CN109765200A - A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics - Google Patents
A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics Download PDFInfo
- Publication number
- CN109765200A CN109765200A CN201910236423.6A CN201910236423A CN109765200A CN 109765200 A CN109765200 A CN 109765200A CN 201910236423 A CN201910236423 A CN 201910236423A CN 109765200 A CN109765200 A CN 109765200A
- Authority
- CN
- China
- Prior art keywords
- adulterated
- angelica
- binary
- preprocessing
- quantitative analysis
- 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
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004445 quantitative analysis Methods 0.000 title claims abstract description 12
- 235000001287 Guettarda speciosa Nutrition 0.000 title claims abstract 7
- 238000004497 NIR spectroscopy Methods 0.000 title claims abstract 5
- 244000061520 Angelica archangelica Species 0.000 title 1
- 230000000694 effects Effects 0.000 claims abstract description 11
- 238000012937 correction Methods 0.000 claims abstract description 5
- 238000009499 grossing Methods 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims abstract description 4
- 241000125175 Angelica Species 0.000 claims abstract 6
- 238000007781 pre-processing Methods 0.000 claims abstract 5
- 241000382455 Angelica sinensis Species 0.000 claims abstract 4
- 238000000985 reflectance spectrum Methods 0.000 claims abstract 2
- 238000010238 partial least squares regression Methods 0.000 claims description 22
- 238000002203 pretreatment Methods 0.000 claims description 7
- 238000002329 infrared spectrum Methods 0.000 description 12
- 238000000354 decomposition reaction Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 11
- 238000001228 spectrum Methods 0.000 description 10
- 239000003814 drug Substances 0.000 description 9
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000009466 transformation Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000002790 cross-validation Methods 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 description 3
- 239000000843 powder Substances 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004587 chromatography analysis Methods 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012628 principal component regression Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 241000208173 Apiaceae Species 0.000 description 1
- 101100127891 Caenorhabditis elegans let-4 gene Proteins 0.000 description 1
- 241000756943 Codonopsis Species 0.000 description 1
- 206010033799 Paralysis Diseases 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 230000003213 activating effect Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 208000007502 anemia Diseases 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 206010003549 asthenia Diseases 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 229940126678 chinese medicines Drugs 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004455 differential thermal analysis Methods 0.000 description 1
- 238000001962 electrophoresis Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000005906 menstruation Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 101150065184 sym-2 gene Proteins 0.000 description 1
- 238000000870 ultraviolet spectroscopy Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
本发明涉及一种基于近红外光谱及化学计量学的二元掺伪当归定量分析方法。具体步骤为先购买当归及相似品若干，配制一定数目的当归掺伪样品；采集掺伪样品的近红外漫反射光谱；采用KS分组方式，将数据集划分为训练集和预测集；确定偏最小二乘回归模型的因子数；再次，考察SG平滑法、多元散射校正、标准正态变量、一阶导数、二阶导数、连续小波变换及其组合的预处理效果，得到最佳预处理方法；最后，采用最佳预处理‑PLSR建模方法对二元掺伪当归定量分析。本发明基于近红外光谱及化学计量学，快速简便，无损样品。本发明适用于二元掺伪当归的定量分析。The invention relates to a method for quantitative analysis of binary adulterated angelica based on near-infrared spectroscopy and chemometrics. The specific steps are to purchase a number of Angelica sinensis and similar products first, prepare a certain number of adulterated samples of Angelica sinensis; collect the near-infrared diffuse reflectance spectrum of the adulterated samples; use the KS grouping method to divide the data set into a training set and a prediction set; determine the partial minimum The number of factors of the quadratic regression model; thirdly, the preprocessing effects of SG smoothing, multivariate scattering correction, standard normal variables, first derivative, second derivative, continuous wavelet transform and their combinations were investigated, and the best preprocessing method was obtained; Finally, the optimal preprocessing-PLSR modeling method was used to quantitatively analyze the binary adulterated Angelica sinensis. The invention is based on near-infrared spectroscopy and chemometrics, and is fast, simple, and does not damage samples. The invention is suitable for quantitative analysis of binary adulterated angelica.
Description
Technical field
The invention belongs to the quantitative analysis tech of Analysis of Chinese Traditional Medicine field Radix Angelicae Sinensis, are related to a kind of based near infrared spectrum and chemistry
The binary of meterological mixes pseudo- Radix Angelicae Sinensis quantitative analysis method.
Background technique
When being classified as Umbellales umbelliferae, it is distributed mainly on high and cold rainy mountain area.Radix Angelicae Sinensis can not only replenishing and activating blood, and
And promoting menstruation dredging collateral, and it is widely used in all various aspects such as weak anaemia, asthenia cold abdominalgia, rheumatism paralysis, menstruction regulating and pain relieving.However due to medicine
The problems such as object scarcity of resources, drug price are higher and demand is big, it is commonplace to mix pseudo- phenomenon for Radix Angelicae Sinensis in market.Common Radix Angelicae Sinensis
Adulterant has Radix Angelicae Pubescentis, Rhizoma Chuanxiong, Rhizoma Atractylodis Macrocephalae etc., and form is approximate and is difficult to the naked eye distinguish, misuses that will lead to curative effect undesirable, or even meeting
Health of human body is threatened, therefore corresponding detection and identification technology is also come into being.
Common TCD identificafion method has chromatography, spectroscopic methodology, differential thermal analysis, random amplification DNA method, scanning electron microscope skill
Art and electrophoresis etc..Wherein, reproducibility is poor when thin-layered chromatography is simple and efficient but complicated component, ultraviolet spectroscopy high sensitivity
But some difficulties are distinguished to Chinese medicine similar in relationship.Near infrared spectrum (NIRS) technology has quick analysis, low cost, without dirt
Dye, being not necessarily to the advantages that pre-treatment and simultaneous determination of multiponents, (China, State of Zhao, Liu Jia, Zhao Qiushuan, the food based near infrared spectrum are mixed
False starch rapid assay methods and system, Chinese invention patent, 2014, CN201410271077.2).NIRS is able to reflect Chinese medicine
Whole difference between material system.Therefore, qualitative analysis is the important branch that NIRS is applied in the field of Chinese medicines, is answered extensively
The true and false for Chinese medicine identifies (Zang Hengchang, Zeng Yingzi, Nie Lei, Yang Hailong, Hu Tian, Yu Hongliang, based on NIR technology
The Radix Codonopsis true and false identify and the place of production determine method, Chinese invention patent, 2015, CN201510141871.X).
Due to the overlapping seriousness and discontinuity of atlas of near infrared spectra, so needing by polynary in Chemical Measurement
Bearing calibration carries out quantitative analysis.Multivariate calibration methods mainly have principal component regression (PCR), Partial Least Squares Regression (PLSR),
Artificial neural network (BP-ANN), support vector regression (SVR) and extreme learning machine (ELM) etc..Wherein PLSR predictive ability is strong
And it is simple to model needs, become one of the multivariate calibration methods being most widely used.Reliable Radix Angelicae Sinensis mixes puppet in order to obtain
Analysis is as a result, the pretreatment of spectrum is very important.Preprocess method mainly has SG smooth, first derivative (1stDer), second order
Derivative (2ndDer), continuous wavelet transform (CWT), multiplicative scatter correction (MSC), standard normal variable (SNV) etc..SG is smooth, one
Order derivative and second dervative are using window number as its important parameter, if window number is too low, it is undesirable to will lead to smooth effect, mistake
Height, which will lead to, relatively to be idealized, and is lost and caused to be distorted compared with multi information.Wavelet transformation there are two important parameter it needs to be determined that, i.e.,
Wavelet function and decomposition scale, the two parameters are directly related to the quality of Pretreated spectra result.So which kind of pretreatment side
Method effect is best, and combination processing effect is needed to choose.
Summary of the invention
The purpose of the present invention is in view of the above problems, using near infrared spectrum as means of testing, by suitable
Preprocess method pre-processes spectrum, models in conjunction with PLSR, and providing a kind of accurately and rapidly binary, to mix pseudo- Radix Angelicae Sinensis quantitative
Analysis method.
The technical scheme comprises the following steps provided by realize the present invention:
1) sample is prepared
From more pharmacy's purchase Radix Angelicae Sinensis and its several batches of adulterant, Chinese medicine is crushed with pulverizer, crosses 120 mesh later
The fine and smooth powder of collection is put into paper bag dry and prepare binary by a certain percentage and mixes pseudo- sample, wherein same match by sieve
Pseudo- sample is mixed with different Radix Angelicae Sinensis 3 Radix Angelicae Sinensis of preparation than lower, seals bottle, number.
2) near infrared spectrum of sample is acquired
The relevant parameter of near-infrared is set and the performance of instrument is tested, acquires the near infrared spectrum of sample.
3) data grouping
Using KS packet mode, data set is divided into training set and forecast set.
4) the optimum factor number of Partial Least-Squares Regression Model is determined
According to cross validation root-mean-square error (RMSECV) with the optimum factor for determining PLSR because of the variation of subnumber (LV)
Number, RMSECV minimum value are corresponding because subnumber is the optimum factor number of PLSR.
5) compare different pretreatments method and extremely combine effect to Pretreated spectra, obtain best preprocess method
SG smooth, first derivative and second order are determined with the variation of window size by predicted root mean square error (RMSEP)
The best window number of derivative, the corresponding window number of RMSEP minimum value are best window number.With wavelet function and divided according to RMSEP
The variation of scale is solved to determine the Optimum wavelet function and decomposition scale of wavelet transformation (CWT).
Under optimal parameter, investigates SG exponential smoothing, standard normal variable (SNV), multiplicative scatter correction (MSC), single order and lead
Number (1stDer), second dervative (2ndDer), the preprocess methods such as CWT and its combination of two carry out pretreated effect to spectrum,
The corresponding preprocess method of RMSEP minimum value is best preprocess method.
6) best pretreatment combines PLSR modeling method to predict unknown sample
PLSR modeling method is combined to predict the constituent content in forecast set sample using best pretreatment.
Pretreated effect is carried out to spectrum the invention has the advantages that comparing different pretreatments method, and then is selected best pre-
Then processing method resettles Partial Least-Squares Regression Model, to realize the accurate identification for mixing binary pseudo- Radix Angelicae Sinensis.
Detailed description of the invention
Fig. 1 is the atlas of near infrared spectra that 81 Radix Angelicae Sinensis Radix Angelicae Pubescentis binary mix pseudo- sample
Fig. 2 is the RMSECV of PLSR modeling with the variation diagram because of subnumber
Fig. 3 is RMSEP with smooth (b) first derivative (c) second dervative of variation diagram (a) SG of window size
Fig. 4 is the RMSEP of wavelet transformation with wavelet function and decomposition scale variation diagram
Fig. 5 is that optimal pretreatment combines PLSR to model to the predicted value of forecast set and relational graph (a) Radix Angelicae Sinensis of true value
SNV-CWT-PLSR modeling；(b) Radix Angelicae Pubescentis MSC-PLSR is modeled
Specific embodiment
To be best understood from the present invention, the present invention will be described in further detail with reference to the following examples, but of the invention
Claimed range is not limited to range represented by embodiment.
Embodiment:
The present embodiment is the quantitative analysis that pseudo- sample is mixed applied to Radix Angelicae Sinensis Radix Angelicae Pubescentis binary, uses near infrared spectrum combinationization
The method for learning meterological.Specific steps are as follows:
1) sample is prepared
From 40, Tianjin, different pharmacies buys 40 batch of Radix Angelicae Sinensis, 39 batch of Radix Angelicae Pubescentis.First by Chinese medicine pulverizer
It crushes, crosses 120 meshes later, the fine and smooth powder of collection is put into paper bag and is dried.By the adulterant Radix Angelicae Pubescentis and Radix Angelicae Sinensis of Radix Angelicae Sinensis
Powder is uniformly mixed according to different proportion, and obtained binary mixes pseudo- sample, wherein the Radix Angelicae Pubescentis to match in every group with Radix Angelicae Sinensis is all pure
Product.Radix Angelicae Sinensis, Radix Angelicae Pubescentis concentration range 0-100% are wherein divided between 1%, 5-95% between being divided into 5%, 97-100% between 0-3% and are divided into
1%, three groups of samples prepared according to same ratio of all useful different sterlings in 27 configuration proportions, so being set in the experiment
In meter, the binary of Radix Angelicae Sinensis mixes puppet and shares 81 samples.Drug is put into plastic bottle later, and marks sample number into spectrum 1-81.Often
A sample theory gross mass is 10g, calculates the Theoretical Mass of each sample each component, after weighing, it is each to record each sample
The actual mass of component, component actual mass obtain the mass percent of each component in sample divided by gross mass, as each group
The target value divided.
2) near infrared spectrum of sample is acquired
Before starting test sample, opens optically focused generation and reach near-infrared spectrometers, first preheat 0.5-1 hours, and to the property of instrument
It can be carried out self-test, wave-length coverage 1000-1800nm is spaced 1nm, then is tested for the property with white correcting plate and reference plate.It
The sample prepared is moved into loading ware with spoon afterwards, the surface of loading ware is smeared smoothly with angle square, makes its uniform fold ware
Face and the sample residue for wiping ware edge, in order to avoid pollution apparatus measures environment.Each sample setting pendulous frequency is 3 times, is turned
Disk carries object ware and rotates together, exports 3 spectrum automatically.In measurement process, white reference plate is utilized to carry out every other hour
Reference guarantees result accurately without generating deviation.Fig. 1 is the atlas of near infrared spectra that Radix Angelicae Sinensis and Radix Angelicae Pubescentis binary mix pseudo- sample.
3) data grouping
Using KS group technology, 2/3 is training set, and 1/3 is forecast set.The instruction of 54 samples is obtained after 81 sample groupings
Practice collection, the forecast set of 27 samples.
4) the optimum factor number of Partial Least-Squares Regression Model is determined
According to cross validation root-mean-square error (RMSECV) with the optimum factor for determining PLSR because of the variation of subnumber (LV)
Number, RMSECV minimum value are corresponding because subnumber is the optimum factor number of PLSR.Fig. 2 shows cross validation root-mean-square error
(RMSECV) with the variation because of subnumber, the RMSECV minimum value of Radix Angelicae Sinensis and Radix Angelicae Pubescentis component is corresponding because subnumber is respectively 15 Hes
The optimum factor number of 14, as PLSR.
5) compare different pretreatments method and extremely combine effect to Pretreated spectra, obtain best preprocess method
SG smooth, first derivative and second order are determined with the variation of window size by predicted root mean square error (RMSEP)
The best window number of derivative, the corresponding window number of RMSEP minimum value are best window number.Fig. 3 (a), (b), (c) are respectively SG flat
Cunning, first derivative, second dervative RMSEP with window size variation diagram.It can thus be concluded that Radix Angelicae Sinensis and Radix Angelicae Pubescentis component SG are smooth
Best window number is respectively 29 and 3, and the best window number of first derivative is 9, and the best window number of second dervative is respectively 17
With 19.
The Optimum wavelet function of wavelet transformation (CWT) is determined with the variation of wavelet function and decomposition scale according to RMSEP
With decomposition scale.Wavelet function include Haar, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12,
db13、db14、db15、db16、db17、db18、db19、db20、coif1、coif2、coif3、coif4、coif5、sym2、
sym3、sym4、sym5、sym6、sym7、sym8、bior1.1、bior1.3、bior1.5、bior2.2、bior2.4、
Bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5 and
Bior6.8, decomposition scale are divided into 1 from 1 to 60, calculate different wavelet functions and the corresponding RMSEP value of decomposition scale.RMSEP
The corresponding wavelet function of minimum value and decomposition scale are Optimum wavelet function and decomposition scale.Fig. 4 shows RMSEP with small echo letter
The variation of several and decomposition scale.Radix Angelicae Sinensis is respectively with decomposition scale with Radix Angelicae Pubescentis component Optimum wavelet function as can be drawn from Figure 4
Coif1 and 49, db2 and 52.
Under optimal parameter, investigates SG exponential smoothing, standard normal variable (SNV), multiplicative scatter correction (MSC), single order and lead
Number (1stDer), second dervative (2ndDer), the preprocess methods such as CWT and its combination of two carry out pretreated effect to spectrum,
The corresponding preprocess method of RMSEP minimum value is best preprocess method.Table 1 is different pretreatments method combination PLSR prediction
RMSEP and R.As can be seen from Table 1, the best pretreatment mode of the corresponding Radix Angelicae Sinensis of RMSEP minimum value and Radix Angelicae Pubescentis is SNV- respectively
CWT-PLSR and MSC-PLSR, the corresponding related coefficient of both best preprocess methods are also the peak in all methods.
The prediction result of 1 different pretreatments method combination PLSR of table modeling
6) best pretreatment combines PLSR modeling method to predict unknown sample
PLSR modeling method is combined to predict the constituent content in forecast set sample using best pretreatment.Fig. 5
(a), (b) is respectively the relationship of Radix Angelicae Sinensis and Radix Angelicae Pubescentis using the SNV-CWT-PLSR and MSC-PLSR predicted value modeled and true value
Figure.As can be seen that predicted value and the linear relationship of true value are fine, related coefficient is all 0.97 or more.Therefore, best pretreatment
It is modeled in conjunction with PLSR, the accurate quantitative analysis of component may be implemented.
Claims (2)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910236423.6A CN109765200A (en) | 2019-03-26 | 2019-03-26 | A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910236423.6A CN109765200A (en) | 2019-03-26 | 2019-03-26 | A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109765200A true CN109765200A (en) | 2019-05-17 |
Family
ID=66460014
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910236423.6A Pending CN109765200A (en) | 2019-03-26 | 2019-03-26 | A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109765200A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110376310A (en) * | 2019-08-20 | 2019-10-25 | 陕西中医药大学 | The detection method of Radix Angelicae Sinensis quality |
-
2019
- 2019-03-26 CN CN201910236423.6A patent/CN109765200A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110376310A (en) * | 2019-08-20 | 2019-10-25 | 陕西中医药大学 | The detection method of Radix Angelicae Sinensis quality |
| CN110376310B (en) * | 2019-08-20 | 2021-10-01 | 陕西中医药大学 | Method for testing the quality of Angelica sinensis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102960096B (en) | Rice single seed vigor nondestructive testing screening method based on near-infrared spectrum | |
| CN103033486B (en) | Method for near infrared spectrum monitoring of quality of pericarpium citri reticulatae and citrus chachiensis hortorum medicinal materials | |
| CN101231274B (en) | A method for rapid determination of allantoin in yam by near-infrared spectroscopy | |
| CN102288572A (en) | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique | |
| Chia et al. | Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison | |
| CN104237157A (en) | Method for utilizing terahertz time-domain spectroscopy technology to detect amino acid content in grain | |
| CN107917897A (en) | The method of the special doctor's food multicomponent content of near infrared ray | |
| CN108760677A (en) | A kind of rhizoma pinellinae praeparata based on near-infrared spectrum technique mixes pseudo- discrimination method | |
| CN104406923A (en) | Method for quantitative detection of amino acid content in grain by THz-TDS technology | |
| Chen et al. | Rapid identification of three varieties of Chrysanthemum with near infrared spectroscopy | |
| CN108593592A (en) | A kind of tuber of pinellia based on near-infrared spectrum technique mixes pseudo- discrimination method | |
| CN105044022A (en) | Method for rapidly nondestructively detecting wheat hardness based on near infrared spectrum technology and application | |
| CN106018321A (en) | Establishment method for single corn seed protein detection model and application of establishment method | |
| CN107132194A (en) | A kind of pseudo-ginseng and its adulterant discrimination method based on uv-vis spectra and Chemical Pattern Recognition | |
| CN106770014A (en) | A kind of near infrared spectrum detection method of rice freshness | |
| KR20030010025A (en) | non-destructive analysis method of one seed grain by near infrared reflectance spectroscopy | |
| WO2020248961A1 (en) | Method for selecting spectral wavenumber without reference value | |
| CN109765200A (en) | A method for quantitative analysis of binary adulterated Angelica based on near-infrared spectroscopy and chemometrics | |
| CN108444943A (en) | A kind of adulterated method for quick identification of coffee based on Fourier Transform Near Infrared | |
| CN106872398A (en) | A kind of HMX explosives moisture method for fast measuring | |
| CN104596983A (en) | Zopiclone tablet rapid identification method based on near infrared spectrum | |
| CN110231305A (en) | A method of DPPH free radical scavenging ability in the odd sub- seed of measurement | |
| CN110231306A (en) | A kind of method of lossless, the quick odd sub- seed protein content of measurement | |
| CN109406447A (en) | A kind of near infrared detection method of tannin in sorghum | |
| CN102539373A (en) | Method for quick and nondestructive measurement of aldioxa content in aldioxa raw material and preparation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190517 |
|
| WD01 | Invention patent application deemed withdrawn after publication |