CN107727591A - A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method - Google Patents
A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method Download PDFInfo
- Publication number
- CN107727591A CN107727591A CN201710928148.5A CN201710928148A CN107727591A CN 107727591 A CN107727591 A CN 107727591A CN 201710928148 A CN201710928148 A CN 201710928148A CN 107727591 A CN107727591 A CN 107727591A
- Authority
- CN
- China
- Prior art keywords
- pseudo
- ginseng
- integrating sphere
- sample
- diffusing reflection
- 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
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
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
A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes the quantitative analysis method of pseudo- pseudo-ginseng, and the uv-vis spectra of pseudo- pseudo-ginseng sample is mixed using integrating sphere diffusing reflection ultraviolet-uisible spectrophotometer scanning ternary；Data set is divided into training set and forecast set；Investigation centralization, sized, minimax normalization, standardization, standard normal variable, multiplicative scatter correction, first derivative, second dervative, continuous wavelet transform, SG smooth pretreating effect, chooses optimal preprocess method；Least square model is established after handling spectrum using optimal preprocess method, and unknown sample is predicted.Present invention introduces integrating sphere diffusing reflection, without sample pretreatment, realizes sample nondestructive direct measurement；It is rapid using uv-vis spectra detection；Spectrum is handled using Chemical Measurement and is modeled, and prediction accuracy is high.The ternary that the present invention is applied to pseudo-ginseng mixes pseudo- quantitative analysis.
Description
Technical field
The invention belongs to Chinese medicine detection technique field, and in particular to one kind is based on integrating sphere diffusing reflection uv-vis spectra
Ternary mix pseudo- pseudo-ginseng quantitative analysis method.
Background technology
Chinese medicine pseudo-ginseng is the dry root of panax araliaceae plant, because it has the effect of dissipate stasis of blood hemostasis, detumescence ding-tong, is
Conventional well sold and in short supply rare rare Chinese medicine.It is expensive because the pseudo-ginseng market demand is big, cause in the market to exist outside a variety of and pseudo-ginseng
See, mix adulterant or adulterant similar in color, have a strong impact on drug safety.Thus pseudo-ginseng is inquired into mix the discriminating of adulterant and quantitatively divide
Analysis, to ensureing that safe medication is significant.
Existing a variety of methods are applied to the discriminating and quantitative study of pseudo-ginseng, such as micro-, visible and near infrared spectrum (PC at present
Nie, D Wu, DW Sun, Potential of visible and near infrared spectroscopy and
pattern recognition for rapid quantification of Notoginseng powder with
Adulterants, Sensors, 2013,13,13820-13834), electronic nose, (Xie Shaopeng, a kind of pseudo-ginseng true and false are excellent for electronic tongues
Bad method for quick identification, Chinese invention patent, 2014, CN201410784877.4) etc..But these methods exist it is expensive,
It is time-consuming, the shortcomings of specificity is not high.Therefore establishing a kind of can accurately measure the method that pseudo-ginseng mixes each component content in adulterant and have
Significance.
Ultraviolet visible spectrometry has many advantages such as instrument price is cheap, quick, accuracy is high, in material qualitative, quantitative
It is widely applied in terms of analysis.Conventional ultra-violet visible spectrum generally requires is made solution by measured object, according to Lambert-
Beer laws, absorbance of the measured object in characteristic wave strong point is established into unitary calibration model with concentration.But Chinese medicine is typically by into hundred
Thousands of kinds of compound groups are into the separation of its active ingredient is cumbersome time-consuming with extracting.The introducing of integrating sphere diffusing reflection annex so that sample
Product are without pretreatment, it is possible to achieve the lossless direct measurement of solid traditional Chinese medicine sample.
For the uv-vis spectra of Chinese medicine, because component is various, it is difficult to search out the characteristic peak of target analytes, therefore
Traditional unitary calibration model can not realize the quantitative analysis of complicated Chinese medicine system.Multivariate calibration techniques pass through in multiple spectrum channels
Founding mathematical models between data and the content of target components, it is possible to achieve the accurate quantitative analysis of complex sample component.Wherein partially most
Small least square method turns into most widely used multivariate calibration methodses because it is simple, quick, parameter is few.
The content of the invention
The purpose of the present invention is to be directed to above-mentioned problem, integrating sphere diffusing reflection detection accessory is introduced, with UV, visible light
Spectrum establishes partial least square model as means of testing, there is provided a kind of ternary mixes the quick, lossless, accurately fixed of pseudo- pseudo-ginseng
Analysis method.
To realize that technical scheme provided by the present invention comprises the following steps：
1) prepare ternary and mix pseudo- pseudo-ginseng sample
It is some from different shop of Chinese medicines purchase pseudo-ginseng and two kinds of medicinal materials similar to pseudo-ginseng respectively, and by pseudo-ginseng and both
Medicinal material according to certain mass percent be configured to ternary mix pseudo- pseudo-ginseng sample several.Ensure all to mix each medicinal material in pseudo- sample
Weight/mass percentage composition scope be 0~100%.
2) uv-vis spectra of sample is gathered
Using the spectrum of integrating sphere diffusing reflection ultraviolet-visual spectrometer device determination sample.Ultraviolet-visual spectrometer device is set
Parameter, wave-length coverage 290-800nm, sweep speed is at a high speed, sampling interval 0.5nm, mensuration mode is reflectivity, slit
A width of 5.0nm, detector cell are position list detector.Scanning the ultraviolet of all samples successively after instrument is preheated 15 minutes can
See spectrum.It will first be put into ultraviolet-uisible spectrophotometer for surveying the absorption cell of baseline and carry out baseline scan, afterwards with small spoon
Candidate drug is put into the absorption cell for surveying solid, smears uniformly, allows it that absorption cell bottom and tight is completely covered, so
After put into ultraviolet-uisible spectrophotometer and be scanned, each Sample Scan once after, absorption cell is taken out and is rotated by 90 ° again
It is secondary to be put into second of scanning of progress.The spectrum that the spectrum of twice sweep is averaged as the sample.
3) data set is divided into training set and forecast set
Using certain packet mode, data set is divided into 2/3 conduct of training set and forecast set, wherein gross sample number
Training set, 1/3 is used as forecast set.
4) factor number of deflected secondary air is determined
According to the cross validation root-mean-square error (RMSECV) of Monte Carlo Cross-Validation with factor number (LV) change
The factor number of partial least square model is determined, factor number corresponding to RMSECV minimum values is optimum factor number.
5) spectral signal is pre-processed using different pretreatments method, it is determined that optimal preprocess method
According to predicted root mean square error (RMSEP) as the change of window determines that SG is smooth, first derivative, second dervative
Window size, window corresponding to RMSEP minimum values are best window；According to RMSEP with the change of wavelet function and decomposition scale
Change the wavelet function and decomposition scale for determining wavelet transformation (CWT), wavelet function and decomposition scale are corresponding to RMSEP minimum values
Optimal parameter.Under optimal parameter, centralization, sized, minimax normalization, standardization, standard normal variable, more is investigated
The preprocess methods such as first scatter correction, first derivative, second dervative, continuous wavelet transform, SG be smooth pre-process to spectrum
The prediction effect of PLS modelings determines optimal preprocess method afterwards, and preprocess method corresponding to RMSEP minimum values is optimal pretreatment
Method.
6) partial least square model is established
Spectroscopic data is pre-processed using optimal preprocess method, using optimum factor number, training set established inclined
Least square model.
7) content of each component in unknown sample is predicted
The spectrum that ternary in forecast set is mixed to pseudo- pseudo-ginseng sample is updated in partial least square model, is predicted various in sample
The percentage composition of component.
The invention has the advantages that Chinese medicine separation and extraction are saved as sample metering system using integrating sphere diffusing reflection annex
Tedious steps, it is possible to achieve the direct measurement of solid Chinese medicine；Using uv-vis spectra as detection means, analyze speed
It hurry up；Establish model using optimal preprocess method combination offset minimum binary, can accurate quantitative analysis ternary pseudo-ginseng mix each group in adulterant
Divide percentage composition.
Brief description of the drawings
Fig. 1 is the ultraviolet-visible spectrogram that pseudo-ginseng curcuma zedoary turmeric ternary mixes pseudo- data
Fig. 2 is that pseudo-ginseng curcuma zedoary turmeric ternary mixes the cross validation root-mean-square error of pseudo- data with the variation diagram of factor number
Fig. 3 is that pseudo-ginseng curcuma zedoary turmeric ternary mixes the predicted root mean square error of pseudo- data with the variation diagram (a) of window size
Smooth (b) first derivative (c) second dervatives of SG
Fig. 4 is that pseudo-ginseng curcuma zedoary turmeric ternary mixes the optimal preprocess method combination PLS models of pseudo- data to forecast set prediction
Graph of a relation (a) the pseudo-ginseng component continuous wavelet transform of predicted value and actual value-offset minimum binary modeling；(b) curcuma zedoary component single order
Derivative-offset minimum binary modeling；(c) turmeric component second dervative-offset minimum binary modeling
Fig. 5 is the ultraviolet-visible spectrogram that pseudo-ginseng curcuma zedoary galangal ternary mixes pseudo- data
Fig. 6 is that pseudo-ginseng curcuma zedoary galangal ternary mixes the cross validation root-mean-square error of pseudo- data with the variation diagram of factor number
Fig. 7 is that pseudo-ginseng curcuma zedoary galangal ternary mixes the predicted root mean square error of pseudo- data with the variation diagram of window size
(a) smooth (b) first derivative (c) second dervatives of SG
Fig. 8 be pseudo-ginseng curcuma zedoary galangal ternary mix pseudo- data optimal preprocess method combination PLS models it is pre- to forecast set
The predicted value of survey and graph of a relation (a) the pseudo-ginseng component centralization of actual value-offset minimum binary model；(b) curcuma zedoary component second order is led
Number-offset minimum binary models；(c) galangal component first derivative-offset minimum binary modeling
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 scope is not limited to the scope represented by embodiment.
Embodiment 1：
The present embodiment is to be applied to integrating sphere diffusing reflection uv-vis spectra to analyze, and mixes puppet to pseudo-ginseng, curcuma zedoary, turmeric ternary
Each component percentage composition carries out quantitative analysis in pseudo-ginseng sample.Specific step is as follows：
1) pseudo-ginseng, curcuma zedoary, turmeric ternary are prepared and mixes pseudo- pseudo-ginseng sample
Same one hundred from Tianjin, Tianjin pharmacy of Beijing Tongrentang, the 14 pharmacy purchase pseudo-ginseng such as profit and big pharmacy that converge respectively
28 kinds of 24 kinds of sample, 26 kinds of curcuma zedoary and turmeric, and pseudo-ginseng and both medicinal materials are configured to three according to certain mass percent
Member mixes pseudo- 66, pseudo-ginseng sample.All quality percent ranges for mixing each medicinal material in pseudo- sample are 0~100%.
2) uv-vis spectra of sample is gathered
Using the light of integrating sphere diffusing reflection ultraviolet-visual spectrometer device (UV-2700, Shimadzu, Japan) measure all samples
Spectrum.Wave-length coverage is 290-800nm, and sweep speed is at a high speed, sampling interval 0.5nm, mensuration mode is reflectivity, and slit is wide
For 5.0nm, detector cell is position list detector.Instrument is preheated 15 minutes and starts test sample.First by for surveying the suction of baseline
Receives pond, which is put into ultraviolet-uisible spectrophotometer, carries out baseline scan, and candidate drug is put into for surveying solid with small spoon afterwards
In absorption cell, smear uniformly, allow it that absorption cell bottom and tight is completely covered, then put into ultraviolet-uisible spectrophotometer
Be scanned, each Sample Scan once after, absorption cell is taken out to be rotated by 90 ° be placed again into progress second and scan.Sweep twice
The spectrum that the spectrum retouched is averaged as the sample.Fig. 1 shows that 66 pseudo-ginseng curcuma zedoary turmeric ternarys mix the ultraviolet of pseudo- sample
Visible ray spectrogram.
3) data set is divided into training set and forecast set
The data of 66 samples are divided with KS methods, the data of 44 samples are made as training set, the data of 22 samples
For forecast set.
4) factor number of deflected secondary air is determined
It is true with the change of factor number (LV) according to the cross validation root-mean-square error (RMSECV) of Monte Carlo Cross-Validation
Determine the factor number of partial least square model, factor number is changed from 1 to 25, factor number corresponding to RMSECV minimum values is most
Good factor number.Fig. 2 shows that pseudo-ginseng curcuma zedoary turmeric ternary mixes the RMSECV of pseudo- data with LV variation diagrams, can from figure
Go out, the optimum factor number of pseudo-ginseng, curcuma zedoary and turmeric component is respectively 7,23,9.
5) spectral signal is pre-processed using different pretreatments method, it is determined that optimal preprocess method
According to predicted root mean square error (RMSEP) as the change determination SG of window is smooth and the window size of derivation,
Window corresponding to RMSEP minimum values is best window.Fig. 3 (a) shows the lower RMSEP of smooth pretreatment with the change of window.
It can be seen that pseudo-ginseng, curcuma zedoary, window is respectively 5,59 and 35 corresponding to turmeric component RMSEP minimum values, it is above-mentioned 3
The best window that individual component smoothly pre-processes.Fig. 3 (b) and Fig. 3 (c) is respectively illustrated under first derivative and second dervative pretreatment
RMSEP with the change of window.From two sub- it can be seen from the figure thats, spectroscopic data respectively in first derivative, second dervative
Under pretreatment, the best window of pseudo-ginseng, curcuma zedoary and turmeric is respectively 57,59,27 and 59,57,59.
According to RMSEP as the change of wavelet function and decomposition scale determines the wavelet function of wavelet transformation (CWT) and divides
Yardstick is solved, wavelet function corresponding to RMSEP minimum values and decomposition scale are optimal parameter.Pseudo-ginseng component in the present embodiment
Wavelet function and decomposition scale corresponding to RMSEP minimum values are respectively Haar and 41, as pseudo-ginseng component Optimum wavelet function and
Decomposition scale.Curcuma zedoary component Optimum wavelet function and decomposition scale are respectively Haar and 30.The Optimum wavelet function of turmeric component
It is respectively coif1 and 51 with decomposition scale.
The different pretreatments method of table 1 mixes pseudo-ginseng curcuma zedoary turmeric ternary pseudo-ginseng the RMSEP of pseudo- sample prediction
Under optimal parameter, offset minimum binary, centralization, sized, minimax normalization, standardization, standard are investigated
The preprocess methods such as normal variate, multiplicative scatter correction, first derivative, second dervative, continuous wavelet transform, SG be smooth are to spectrum
The prediction effect that PLS is modeled after being pre-processed determines optimal preprocess method.Table 1 shows different pretreatments method
RMSEP values.As can be seen from the table, pseudo-ginseng, curcuma zedoary, preprocess method corresponding to turmeric component minimum RMSEP values are respectively to connect
Continuous wavelet transformation, first derivative, second dervative, the optimal preprocess method as these three components.
6) partial least square model is established
For pseudo-ginseng, curcuma zedoary, turmeric component, continuous wavelet transform, first derivative, second dervative is respectively adopted as optimal
Preprocess method is predicted to spectrum, and optimum factor number is respectively 7,23,9, and partial least square model is established to training set.
7) content of each component in unknown sample is predicted
The spectrum that ternary in forecast set is mixed to pseudo- pseudo-ginseng sample is updated in partial least square model, and prediction ternary mixes puppet three
The percentage composition of each component in seven samples.
Fig. 4 is shown establishes PLS models after optimal pretreatment, ternary mix the predicted value of pseudo- pseudo-ginseng sample each component with
The relation of actual value.It can be seen that pseudo-ginseng, the predicted value of three components of curcuma zedoary and turmeric and actual value have well
Correlation, coefficient correlation have respectively reached 0.9974,0.9924 and 0.9954.Therefore, integrating sphere diffusing reflection uv-vis spectra
Technology combine optimal pretreatment-offset minimum binary modeling method can accurately, quickly, nondestructively quantitative analysis ternary, curcuma zedoary, ginger
Rhizoma soulieae vaginatae mixes the content of each component in pseudo- sample.
Embodiment 2：
The present embodiment is to be applied to integrating sphere diffusing reflection uv-vis spectra to analyze, and pseudo-ginseng, curcuma zedoary, galangal ternary are mixed
Each component percentage composition carries out quantitative analysis in pseudo- pseudo-ginseng sample.Specific step is as follows：
1) prepare ternary and mix pseudo- pseudo-ginseng sample
It is some from different shop of Chinese medicines's purchase pseudo-ginseng, curcuma zedoary and galangal respectively, and by pseudo-ginseng and both medicinal materials according to
Certain mass percent be configured to ternary mix pseudo- pseudo-ginseng sample several.All concentration ranges for mixing each medicinal material in pseudo- sample are 0
~100%, ternary is configured to altogether mixes pseudo- 66, pseudo-ginseng sample.
2) uv-vis spectra of sample is gathered
Using the light of integrating sphere diffusing reflection ultraviolet-visual spectrometer device (UV-2700, Shimadzu, Japan) measure all samples
Spectrum.Wave-length coverage is 290-800nm, and sweep speed is at a high speed, sampling interval 0.5nm, mensuration mode is reflectivity, and slit is wide
For 5.0nm, detector cell is position list detector.Instrument is preheated 15 minutes and starts test sample.First by for surveying the suction of baseline
Receives pond, which is put into ultraviolet-uisible spectrophotometer, carries out baseline scan, and candidate drug is put into for surveying solid with small spoon afterwards
In absorption cell, smear uniformly, allow it that absorption cell bottom and tight is completely covered, then put into ultraviolet-uisible spectrophotometer
Be scanned, each Sample Scan once after, absorption cell is taken out to be rotated by 90 ° be placed again into progress second and scan.Sweep twice
The spectrum that the spectrum retouched is averaged as the sample.Fig. 5 shows that 66 pseudo-ginseng curcuma zedoary galangal ternarys mix the purple of pseudo- sample
Outer visible ray spectrogram.
3) data set is divided into training set and forecast set
Data are divided with KS methods, the 2/3 of total number of samples is used as training set, and 1/3 is used as forecast set.
4) factor number of deflected secondary air is determined
It is true with the change of factor number (LV) according to the cross validation root-mean-square error (RMSECV) of Monte Carlo Cross-Validation
Determine the factor number of partial least square model, factor number is changed from 1 to 25, factor number corresponding to RMSECV minimum values is most
Good factor number.Fig. 6 shows that pseudo-ginseng curcuma zedoary galangal ternary mixes pseudo- RMSECV with LV variation diagrams, it can be seen that three
7th, curcuma zedoary and the optimum factor number of galangal component are respectively 16,17,18.
5) spectral signal is pre-processed using different pretreatments method, it is determined that optimal preprocess method
According to predicted root mean square error (RMSEP) as the change determination SG of window is smooth and the window size of derivation.Fig. 7
(a) show the lower RMSEP of smooth pretreatment with the change of window.It can be seen that pseudo-ginseng, curcuma zedoary, galangal component
Window corresponding to RMSEP minimum values is respectively 3,59 and 59, the best window smoothly pre-processed for above-mentioned 3 components.Fig. 7 (b)
With Fig. 7 (c) respectively illustrate first derivative and second dervative pretreatment under RMSEP with the change of window.Can from two figures
To find out, spectroscopic data respectively under first derivative, the pretreatment of second dervative, divide by the best window of pseudo-ginseng, curcuma zedoary and turmeric
Wei 27,41,57 and 59,59,53.
According to RMSEP as the change of wavelet function and decomposition scale determines the wavelet function of wavelet transformation (CWT) and divides
Solve yardstick.After processing ternary pseudo-ginseng mixes adulterant data, it can be deduced that wavelet function influences to be much smaller than decomposition scale on prediction result
Influence.It is respectively Haar and 32 to mix wavelet function and decomposition scale corresponding to the RMSEP minimum values of pseudo-ginseng in adulterant.Curcuma zedoary and
The Optimum wavelet function and decomposition scale of galangal are respectively Haar, bior1.3 and 46,60.
Under optimal parameter, offset minimum binary, centralization, sized, minimax normalization, standardization, standard are investigated
The preprocess methods such as normal variate, multiplicative scatter correction, first derivative, second dervative, continuous wavelet transform, SG be smooth are to spectrum
The prediction effect that PLS is modeled after being pre-processed, it is determined that optimal preprocess method.Table 2 shows different pretreatments method to three
Seven curcuma zedoary galangal ternary pseudo-ginseng mix the RMSEP of pseudo- sample prediction.As can be seen from the table, to pseudo-ginseng, curcuma zedoary, galangal group
Point, preprocess method corresponding to minimum RMSEP values is respectively centralization, second dervative, first derivative, therefore, these three are pre-
Optimal preprocess method of the processing method as these three components.
6) partial least square model is established
For pseudo-ginseng, curcuma zedoary, galangal component, centralization, second dervative, first derivative is respectively adopted as optimal pre- place
Reason method, using optimum factor number, partial least square model is established to training set.
7) content of each component in unknown sample is predicted
The spectrum that ternary in forecast set is mixed to pseudo- pseudo-ginseng sample is updated in partial least square model, predicts sample each component
Percentage composition.Fig. 8 is shown establishes PLS models after optimal pretreatment, and ternary mixes the prediction of pseudo- pseudo-ginseng sample each component
Value and the relation of actual value, it can be seen that ternary pseudo-ginseng curcuma zedoary galangal mix in pseudo- data respectively to pseudo-ginseng, curcuma zedoary and
The predicted value and actual value that galangal is predicted have a good correlation, coefficient correlation respectively reached 0.9971,
0.9971 and 0.9986.As a result show after optimal preprocess method handles spectrum, build the prediction of partial least square model
Ability improves a lot.
Therefore, integrating sphere diffusing reflection UV-Vis spectroscopic techniques can be real well with reference to suitable chemometrics method
Existing ternary pseudo-ginseng mixes the accurate quantitative analysis of each component in adulterant.
The different pretreatments method of table 2 mixes pseudo-ginseng curcuma zedoary galangal ternary pseudo-ginseng the RMSEP of pseudo- sample prediction
Claims (5)
1. a kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method, it is characterised in that：
Collect some pseudo-ginseng ternarys and mix pseudo- sample, the parameter of integrating sphere diffusing reflection ultraviolet-visual spectrometer device is set, scans sample successively
Ultraviolet spectra, attempt different preprocess methods, collection spectrum pre-processed, it is determined that optimal preprocess method, most
Least square model is established on the basis of good preprocess method, pseudo-ginseng content in unknown sample is predicted using model.
2. a kind of ternary based on integrating sphere diffusing reflection uv-vis spectra according to claim 1 is mixed pseudo- pseudo-ginseng and quantitatively divided
Analysis method, it is characterised in that：It is 290-800nm that the parameter of ultraviolet-visual spectrometer device, which is arranged to sample wave-length coverage, scanning speed
Spend at a high speed, sampling interval 0.5nm, mensuration mode is reflectivity, and a width of 5.0nm of slit, detector cell is that position is singly examined
Survey device.
3. a kind of ternary based on integrating sphere diffusing reflection uv-vis spectra according to claim 1 is mixed pseudo- pseudo-ginseng and quantitatively divided
Analysis method, it is characterised in that：Described preprocess method includes centralization, sized, minimax normalization, standardization, mark
Quasi- normal variate, multiplicative scatter correction, first derivative, second dervative, continuous wavelet transform, SG are smooth.
4. a kind of ternary based on integrating sphere diffusing reflection uv-vis spectra according to claim 1 is mixed pseudo- pseudo-ginseng and quantitatively divided
Analysis method, it is characterised in that：The system of selection of described optimal preprocess method is：Light is handled using no preprocess method
After spectrum, partial least square model is established, obtains the predicted root mean square error of every kind of method.Predicted root mean square error minimum value is corresponding
Preprocess method be optimal preprocess method.
5. a kind of ternary based on integrating sphere diffusing reflection uv-vis spectra according to claim 1 is mixed pseudo- pseudo-ginseng and quantitatively divided
Analysis method, it is characterised in that：To the limitednumber system of pseudo-ginseng adulterant, pattern, shape and adulterant similar in pseudo-ginseng can be carried out
Mix pseudo- quantitative.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710928148.5A CN107727591A (en) | 2017-09-27 | 2017-09-27 | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710928148.5A CN107727591A (en) | 2017-09-27 | 2017-09-27 | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107727591A true CN107727591A (en) | 2018-02-23 |
Family
ID=61209727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710928148.5A Pending CN107727591A (en) | 2017-09-27 | 2017-09-27 | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107727591A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111257263A (en) * | 2020-03-04 | 2020-06-09 | 浙江方圆检测集团股份有限公司 | Method for identifying chemical composition and structural morphology consistency of pearl surface layer by using ultraviolet-visible diffuse reflection spectrum |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103411895A (en) * | 2013-08-23 | 2013-11-27 | 中国检验检疫科学研究院 | Near infrared spectrum identification method of adulteration of pearl powder |
| CN104297201A (en) * | 2014-11-03 | 2015-01-21 | 天津工业大学 | Method for quickly, accurately and quantitatively detecting ratio of various oil components in blend oil |
| CN107036999A (en) * | 2016-11-15 | 2017-08-11 | 天津工业大学 | A kind of five yuan of ready-mixed oil quantitative analysis methods based near infrared spectrum and Chemical Measurement |
| CN107132194A (en) * | 2017-07-10 | 2017-09-05 | 天津工业大学 | A kind of pseudo-ginseng and its adulterant discrimination method based on uv-vis spectra and Chemical Pattern Recognition |
-
2017
- 2017-09-27 CN CN201710928148.5A patent/CN107727591A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103411895A (en) * | 2013-08-23 | 2013-11-27 | 中国检验检疫科学研究院 | Near infrared spectrum identification method of adulteration of pearl powder |
| CN104297201A (en) * | 2014-11-03 | 2015-01-21 | 天津工业大学 | Method for quickly, accurately and quantitatively detecting ratio of various oil components in blend oil |
| CN107036999A (en) * | 2016-11-15 | 2017-08-11 | 天津工业大学 | A kind of five yuan of ready-mixed oil quantitative analysis methods based near infrared spectrum and Chemical Measurement |
| CN107132194A (en) * | 2017-07-10 | 2017-09-05 | 天津工业大学 | A kind of pseudo-ginseng and its adulterant discrimination method based on uv-vis spectra and Chemical Pattern Recognition |
Non-Patent Citations (1)
| Title |
|---|
| 明晶 等: "基于近红外光谱和SVM算法对琥珀掺伪的定性鉴别与定量分析", 《中药材》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111257263A (en) * | 2020-03-04 | 2020-06-09 | 浙江方圆检测集团股份有限公司 | Method for identifying chemical composition and structural morphology consistency of pearl surface layer by using ultraviolet-visible diffuse reflection spectrum |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1982872B (en) | Near-infrared diffuse reflection spectral method for fastly inspecting drop water content | |
| CN104062257B (en) | A kind of based on the method for general flavone content near infrared ray solution | |
| Chen et al. | Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques | |
| Burger et al. | Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples | |
| CN106841083A (en) | Sesame oil quality detecting method based on near-infrared spectrum technique | |
| CN101216419A (en) | Method for quickly detecting yellow wine quality index | |
| CN102175648A (en) | Method for distinguishing variety of fritillaria and detecting total alkaloid content of fritillaria by virtue of near infrared spectrum | |
| CN106770003A (en) | Wood Identification Method and system based on near-infrared spectrum technique | |
| CN103411895B (en) | Pseudo-near infrared spectrum identification method mixed by pearl powder | |
| CN109142263A (en) | A kind of component dispersing uniformity on-line testing method of solid propellant preparation process | |
| Wulandari et al. | Determination of total flavonoid content in medicinal plant leaves powder using infrared spectroscopy and chemometrics | |
| CN108663337A (en) | A kind of method and its application measuring tanshinone component | |
| CN113655027A (en) | Method for rapidly detecting tannin content in plant by near infrared | |
| CN109799224A (en) | Quickly detect the method and application of protein concentration in Chinese medicine extract | |
| CN109507123A (en) | A kind of discrimination method of Radix Angelicae Sinensis based on UV-vis DRS spectrum and Chemical Pattern Recognition and its similar product | |
| CN107727591A (en) | A kind of ternary based on integrating sphere diffusing reflection uv-vis spectra mixes pseudo- pseudo-ginseng quantitative analysis method | |
| CN1982873B (en) | Near-infrared diffuse reflection spectral method for fastly inspecting drop appearance quality | |
| CN107271396A (en) | The quick determination method of general flavone content in a kind of tealeaves | |
| CN109030410B (en) | Construction method of royal jelly near-infrared quantitative correction model and royal jelly detection method | |
| CN107703095A (en) | A kind of binary based near infrared spectrum and Chemical Measurement mixes pseudo- pseudo-ginseng quantitative analysis method | |
| CN107703074A (en) | One kind mixes the fast and accurately quantitative analysis method of pseudo- pseudo-ginseng for quaternary | |
| CN107036997A (en) | Method and application using the preparation process of near infrared spectroscopy quick detection qizhi weitong granules | |
| CN108693140A (en) | A kind of method of muskone content in quick detection Xingnaojing oral preparation | |
| CN105466878B (en) | A method of utilizing lycopene in infrared spectrum measurement catsup | |
| CN108072628A (en) | A kind of near infrared spectroscopy for differentiating Medical Packing Materials material |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180223 |