, the resolution is 8cm < -1 >, the scanning times are 64 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, and the relative humidity is 45%. Collecting each sample for 3 times, calculating average spectrum, preheating the spectrometer for more than 1 hr before collecting spectrum, keeping indoor temperature and humidity substantially consistent, loading Saviae Miltiorrhizae radix sample into a rotary cup matched with the instrument to collect spectrum, and collecting spectrum as shown in figure 2.
2.3 pretreatment of the spectra
In the spectrum acquisition process, noise information which influences the model prediction effect, such as high-frequency noise, baseline drift and the like, is usually generated, so that the spectrum needs to be preprocessed before a correction set model is established, and chemometric software TQ analysis software is used for preprocessing, such as derivation smoothing and the like, on all near infrared spectrums of the salvia miltiorrhiza.
2.4, establishing a principal component-Mahalanobis distance discrimination model:
in the modeling process, the average spectrum is calculated, and then a classification model is established by estimating the change of each wave point in the analysis area. In the discriminant analysis of multivariate statistics, the discrimination attribution of a sample point is discriminated by adopting the Mahalanobis distance, the Mahalanobis distance is one of generalized square distances, three parameters of mean value, variance and covariance are effectively considered on the basis of the multivariate normal distribution theory, and the Markov distance is a comprehensive index capable of comprehensively describing the overall multivariate structure.
Assuming that there are two normally distributed populations G1 and G2, x ∈ R is a new sample point, defining the Mahalanobis distance of x to G1 and G2 as d (x, G, G)1) And d (x, G)2)：
Figure BDA0001884930460000211
Figure BDA0001884930460000212
In the formula of1And mu2Mean matrix of global G1 and G2; s1 and S2 are covariance matrices of the overall G1 and G2.
The discrimination rules are as follows:
Figure BDA0001884930460000213
2.5 prediction results of the model:
in order to test the accuracy of the prediction of the built model, 15 models are randomly extracted, the identification capability of the model is externally tested, the sample is processed and then subjected to spectrum collection by near infrared, and finally the spectrum is subjected to grade prediction by the built grade evaluation model, which is shown in figure 3. The results are shown in Table 2.
TABLE 2 grade evaluation results
Figure BDA0001884930460000214
And (4) conclusion: as can be seen from the table, the predicted result of the model is basically consistent with the actual result, and the discrimination rate of the model is calculated to be 93.3%.
Experimental example two
In this example, the same apparatus and method as in the first example were used to establish a model and verify the discrimination of the model, and the composition of the sample is also shown in table 2. The difference between this embodiment and the first embodiment is only that:
1. in this embodiment, when collecting the spectrum, a known type of salvia miltiorrhiza decoction piece sample is weighed, and after being crushed, the sample is sieved by a 300-mesh sieve, and the obtained sample powder uses an integrating sphere to collect a near-infrared spectrogram, and parameters of a near-infrared spectrometer are set as follows: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 9cm < -1 >, the scanning times are 67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 25 ℃, the relative humidity is 50 percent, each sample is collected for 3 times, and the average spectrum is obtained; before spectrum collection, the spectrometer is preheated for more than 1 hour, after the indoor temperature and humidity are kept basically consistent, a salvia miltiorrhiza sample is placed into a rotating cup matched with the spectrometer to collect the spectrum, and finally the spectrum is subjected to grade prediction by using the established grade evaluation model, and the result is shown in table 3.
2. This embodiment establishes an authentication model with 30 as the principal component number.
TABLE 3 grade evaluation results
Figure BDA0001884930460000221
The obtained model was verified by using the verification set, and the authentication rate of the model was 100%.
Experimental example III
The method comprises the following steps: near infrared spectrum identification method of CN200910069865.2 salvia miltiorrhiza bunge
In this embodiment, the same instrument and method as the near infrared spectrum identification method of CN200910069865.2 salvia miltiorrhiza is used to establish a model and verify the identification rate of the model, the sample is processed in the same way as the near infrared spectrum identification method of CN200910069865.2 salvia miltiorrhiza, and the near infrared spectrum is collected by the diffuse reflection optical fiber accessory of the near infrared spectrometer shown in table 2 under the following conditions: the scanning range is 10000-4000cm-1, the scanning times are 32 times, the resolution is 8cm-1, and the results are shown in Table 4:
TABLE 4 grade evaluation results
Figure BDA0001884930460000231
The obtained model was verified by the validation set and found to have an authentication rate of 80%. As can be seen from the table, the method has low classification and identification rate for the quality grade of the salvia miltiorrhiza decoction pieces and poor quality grade judgment effect.
To summarize: the prediction result of the model is basically consistent with the actual result, the quality grade of the salvia miltiorrhiza decoction pieces can be rapidly and accurately judged, the experimental instrument is simple to operate, the sample is not damaged, and the environmental pollution is avoided.
While the invention has been described in detail in the foregoing by way of general description, specific embodiments and experiments, it will be apparent to those skilled in the art that certain changes and modifications may be made therein based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (3)

1. A method for establishing quality grading of salvia miltiorrhiza decoction pieces by utilizing a near infrared technology is characterized by comprising the following steps:
(1) the visual quality evaluation of the salvia miltiorrhiza decoction pieces is established on the basis of the identification of appearance, smell and taste: observing the color and texture of different batches of salvia miltiorrhiza decoction pieces with different specifications, measuring the thickness, width, length and quality form indexes of the salvia miltiorrhiza decoction pieces, carrying out odor and taste identification, and establishing visual quality evaluation;
(2) establishing a pharmacodynamic ingredient system: carrying out fingerprint spectrum, multi-index component quantitative analysis and active component group content research on each level of salvia miltiorrhiza decoction pieces in the step (1), and analyzing the obtained data;
(3) introducing the mass constant of the traditional Chinese medicine through the effective components: measuring the contents of salvianolic acid B and tanshinone by HPLC, respectively giving a weighting coefficient of 10:1 according to the contents of salvianolic acid B and tanshinone, and calculating to obtain final Chinese medicinal mass constant, and establishing a classification method of Saviae Miltiorrhizae radix decoction pieces by combining the Chinese medicinal mass constant with traditional quality evaluation method and effective components;
(4) collecting a near infrared spectrogram of a salvia miltiorrhiza decoction piece sample with known grade classification, preprocessing the spectrogram, and establishing a principal component-mahalanobis distance discrimination model: weighing a known type of salvia miltiorrhiza decoction piece sample, crushing, sieving with a 300-mesh sieve, collecting a near-infrared spectrogram of the obtained sample powder by using an integrating sphere, and setting parameters of a near-infrared spectrometer: the spectrum collection range is 10000-4000cm < -1 >, the resolution is 8-10 cm < -1 >, the scanning times are 64-67 times, the data format is Absorbance, the optimized energy gain is 2x, the temperature is 20-25 ℃, the relative humidity is 45-50%, each sample is collected for 3 times, and the average spectrum is obtained; the obtained near-infrared spectrogram is subjected to batch normalization processing, batch baseline correction processing and abnormal sample point elimination processing in sequence by using chemometrics software TQ Analyst, similar medicinal materials are classified and identified by using a chemical pattern recognition method, a Mahalanobis distance discriminant analysis is adopted to divide a sample into a training set and a prediction set, the classification effect is judged by the prediction accuracy of the prediction set, and the spectrum is subjected to grade prediction by using an established grade evaluation model.
2. The method for quality classification as claimed in claim 1, wherein the mass constant of Danshen decoction pieces is in the range of 1.42-13.02.
3. The quality grading method according to claim 2, wherein the quality constants of the salvia miltiorrhiza decoction pieces are within the following range: 6.97-13.02; the mass constant range of the second-level decoction pieces is as follows: 3.22 to 5.93; the range of the mass constant of the third-level decoction pieces is as follows: 1.42 to 2.69.
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