CN109297929A - A method of salvia piece quality grading is established using near infrared technology - Google Patents
A method of salvia piece quality grading is established using near infrared technology Download PDFInfo
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- CN109297929A CN109297929A CN201811442253.9A CN201811442253A CN109297929A CN 109297929 A CN109297929 A CN 109297929A CN 201811442253 A CN201811442253 A CN 201811442253A CN 109297929 A CN109297929 A CN 109297929A
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- quality
- salvia piece
- sample
- salvia
- piece
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- 238000000034 method Methods 0.000 title claims abstract description 123
- 238000005516 engineering process Methods 0.000 title claims abstract description 15
- 240000007164 Salvia officinalis Species 0.000 title description 141
- 239000003814 drug Substances 0.000 claims abstract description 119
- 238000011156 evaluation Methods 0.000 claims abstract description 36
- 241001072909 Salvia Species 0.000 claims abstract description 31
- 238000013441 quality evaluation Methods 0.000 claims abstract description 28
- 238000001228 spectrum Methods 0.000 claims description 65
- 239000000463 material Substances 0.000 claims description 58
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- 238000002329 infrared spectrum Methods 0.000 claims description 34
- 238000004458 analytical method Methods 0.000 claims description 33
- 239000002253 acid Substances 0.000 claims description 28
- 239000004615 ingredient Substances 0.000 claims description 25
- 229930183118 Tanshinone Natural products 0.000 claims description 23
- 238000012545 processing Methods 0.000 claims description 23
- 239000000843 powder Substances 0.000 claims description 17
- 238000002835 absorbance Methods 0.000 claims description 14
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- 238000011160 research Methods 0.000 description 10
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- IBGBGRVKPALMCQ-UHFFFAOYSA-N 3,4-dihydroxybenzaldehyde Chemical compound OC1=CC=C(C=O)C=C1O IBGBGRVKPALMCQ-UHFFFAOYSA-N 0.000 description 6
- YQUVCSBJEUQKSH-UHFFFAOYSA-N 3,4-dihydroxybenzoic acid Chemical compound OC(=O)C1=CC=C(O)C(O)=C1 YQUVCSBJEUQKSH-UHFFFAOYSA-N 0.000 description 6
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- 238000004364 calculation method Methods 0.000 description 6
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- 238000012360 testing method Methods 0.000 description 6
- QAIPRVGONGVQAS-DUXPYHPUSA-N trans-caffeic acid Chemical compound OC(=O)\C=C\C1=CC=C(O)C(O)=C1 QAIPRVGONGVQAS-DUXPYHPUSA-N 0.000 description 6
- GVKKJJOMQCNPGB-JTQLQIEISA-N Cryptotanshinone Chemical compound O=C1C(=O)C2=C3CCCC(C)(C)C3=CC=C2C2=C1[C@@H](C)CO2 GVKKJJOMQCNPGB-JTQLQIEISA-N 0.000 description 5
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- TVHVQJFBWRLYOD-UHFFFAOYSA-N rosmarinic acid Natural products OC(=O)C(Cc1ccc(O)c(O)c1)OC(=Cc2ccc(O)c(O)c2)C=O TVHVQJFBWRLYOD-UHFFFAOYSA-N 0.000 description 5
- BDAGIHXWWSANSR-UHFFFAOYSA-N methanoic acid Natural products OC=O BDAGIHXWWSANSR-UHFFFAOYSA-N 0.000 description 4
- 150000007965 phenolic acids Chemical class 0.000 description 4
- ACEAELOMUCBPJP-UHFFFAOYSA-N (E)-3,4,5-trihydroxycinnamic acid Natural products OC(=O)C=CC1=CC(O)=C(O)C(O)=C1 ACEAELOMUCBPJP-UHFFFAOYSA-N 0.000 description 3
- 229940074360 caffeic acid Drugs 0.000 description 3
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- QAIPRVGONGVQAS-UHFFFAOYSA-N cis-caffeic acid Natural products OC(=O)C=CC1=CC=C(O)C(O)=C1 QAIPRVGONGVQAS-UHFFFAOYSA-N 0.000 description 3
- 229960003371 protocatechualdehyde Drugs 0.000 description 3
- HARGZZNYNSYSGJ-UHFFFAOYSA-N 1,2 dihydrotanshinquinone Natural products C1=CC2=C(C)C=CC=C2C(C(=O)C2=O)=C1C1=C2C(C)CO1 HARGZZNYNSYSGJ-UHFFFAOYSA-N 0.000 description 2
- AZQWKYJCGOJGHM-UHFFFAOYSA-N 1,4-benzoquinone Chemical compound O=C1C=CC(=O)C=C1 AZQWKYJCGOJGHM-UHFFFAOYSA-N 0.000 description 2
- OSWFIVFLDKOXQC-UHFFFAOYSA-N 4-(3-methoxyphenyl)aniline Chemical compound COC1=CC=CC(C=2C=CC(N)=CC=2)=C1 OSWFIVFLDKOXQC-UHFFFAOYSA-N 0.000 description 2
- HARGZZNYNSYSGJ-JTQLQIEISA-N Dihydrotanshinone I Chemical compound C1=CC2=C(C)C=CC=C2C(C(=O)C2=O)=C1C1=C2[C@@H](C)CO1 HARGZZNYNSYSGJ-JTQLQIEISA-N 0.000 description 2
- LYCAIKOWRPUZTN-UHFFFAOYSA-N Ethylene glycol Chemical compound OCCO LYCAIKOWRPUZTN-UHFFFAOYSA-N 0.000 description 2
- YMGFTDKNIWPMGF-QHCPKHFHSA-N Salvianolic acid A Natural products OC(=O)[C@H](Cc1ccc(O)c(O)c1)OC(=O)C=Cc2ccc(O)c(O)c2C=Cc3ccc(O)c(O)c3 YMGFTDKNIWPMGF-QHCPKHFHSA-N 0.000 description 2
- HYXITZLLTYIPOF-UHFFFAOYSA-N Tanshinone II Natural products O=C1C(=O)C2=C3CCCC(C)(C)C3=CC=C2C2=C1C(C)=CO2 HYXITZLLTYIPOF-UHFFFAOYSA-N 0.000 description 2
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- 235000013305 food Nutrition 0.000 description 2
- 235000019253 formic acid Nutrition 0.000 description 2
- 235000008216 herbs Nutrition 0.000 description 2
- -1 ketone compounds Chemical class 0.000 description 2
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- 238000004867 photoacoustic spectroscopy Methods 0.000 description 2
- 238000010298 pulverizing process Methods 0.000 description 2
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- AZEZEAABTDXEHR-UHFFFAOYSA-M sodium;1,6,6-trimethyl-10,11-dioxo-8,9-dihydro-7h-naphtho[1,2-g][1]benzofuran-2-sulfonate Chemical compound [Na+].C12=CC=C(C(CCC3)(C)C)C3=C2C(=O)C(=O)C2=C1OC(S([O-])(=O)=O)=C2C AZEZEAABTDXEHR-UHFFFAOYSA-M 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
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- PAFLSMZLRSPALU-MRVPVSSYSA-N (2R)-3-(3,4-dihydroxyphenyl)lactic acid Chemical compound OC(=O)[C@H](O)CC1=CC=C(O)C(O)=C1 PAFLSMZLRSPALU-MRVPVSSYSA-N 0.000 description 1
- RTKDBIDPGKCZJS-KPZWWZAWSA-N (6r,7s)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7h-naphtho[1,2-g][1]benzofuran-10,11-dione Chemical compound C1=CC([C@@]([C@@H](O)CC2)(C)O)=C2C(C(=O)C2=O)=C1C1=C2C(C)=CO1 RTKDBIDPGKCZJS-KPZWWZAWSA-N 0.000 description 1
- TZHMQUSSYNZSTA-GOSISDBHSA-N (6s)-6-hydroxy-6-(hydroxymethyl)-1-methyl-8,9-dihydro-7h-naphtho[1,2-g][1]benzofuran-10,11-dione Chemical compound O=C1C(=O)C2=C3CCC[C@@](O)(CO)C3=CC=C2C2=C1C(C)=CO2 TZHMQUSSYNZSTA-GOSISDBHSA-N 0.000 description 1
- YMGFTDKNIWPMGF-AGYDPFETSA-N 3-(3,4-dihydroxyphenyl)-2-[(e)-3-[2-[(e)-2-(3,4-dihydroxyphenyl)ethenyl]-3,4-dihydroxyphenyl]prop-2-enoyl]oxypropanoic acid Chemical compound C=1C=C(O)C(O)=C(\C=C\C=2C=C(O)C(O)=CC=2)C=1/C=C/C(=O)OC(C(=O)O)CC1=CC=C(O)C(O)=C1 YMGFTDKNIWPMGF-AGYDPFETSA-N 0.000 description 1
- 206010002383 Angina Pectoris Diseases 0.000 description 1
- VDYMGLBSIBHGCP-UHFFFAOYSA-N C1=CC2=C(C)C=CC=C2C(C(=O)O2)=C1C1=C2C(C)=CO1 Chemical compound C1=CC2=C(C)C=CC=C2C(C(=O)O2)=C1C1=C2C(C)=CO1 VDYMGLBSIBHGCP-UHFFFAOYSA-N 0.000 description 1
- 206010007247 Carbuncle Diseases 0.000 description 1
- QRYRORQUOLYVBU-VBKZILBWSA-N Carnosic acid Natural products CC([C@@H]1CC2)(C)CCC[C@]1(C(O)=O)C1=C2C=C(C(C)C)C(O)=C1O QRYRORQUOLYVBU-VBKZILBWSA-N 0.000 description 1
- XUSYGBPHQBWGAD-PJSUUKDQSA-N Carnosol Chemical compound CC([C@@H]1C2)(C)CCC[C@@]11C(=O)O[C@@H]2C2=C1C(O)=C(O)C(C(C)C)=C2 XUSYGBPHQBWGAD-PJSUUKDQSA-N 0.000 description 1
- MMFRMKXYTWBMOM-UHFFFAOYSA-N Carnosol Natural products CCc1cc2C3CC4C(C)(C)CCCC4(C(=O)O3)c2c(O)c1O MMFRMKXYTWBMOM-UHFFFAOYSA-N 0.000 description 1
- PAFLSMZLRSPALU-QMMMGPOBSA-N Danshensu Natural products OC(=O)[C@@H](O)CC1=CC=C(O)C(O)=C1 PAFLSMZLRSPALU-QMMMGPOBSA-N 0.000 description 1
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000004566 IR spectroscopy Methods 0.000 description 1
- 241000207923 Lamiaceae Species 0.000 description 1
- 229930194927 Miltionone Natural products 0.000 description 1
- LGZUUBFLEYOEEX-UHFFFAOYSA-N Neo-tanshinlactone Natural products C12=CC=C3C(C)=CC=CC3=C1OC(=O)C1=C2OC=C1C LGZUUBFLEYOEEX-UHFFFAOYSA-N 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- PAFLSMZLRSPALU-UHFFFAOYSA-N Salvianic acid A Natural products OC(=O)C(O)CC1=CC=C(O)C(O)=C1 PAFLSMZLRSPALU-UHFFFAOYSA-N 0.000 description 1
- YMGFTDKNIWPMGF-UCPJVGPRSA-N Salvianolic acid A Chemical compound C([C@H](C(=O)O)OC(=O)\C=C\C=1C(=C(O)C(O)=CC=1)\C=C\C=1C=C(O)C(O)=CC=1)C1=CC=C(O)C(O)=C1 YMGFTDKNIWPMGF-UCPJVGPRSA-N 0.000 description 1
- TZHMQUSSYNZSTA-UHFFFAOYSA-N Tanshindiol A Natural products O=C1C(=O)C2=C3CCCC(O)(CO)C3=CC=C2C2=C1C(C)=CO2 TZHMQUSSYNZSTA-UHFFFAOYSA-N 0.000 description 1
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- 239000003963 antioxidant agent Substances 0.000 description 1
- 230000003078 antioxidant effect Effects 0.000 description 1
- 235000006708 antioxidants Nutrition 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
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- VPLLTGLLUHLIHA-UHFFFAOYSA-N dicyclohexyl(phenyl)phosphane Chemical compound C1CCCCC1P(C=1C=CC=CC=1)C1CCCCC1 VPLLTGLLUHLIHA-UHFFFAOYSA-N 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
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- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- WGCNASOHLSPBMP-UHFFFAOYSA-N hydroxyacetaldehyde Natural products OCC=O WGCNASOHLSPBMP-UHFFFAOYSA-N 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 150000002576 ketones Chemical class 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 210000004185 liver Anatomy 0.000 description 1
- XHALVRQBZGZHFE-KRWDZBQOSA-N methyl rosmarinate Natural products COC(=O)[C@H](Cc1ccc(O)c(O)c1)OC(=O)C=Cc2ccc(O)c(O)c2 XHALVRQBZGZHFE-KRWDZBQOSA-N 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000008188 pellet Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- XHALVRQBZGZHFE-UHFFFAOYSA-N rosmarinic acid methyl ester Natural products C=1C=C(O)C(O)=CC=1C=CC(=O)OC(C(=O)OC)CC1=CC=C(O)C(O)=C1 XHALVRQBZGZHFE-UHFFFAOYSA-N 0.000 description 1
- 229930183842 salvianolic acid Natural products 0.000 description 1
- PRYXPGFZVGZNBL-ADLFWFRXSA-N salviol Chemical compound CC(C)c1cc2CC[C@H]3C(C)(C)C[C@H](O)C[C@]3(C)c2cc1O PRYXPGFZVGZNBL-ADLFWFRXSA-N 0.000 description 1
- AJSGWTLZEUBGFV-UHFFFAOYSA-N salviol Natural products CC(C)c1cc2CCC3C(CC(O)CC3(C)C)c2cc1O AJSGWTLZEUBGFV-UHFFFAOYSA-N 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- RTKDBIDPGKCZJS-UHFFFAOYSA-N tanshindiol B Natural products C1=CC(C(C(O)CC2)(C)O)=C2C(C(=O)C2=O)=C1C1=C2C(C)=CO1 RTKDBIDPGKCZJS-UHFFFAOYSA-N 0.000 description 1
- 229930192675 tanshinlactone Natural products 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
- G01N2021/3572—Preparation of samples, e.g. salt matrices
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N2030/022—Column chromatography characterised by the kind of separation mechanism
- G01N2030/027—Liquid chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
- G01N2030/062—Preparation extracting sample from raw material
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- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Medicines Containing Plant Substances (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present invention relates to a kind of new methods that salvia piece classification is established using near infrared technology, specifically on the basis of traditional quality evaluation method, composition and effectiveness and pharmacological action in conjunction with salvia piece establish the new method of salvia piece classification using near-infrared spectral analysis technology.The method that the present invention uses have the advantages that it is quick, lossless, not can cause environmental pollution.Using near infrared technology, the attribute of traditional Chinese medicine medicine materical crude slice, the correlation of effective component, modern pharmacology are disclosed, provides simple and effective method for the classification of salvia piece, so that the evaluation of salvia piece prepared slice quality is with more scientific and practicability.
Description
Technical field
The present invention relates to the methods of the quality grading of the prepared slices of Chinese crude drugs, and in particular to a kind of to establish Radix Salviae Miltiorrhizae using near infrared technology
The method of prepared slice quality classification, belongs to Chinese medicine study technical field
Background technique
Radix Salviae Miltiorrhizae is the dry root and rhizome of Lamiaceae plant Radix Salviae Miltiorrhizae (Salvia miltiorrhiza Bge.), has dissolving stasis to stop
Bitterly, the effect of blood circulation, cool blood to disappear carbuncle, liver protection, antimicrobial antiphlogistic.Clinically it is mainly used for treating coronary heart disease and angina pectoris, effect
Fruit is preferable.
Radix Salviae Miltiorrhizae main chemical compositions are broadly divided into fat-soluble and water soluble ingredient, and the liposoluble constituent of Radix Salviae Miltiorrhizae is mostly conjugation
Quinone, ketone compounds, in orange-yellow and orange red, Cryptotanshinone is the main component of Radix Salviae Miltiorrhizae antibacterial.The water soluble ingredient of Radix Salviae Miltiorrhizae
Predominantly phenolic acid, including: Salvianolic acid A is also known as danshensu, salviol acid A, root of red-rooted salvia phenolic acid B, Rosmarinic acid, coffee
Coffee acid, potassium, Rosmarinic acid, methyl rosmarinate, carnosol, tanshinlactone, Tanshindiol A, Tanshindiol B, Radix Salviae Miltiorrhizae glycol
C, miltionone IV.Water-soluble salvianolic acid has anti-oxidant, anticoagulation, antithrombus formation, Adjust-blood lipid and cytoprotection
Effect.
Currently, salvia piece matter quantifier elimination, reported to identify primarily with regard to the true and false, such as
The near infrared spectrum of the near infrared spectrum identification method Radix Salviae Miltiorrhizae of CN200910069865.2 Radix Salviae Miltiorrhizae identifies, CN103674996B- mono-
The method that kind identifies red rooted salvia or spin-off, the method for establishing salvia piece quality grading using near infrared technology, there is not yet
Report.
Radix Salviae Miltiorrhizae is very widely used, the quality of medicinal material and medicine materical crude slice, is the basis of medicine preparation quality, and in the market medicinal material and
The quality of medicine materical crude slice is mostly very different, and therefore, in addition to the true and false of identification salvia piece, the division of credit rating is also extremely important；
The credit rating for dividing salvia piece, is not only the economic benefit for improving Radix Salviae Miltiorrhizae, is also the matter of medicine preparation related to Radix Salviae Miltiorrhizae
Amount provides safeguard, and it is extremely urgent for establishing the quality grading method of medicine materical crude slice.
Currently, it is so additional fractionation mode that salvia piece grade classification, which is appointed, be according to its character (shape, diameter, appearance,
Quality etc.) it is divided into three grades, however due to the change of medicine materical crude slice raw medicinal material growing environment, make raw medicinal material appearance and inherent product
Matter also significantly changes therewith.So as to cause the variation of medicine materical crude slice appearance, caused to traditional prepared slice quality stage division application
Difficulty.Therefore, it is necessary to establish a set of science, the salvia piece grade evaluation criterion that reasonable, strong operability, practicability are good is come
Objectively judge salvia piece quality good or not.
On the basis of traditional quality evaluation method, composition and effectiveness and pharmacological action in conjunction with salvia piece, by with medicine
Based on material Radix Salviae Miltiorrhizae is with identical pharmacological action and having the tanshin polyphenolic acid B of drug action, Cryptotanshinone, it is based near infrared spectrum
Analytical technology establishes the new method of a salvia piece quality grading.This method is quick, it is lossless, dirt will not be caused to environment
Dye.Therefore near infrared technology is utilized, the attribute of traditional Chinese medicine medicine materical crude slice, the correlation of effective component, modern pharmacology are disclosed, so that
The evaluation of salvia piece prepared slice quality is with more scientific and practicability.
This can not only ensure the good efficacy of clinical application, and high-quality for the reasonable disposition of salvia piece, realization
Favorable rates, standard market and conducive to relevant department supervision be of great significance.
Near-infrared spectrum technique has preferable distinguishing ability, and analysis speed is fast, and accuracy rate is high, is answered extensively at present
For fields such as high-molecular compound content detection, optoacoustic spectroscopy research, Study of Medicinal Herbs, food safety and object identifications.It is comprehensive
On, this experiment passes through the new method that near-infrared spectrum technique is classified based on salvia piece, realizes quick to salvia piece grade, quasi-
Really, lossless identification.
Summary of the invention
The purpose of the present invention is overcome in the prior art since Salvia miltiorrhiza Growth environment difference leads to appearance and interior quality not
It determines, quality discrimination difficulty is big, the subjective factor of operator, and ability experience etc. leads to salvia piece quality grade compartmentalization difficulty
Greatly, there is the defects of not scientific stable, a kind of method for establishing salvia piece quality grading using near infrared technology is provided.
It is a kind of quick the purpose of the present invention is in view of the above problems, providing, accurately, lossless salvia piece quality
The method of classification.
This method specifically includes the following steps:
(1) by establishing the evaluation of salvia piece perceptual quality based on appearance, smell, taste identification；
(2) effective component system is established；
(3) by effective component, traditional Chinese medicine quality constant is introduced；
(4) atlas of near infrared spectra of the salvia piece sample of acquisition known grades classification, carries out spectrogram pretreatment, establishes master
Ingredient-mahalanobis distance discrimination model divides salvia piece credit rating.
The method of the step (1): the color of the salvia piece of observation different batches different size, texture, and measure pellet
Join thickness, width, length, the quality morphological index of medicine materical crude slice, carries out smell, taste identification, establish perceptual quality evaluation.
The step (2) establishes effective component system are as follows: and rank salvia piece each in step (1) is crushed, is sieved, number,
Finger-print, Multi-component quantitation, active ingredient group determination study are carried out, the data obtained is analyzed.
The step (3) is by effective component, the method for introducing traditional Chinese medicine quality constant are as follows: wherein red using the measurement of HPLC method
The content of phenolic acid B and tanshinone is divided red by traditional Chinese medicine quality constant in conjunction with traditional quality evaluation method and effective component
Join prepared slice quality specification grade, establishes salvia piece stage division.
The method that the step (4) divides salvia piece credit rating specifically: crush the salvia piece of Known Species
Sieving, gained sample powder acquire atlas of near infrared spectra, and gained spectrogram Applied Chemometrics software is successively returned by batch
One change processing, batch baseline correction processing and the processing of rejecting abnormalities sample point, using Chemical Pattern Recognition method to similar medicine
Material carries out taxonomic history, using the linear classification method principal component i.e. discriminant analysis for having supervision.
The taxonomic history are as follows: sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set
To judge.
The acquisition method of the atlas of near infrared spectra are as follows: weigh salvia piece sample, smash it through 300 meshes, gained sample
Product powder acquires near-infrared spectrogram using integrating sphere, near infrared spectrometer parameter setting: spectra collection range 10000~
4000cm-1, resolution ratio are 8~10cm-1, scanning times 64~67 times, data format Absorbance, optimize energy gain
For 2x, 20~25 DEG C of temperature, relative humidity 45%~50%, each sample is acquired 3 times, seeks averaged spectrum.
The method specifically includes the following steps:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content, salvia piece stage division is established in conjunction with traditional quality evaluation method and effective component by traditional Chinese medicine quality constant；
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, establishes principal component-
Mahalanobis distance discrimination model: weighing the salvia piece sample of Known Species, smashes it through 300 meshes, and gained sample powder uses
Integrating sphere acquires near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio
For 8~10cm-1, scanning times 64~67 times, data format Absorbance, optimization energy gain is 2x, temperature 20~25
DEG C, relative humidity 45%~50%, each sample acquires 3 times, seeks averaged spectrum；Gained spectrogram Applied Chemometrics is soft
Part TQ Analyst successively passes through batch normalized, batch baseline correction processing and the processing of rejecting abnormalities sample point, makes
Taxonomic history is carried out to similar medicinal material with Chemical Pattern Recognition method, using discriminant analysis, sample is divided into training
Collection and forecast set, classifying quality predict that accuracy judges by forecast set, with the level evaluation model established to spectrum into
Row grade forecast.
Salvia piece quality constant range is 1.42~13.02, wherein level-one prepared slice quality constant range are as follows: 6.97~
13.02；Second level prepared slice quality constant range are as follows: 3.22~5.93；Three-level prepared slice quality constant range are as follows: 1.42~2.69.
The method of the salvia piece quality grading the following steps are included:
(1) it sample collection and classification: using different grades of salvia piece as research object, collects from the more of different manufacturers
Solid pulverizing medicinal materials are sieved by a sample, number.
(2) near infrared spectrum data acquires: determining the parameter of near infrared spectrum test, chooses the medicinal powder of suitable mesh number
Sample acquisition near-infrared diffuses spectrum signal.
(3) Pretreated spectra: gained spectrogram Applied Chemometrics software successively passes through batch normalized, batch
Baseline correction processing and the processing of rejecting abnormalities sample point
(4) it establishes principal component-mahalanobis distance discrimination model: in modeling process, first calculating averaged spectrum, then pass through estimation
Disaggregated model is established in the variation of each wave point in analyzed area.In the discriminant analysis of multivariate statistics, using mahalanobis distance, come
Differentiate the differentiation ownership of sample point, mahalanobis distance is one kind of General Quadratic distance, based on multivariate normal distributions theory, is had
Three mean value, variance, covariance parameters are considered to effect, are the overall targets that can describe overall multi-factor structure comprehensively.
Preferably, the salvia piece quality grading method the following steps are included:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that rank salvia piece each in step 1 is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content, salvia piece stage division is established in conjunction with traditional quality evaluation method and effective component by traditional Chinese medicine quality constant；
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, establishes principal component-
Mahalanobis distance discrimination model: weighing the salvia piece sample of Known Species, smashes it through 300 meshes, and gained sample powder uses
Integrating sphere acquires near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio
For 8~10cm-1, scanning times 64~67 times, data format Absorbance, optimization energy gain is 2x, temperature 20~25
DEG C, relative humidity 45%~50%, each sample acquires 3 times, seeks averaged spectrum；Gained spectrogram Applied Chemometrics is soft
Part TQ Analyst successively passes through batch normalized, batch baseline correction processing and the processing of rejecting abnormalities sample point, makes
Taxonomic history is carried out to similar medicinal material with Chemical Pattern Recognition method, using discriminant analysis, sample is divided into training
Collection and forecast set, classifying quality predict that accuracy judges by forecast set, with the level evaluation model established to spectrum into
Row grade forecast.
Still more preferably, the salvia piece quality grading method the following steps are included:
(1) by establishing perceptual quality evaluation: by different batches different size based on appearance, smell, taste identification
Salvia piece, observe color, the texture of salvia piece, and measure the thickness of salvia piece, width, length, quality form and refer to
Mark carries out smell, taste identification, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that rank salvia piece each in step 1 is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content, salvia piece stage division is established in conjunction with traditional quality evaluation method and effective component by traditional Chinese medicine quality constant；
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, establishes principal component-
Mahalanobis distance discrimination model: weighing the salvia piece sample of Known Species, smashes it through 300 meshes, and gained sample powder uses
Integrating sphere acquires near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio
For 8cm-1, scanning times 64~67 times, data format Absorbance, optimization energy gain is 2x, 25 DEG C of temperature, relatively
Humidity 45%~50%, each sample acquire 3 times, seek averaged spectrum；Gained spectrogram Applied Chemometrics software TQ
Analyst successively passes through batch normalized, batch baseline correction processing and the processing of rejecting abnormalities sample point, uses chemistry
Mode identification method carries out taxonomic history to similar medicinal material, and using discriminant analysis, sample is divided into training set and pre-
Collection is surveyed, classifying quality predicts accuracy by forecast set to judge, carries out grade to spectrum with the level evaluation model established
Prediction.
The invention has the following advantages that
1, on the basis of traditional quality evaluation method, composition and effectiveness and pharmacological action in conjunction with salvia piece, by with
Based on medicinal material Radix Salviae Miltiorrhizae is with identical pharmacological action and having the tanshin polyphenolic acid B of drug action, Cryptotanshinone, it is based near infrared light
Spectral analysis technology establishes a set of science, the method for the salvia piece grade evaluation that reasonable, strong operability, practicability are good.
2, this method it is quick, it is lossless, not can cause environmental pollution, using near infrared technology, disclose traditional Chinese medicine drink
The correlation of the attribute of piece, effective component, modern pharmacology, so that the evaluation of salvia piece prepared slice quality is with more scientific and practical
Property, it can not only ensure the good efficacy of clinical application, and for the reasonable disposition of salvia piece, realization high quality and favourable price, rule
Model market and conducive to relevant department supervision be of great significance.
Detailed description of the invention:
Fig. 1: sample size HPLC chromatogram: 1. protocatechuic acid；2. protocatechualdehyde；3. caffeic acid；4. Rosmarinic acid；5. red
Phenolic acid B；6. dihydrotanshinone Ⅰ；7. Cryptotanshinone；8. salvia miltiorrhiza bge I；9 tanshinone IIAs.
Fig. 2: sample spectrum diagram
Fig. 3: salvia piece level region component, wherein " " expression level-one Radix Salviae Miltiorrhizae, " zero
" indicate that second level Radix Salviae Miltiorrhizae, " △ " indicate three-level Radix Salviae Miltiorrhizae
Specific embodiment:
To be best understood from the present invention, the present invention will be described in further detail with reference to the following examples, but of the invention
Claimed range is not limited to the range of embodiment expression.
The new method that the classification of a salvia piece is established based near infrared technology, initially sets up the classical quality of Conventional wisdom
Evaluation is provided intuitive, easy based on establishing Conventional wisdom identification (appearance, smell, taste etc.) for salvia piece classification
Grade scale.Then Qualitative fingerprint analysis, Multi-component quantitation, active ingredient group content etc. are carried out as far as possible
Embody the difference between different brackets medicine materical crude slice.And embody the active ingredient of Radix Salviae Miltiorrhizae pharmacology.It, will be traditional by traditional Chinese medicine quality constant method
Quality evaluation and effective component, pharmacological basis are combined, and construct a new method for salvia piece classification.And apply near infrared light
Spectral analysis technology carries out spectral scan acquisition to different grades of salvia piece, finally establishes different mode recognition methods to sample
The classification capacity of product.
Embodiment 1
Establish salvia piece quality grading method:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes the evaluation of salvia piece perceptual quality；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius) (4) acquire the Radix Salviae Miltiorrhizae of known grades classification
The atlas of near infrared spectra spectrogram of medicine materical crude slice sample pre-processes, and scans the close of whole salvia pieces using Fourier-type near infrared spectrometer
Infrared spectroscopy,
It establishes principal component-mahalanobis distance discrimination model: weighing the salvia piece sample of Known Species, smash it through 100 mesh
Sieve, gained sample powder acquire near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection range using integrating sphere
10000~4000cm-1, resolution ratio 8cm-1 scanning times 64 times, data format Absorbance, optimize energy gain
For 2x, 20 DEG C of temperature, relative humidity 45%, each sample is acquired 3 times, seeks averaged spectrum；
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Embodiment 2
Establish salvia piece quality grading method:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes the evaluation of salvia piece perceptual quality；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius)
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, using Fourier
Type near infrared spectrometer scans the near infrared spectrum of whole salvia pieces, establishes principal component-mahalanobis distance discrimination model: weighing
The salvia piece sample for knowing type smashes it through 140 meshes, and gained sample powder acquires near-infrared spectrogram using integrating sphere, closely
Infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio are 8~10cm-1, scanning times 65
Secondary, data format Absorbance, optimization energy gain is 2x, and 22 DEG C of temperature, relative humidity 47%, each sample acquires 3
It is secondary, seek averaged spectrum.
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Embodiment 3
Establish salvia piece quality grading method:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius)
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, using Fourier
Type near infrared spectrometer scans the near infrared spectrum of whole salvia pieces, establishes principal component-mahalanobis distance discrimination model: weighing
The salvia piece sample for knowing type smashes it through 150 meshes, and gained sample powder acquires near-infrared spectrogram using integrating sphere, closely
Infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio 8cm-1 scanning times 64 times, are counted
It is Absorbance according to format, optimization energy gain is 2x, and 23 DEG C of temperature, relative humidity 48%, each sample acquires 3 times, asks
Take average spectrum；
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Embodiment 4
Establish salvia piece quality grading method:
(1) by establishing perceptual quality evaluation: by different batches different size based on appearance, smell, taste identification
Salvia piece, observe color, the texture of salvia piece, and measure the thickness of salvia piece, width, length, quality form and refer to
Mark carries out smell, taste identification, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius)
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, using Fourier
Type near infrared spectrometer scans the near infrared spectrum of whole salvia pieces, establishes principal component-mahalanobis distance discrimination model: weighing
The salvia piece sample for knowing type smashes it through 200 meshes, and gained sample powder acquires near-infrared spectrogram using integrating sphere, closely
Infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio be 8~, scanning times 67 times, data
Format is Absorbance, and optimization energy gain is 2x, and 25 DEG C of temperature, relative humidity 48%, each sample acquires 3 times, is sought
Averaged spectrum；
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Embodiment 5
Establish salvia piece quality grading method:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius)
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, using Fourier
Type near infrared spectrometer scans the near infrared spectrum of whole salvia pieces, establishes principal component-mahalanobis distance discrimination model: weighing
The salvia piece sample for knowing type smashes it through 270 meshes, and gained sample powder acquires near-infrared spectrogram using integrating sphere, closely
Infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio 8cm-1 scanning times 65 times, are counted
It is Absorbance according to format, optimization energy gain is 2x, and 25 DEG C of temperature, relative humidity 50%, each sample acquires 3 times, asks
Take average spectrum；
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Embodiment 6
Establish salvia piece quality grading method:
(1) by establishing the evaluation of salvia piece perceptual quality: by different batches based on appearance, smell, taste identification
The salvia piece of different size, observes color, the texture of salvia piece, and measures the thickness, width, length, matter of salvia piece
Morphological index is measured, smell, taste identification is carried out, establishes perceptual quality evaluation；
(2) establish effective component system: it is fixed that each rank salvia piece of step (1) is carried out finger-print, multi-target ingredient
Amount analysis, active ingredient group determination study, analyze the data obtained；
(3) it by effective component, introduces traditional Chinese medicine quality constant: wherein tanshin polyphenolic acid B and tanshinone is measured using HPLC method
Content give weighting coefficient (10:1) respectively and calculate again while according to tanshin polyphenolic acid B and tanshinone amount, obtain last
Traditional Chinese medicine quality constant salvia piece point in conjunction with traditional quality evaluation method and effective component, is established by traditional Chinese medicine quality constant
Grade method；
Traditional Chinese medicine quality constant (A), abbreviation quality constant define the quality (M) and its thickness (h) for ingredient in unit Chinese medicine
The ratio between square, A=M/h2.In order to simplify research, tubers unit medicinal material is considered as standard cylinder, can derive new shape
Formula.It can thus be seen that quality constant is directly proportional to the size of medicine materical crude slice, index components content, it is inversely proportional with the thickness of medicine materical crude slice.Cause
And piece shape is bigger, index components content is higher, in the thinner medicine materical crude slice of regulatory specifications range inner sheet thickness, quality constant is bigger.Matter
Amount constant is bigger, and specification is higher.In traditional character grade evaluation method, the size of piece shape is the evaluation index of most critical.
In general, piece shape is bigger, grade is also higher.In the evaluation method based on component content, the content of ingredient is higher, etc.
Grade is higher.
V is unit medicinal material volume: V=π r2H (r is radius, and h is thickness)
M is unit quality of medicinal material: m=PV=ρ π r2H (ρ is density)
M is unit quality of medicinal material: M=cm=c ρ π r2H (c is component content)
For salvia piece quality constant, common round medicine materical crude slice calculation formula are as follows:
(n is the number for studying medicinal material)
It is after simplificationH is the overall thickness for studying medicinal material, and M ' is the gross mass of study sample index components, red
Joining medicine materical crude slice is similar round or oval medicine materical crude slice, and piece shape coefficient a is introduced in formula, and a is the salvia piece short radius (width of medicine materical crude slice
Degree) with the ratio of major radius (length of medicine materical crude slice), then formula evolves into:
(r1 is short radius, and r2 is major radius)
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, using Fourier
Type near infrared spectrometer scans the near infrared spectrum of whole salvia pieces, establishes principal component-mahalanobis distance discrimination model: weighing
The salvia piece sample for knowing type smashes it through 300 meshes, and gained sample powder acquires near-infrared spectrogram using integrating sphere, closely
Infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio 8cm-1 scanning times 64 times, are counted
It is Absorbance according to format, optimization energy gain is 2x, and 25 DEG C of temperature, relative humidity 50%, each sample acquires 3 times, asks
Take average spectrum；
Gained spectrogram Applied Chemometrics software TQ Analyst successively passes through batch normalized, batch baseline
Correction process and the processing of rejecting abnormalities sample point carry out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, adopt
With discriminant analysis, sample is divided into training set and forecast set, classifying quality predicts accuracy by forecast set to sentence
It is disconnected, grade forecast is carried out to spectrum with the level evaluation model established.
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
Near-infrared spectrum technique has preferable distinguishing ability, and analysis speed is fast, and accuracy rate is high, is answered extensively at present
For fields such as high-molecular compound content detection, optoacoustic spectroscopy research, Study of Medicinal Herbs, food safety and object identifications.?
In application in TCM field, near-infrared has been successfully realized index ingredient in the identification to medicinal material kind and different sources Chinese medicine
Quick measurement.
Experimental example one
1. instrument and material
1.1 instrument
Electronic balance: Mei Teletuo benefit (MS105)
High performance liquid chromatograph: Agilent 1260；
Chromatographic column: Diamonsil5um C18,250 × 4.6mm (8997474)；
U.S.'s match is silent to fly-generation that AntarisII type Fourier transform near infrared instrument；
SabIR diffusing reflection optical fiber probe attachment；
Software: Result software (match is silent to fly-generation that company) is used for the acquisition of spectrum, TQ
Analyst6.2 software (match is silent to fly-generation that company) is for the pretreatment of spectrum and the calculating of algorithm.
1.2 sample source
Mobility: acetonitrile (chromatographically pure), 0.1% formic acid
Reference substance: protocatechuic acid, protocatechualdehyde, caffeic acid, Rosmarinic acid, tanshin polyphenolic acid B, dihydrotanshinone Ⅰ, hidden Radix Salviae Miltiorrhizae
Ketone, salvia miltiorrhiza bge I, tanshinone IIA.
Test sample: using different grades of salvia piece as research object, multiple samples from different manufacturers is collected, will be consolidated
Body pulverizing medicinal materials, sieving, the preparation of test solution: take number are as follows: 1-1,1-2,1-3 ... 1-8,2-1,2-2,2-3 ...
2-8,3-1,3-2,3-3 ... 3-8 obtain test sample, take about lg.
2. method
2.1 salvia pieces are collected and classification
Different grades of salvia piece is collected respectively, and it is measurement object that every batch of, which randomly selects 100 medicine materical crude slice, and measurement is red respectively
The morphological parameters (including thickness, length, width and quality) for joining medicine materical crude slice measure wherein tanshin polyphenolic acid B and tanshinone using HPLC method
The content of class.
Chromatographic condition: chromatographic column: Diamonsil 5um C18,250 X 4.6mm (8997474)；Mobile phase: acetonitrile (B):
0.1% formic acid (A), gradient elution (0-10min:10%-20%B, 10-17min:20%B, 17-45min:20-33%B, 45-
90min:33-100%B), flow velocity: 1ml/min；Column temperature: 35 DEG C；Wavelength: 280nm.
The preparation of reference substance solution: tanshin polyphenolic acid B, protocatechuic acid, protocatechualdehyde, caffeic acid, Rosmarinic acid, tanshin polyphenolic acid B, two
Hydrogen salvia miltiorrhiza bge I, Cryptotanshinone, salvia miltiorrhiza bge I, appropriate tanshinone IIA, it is accurately weighed, add methanol that every 1ml is made containing each reference substance
The mixed reference substance solution of 45ug.
The preparation of test solution: number is taken are as follows: take number are as follows: 1-1,1-2,1-3 ... 1-8,2-1,2-2,2-3 ...
2-8,3-1,3-2,3-3 ... 3-8 obtain test sample, take about lg, and essence is weighed, set tool stopper bottle, and 50% methanol 50ml is added in precision,
Weighed weight, ultrasonic reason rate 500W are filled in, frequency 40kHz 30 minutes, is let cool, then weighed weight, supplies less loss with 50% methanol
Weight shakes up, filtration, continue filter to get.Precision draws reference substance and each 10ul of test sample liquid, injects chromatography, measures, note
Chromatogram is recorded, sample chromatogram figure is shown in Fig. 1, calculates by external standard method, and the content results of tanshin polyphenolic acid B and tanshinone are shown in Table 1.
The content results table of 1 tanshin polyphenolic acid B of table and tanshinone
Salvia piece quality constant range by parameter in upper table using content after weighting as foundation is 1.42~13.02,
Wherein level-one prepared slice quality constant range are as follows: 6.97~13.02；Second level prepared slice quality constant range are as follows: 3.22~5.93；Three-level
Prepared slice quality constant range are as follows: 1.42~2.69.
The acquisition of 2.2 spectrum
Fly-generation that AntarisII type Fourier transform near infrared instrument using U.S.'s match is silent, is smashed it through using pulverizer
300 meshes, gained sample powder acquire near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection model using integrating sphere
10000~4000cm-1, resolution ratio 8cm-1 are enclosed, scanning times 64 times, data format Absorbance, optimization energy increases
Benefit be 2x, 25 DEG C of temperature, relative humidity 45%.Each sample acquires 3 times, seeks averaged spectrum, before spectra collection, by spectrometer
It is more than hour to preheat 1, after keeping room temperature and humidity almost the same, Radix Salviae Miltiorrhizae sample is packed into rotation matched with the instrument
Spectrum is acquired in cup, spectrogram is shown in Fig. 2
The pretreatment of 2.3 spectrum
During spectra collection, it will usually which generating high-frequency noise and baseline drift etc. influences the noise of forecast result of model
Therefore information needs to pre-process spectrum before establishing calibration set model, soft with stoichiometry software TQ Analyst
Part carries out derivation to Radix Salviae Miltiorrhizae whole near infrared spectrum and smoothly waits pretreatment.
2.4 establish principal component-mahalanobis distance discrimination model:
In modeling process, averaged spectrum is first calculated, is then established by the variation of estimation each wave point in analyzed area
Disaggregated model.In the discriminant analysis of multivariate statistics, using mahalanobis distance, to differentiate the differentiation ownership of sample point, mahalanobis distance
It is one kind of General Quadratic distance, based on multivariate normal distributions theory, effectively considers mean value, variance, covariance three
A parameter is the overall target that can describe overall multi-factor structure comprehensively.
Assuming that overall G1 and G2, x the ∈ R there are two Normal Distribution are a new sample points, define x to G1's and G2
Mahalanobis distance is d (x, G1) and d (x, G2):
μ in formula1And μ2For the mean value battle array of overall G1 and G2；S1 and S2 is the covariance matrix of totality G1 and G2.
Decision rule is as follows:
The prediction result of 2.5 models:
In order to examine the accuracy of the above model built prediction, 15 are randomly selected, the identification capacity of model is carried out
External inspection, sample carry out spectra collection after processing, with near-infrared, finally with established level evaluation model to spectrum into
It has gone grade forecast, has seen Fig. 3.It the results are shown in Table 2.
2 rank judging results of table
Conclusion: as can be seen from the table, the prediction result of model is almost the same with actual result, is computed, the identification of model
Rate is 93.3%.
Experimental example two
The present embodiment uses and the identical instrument and method of embodiment one establish model and verify the resolution of model, sample
Composition it is also as shown in table 2.The difference of the present embodiment and embodiment one is only that:
1. the present embodiment acquires spectrum, the salvia piece sample of Known Species is weighed, 300 meshes, gained are smashed it through
Sample powder acquires near-infrared spectrogram using integrating sphere, near infrared spectrometer parameter setting: spectra collection range 10000~
4000cm-1, resolution ratio 9cm-1, scanning times 67 times, data format Absorbance, optimization energy gain is 2x, temperature
25 DEG C of degree, relative humidity 50%, each sample acquire 3 times, seek averaged spectrum；Before spectra collection, spectrometer is preheated 1 small
When more than, keep room temperature and humidity it is almost the same after, Radix Salviae Miltiorrhizae sample is fitted into rotating cup matched with the instrument and is acquired
Spectrum has finally carried out grade forecast to spectrum with the level evaluation model established, the results are shown in Table 3.
2. the present embodiment is established using 30 as number of principal components identifies model.
3 rank judging results of table
The model discovery obtained with verifying collection verifying, the resolution of the model are 100%.
Experimental example three
Method source: the near infrared spectrum of the near infrared spectrum identification method Radix Salviae Miltiorrhizae of CN200910069865.2 Radix Salviae Miltiorrhizae identifies
Method
The present embodiment uses instrument identical with the near infrared spectrum identification method of CN200910069865.2 Radix Salviae Miltiorrhizae and side
Method establishes model and verifies the resolution of model, and the processing of sample is also reflected with the near infrared spectrum of CN200910069865.2 Radix Salviae Miltiorrhizae
Other method is identical, and the composition of sample near infrared spectrometer diffusing reflection optical fiber attachment as shown in table 2 acquires close red under the following conditions
External spectrum: scanning range 10000-4000cm-1, scanning times 32 times, resolution ratio 8cm-1 the results are shown in Table 4:
4 rank judging results of table
The model discovery obtained with verifying collection verifying, the resolution of the model are 80%.As can be seen from the table, this method pair
It is relatively low in the division resolution of the quality scale of salvia piece, it is poor to judge quality scale effect.
Summarize: the prediction result of model of the present invention and actual result are almost the same, can quick and precisely determine salvia piece
Quality scale, laboratory apparatus is easy to operate, and sample nondestructive not can cause environmental pollution.
Although above having used general explanation, specific embodiment and test, the present invention is made to retouch in detail
State, but on the basis of the present invention, it can be made it is some modify or improve, this is aobvious and easy to those skilled in the art
See.Therefore, these modifications or improvements without departing from theon the basis of the spirit of the present invention, belong to claimed
Range.
Claims (10)
1. a kind of method for establishing salvia piece quality grading using near infrared technology, which is characterized in that the method includes with
Lower step:
(1) by establishing the evaluation of salvia piece perceptual quality based on appearance, smell, taste identification；
(2) effective component system is established；
(3) by effective component, traditional Chinese medicine quality constant is introduced；
(4) acquisition known grades classification salvia piece sample atlas of near infrared spectra, carry out spectrogram pretreatment, establish it is main at
Point-mahalanobis distance discrimination model, divide salvia piece credit rating.
2. the method for quality grading according to claim 1, which is characterized in that the method for the step (1): observation is different
The color of the salvia piece of batch different size, texture, and measure the thickness of salvia piece, width, length, quality form and refer to
Mark carries out smell, taste identification, establishes perceptual quality evaluation.
3. the method for quality grading according to claim 1, which is characterized in that the step (2) establishes effective component body
System are as follows: rank salvia piece each in step (1) is crushed, is sieved, number carries out finger-print, Multi-component quantitation, work
Property components group determination study, analyze the data obtained.
4. the method for quality grading according to claim 1, which is characterized in that the step (3) is drawn by effective component
Enter the method for traditional Chinese medicine quality constant are as follows: the content that wherein tanshin polyphenolic acid B and tanshinone are measured using HPLC method passes through traditional Chinese medicine quality
Constant divides salvia piece specification of quality grade in conjunction with traditional quality evaluation method and effective component, establishes salvia piece classification
Method.
5. the method for quality grading according to claim 1, which is characterized in that the step (4) divides salvia piece matter
The method for measuring grade specifically: the salvia piece of Known Species is pulverized and sieved, gained sample powder acquires atlas of near infrared spectra,
Gained spectrogram Applied Chemometrics software successively passes through batch normalized, batch baseline correction processing and rejects different
Normal sample point processing carries out taxonomic history to similar medicinal material using Chemical Pattern Recognition method, using the linear classification for having supervision
Method principal component, that is, discriminant analysis.
6. the method for quality grading according to claim 5, which is characterized in that the taxonomic history are as follows: sample is divided into
Training set and forecast set, classifying quality predict accuracy by forecast set to judge.
7. the method for quality grading according to claim 1, which is characterized in that the acquisition method of the atlas of near infrared spectra
Are as follows: salvia piece sample is weighed, smashes it through 300 meshes, gained sample powder acquires near-infrared spectrogram using integrating sphere, close red
External spectrum instrument parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio are 8~10cm-1, scanning times 64~67
It is secondary, data format Absorbance, optimization energy gain be 2x, 20~25 DEG C of temperature, relative humidity 45%~50%, each
Sample acquires 3 times, seeks averaged spectrum.
8. the method for quality grading according to any one of claims 1 to 7, which is characterized in that the method includes following
Step:
(1) by establishing the evaluation of salvia piece perceptual quality based on appearance, smell, taste identification: by different batches difference
The salvia piece of specification, observes color, the texture of salvia piece, and measures the thickness, width, length, quality shape of salvia piece
State index carries out smell, taste identification, establishes perceptual quality evaluation；
(2) effective component system is established: by each rank salvia piece of step (1) carries out finger-print, multi-target ingredient quantitatively divides
The data obtained is analyzed in analysis, active ingredient group determination study；
(3) by effective component, introduce traditional Chinese medicine quality constant: using the measurement of HPLC method, wherein tanshin polyphenolic acid B and tanshinone contain
Amount, while according to tanshin polyphenolic acid B and tanshinone amount, weighting coefficient (10:1) is given respectively and is calculated again, is obtained in last
Medicine quality constant establishes salvia piece classification side in conjunction with traditional quality evaluation method and effective component by traditional Chinese medicine quality constant
Method；
(4) the atlas of near infrared spectra spectrogram pretreatment of the salvia piece sample of acquisition known grades classification, establishes principal component-geneva
Distance discrimination model: weighing the salvia piece sample of Known Species, smashes it through 300 meshes, and gained sample powder uses integral
Ball acquire near-infrared spectrogram, near infrared spectrometer parameter setting: spectra collection 10000~4000cm-1 of range, resolution ratio be 8~
10cm-1, scanning times 64~67 times, data format Absorbance, optimization energy gain be 2x, 20~25 DEG C of temperature, phase
To humidity 45%~50%, each sample is acquired 3 times, seeks averaged spectrum；Gained spectrogram Applied Chemometrics software TQ
Analyst successively passes through batch normalized, batch baseline correction processing and the processing of rejecting abnormalities sample point, uses chemistry
Mode identification method carries out taxonomic history to similar medicinal material, and using discriminant analysis, sample is divided into training set and pre-
Collection is surveyed, classifying quality predicts accuracy by forecast set to judge, carries out grade to spectrum with the level evaluation model established
Prediction.
9. the method for quality grading according to claim 8, which is characterized in that salvia piece quality constant range is 1.42
~13.02.
10. the method for quality grading according to claim 9, which is characterized in that the salvia piece quality constant is wherein
Level-one prepared slice quality constant range are as follows: 6.97~13.02；Second level prepared slice quality constant range are as follows: 3.22~5.93；Three-level medicine materical crude slice
Quality constant range are as follows: 1.42~2.69.
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| CN109932332A (en) * | 2019-03-18 | 2019-06-25 | 陇西保和堂药业有限责任公司 | A kind of method of infrared spectroscopy detection Chinese medicine |
| CN110376312A (en) * | 2019-08-20 | 2019-10-25 | 陕西中医药大学 | Traditional Chinese medicine quality level detection method |
| CN113390822A (en) * | 2021-06-15 | 2021-09-14 | 贵州吾茶大健康产业发展有限公司 | Method for judging astringent taste grade of matcha by utilizing near infrared technology |
| CN113433274A (en) * | 2021-08-11 | 2021-09-24 | 张大宁 | Detection method of traditional Chinese medicine decoction pieces |
| CN113887389A (en) * | 2021-09-29 | 2022-01-04 | 湖南省博世康中医药有限公司 | Quality inspection and classification method for traditional Chinese medicine decoction pieces based on image recognition |
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| CN113887389A (en) * | 2021-09-29 | 2022-01-04 | 湖南省博世康中医药有限公司 | Quality inspection and classification method for traditional Chinese medicine decoction pieces based on image recognition |
| CN115184283A (en) * | 2022-09-09 | 2022-10-14 | 张家港市绿色中药饮片有限公司 | Intelligent screening method and system for traditional Chinese medicine decoction pieces |
| CN116793990A (en) * | 2023-07-13 | 2023-09-22 | 黄冈职业技术学院 | Five-flavor quantitative characterization method of plant traditional Chinese medicine |
| CN116793990B (en) * | 2023-07-13 | 2024-03-26 | 黄冈职业技术学院 | Five-flavor quantitative characterization method of plant traditional Chinese medicine |
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