CN106198446A - The method of L-Borneol content near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder - Google Patents
The method of L-Borneol content near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 43
- DTGKSKDOIYIVQL-QXFUBDJGSA-N (-)-borneol Chemical compound C1C[C@]2(C)[C@H](O)C[C@H]1C2(C)C DTGKSKDOIYIVQL-QXFUBDJGSA-N 0.000 title claims abstract description 41
- 229930006703 (-)-borneol Natural products 0.000 title claims abstract description 41
- 239000000843 powder Substances 0.000 title claims abstract description 22
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 16
- 238000012360 testing method Methods 0.000 title claims abstract description 11
- 238000001228 spectrum Methods 0.000 claims abstract description 22
- 238000011156 evaluation Methods 0.000 claims abstract description 5
- 238000003556 assay Methods 0.000 claims abstract description 4
- 230000003595 spectral effect Effects 0.000 claims abstract description 3
- 238000004817 gas chromatography Methods 0.000 claims description 5
- DTGKSKDOIYIVQL-WEDXCCLWSA-N (+)-borneol Chemical compound C1C[C@@]2(C)[C@@H](O)C[C@@H]1C2(C)C DTGKSKDOIYIVQL-WEDXCCLWSA-N 0.000 claims description 4
- 238000010987 Kennard-Stone algorithm Methods 0.000 claims description 4
- 238000004611 spectroscopical analysis Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 2
- 239000004575 stone Substances 0.000 claims 1
- 239000012567 medical material Substances 0.000 abstract description 2
- 238000012372 quality testing Methods 0.000 abstract 1
- XEKOWRVHYACXOJ-UHFFFAOYSA-N Ethyl acetate Chemical compound CCOC(C)=O XEKOWRVHYACXOJ-UHFFFAOYSA-N 0.000 description 15
- 238000002790 cross-validation Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 239000003153 chemical reaction reagent Substances 0.000 description 5
- 238000012937 correction Methods 0.000 description 5
- 239000003814 drug Substances 0.000 description 5
- 238000007781 pre-processing Methods 0.000 description 5
- OSWPMRLSEDHDFF-UHFFFAOYSA-N methyl salicylate Chemical compound COC(=O)C1=CC=CC=C1O OSWPMRLSEDHDFF-UHFFFAOYSA-N 0.000 description 4
- 238000010792 warming Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 239000013558 reference substance Substances 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000002604 ultrasonography Methods 0.000 description 3
- 240000000572 Blumea balsamifera Species 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000706 filtrate Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 229960001047 methyl salicylate Drugs 0.000 description 2
- 239000010453 quartz Substances 0.000 description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
- 239000000341 volatile oil Substances 0.000 description 2
- 235000010894 Artemisia argyi Nutrition 0.000 description 1
- 241000208838 Asteraceae Species 0.000 description 1
- 241001252601 Blumea Species 0.000 description 1
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 description 1
- 244000062793 Sorghum vulgare Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000000844 anti-bacterial effect Effects 0.000 description 1
- 230000003064 anti-oxidating effect Effects 0.000 description 1
- 230000000259 anti-tumor effect Effects 0.000 description 1
- 244000030166 artemisia Species 0.000 description 1
- 235000009120 camo Nutrition 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 239000012982 microporous membrane Substances 0.000 description 1
- 235000019713 millet Nutrition 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 244000045947 parasite Species 0.000 description 1
- 210000003800 pharynx Anatomy 0.000 description 1
- 239000006187 pill Substances 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000007430 reference method Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
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- 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
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- 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
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- 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
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Abstract
The invention discloses the method for L-Borneol content near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder.Comprise the steps: the collection of S1. original spectral data；S2. the mensuration of reference value；S3. the foundation of calibration model: the calibration set near infrared spectrum data of Herba Blumeae Balsamiferae leaf powder sample that step S1 gathers is carried out derivative and combines SG and smooth pretreatment, select optimal latent variable because of subnumber, the interval partial least square of application combination determines the wave-length coverage of optimal calibration model, by model evaluation, establish second dervative and combine the L-Borneol SiPLS model of Pretreated spectra smooth for SG；S4. the checking of calibration model；S5. unknown sample L-Borneol assay.Compare current Gas Chromatographic Determination, the advantages such as it is accurate, quick, lossless that the inventive method has, can improve the quality testing efficiency of Herba Blumeae Balsamiferae medical material.
Description
Technical field
The present invention relates to medical material index component analysis technical field, in particular it relates to a kind of near infrared spectrum is quickly surveyed
Determine the method for L-Borneol content in Herba Blumeae Balsamiferae leaf powder.
Background technology
Herba Blumeae Balsamiferae (Blumeabalsamifera (L.) DC.) has another name called Balsamiferou Blumea Herb, for Compositae herbaceos perennial, at me
State is distributed widely in the provinces and regions such as Hainan, Guizhou, Guangxi, Yunnan.Herba Blumeae Balsamiferae blade mainly (is mainly composed of left-handed dragon containing volatile oil
Brain), it is used as refined Chinese medicine Blumeae preparatum Tabellae.There is the multiple pharmacologically actives such as antibacterial, parasite killing, antioxidation, antityrosinase and antitumor.Chinese mugwort
Fragrant volatile oil of receiving is successfully applied to the Chinese patent medicine kind such as " JINHOUJIAN PENWUJI ", " the vertical refreshing drop pill of pharynx ".Therefore, L-Borneol
It it is the key index of Herba Blumeae Balsamiferae quality control.
In Herba Blumeae Balsamiferae blade, L-Borneol content currently mainly uses gas chromatogram (GC) method to measure.As summer millet etc. (in
Patent medicine, 2011), (Guiyang College of Traditional Chinese Medicine's journal, 2013), Wang Yuanhui etc. (modern food science and technology, 2014) such as Liu Jintao, come in good fortune
Gas chromatography is all used to establish the mensuration side of L-Borneol content in Herba Blumeae Balsamiferae blade Deng (contemporary Chinese Chinese medicine, 2014)
Method, but all these method all exists, and operation is relatively cumbersome, the test period is longer, need to consume the shortcomings such as chemistry reagent, therefore
It is necessary to set up a kind of assay method quick, simple, free of contamination.
Near infrared spectrum (NIR) is mainly by the absorption band of the frequency multiplication of the hydric groups such as C-H, N-H and O-H with combination frequency
Composition.This spectrum district signal easily extracts, and quantity of information is the abundantest, and most of material has response near infrared region.Make
For a kind of indirectly analytical technology, it is combined with Chemical Measurement and can realize quick, accurate, the lossless quantitative analysis to sample.
The most not yet it is related to apply the relevant report of L-Borneol content in NIR technology quantitative determination Herba Blumeae Balsamiferae leaf powder.
Summary of the invention
For the above-mentioned deficiency of prior art, the present invention provides left in a kind of near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder
The method of rotation Borneolum Syntheticum content.The method compares current gas chromatogram (GC) algoscopy, has quick, simple, low cost, without dirty
The advantages such as dye.
To achieve these goals, the present invention is achieved by the following technical programs:
In a kind of near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder, the method for L-Borneol content, comprises the steps:
S1. the collection of original spectral data: collect particular batch Herba Blumeae Balsamiferae leaf sample, gathers near infrared spectrum after pretreatment
Data；
S2. the mensuration of reference value: use L-Borneol content in gas chromatography determination Herba Blumeae Balsamiferae leaf powder；
S3. the foundation of calibration model: use Kennard-Stone algorithm that all Herba Blumeae Balsamiferae leaf powder samples are divided into calibration set
With checking collection, the calibration set near infrared spectrum data of Herba Blumeae Balsamiferae leaf powder sample step S1 gathered carries out pretreatment, selects
Good latent variable is because of subnumber, and the interval partial least square (SiPLS) of application combination determines the wave-length coverage of optimal calibration model
It is 4601～4894-1, 5504～6102cm-1, set up L-Borneol SiPLS model；
S4. the checking of calibration model: utilize checking collection sample that calibration model is carried out external certificate, use suitable model
Calibration model is evaluated by evaluating, calculates predictive value and the dependency of actual value；
S5. unknown sample assay: utilize authenticated calibration model to measure the L-Borneol of unknown Herba Blumeae Balsamiferae leaf powder
Content.
Preferably, the near infrared spectrum described in step S1 uses Antaris Fourier Transform Near Infrared instrument, to rotate
Integrating sphere diffuse-reflectance mode is collected, sweep limits 4000～10000cm-1, resolution 8cm-1, scanning times 64 times；With in instrument
The air in portion is background registration Log (1/R), each Sample Scan 1 time；All spectroscopic datas are by Thermo Scientific
Result software is collected and is achieved.
Preferably, the method for the gas Chromatographic Determination L-Borneol content described in step S2 is: accurately weighed Herba Blumeae Balsamiferae leaf
Sheet powder (crossing 20 mesh sieves) about 2g, is placed in 50mL tool plug triangular flask, adds ethyl acetate 25mL, and weighed weight, 40kHz's
Extract 30min under ultrasound condition, let cool, supply the weight of less loss by ethyl acetate, shake up, stand, through 0.22 μm microporous filter membrane
Filter, take subsequent filtrate to be measured.It is measured under following chromatographic condition: HP-5 quartz capillary chromatographic column (0.32mm × 30m,
0.25μm)；With 80 DEG C as initial temperature, keep 2min, be then warming up to 100 DEG C with 5 DEG C/min, then be warming up to 20 DEG C/min
200℃；Injector temperature is 220 DEG C；Fid detector temperature is 240 DEG C；Sample size 0.6 μ L, split ratio is 9:1.Each sample
Measure 3 times and calculate meansigma methods；
Preferably, optimal latent variable described in step S3 because of subnumber be to calculate minimum pre-by a cross-validation method (LOO-CV)
Survey residual sum of squares (RSS) (PRESS) value to predict and obtain.
Preferably, calibration model evaluation described in step S4 depend on correction root-mean-square error (RMSEC), cross validation mean square
Root error (RMSECV), correlation coefficient (R2), the parameter such as predicted root mean square error (RMSEP).
Preferably, all Herba Blumeae Balsamiferae leaf powder samples using Kennard-Stone algorithm to be measured by S1 by 2:1 are divided into school
Just collecting and verifying collection；Wherein calibration set 72, checking collection 36.
Predict optimal latent by staying a cross-validation method (LOO-CV) to calculate minimum predictive residual error sum of squares (PRESS) value
Variable Factors number is 6.
Compared with prior art, the method have the advantages that
The present invention uses diffuse-reflectance NIR method to establish the quantitative determination model of L-Borneol in Herba Blumeae Balsamiferae leaf powder.This school
Positive model is built by PLS method, by GC method on the basis of the wavelength of the interval partial least square (SiPLS) of combination selects
Preferable concordance is shown between the L-Borneol value and the NIR predictive value that measure.According to the parameter of model, NIR technology can be real
Now to the detection of L-Borneol content in Herba Blumeae Balsamiferae.Compare current Gas Chromatographic Determination, the method have quick, simple,
Low cost, the advantage such as pollution-free.
Accompanying drawing explanation
Fig. 1 is L-Borneol reference substance and test sample GC chromatogram, and wherein a is reference substance, and b is test sample；1 is left-handed dragon
Brain, 2 is methyl salicylate.
Fig. 2 is separate sources Herba Blumeae Balsamiferae NIR primary light spectrogram.
Fig. 3 is L-Borneol PRESS figure under different Pretreated spectra.
Fig. 4 is that the L-Borneol of 2D+SG (9) pretreatment quantitative determines the modeling interval choosing using SiPLS algorithm under model
Select.
Fig. 5 is L-Borneol measured value and predictive value dependency under SiPLS model.
Detailed description of the invention
Below in conjunction with Figure of description and specific embodiment the present invention made and elaborating further, described embodiment
It is served only for explaining the present invention, is not intended to limit the scope of the present invention.Test method used in following embodiment is as without special
Different explanation, is conventional method；The material that used, reagent etc., if no special instructions, for the reagent commercially obtained
And material.
Embodiment 1
Specimen in use of the present invention amounts to 109 parts, within 2013, is collected in Hainan, Guizhou and Guangxi, Pang Yu recent studies on person reflect
It is set to Herba Blumeae Balsamiferae (Blumeabalsamifera (L.) DC.).
Table 1 separate sources Herba Blumeae Balsamiferae sample message
Instrument, software and the reagent that the present invention relates to be: NIR analyzes: Antaris Fourier Transform Near Infrared instrument
(Thermo Nicolet company of the U.S.) is furnished with InGaAs detector, integrating sphere diffuse-reflectance sampling system, Thermo
Scientific Result operates software；Application Unscrambler 9.7 software (CAMO software, Norway) carries out spectrum and locates in advance
Reason and model calculate；SiPLS algorithmic tool bag is by (http://www.models.life.ku.dk/ such as Norgaard
IToolbox) provide；Additive method is to have been write by inventor's amendment on the basis of Norgaard scheduling algorithm.GC analyzes: peace
Prompt human relations 7890A gas chromatograph, flame ionization ditector (FID), 16 automatic samplers of Agilent G4513A,
Sartarius CPA225D electronic analytical balance, KQ-500DB type Ultrasound Instrument, (AlfaAesar chemistry has L-Borneol reference substance
Limit company, purity > 98%), methyl salicylate (Tianjin recovery fine chemistry industry institute, purity ＞ 99.5%), ethyl acetate etc.
Reagent is domestic analytical pure.
The collection of original spectrum: the NIR spectra of Herba Blumeae Balsamiferae powder uses Antaris Fourier Transform Near Infrared instrument,
Collect in Rotation Product bulb separation diffuse-reflectance mode, sweep limits 4000～10000cm-1, resolution 8cm-1, scanning times 64 times.With
The air of instrument internal is background registration Log (1/R), each Sample Scan 1 time.All spectroscopic datas are by Thermo
Scientific Result software is collected and is achieved.
The foundation of reference method and sample L-Borneol content statistics: accurately weighed Herba Blumeae Balsamiferae blade powder (crosses 20 mesh sieves)
About 2g, is placed in 50mL tool plug triangular flask, adds ethyl acetate 25mL, weighed weight, extract under the ultrasound condition of 40kHz
30min, lets cool, and supplies the weight of less loss by ethyl acetate, shakes up, and stands, through 0.22 μm filtering with microporous membrane, takes subsequent filtrate and treat
Survey.It is measured under following chromatographic condition: HP-5 quartz capillary chromatographic column (0.32mm × 30m, 0.25 μm)；With 80 DEG C it is
Initial temperature, keeps 2min, is then warming up to 100 DEG C with 5 DEG C/min, then is warming up to 200 DEG C with 20 DEG C/min；Injector temperature
It it is 220 DEG C；Fid detector temperature is 240 DEG C；Sample size 0.6 μ L, split ratio is 9:1.Each sample determination calculates for 3 times average
Value.Carrying out methodological study (table 2) before sample determination, the method that the explanation of its data is set up is accurate and effective.Fig. 1 is given
GC chromatogram containing sample and standard substance, sample L-Borneol retention time is consistent with standard substance.As can be seen from Table 3, all
109 parts of Herba Blumeae Balsamiferae sample L-Borneol content ranges are between 0～13.8mg/g, and average out to 5.20mg/g, it is left that sample is produced in Hainan
Rotation Borneolum Syntheticum content is slightly above Guizhou and produces.
Table 2 GC methodology parameter and standard curve
Table 3 Herba Blumeae Balsamiferae sample L-Borneol content is added up
NIR spectra feature and outlier select: the original spectrum of all of 109 parts of samples is as shown in Figure 2.Present substantially
Spectrum superposition and baseline drift phenomenon, wherein two grades of frequency multiplication districts (FCOT, 7100～4900cm-1) and combination frequency district (CR,
4900～4000cm-1) present bigger fluctuation.Wherein there is a sample away from other spectrum, as outlier and reject.
According to Kennard-Stone (KS) algorithm, by 2:1, data set being divided into calibration set and checking collection, wherein calibration set 72, test
Card collection 36.
Pretreated spectra: Fig. 2 shows that averaged spectrum is overlapping serious, and with the interference factor such as random noise, baseline drift,
Need to eliminate various noises and interference factor, to extract object to be measured near infrared spectrum with rational preprocessing procedures
Characteristic information.The present invention compares the effect of the quantitative model various method of several Pretreated spectra, as smooth in SNV, SG, MSC and
SG combines Spectra Derivative.Table 4 is the major parameter of L-Borneol PLS model under different preprocessing procedures, tests according to intersection
The result of card selects suitable preprocessing procedures.SG smoothing techniques pre-processed spectrum, L-Borneol PLS model is combined with derivative
RMSEC and RMSECV minimum, R2Close to 1.Result is consistent with Fig. 3, shows that combining SG by derivative smooths a pre-processed spectrum left side
Rotation Borneolum Syntheticum model is preferable.
The major parameter of L-Borneol PLS model under the different preprocessing procedures of table 4
Note: Raw: original spectrum；MSC: multiplicative scatter correction method；SNV: standard normal variable；SG:Savitzky Golay
Smooth；1D: first derivative；2D: second dervative；RMSEC: correction root-mean-square error；RMSECV: cross validation root-mean-square error；
R2: correlation coefficient
The determination of the optimal latent variable factor: predict by staying a cross-validation method (LOO-CV) to calculate minimum PRESS value
Optimal latent variable is because of subnumber.Generally PRESS schemes upper first minima and is used as being defined as optimal latent variable because of subnumber.Fig. 3 is
The latent variable factor under different pretreatments and PRESS value.Increasing because of quantum count along with latent variable, PERSS value diminishes.Pre-with other
Processing method compares, and derivative combination S G is smooth has obvious advantage, and its PRESS value drastically drops to 8 latent variable factors.And its
His preprocess method effectively can not be separated to useful information from superposition spectrum.
The selection that calibration model modeling is interval: the interval partial least square (SiPLS) of application combination selects calibration model
Wave-length coverage, spectra collection is divided into different intervals, according to minimum root-mean-square error (RMSE) determine optimal subinterval and
A combination thereof.The parameter of SiPLS algorithm needs optimization to include sub-district quantity and combination.Inventor is previously reported by SiPLS algorithm
Good parameter is 20 Ge Zi districts and the combination in 3 Ge Zi districts.Therefore, the present invention 3, the 6 and 7 three Ge Zi districts by 6 latent variable factors
Combination sets up optimal L-Borneol forecast model, i.e. Wavelength distribution 4601～4894-1, 5504～6102cm-1(such as Fig. 4 institute
Show).
The foundation of calibration model and checking: best model evaluation depends on RMSEC, RMSECV, it was predicted that mean square deviation) RMSEP,
R2Etc. parameter.Shown in table 5, combine the L-Borneol SiPLS mould of the Pretreated spectra of SG smooth (2D+SG (9)) with second dervative
Type is best.By checking 36 parts of samples of collection, calibration model is verified, find the quartile RMSEP of checking collection0.25,
RMSEP0.50, RMSEP0.75, RMSEP1.0, respectively 0.0532mg/g, 0.0635mg/g, 0.0629mg/g and 0.0779mg/g,
Relatively.The R of checking collection2It is 0.9069 (see Fig. 5).Result above shows, the SiPLS model tool of L-Borneol is the most pre-
The property surveyed.Meanwhile, Fig. 5 provides L-Borneol measured value and predictive value dependency under SiPLS model, understands predictive value and actual measurement from figure
Value closely, presents preferable dependency.
The major parameter of L-Borneol SiPLS model is set up under the different preprocessing procedures of table 5
Note: Raw: original spectrum；MSC: multiplicative scatter correction method；SNV: standard normal variable；SG:Savitzky Golay
Smooth；1D: first derivative；2D: second dervative；RMSEC: correction root-mean-square error；RMSECV: cross validation root-mean-square error；
R2: correlation coefficient
The present invention uses diffuse-reflectance NIR method to establish the quantitative determination model of L-Borneol in Herba Blumeae Balsamiferae.This straightening die
Type is built by PLS method on the basis of the wavelength of the interval partial least square (SiPLS) of combination selects.Measured by GC method
L-Borneol value and NIR predictive value between show preferable concordance.According to the parameter of model, it is right that NIR technology can realize
The detection of L-Borneol content in Herba Blumeae Balsamiferae.The method has quick, simple, low cost, the advantage such as pollution-free.
Claims (5)
1. the method for L-Borneol content in a near infrared spectrum quick test Herba Blumeae Balsamiferae leaf powder, it is characterised in that include as
Lower step:
S1. the collection of original spectral data: collect particular batch Herba Blumeae Balsamiferae leaf sample, gathers near infrared spectrum number after pretreatment
According to；
S2. the mensuration of reference value: use L-Borneol content in gas chromatography determination Herba Blumeae Balsamiferae leaf powder；
S3. the foundation of calibration model: all Herba Blumeae Balsamiferae leaf powder samples using Kennard-Stone method to be measured by S1 are divided into school
Just collecting and verifying collection, the near infrared spectrum data of calibration set sample being carried out second dervative and combines SG and smooth pretreatment, select
Good latent variable because of subnumber, the interval partial least square of application combination determine the wave-length coverage of optimal calibration model be 4601～
4894-1, 5504～6102 cm-1, by model evaluation, establish second dervative and combine the left-handed of Pretreated spectra smooth for SG
Borneolum Syntheticum SiPLS model；
S4. the checking of calibration model: utilize checking collection sample that calibration model carries out external certificate, calculate predictive value and actual value
Dependency；
S5. unknown sample assay: utilize authenticated calibration model to measure the L-Borneol content of unknown Herba Blumeae Balsamiferae leaf powder.
Method the most according to claim 1, it is characterised in that gather the method for near infrared spectrum data described in S1 for using
Antaris Fourier Transform Near Infrared instrument, collects in Rotation Product bulb separation diffuse-reflectance mode, sweep limits 4000～
10000cm-1, resolution 8 cm-1, scanning times 64 times；With the air of instrument internal for background registration Log (1/R), each sample
Product scan 1 time；All spectroscopic datas are collected by Thermo Scientific Result software and are achieved.
Method the most according to claim 1, it is characterised in that optimal latent variable described in S3 is to be tested by an intersection because of subnumber
Demonstration calculates minimum predictive residual error sum of squares PRESS value and predicts and obtain.
Method the most according to claim 1, it is characterised in that to correct root-mean-square error, friendship during model evaluation described in S3
Fork checking root-mean-square error, correlation coefficient and predicted root mean square error are parameter.
Method the most according to claim 1, it is characterised in that use Kennard-Stone algorithm to press 2:1 and S1 is measured
All Herba Blumeae Balsamiferae leaf powder samples be divided into calibration set and checking collection.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107132198A (en) * | 2017-04-18 | 2017-09-05 | 浙江中烟工业有限责任公司 | A kind of near infrared spectrum data preprocess method |
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| CN107132198A (en) * | 2017-04-18 | 2017-09-05 | 浙江中烟工业有限责任公司 | A kind of near infrared spectrum data preprocess method |
| CN108007898A (en) * | 2017-09-04 | 2018-05-08 | 无锡济民可信山禾药业股份有限公司 | A kind of quickly L-Borneol medicinal material detection method |
| CN109211829A (en) * | 2018-07-31 | 2019-01-15 | 湖南省水稻研究所 | A method of moisture content in the near infrared spectroscopy measurement rice based on SiPLS |
| CN109085136A (en) * | 2018-08-07 | 2018-12-25 | 山东大学 | The method of near-infrared diffusing reflection spectrum measurement cement slurry oxide components content |
| CN109991206A (en) * | 2019-04-10 | 2019-07-09 | 西安石油大学 | One kind is based on Partial Least Squares to the total alcohol content method for measuring of alcohol gasoline |
| CN109991206B (en) * | 2019-04-10 | 2021-08-27 | 西安石油大学 | Method for measuring total alcohol content of alcohol gasoline based on partial least square method |
| CN112528806A (en) * | 2020-12-02 | 2021-03-19 | 广东省农业科学院农产品公共监测中心 | Single tea aroma type classification method and device based on bionic olfaction |
| CN117420095A (en) * | 2023-12-15 | 2024-01-19 | 乐比(广州)健康产业有限公司 | Nasal spray ingredient detection method |
| CN117420095B (en) * | 2023-12-15 | 2024-03-01 | 乐比(广州)健康产业有限公司 | Nasal spray ingredient detection method |
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