CN111220561A - Infrared spectrum identification method for origin of swertia mussotii - Google Patents
Infrared spectrum identification method for origin of swertia mussotii Download PDFInfo
- Publication number
- CN111220561A CN111220561A CN201811408926.9A CN201811408926A CN111220561A CN 111220561 A CN111220561 A CN 111220561A CN 201811408926 A CN201811408926 A CN 201811408926A CN 111220561 A CN111220561 A CN 111220561A
- Authority
- CN
- China
- Prior art keywords
- sample
- swertia mussotii
- infrared spectrum
- infrared
- spectrum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 241000977602 Swertia mussotii Species 0.000 title claims abstract description 73
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000001228 spectrum Methods 0.000 claims abstract description 37
- IOLCXVTUBQKXJR-UHFFFAOYSA-M potassium bromide Chemical compound [K+].[Br-] IOLCXVTUBQKXJR-UHFFFAOYSA-M 0.000 claims abstract description 32
- 238000005102 attenuated total reflection Methods 0.000 claims abstract description 22
- 238000002360 preparation method Methods 0.000 claims abstract description 5
- 238000002156 mixing Methods 0.000 claims abstract description 4
- 238000012360 testing method Methods 0.000 claims description 40
- 238000010521 absorption reaction Methods 0.000 claims description 38
- HEYZWPRKKUGDCR-QBXMEVCASA-N Swertiamarin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1O[C@H]1[C@H](C=C)[C@]2(O)CCOC(=O)C2=CO1 HEYZWPRKKUGDCR-QBXMEVCASA-N 0.000 claims description 7
- HEYZWPRKKUGDCR-WRMJXEAJSA-N Swertiamarin Natural products O([C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1)[C@H]1[C@@H](C=C)[C@@]2(O)C(C(=O)OCC2)=CO1 HEYZWPRKKUGDCR-WRMJXEAJSA-N 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 7
- DUAGQYUORDTXOR-GPQRQXLASA-N Gentiopicrin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1O[C@H]1[C@H](C=C)C2=CCOC(=O)C2=CO1 DUAGQYUORDTXOR-GPQRQXLASA-N 0.000 claims description 6
- DUAGQYUORDTXOR-WULZUDSJSA-N Gentiopicrin Natural products O([C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1)[C@H]1[C@@H](C=C)C=2C(C(=O)OCC=2)=CO1 DUAGQYUORDTXOR-WULZUDSJSA-N 0.000 claims description 6
- 238000001035 drying Methods 0.000 claims description 5
- 238000001559 infrared map Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 abstract description 4
- 238000007873 sieving Methods 0.000 abstract description 2
- 238000000643 oven drying Methods 0.000 abstract 1
- 238000010298 pulverizing process Methods 0.000 abstract 1
- 238000002835 absorbance Methods 0.000 description 17
- 238000004458 analytical method Methods 0.000 description 15
- 238000002834 transmittance Methods 0.000 description 7
- 238000002474 experimental method Methods 0.000 description 5
- 102000003712 Complement factor B Human genes 0.000 description 4
- 108090000056 Complement factor B Proteins 0.000 description 4
- 241001530209 Swertia Species 0.000 description 4
- 238000000540 analysis of variance Methods 0.000 description 4
- AEDDIBAIWPIIBD-ZJKJAXBQSA-N mangiferin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1C1=C(O)C=C(OC=2C(=CC(O)=C(O)C=2)C2=O)C2=C1O AEDDIBAIWPIIBD-ZJKJAXBQSA-N 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- YWQSXCGKJDUYTL-UHFFFAOYSA-N Mangiferin Natural products CC(CCC=C(C)C)C1CC(C)C2C3CCC4C(C)(C)CCCC45CC35CCC12C YWQSXCGKJDUYTL-UHFFFAOYSA-N 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 229940043357 mangiferin Drugs 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- MIJYXULNPSFWEK-GTOFXWBISA-N 3beta-hydroxyolean-12-en-28-oic acid Chemical compound C1C[C@H](O)C(C)(C)[C@@H]2CC[C@@]3(C)[C@]4(C)CC[C@@]5(C(O)=O)CCC(C)(C)C[C@H]5C4=CC[C@@H]3[C@]21C MIJYXULNPSFWEK-GTOFXWBISA-N 0.000 description 1
- JKLISIRFYWXLQG-UHFFFAOYSA-N Epioleonolsaeure Natural products C1CC(O)C(C)(C)C2CCC3(C)C4(C)CCC5(C(O)=O)CCC(C)(C)CC5C4CCC3C21C JKLISIRFYWXLQG-UHFFFAOYSA-N 0.000 description 1
- 241001071804 Gentianaceae Species 0.000 description 1
- YBRJHZPWOMJYKQ-UHFFFAOYSA-N Oleanolic acid Natural products CC1(C)CC2C3=CCC4C5(C)CCC(O)C(C)(C)C5CCC4(C)C3(C)CCC2(C1)C(=O)O YBRJHZPWOMJYKQ-UHFFFAOYSA-N 0.000 description 1
- MIJYXULNPSFWEK-UHFFFAOYSA-N Oleanolinsaeure Natural products C1CC(O)C(C)(C)C2CCC3(C)C4(C)CCC5(C(O)=O)CCC(C)(C)CC5C4=CCC3C21C MIJYXULNPSFWEK-UHFFFAOYSA-N 0.000 description 1
- OZWDYLLMBFLSNH-UHFFFAOYSA-N Swertisin Natural products COc1cc2OC(=CC(=O)c2c(O)c1OC3OC(CO)C(O)C(O)C3O)c4cccc(O)c4 OZWDYLLMBFLSNH-UHFFFAOYSA-N 0.000 description 1
- 239000004480 active ingredient Substances 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 201000001352 cholecystitis Diseases 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 201000006549 dyspepsia Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000011049 filling Methods 0.000 description 1
- 229930003935 flavonoid Natural products 0.000 description 1
- 150000002215 flavonoids Chemical class 0.000 description 1
- 235000017173 flavonoids Nutrition 0.000 description 1
- 208000006454 hepatitis Diseases 0.000 description 1
- 231100000283 hepatitis Toxicity 0.000 description 1
- 238000004128 high performance liquid chromatography Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000004811 liquid chromatography Methods 0.000 description 1
- 238000004476 mid-IR spectroscopy Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000004570 mortar (masonry) Substances 0.000 description 1
- 229940100243 oleanolic acid Drugs 0.000 description 1
- 230000000144 pharmacologic effect Effects 0.000 description 1
- HZLWUYJLOIAQFC-UHFFFAOYSA-N prosapogenin PS-A Natural products C12CC(C)(C)CCC2(C(O)=O)CCC(C2(CCC3C4(C)C)C)(C)C1=CCC2C3(C)CCC4OC1OCC(O)C(O)C1O HZLWUYJLOIAQFC-UHFFFAOYSA-N 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000013112 stability test Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- ABRULANJVVJLFI-DGHBBABESA-N swertisin Chemical compound COC1=CC=2OC(C=3C=CC(O)=CC=3)=CC(=O)C=2C(O)=C1[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O ABRULANJVVJLFI-DGHBBABESA-N 0.000 description 1
- UJMBCXLDXJUMFB-GLCFPVLVSA-K tartrazine Chemical compound [Na+].[Na+].[Na+].[O-]C(=O)C1=NN(C=2C=CC(=CC=2)S([O-])(=O)=O)C(=O)C1\N=N\C1=CC=C(S([O-])(=O)=O)C=C1 UJMBCXLDXJUMFB-GLCFPVLVSA-K 0.000 description 1
- 229960000943 tartrazine Drugs 0.000 description 1
- 235000012756 tartrazine Nutrition 0.000 description 1
- 239000004149 tartrazine Substances 0.000 description 1
- 150000003648 triterpenes Chemical class 0.000 description 1
- 150000007964 xanthones Chemical class 0.000 description 1
Images
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
- G01N21/3563—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
- 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
Landscapes
- 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)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides an infrared spectrum identification method of a producing area of swertia mussotii. The method is characterized in that: it comprises the following steps: a. sample preparation: pulverizing the sample, sieving, and oven drying at constant temperature; b. collecting an infrared spectrum: mixing the sample obtained in the step a with potassium bromide uniformly according to a proportion, tabletting, setting parameters of an infrared spectrometer, and scanning to obtain a middle infrared spectrum; c. collecting an ATR map: taking the sample in the step a, setting parameters of an infrared spectrometer, and scanning to obtain an ATR (attenuated total reflectance) spectrum; d. collecting a near-infrared spectrum: taking the sample in the step a, setting parameters of an infrared spectrometer, and scanning to obtain a near-infrared spectrum; e. and d, analyzing the maps obtained in the steps b to d. The infrared spectrum identification method of the origin of the swertia mussotii Franch is simple to operate, good in repeatability and stability and high in accuracy, and can identify original medicinal materials of the swertia mussotii Franch from different sources and origin places through infrared spectrum scanning and rapidly realize the identification of the origin of the swertia mussotii Franch.
Description
Technical Field
The invention belongs to the technical field of medicinal material detection, and particularly relates to an infrared spectrum identification method of swertia mussotii and an identification method of a producing area of the swertia mussotii.
Background
The Swertia mussotii Franch (Swertia mussotii Franch.) genus (Gentianaceae) Swertia genus (Swertia L.) plant is a single herb commonly used in Tibetan nations, and the traditional Tibetan medicine is a representative variety of Tibetan capillaris. It is mainly used for treating dyspepsia, cholecystitis, various hepatitis, etc. At present, researches on pharmacological active ingredients in swertia mussotii and extracts are mostly carried out on secoiridoid compounds such as swertiamarin and gentiopicrin, xanthone compounds such as mangiferin, triterpenoid compounds such as oleanolic acid and flavonoid substances such as swertisin. As a wild plant on a treasure plateau, the research on the medicinal components of the swertia mussotii is deepened continuously along with the rapid development of national medicines, and the demand is increased day by day.
The swertia mussotii medicinal components in different producing areas have obviously different contents and different quality. Baoyi (2006, quality evaluation of Tibetan swertia and chemical component research of tartrazine) obtains the swertia mussotii of the same genus and different origins through high performance liquid chromatography, the liquid fingerprint spectra of the swertia mussotii have obvious difference, but the liquid chromatography method needs to extract the effective components in the swertia mussotii by using chemical reagents, the operation is relatively complex, time and labor are wasted, the analysis efficiency is low when a plurality of samples and components are complex, the result is influenced by the sample extraction method, errors also exist, and the purpose of rapid and accurate analysis cannot be achieved.
Disclosure of Invention
In order to solve the problems, the invention provides a method for identifying swertia mussotii infrared spectrum, which is characterized by comprising the following steps: it comprises the following steps:
a. sample preparation: taking a test sample, crushing, screening by a 100-mesh screen and drying;
b. collecting an infrared spectrum: mixing the sample obtained in the step a with potassium bromide uniformly, tabletting, setting parameters of an infrared spectrometer, and scanning to obtain a middle infrared spectrum;
c. collecting an ATR map: taking the sample in the step a, and scanning to obtain an ATR (attenuated total reflectance) spectrum;
d. collecting a near-infrared spectrum: taking the sample in the step a, and scanning to obtain a near-infrared spectrum;
e. and d, analyzing the maps obtained in the steps b to d.
Further, the drying temperature in the step a is 45 ℃ and the time is 24 hours; and/or the ratio of the sample to KBr in the step b is 1: 100.
Further, the parameters in steps b and c are: the wave number is 4000-400 cm-1Scanning times 8 times, resolution 6cm-1。
Further, the parameters in step d are: the wave number range is 10000-4000 cm-1Scanning times of 64 times and resolution of 6cm-1。
Furthermore, in the map in the step e, the middle infrared map shows that 5 common characteristic peaks of swertia mussotii are preserved, and the common characteristic peaks are 3406cm respectively-1、2924cm-1、1614cm-1、1272cm-1、1074cm-1。
Furthermore, in the map in the step e, the ATR map shows that 7 common characteristic peaks of swertia mussotii are shown, and the common characteristic peaks are 3295cm-1、2921cm-1、1608cm-1、1413cm-1、1369cm-1、1266cm-1、1018cm-1。
Further, step (ii)In the spectrum described in sudden e, the near infrared spectrum shows that there should be 4 common peaks of swertia mussotii Franch, which are 6853cm respectively-1、5785cm-1、5178cm-1、4724cm-1。
The invention also provides an infrared spectrum identification method of the origin of the swertia mussotii Franch, which is characterized by comprising the following steps:
1) taking a sample to be detected of a swertia mussotii Franch;
2) detecting according to the method in the steps a-d;
3) analyzing the map obtained in the step 2).
Further, in the spectrum in the step 3), the wave number of the infrared spectrum in different swertia mussotii is 1074cm compared with that in the infrared spectrum in different swertia mussotii-1、3406cm-1The wave number of ATR pattern comparison is 2921cm-1、821cm-1The wave number of the near infrared spectrum is 8354cm-1、5785cm-1The absorption peaks are different, which indicates that the producing areas of the swertia mussotii are different; the absorption peaks are the same, which indicates that the producing areas of the swertia mussotii are the same.
Furthermore, the main chemical components of swertia mussotii var catarrhalis Makino corresponding to the absorption peak are swertiamarin, gentiopicroside and the like.
The method for identifying the origin of the swertia mussotii comprises the following steps of carrying out infrared spectrum scanning on the swertia mussotii, and carrying out infrared spectrum identification on the swertia mussotii.
Obviously, many modifications, substitutions, and variations are possible in light of the above teachings of the invention, without departing from the basic technical spirit of the invention, as defined by the following claims.
The present invention will be described in further detail with reference to the following examples. This should not be understood as limiting the scope of the above-described subject matter of the present invention to the following examples. All the technologies realized based on the above contents of the present invention belong to the scope of the present invention.
Drawings
FIG. 1 distribution diagram of sampling points
FIG. 2 shows the infrared spectrum of swertia mussotii in different producing areas
FIG. 3 ATR spectrogram of swertia mussotii Franch in different producing areas
FIG. 4 shows the near-infrared spectrum of swertia mussotii in different producing areas
FIG. 5 is an average spectrum of infrared orthogonal test spectrum
FIG. 6 comparison of theoretical optimal combination and actual optimal combination profiles
FIG. 7 is a graph of an average spectrum of a near-infrared orthogonal test spectrogram
FIG. 82 No. 5 and No. 15 Norris first derivative spectra
FIG. 9 comparison of near-infrared theoretical optimal combination with actual optimal combination maps
FIG. 10 shows the mid-infrared spectrum of swertiamarin
FIG. 11 shows a mid-infrared spectrogram of swertiamarin
FIG. 12 mid-infrared spectrogram of gentiopicroside
FIG. 13 mid-infrared spectrum of mangiferin
FIG. 14 results of clustering infrared common peak absorbance data in swertia mussotii
Detailed Description
The method selects Yushu area of Qinghai province, Ganzinu area of Sichuan province and Abamema area of Sichuan province as main investigation areas, adopts a route investigation method, and intensively carries out the sample collection work of the swertia mussotii, and the sample collection information (table 1) and the sampling route (figure 1) of the swertia mussotii.
TABLE 1 swertia mussotii sample collection information table
Example 1 identification of swertia mussotii and identification of origin
a. Sample preparation: respectively crushing swertia mussotii medicinal materials, sieving with a 100-mesh sieve, and drying in an oven at 45 ℃ for 24h until the materials are dried;
b. collecting an infrared spectrum: and (b) taking the sample in the step a, uniformly mixing the sample with potassium bromide in a mortar according to the mass ratio of 1:100, filling the mixture into a mold, and tabletting the mixture on a tabletting machine. Setting spectrum acquisition conditions: the wave number is 4000-400 cm-1Scanning times 8 times, resolution 6cm-1Detecting the mid-infrared spectrum;
c. collecting an ATR map: taking the sample in the step a, and setting spectrum acquisition conditions: the wave number is 4000-400 cm-1Scanning times 8 times, resolution 6cm-1Detecting an ATR spectrum;
d. collecting a near-infrared spectrum: taking the sample in the step a, and setting spectrum acquisition conditions: the wave number range is 10000-4000 cm-1Scanning times of 64 times and resolution of 6cm-1Detecting the near infrared spectrum;
e. and d, analyzing the maps obtained in the steps b to d.
The spectrum of the mid-infrared spectrum obtained by the detection in the step b is shown in a figure 2, and the mid-infrared absorption data is shown in a table 2.
As can be seen from fig. 2 and table 2: the infrared one-dimensional spectrogram in 22 parts of swertia mussotii Franch has high similarity degree and good spectrogram consistency. The total number of characteristic peaks is 5, and the characteristic peaks are respectively as follows: 3406cm-1、2924cm-1、1614cm-1、1272cm-1、1074cm-1. According to infrared absorption data (table 2) in swertia mussotii, the number of absorption peaks of swertia mussotii in a P22 producing area is the minimum; p1 at 824cm-1No absorption; p2 at 1513cm-1No absorption; p7 at 1513cm-1And 1423cm-1There is no absorption; p12 at 1154cm-1Special absorption is performed; p15 at 824cm-1And 593cm-1There is no absorption; p18 at 593cm-1There is no absorption; p19 at 1377cm-1There is no absorption.
The ATR spectra obtained from step c are shown in fig. 3, and the ATR absorption data are shown in table 3.
As can be seen from fig. 3 and table 3: the ATR spectrum of 22 portions of swertia mussotii Franch has high similarity degree and good spectrum consistency. The total number of characteristic peaks is 7, and the characteristic peaks are respectively as follows: 3295cm-1、2921cm-1、1608cm-1、1413cm-1、1369cm-1、1266cm-1、1018cm-1. According to the ATR absorption data of swertia mussotii Franch (Table 3), P7 is 1514cm-1And 522cm-1There is no absorption; p15 at 821cm-1There is no absorption; p17 at 750cm-1There is a special absorption.
The near infrared spectrum detected in step d is shown in FIG. 4, and the near infrared absorption data is shown in Table 4.
As can be seen from FIG. 4 and Table 4, 22 swertia mussotii near-infrared spectrograms have high similarity and good consistency. The total number of characteristic peaks is 4, and the characteristic peaks are 6853cm respectively-1、5785cm-1、5178cm-1、4724cm-1. According to the near infrared absorption data of swertia mussotii Franch (Table 4), P18 is 4325cm-1And 4256cm-1No absorption is observed; p19 at 8354cm-1There is no absorption; p20 at 4256cm-1There is no absorption; p22 at 8354cm-1And 4325cm-1There is no absorption.
The intermediate infrared, ATR and near infrared spectra obtained in the steps b-d can be known, and the origin of the swertia mussotii can not be distinguished well from the origin of the swertia mussotii in different origins by any spectra, and 3 spectra are combined for distinguishing the origin.
As can be seen from RSD values in the mid-infrared absorbance (Table 2), ATR absorbance (Table 3) and near-infrared absorbance (Table 4), the absorption difference of the swertia mussotii of different origins is the largest in the same wave number of ATR, and the absorption difference is the smallest in the same wave number of near-infrared. The absorption difference of the same wave number in the near infrared is larger, namely 8354cm-1And 5785cm-1(ii) a 1074cm is the medium infrared ray with the same wave number and the larger absorption difference-1、3406cm-1(ii) a The larger absorption difference of the same wave number in the ATR is 2921cm-1、821cm-1。
The following test examples specifically illustrate the advantageous effects of the present invention:
experimental example 1 optimization of collection conditions of mid-infrared spectrum of swertia mussotii
1. Quadrature test
Considering factors that may affect infrared spectral acquisition (table 5), a 3-factor 4-level orthogonal test table (table 6) was designed for 16 sets of tests. Randomly selecting a swertia mussotii sample to carry out full-waveband spectrum scanning, carrying out spectrum collection of 16 groups of tests according to conditions, calculating an average spectrum (n is 3, see figure 5) of each test, and selecting an actual optimal combination according to the maximum absorbance and the maximum transmittance.
TABLE 5 infrared orthogonal test factor horizon
TABLE 6 orthogonal test table for mid-infrared condition
When selecting a sample spectrogram, the absorbance value and the transmittance of the sample need to be considered, namely the absorbance value of the sample is in the range of 0.8-1.2 and about 1.0 is the best, and the transmittance needs to be more than 75 percent. The results show that the test No. 5 is more suitable, with the absorbance value of the sample being 0.898 and the sample transmittance being 77.87%, so the actual best combination is No. 5, namely A2B1C2(sample to KBr ratio 1:100, scan times 8 times, resolution 4cm-1)。
2. Range analysis
Performing range analysis on the test results (table 7), wherein for the maximum absorbance value, the sequence of the influence factors from large to small is A > B > C, namely the ratio of the sample to KBr > the scanning times > resolution; for the maximum transmittance, the influence factors are sequentially A > C > B from large to small, namely the ratio of the sample to KBr > resolution > scanning times.
For maximum absorbance values, factor A gives the best results at level 2, factor B gives the best results at level 1, and factor C gives the best results at level 3, so the theoretically best combination is A2B1C3(i.e., sample to KBr ratio of 1:100, scan times of 8, resolution of 6cm-1) (ii) a For transmittance, factor A gives the best results at 4 levels, factor B gives the best results at 1 level, and factor C gives the best results at 4 levels, so the theoretical optimal combination is A4B1C4(i.e., sample to KBr ratio of 1:200, scan times of 8 times, resolution of 8cm-1)。
For factor A, the absorbance within the range of 0.8-1.2 is best with a result of about 1.0, so A is selected2(ii) a For factor B, both optimal combinations are B1Thus selecting B1(ii) a For factor C, the higher the resolution, the more apparent the pattern noise, so C is chosen3Therefore, the optimal theoretical combination is A2B1C3(i.e., sample to KBr ratio of 1:100, scan times of 8, resolution of 6cm-1)。
TABLE 7 results of range analysis of mid-IR orthogonal test
3. Comparison of theoretical optimal combination with actual optimal combination
Fig. 6 shows a comparison result between the theoretical optimal combination spectrogram and the actual optimal combination spectrogram, which shows that the theoretical optimal combination spectrogram is smoother and has less spectral burrs than the actual optimal combination spectrogram. The analysis and comparison of the two results of 3 times of experiments show that the correlation coefficients of the actual optimal combination of the results of 3 times of experiments are respectively as follows: 1.0000, 0.9858, 0.9854, RSD value 0.832%; the correlation coefficients of the theoretical optimal combination of the 3 test results are respectively as follows: 1.0000, 0.9949, 0.9948, RSD value of 0.297%, indicating poor reproducibility of the actual optimum combination, so the theoretical optimum combination condition a was selected during the test2B1C3(i.e., sample to KBr ratio of 1:100, scan times of 8, resolution of 6cm-1)。
4. Analysis of variance
From the results of the anova (table 8), the ratio of the sample to KBr significantly affected the results, but the number of scans and the resolution did not significantly affect the results. And the influence of various factors on the result can be known according to the F value, and for the absorbance value: ratio > scan number > resolution, for transmittance: sample preparation: KBr ratio > resolution > scan number, which is consistent with the results of the range analysis.
Table 8 analysis results of variance in mid-infrared orthogonal test
Note: the influence of the expression factors on the results reaches a very significant level
5. Methodology validation
The methodological verification test is carried out by taking the aforementioned sample of swertia mussotii Franch as a test sample and testing under the condition of determining the collection condition of a pre-test spectrum, namely the ratio of the sample to KBr is 1:100, the scanning times are 8 times, and the resolution is 6cm-1。
5.1 repeatability
The same sample is continuously pressed for 6 times, and the correlation coefficients of 6 times are respectively 1.0000, 0.9979, 0.9977, 0.9972, 0.9967 and 0.9948 by taking one time as a standard, and the RSD value is 0.170%, which indicates that the test has better repeatability.
5.2 precision
The same tablet of the same sample is continuously measured for 6 times, and by taking one time as a standard, the correlation coefficients of the measured 6 times are respectively 1.0000, 0.9986, 0.9976, 0.9974, 0.9973 and 0.9960, and the RSD value is 0.135%, which indicates that the precision of the test is better.
5.3 stability
The same sample is pressed to obtain the same tablet, the tablet is placed in a dryer for storage, the tablet is measured once every 1 hour, the continuous measurement is carried out for 6 times, the correlation coefficients of the measured 6 times are respectively 1.0000, 0.9973, 0.9968, 0.9955, 0.9953 and 0.9946 by taking one time as a standard, and the RSD value is 0.195%, which indicates that the stability of the test is better.
Experimental example 2 optimization of near infrared spectrum collection conditions of swertia mussotii
1.1, orthogonal test
Considering factors that may affect the acquisition of near infrared spectra (table 9), a 3-factor 4-level orthogonal test table (table 10) was designed for 16 sets of experiments. Taking a swertia mussotii sample to carry out full-waveband spectrum scanning, carrying out spectrum collection of 16 groups of experiments according to conditions, calculating an average spectrum (n is 3, see figure 7) of each experiment, and selecting an actual optimal combination according to a peak value (the peak value reflects a signal-to-noise ratio to a certain extent).
TABLE 9 near-infrared orthogonal test factor horizon
As can be seen from table 10, the peak value was 5 # with the highest peak value, and was next 2 # and 15 # with the correlation coefficients of the spectra acquired three times for No. 2 being 1.0000, 0.9415, 0.9386(RSD of 3.61%), the correlation coefficients of the spectra acquired three times for No. 5 being 1.0000, 0.8413, 0.8319(RSD of 10.60%), and the correlation coefficients of the spectra acquired three times for No. 15 being 1.0000, 0.9960, 0.9959(RSD of 0.23%). Meanwhile, the near infrared spectrum and the near infrared Norris first-order derivative spectrum (figure 8) are combined, and the spectrum No. 15 is smoother than the spectrums No. 2 and No. 5, so that the spectrum No. 15 is selected as an actual optimal combination.
TABLE 10 near-infrared orthogonal test Table
1.2, range analysis
The results of the above tests were analyzed for range differences (Table 11), and the magnitude of the influence of each factor on the absorbance was: size of particle>Resolution ratio>The number of scans. The best results are obtained at level 1 for factor A, 1 for factor B and 2 for factor C, so the best theoretical combination is A1B1C2(i.e. 8 scans at 2cm resolution-1Particle size 100 mesh).
TABLE 11 results of range analysis in near-infrared orthogonal test
1.3 comparison of the actual optimal combination with the theoretical optimal combination
The correlation coefficients of the actual optimal combined cubic spectrograms are 1.0000, 0.9966 and 0.9959, the RSD value is 0.219%, the correlation coefficients of the theoretical optimal combined cubic spectrogram are 1.0000, 0.7349 and 0.7076, the RSD value is 16.151%, the theoretical and actual near infrared spectrum and the Norris first-order derivative spectrum (figure 9) are combined, the actual optimal combination is smoother than the theoretical optimal combination spectrogram, so the experimental acquisition conditions are acquired according to the actual optimal combination, namely the scanning times are 64 times, and the resolution is 6cm-1The granularity is 100 meshes.
1.4 analysis of variance
The analysis of variance in the near-infrared orthogonal test is shown in table 4, and it is known that the influence of the granularity on the result reaches an extremely significant level, and the influence of the resolution on the result reaches a significant level; according to the magnitude of the F value, the influence of the three factors on the result is as follows: granularity > resolution > scan times, consistent with range analysis results.
TABLE 12 analysis of variance in near-infrared orthogonal test
Note: indicates that the influence of the factors on the results reaches a very significant level
1.5 methodology validation
The methodology verification test is carried out by taking swertia mussotii Franch as a sample under the spectrum collection condition determined by a pre-test, namely: the scanning times are 64 times, and the resolution is 6cm-1The granularity is 100 meshes.
1.5.1 repeatability test
The same sample is continuously tested for 6 times by different samples, one time is taken as a standard, the correlation coefficients of the 6 times are respectively 1.0000, 0.9961, 0.9960 and 0.9958, and the RSD value is 0.163 percent, which indicates that the test has better repeatability.
1.5.2 precision test
The same sample of the same sample is continuously measured for 6 times, wherein one time is taken as a standard, the correlation coefficients of the 6 times are respectively 1.0000, 0.9959, 0.9959, 0.9959, 0.9956 and 0.9955, and the RSD value is 0.174%, which shows that the precision of the test is better.
1.5.3 stability test
The same sample of the same sample is continuously measured for 6 times, the sample is placed in a dryer for storage once after the measurement is finished, the sample is measured once every 1h, the total number of the measurement is 6, and the correlation coefficients of the 6 times are respectively 1.0000, 0.9979, 0.9974, 0.9973 and the RSD value is 0.106 percent by taking one of the times as a standard, which indicates that the test has better stability.
Experimental example 3 analysis of differences in ingredients of swertia mussotii in different producing areas
The absorption difference at the same wave number is large at 1074cm in the mid-infrared absorbance of example 1 (Table 2)-1、3406cm-12921cm, with a large absorption difference at the same wavenumber as the ATR absorbance (Table 3)-1、821cm-18354cm having a large absorption difference at the same wave number as the near-infrared absorbance (Table 4)-1And 5785cm-1It is known from the infrared absorption analysis of swertia mussotii (Table 13) and the middle infrared spectra (FIGS. 10-13) of swertiamarin, gentiopicroside, etc., which are the main chemical components thereofThe absorption peaks with large differences correspond to the main chemical components of swertiamarin, gentiopicroside and the like in swertia mussotii, so that the difference of swertia mussotii in different producing areas is mainly reflected in the difference of the main chemical component contents.
TABLE 13 infrared absorption analysis of swertia mussotii
Experimental example 4 Cluster analysis of swertia mussotii in different producing areas
Clustering analysis was performed using the mid-infrared absorbance (table 2) of example 1 as a variable, and the results are shown in fig. 14. The samples of 22 sampling points were roughly divided into 2 groups according to the clustering result, the first group comprised 14 samples: p1, P2, P15, P3, P4, P9, P14, P11, P6, P12, P7, P8, P10, P13; the second set included 8 samples: p5, P19, P16, P17, P20, P21, P18 and P22. Combining a sample information table (table 1) of the swertia mussotii in the western Sichuan and an infrared clustering result (fig. 14) of the swertia mussotii in 22 points, 14 samples of P1-P14 are from the interior of Qinghai province, 8 samples of P15-P22 are from the interior of Sichuan province, P5 from the Qinghai province is classified into the Sichuan province in 22 samples from two provinces, P15 from the Sichuan province is classified into the Qinghai province, and the rest 20 samples are correctly classified, so that the classification accuracy of the samples in the provinces is 90.91 percent by using the method. The origin identification of the collected swertia mussotii sample can be better carried out.
Claims (10)
1. An infrared spectrum identification method of swertia mussotii Franch is characterized in that: it comprises the following steps:
a. sample preparation: taking a test sample, crushing, screening by a 100-mesh screen and drying;
b. collecting an infrared spectrum: mixing the sample obtained in the step a with potassium bromide uniformly, tabletting, setting parameters of an infrared spectrometer, and scanning to obtain a middle infrared spectrum;
c. collecting an ATR map: taking the sample in the step a, and scanning to obtain an ATR (attenuated total reflectance) spectrum;
d. collecting a near-infrared spectrum: taking the sample in the step a, and scanning to obtain a near-infrared spectrum;
e. and d, analyzing the maps obtained in the steps b to d.
2. The identification method according to claim 1, characterized in that: the drying temperature in the step a is 45 ℃ and the time is 24 hours; and/or the ratio of the sample to KBr in the step b is 1: 100.
3. The identification method according to claim 1, characterized in that: the parameters in the steps b and c are as follows: the wave number is 4000-400 cm-1Scanning times 8 times, resolution 6cm-1。
4. The identification method according to claim 1, characterized in that: the parameters in the step d are as follows: the wave number range is 10000-4000 cm-1Scanning times of 64 times and resolution of 6cm-1。
5. The identification method according to claim 1, characterized in that: in the map of the step e, the middle infrared map shows that 5 common characteristic peaks of swertia mussotii are 3406cm-1、2924cm-1、1614cm-1、1272cm-1、1074cm-1。
6. The identification method according to claim 1, characterized in that: in the map in the step e, the ATR map shows that 7 common characteristic peaks are required for swertia mussotii Franch, and the characteristic peaks are 3295cm-1、2921cm-1、1608cm-1、1413cm-1、1369cm-1、1266cm-1、1018cm-1。
7. The identification method according to claim 1, characterized in that: in the map of the step e, the near infrared map shows that the swertia mussotii Franch should have 4 common peaks which are 6853cm respectively-1、5785cm-1、5178cm-1、4724cm-1。
8. An infrared spectrum identification method for a producing area of a swertia mussotii Franch is characterized by comprising the following steps:
1) taking a sample to be detected of a swertia mussotii Franch;
2) detected according to the method of steps a-d of claim 1;
3) analyzing the map obtained in the step 2).
9. The authentication method according to claim 8, wherein: in the spectrum in the step 3), the wave number of the infrared spectrum in different swertia mussotii Franch is 1074cm-1、3406cm-1The wave number of ATR pattern comparison is 2921cm-1、821cm-1The wave number of the near infrared spectrum is 8354cm-1、5785cm-1The absorption peaks are different, which indicates that the producing areas of the swertia mussotii are different; the absorption peaks are the same, which indicates that the producing areas of the swertia mussotii are the same.
10. The authentication method according to claim 9, wherein: the main chemical components of swertia mussotii corresponding to the absorption peak are swertiamarin, gentiopicroside and the like.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811408926.9A CN111220561A (en) | 2018-11-23 | 2018-11-23 | Infrared spectrum identification method for origin of swertia mussotii |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811408926.9A CN111220561A (en) | 2018-11-23 | 2018-11-23 | Infrared spectrum identification method for origin of swertia mussotii |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111220561A true CN111220561A (en) | 2020-06-02 |
Family
ID=70831871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811408926.9A Pending CN111220561A (en) | 2018-11-23 | 2018-11-23 | Infrared spectrum identification method for origin of swertia mussotii |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111220561A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116148394A (en) * | 2022-12-21 | 2023-05-23 | 中国科学院西北高原生物研究所 | Method and system for measuring content of iridoid glycoside compounds in swertia davidiana |
-
2018
- 2018-11-23 CN CN201811408926.9A patent/CN111220561A/en active Pending
Non-Patent Citations (3)
| Title |
|---|
| 孙惠丽: "应用红外光谱技术进行中药材检测的研究", 《中国优秀硕士学位论文全文数据库》 * |
| 狄准: "川东獐牙菜及其两种近缘种的指纹图谱分析", 《中国优秀硕士学位论文全文数据库》 * |
| 陈铁寓等: "不同产地两面针衰减全反射傅立叶红外光谱特征及其应用", 《今日药学》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116148394A (en) * | 2022-12-21 | 2023-05-23 | 中国科学院西北高原生物研究所 | Method and system for measuring content of iridoid glycoside compounds in swertia davidiana |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104251839B (en) | Spectrum separation detection method of compositions of south-Xinjiang red date sample for south-Xinjiang red date modeling | |
| CN103278473B (en) | The mensuration of pipering and moisture and method for evaluating quality in white pepper | |
| CN101231274B (en) | Method for rapid measuring allantoin content in yam using near infrared spectrum | |
| CN107478595B (en) | Method for rapidly identifying authenticity of pearl powder and quantitatively predicting content of adulterated shell powder | |
| CN103411895B (en) | Pseudo-near infrared spectrum identification method mixed by pearl powder | |
| CN111007032A (en) | Near-infrared spectroscopy for rapidly and nondestructively identifying liquorice and pseudo-product glycyrrhiza spinosa | |
| Fan et al. | Geographical-origin discrimination and volatile oil quantitative analysis of Zanthoxylum bungeanum Maxim. with a portable near-infrared spectrometer | |
| CN113655027B (en) | Method for near infrared rapid detection of tannin content in plants | |
| Soni et al. | A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean | |
| CN107449849B (en) | Traditional Chinese medicine identification method | |
| CN111220561A (en) | Infrared spectrum identification method for origin of swertia mussotii | |
| CN117874609A (en) | Universal rapid method for rapidly identifying whether natural product is specific production place | |
| CN113176227A (en) | Method for rapidly predicting adulteration of dendrobium huoshanense in dendrobium hunan | |
| CN105699314B (en) | A method of detecting soil stabilization carbon isotope ratio using middle infrared spectrum | |
| CN105784629B (en) | The method that the stable carbon isotope ratio of soil is quickly detected using middle infrared spectrum | |
| Ni et al. | Discrimination of Radix Paeoniae varieties on the basis of their geographical origin by a novel method combining high-performance liquid chromatography and Fourier transform infrared spectroscopy measurements | |
| CN109932332A (en) | A kind of method of infrared spectroscopy detection Chinese medicine | |
| CN115452758A (en) | Rice category identification model, construction method thereof and method for identifying rice category | |
| CN111220562A (en) | Method for identifying different species of swertia | |
| CN114076745A (en) | Saffron identification method based on cloud-interconnection portable near-infrared technology and adulterated product quantitative prediction method thereof | |
| CN112684023A (en) | Rapid detection method for quality of magnolia officinalis medicinal material and screening method for magnolia officinalis medicinal material | |
| CN107561036B (en) | Rhizoma bletillae variety and authenticity detection method | |
| Sha et al. | Enhanced Raman and Mid-Infrared Spectroscopic Discrimination of Geographical Origin of Rice by Data Mining and Data Fusion | |
| CN108956527B (en) | Method for rapidly detecting cyclic adenosine monophosphate cAMP content in red dates | |
| CN110596175B (en) | Based on 1 Method for comparing and distinguishing iron-stick yam and other varieties of yam in Shang county through HNMR metabonomics technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200602 |
|
| RJ01 | Rejection of invention patent application after publication |