CN106198447A - Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique - Google Patents
Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique Download PDFInfo
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- CN106198447A CN106198447A CN201610551981.8A CN201610551981A CN106198447A CN 106198447 A CN106198447 A CN 106198447A CN 201610551981 A CN201610551981 A CN 201610551981A CN 106198447 A CN106198447 A CN 106198447A
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- 239000000126 substance Substances 0.000 title claims abstract description 64
- 239000003337 fertilizer Substances 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 18
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims abstract description 24
- 238000001228 spectrum Methods 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims abstract description 12
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims abstract description 12
- 229910052757 nitrogen Inorganic materials 0.000 claims abstract description 12
- 229910052698 phosphorus Inorganic materials 0.000 claims abstract description 12
- 239000011574 phosphorus Substances 0.000 claims abstract description 12
- 239000011591 potassium Substances 0.000 claims abstract description 12
- 229910052700 potassium Inorganic materials 0.000 claims abstract description 12
- 238000004519 manufacturing process Methods 0.000 claims abstract description 10
- 238000000985 reflectance spectrum Methods 0.000 claims abstract description 5
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims abstract description 4
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 3
- 238000001311 chemical methods and process Methods 0.000 claims abstract 4
- SMWDFEZZVXVKRB-UHFFFAOYSA-N Quinoline Chemical compound N1=CC=CC2=CC=CC=C21 SMWDFEZZVXVKRB-UHFFFAOYSA-N 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 238000012417 linear regression Methods 0.000 claims description 6
- DHRLEVQXOMLTIM-UHFFFAOYSA-N phosphoric acid;trioxomolybdenum Chemical compound O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.O=[Mo](=O)=O.OP(O)(O)=O DHRLEVQXOMLTIM-UHFFFAOYSA-N 0.000 claims description 4
- 239000004575 stone Substances 0.000 claims description 4
- WUUHFRRPHJEEKV-UHFFFAOYSA-N tripotassium borate Chemical compound [K+].[K+].[K+].[O-]B([O-])[O-] WUUHFRRPHJEEKV-UHFFFAOYSA-N 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 3
- 239000000843 powder Substances 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 239000000470 constituent Substances 0.000 claims 2
- 238000013528 artificial neural network Methods 0.000 claims 1
- 238000011425 standardization method Methods 0.000 claims 1
- 238000005259 measurement Methods 0.000 abstract description 7
- 150000001875 compounds Chemical class 0.000 abstract description 4
- 238000003912 environmental pollution Methods 0.000 abstract description 3
- 239000000463 material Substances 0.000 abstract description 3
- 239000003153 chemical reaction reagent Substances 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 4
- 238000012628 principal component regression Methods 0.000 description 4
- 238000002203 pretreatment Methods 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 235000016709 nutrition Nutrition 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000004313 glare Effects 0.000 description 1
- 229910052736 halogen Inorganic materials 0.000 description 1
- 150000002367 halogens Chemical class 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 description 1
- 229910052721 tungsten Inorganic materials 0.000 description 1
- 239000010937 tungsten Substances 0.000 description 1
- 238000001845 vibrational spectrum Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/286—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q involving mechanical work, e.g. chopping, disintegrating, compacting, homogenising
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/286—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q involving mechanical work, e.g. chopping, disintegrating, compacting, homogenising
- G01N2001/2866—Grinding or homogeneising
Abstract
The present invention provides a kind of Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique, first Chemical Mixed Fertilizer sample set is acquired, then each sample is divided into two parts, portion utilizes standard chemical process to measure total nitrogen content, total phosphorus content and total potassium content, another part utilizes near infrared spectrometer to measure its reflectance spectrum, again spectrum is carried out pretreatment, choose calibration samples collection, utilize the content data measured by the spectroscopic data of this calibration samples collection and standard chemical process, set up calibration model by multivariate regression algorithm or preferably go out characteristic wavelength and directly set up multivariate calibration model, by this calibration model, the Contents of Main Components of Chemical Mixed Fertilizer sample is measured.The present invention substantially reduces the analysis measurement time, during without a large amount of reaction reagents, not only save substantial amounts of manpower and materials, do not result in environmental pollution simultaneously, can effectively solve conventional offline or sampling Detection time-consumingly long, inefficient problem in tradition production of compound fertilizer.
Description
Technical field
The present invention relates to chemical fertilizer production Quality Control Technology field, be specifically related to a kind of one-tenth based on near-infrared spectrum technique
Divide content assaying method, for quick, lossless, the multiple main component of quantitative measurement Chemical Mixed Fertilizer.
Background technology
Chemical fertilizer is requisite valuable cargo in modern agriculture, uses chemical fertilizer and can be effectively increased grain yield.Research table
Bright, the best results of multiple application of mixed fertilizers, therefore major part chemical fertilizer production producer is all to produce containing two or more at present
The Chemical Mixed Fertilizer of the required nutritional labeling of plant growth.Due to the very different and technologic problem of manufacturer, cause Chemical Mixed Fertilizer
Middle nutrition composition is not be completely fixed, but fluctuates within a certain range, therefore to Chemical Mixed Fertilizer principle active component quickly,
Accurately measure, to chemical fertilizer quality significant to control and follow-up accurate Fertilising implement.
The main component of Chemical Mixed Fertilizer includes total nitrogen, total phosphorus and total potassium etc., and traditional detection method is many to be carried out in the lab,
Wherein total nitrogen content measures and mainly uses Kjeldahl's method, total phosphorus content to measure main employing phosphomolybdic acid quinoline gravimetric method, total potassium
Assay mainly uses tetraphenyl borate potassium gravimetric method etc., these chemical methodes relate to weighing, dissolve, digest, distill, titrate with
And the operating procedure of the series of complex such as calculating, not only waste time and energy, cost intensive, also experiment operator can be caused necessarily
Danger and cause environmental pollution.
Near infrared spectrum is between visible ray and middle-infrared band, and wave-length coverage is 780～2526nm.Near infrared spectrum
Belong to molecular vibration spectrum, result from the vibration of covalent chemical bond anharmonic energy level, be frequency multiplication and the combination frequency of anharmonic vibration.In principle
The possibility of the most useful near-infrared spectrum analysis of material of near infrared spectrum can be produced, and near-infrared spectral analytical method shows
Series of advantages, the most quickly, simplicity, low cost, non-destructive and simultaneous determination of multiponents etc..Fast along with Chemical Measurement
Speed development, near-infrared spectral analysis technology is widely used in fields such as petrochemical industry, food, medicines, and these are all
This technology is applied to the main component detection of Chemical Mixed Fertilizer and provides feasibility by us.
Summary of the invention
The present invention provide a kind of can Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique, with
The quick mensuration of multiple main component, effectively supervision Chemical Mixed Fertilizer quality during effectively meeting production of compound fertilizer.
For solving above-mentioned technical problem, the present invention adopts the following technical scheme that
A kind of Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique, comprises the steps:
1) collection of Chemical Mixed Fertilizer sample set: gather the Chemical Mixed Fertilizer sample of different batches, indicate sampling time and production batch,
It is ground into powder again；
2) Chemical Mixed Fertilizer sample Contents of Main Components measures and spectra collection: each sample is divided into two parts, and a utilization is marked
Quasi-chemical gauging total nitrogen content, total phosphorus content and total potassium content, wherein the mensuration of total nitrogen content uses Kjeldahl's method, always
Phosphorus detection uses phosphomolybdic acid quinoline gravimetric method, and total potassium content measures and uses tetraphenyl borate potassium gravimetric method；Another part utilizes near
Its reflectance spectrum measured by infrared spectrometer, and measurement (3～4 time) is repeated several times, and is averaged the spectrum approximation standard as this sample
Spectrum；
3) spectrum is carried out spectral information that pretreatment, i.e. near infrared spectrometer gathered in addition to useful information, also
The path-length error etc. that the noise that causes including noise, bias light and the veiling glare of instrument itself, sample particle size cause, because of
This, when later use Chemical Measurement sets up model, it is necessary to use suitable preprocess method；Preferably, described spectrum is pre-
Processing method includes smoothing, the conversion of derivative, standard normal variable, multiplicative scatter correction and Orthogonal Signal Correction Analyze；
4) foundation of calibration model: choose calibration samples collection, utilizes spectroscopic data and the standard chemical of this calibration samples collection
Content data measured by method, sets up calibration model by multivariate regression algorithm or preferably goes out characteristic wavelength and directly set up many
Unit's calibration model；
Preferably, the choosing method of described calibration samples collection includes Kennard-Stone method, i.e. based on sample spectra
Euclidean distance selects calibration samples collection；Or first spectrum is carried out principal component analysis, according to principal component scores as characteristic variable
Carry out Kennard-Stone method choice calibration samples collection；
Preferably, described multivariate regression algorithm includes multiple linear regression analysis method and multiple nonlinear regression method, wherein
Multiple linear regression analysis method includes principal component regression and partial least square method, and multiple nonlinear regression method includes ANN
Network and support vector regression；
Preferably, described characteristic wavelength system of selection include correlation coefficient process, method of gradual regression, without information variable method of elimination
And genetic algorithm；
5) mensuration of the Contents of Main Components of Chemical Mixed Fertilizer sample: first scan the near infrared spectrum of Chemical Mixed Fertilizer sample, by institute
The near infrared spectrum of the Chemical Mixed Fertilizer sample collected brings calibration model into, and calculating i.e. can get the main component of Chemical Mixed Fertilizer sample and contains
Amount.
Analysis involved by this patent is to liking complicated sample system, it is impossible to by artificial preparation acquisition correcting sample,
Must collect actual sample, the Chemical Mixed Fertilizer sample collected on a production line contains substantial amounts of repeated sample, it is therefore necessary to
Pick out representative sample and set up calibration model；It is modeled being possible not only to reduce by selected representative sample
The memory space of model library, raising modeling speed, more can improve the scope of application of model, it is simple to model is more by less sample
New and safeguard；Therefore in step 4) and step 5) between also need to arrange the checking of calibration model, concrete grammar is as follows:
Choose multiple forecast set samples of known component content, after the near infrared spectrum of forecast set sample is carried out pretreatment
Substitute in institute's positive model for school building, calculate the total nitrogen of forecast set sample, total phosphorus and total potassium content respectively, use correlation coefficient respectively
(R), predicted root mean square error (RMSEP) and relation analysis error (RPD) are as the standard forecast of regression model to being set up
Can be evaluated.
From above technical scheme, the present invention has feature simple, quick, accurate, efficient, free of contamination, the method
Substantially reduce the analysis measurement time, during without a large amount of reaction reagents, not only save substantial amounts of manpower and materials, the most not
Environmental pollution can be caused；Measurement result is more accurate, and error is little, can effectively solve in tradition production of compound fertilizer conventional offline or take out
Time-consuming long, the inefficient problem of sample detection, provides technical support for online quality monitoring during production of compound fertilizer.
Accompanying drawing explanation
Fig. 1 is the theory diagram of the present invention.
Detailed description of the invention
The present invention is realized by detailed description below, but the present invention is not limited to this.
Embodiment 1
1. the collection of Chemical Mixed Fertilizer sample and pretreatment.
Gathering 174 Chemical Mixed Fertilizer samples of different batches, every part of sample controls to put into after about 0.5kg, sampling transparent
Valve bag, indicates sampling time and production batch, then is ground into powder.Each sample is divided into two parts, and portion is sent to chemistry
Laboratory measures, and another part of sample utilizes near infrared spectrometer to measure its reflectance spectrum.
2. the mensuration of Chemical Mixed Fertilizer sample Contents of Main Components.
Use in Kjeldahl's method, phosphomolybdic acid quinoline gravimetric method and tetraphenyl borate potassium gravimetric detemination Chemical Mixed Fertilizer respectively is total
Nitrogen content, total phosphorus content and total potassium content.
3. the collection of Chemical Mixed Fertilizer sample spectrum.
Use the C9914GB near infrared spectrometer of the USB4000 visible spectrophotometer of Ocean Optics and Bin Song company with
And halogen tungsten lamp light source gathers the diffuse-reflectance spectrum of Chemical Mixed Fertilizer sample.Whole spectral region is 342～2221nm, and each sample repeats
Measure 3～4 times, be averaged the spectrum standard spectrum as this sample.
4. the selection of calibration samples collection.
First our rejecting abnormalities sample, the method (PCA-MD) using principal component analysis to combine with mahalanobis distance, take
Threshold value is the standard deviation that mahalanobis distance average adds three times, and the sample of the most numbered 8 and 136 is judged as exceptional sample, by it
Delete, remain 172 samples.KS method is used to select 124 samples as calibration samples collection, remaining 43 sample conducts again
Forecast set.
5. the foundation of calibration model.
Spectrum is carried out different pretreatments, re-uses principal component regression method (PCR), adopt respectively for different premeasurings
With staying a cross validation method, choose optimal number of principal components according to predictive residual error sum of squares.
6. the checking of calibration model.
Choose 43 forecast set samples of known component content, the near infrared spectrum of forecast set sample is carried out identical pre-
Process (multiplicative scatter correction and smooth) and substitute in institute's positive model for school building afterwards, calculate respectively the total nitrogen of forecast set sample, total phosphorus and
Total potassium content.Use correlation coefficient (R), predicted root mean square error (RMSEP) and relation analysis error (RPD) as standard respectively
The forecast of regression model performance set up is evaluated.
Table 1 is by using the analysis result of PCR method built Chemical Mixed Fertilizer Contents of Main Components model, wherein to total in Chemical Mixed Fertilizer
Nitrogen, total phosphorus and total potassium content are calculated.
Embodiment 2
1. the collection of Chemical Mixed Fertilizer sample and pretreatment are with embodiment 1.
2. the mensuration of Chemical Mixed Fertilizer sample Contents of Main Components is with embodiment 1.
3. the collection of Chemical Mixed Fertilizer sample spectrum is with embodiment 1.
4. the selection of calibration samples collection is with embodiment 1.
5. the foundation of calibration model.
Spectrum is carried out different pretreatments, re-uses genetic algorithm (GA) preferred feature wavelength, according to the spy preferably gone out
Levy wavelength and set up multiple linear regression model.
6. the checking of calibration model is with embodiment 1.
Table 2 is analysis result based on GA method set up Chemical Mixed Fertilizer Contents of Main Components model.
Embodiment 3
1. the collection of Chemical Mixed Fertilizer sample and pretreatment are with embodiment 1.
2. the mensuration of Chemical Mixed Fertilizer sample Contents of Main Components is with embodiment 1.
3. the collection of Chemical Mixed Fertilizer sample spectrum is with embodiment 1.
4. the selection of calibration samples collection is with embodiment 1.
5. the foundation of calibration model.
Spectrum is carried out different pretreatments, re-uses successive projection algorithm (SPA) preferred feature wavelength, according to preferably going out
Characteristic wavelength set up multiple linear regression model.
6. the checking of calibration model is with embodiment 1.
Table 3 is analysis result based on SPA method set up Chemical Mixed Fertilizer Contents of Main Components model.
To sum up, said method built forecast model effect is preferable, can be used for the on-line checking of Chemical Mixed Fertilizer main component.
The above embodiment is only to be described the preferred embodiment of the present invention, the not model to the present invention
Enclose and be defined, on the premise of designing spirit without departing from the present invention, the those of ordinary skill in the art technical side to the present invention
Various deformation that case is made and improvement, all should fall in the protection domain that claims of the present invention determines.
Claims (7)
1. a Chemical Mixed Fertilizer main component harmless quantitative detection method based on near-infrared spectrum technique, it is characterised in that include
Following steps:
1) collection of Chemical Mixed Fertilizer sample set: gather the Chemical Mixed Fertilizer sample of different batches, indicate sampling time and production batch, then grind
Grinds powder；
2) Chemical Mixed Fertilizer sample Contents of Main Components measures and spectra collection: each sample is divided into two parts, and portion utilizes standardization
Method measures total nitrogen content, total phosphorus content and total potassium content；Another part utilizes near infrared spectrometer to measure its reflectance spectrum, many
Secondary repeated measure, is averaged the spectrum approximation standard spectrum as this sample；
3) spectrum is carried out pretreatment；
4) foundation of calibration model: choose calibration samples collection, utilizes spectroscopic data and the standard chemical process of this calibration samples collection
Measured content data, sets up calibration model by multivariate regression algorithm or preferably goes out characteristic wavelength and directly set up polynary school
Positive model；
5) mensuration of the Contents of Main Components of Chemical Mixed Fertilizer sample: first scan the near infrared spectrum of Chemical Mixed Fertilizer sample, will be gathered
To the near infrared spectrum of Chemical Mixed Fertilizer sample bring calibration model into, calculate the Contents of Main Components that i.e. can get Chemical Mixed Fertilizer sample.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that described correction sample
The standard chemical process of main constituent content measured by this collection: total nitrogen content measures and uses Kjeldahl's method, and total phosphorus content measures and adopts
By phosphomolybdic acid quinoline gravimetric method, total potassium content measures and uses tetraphenyl borate potassium gravimetric method.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that described spectrum is pre-
Processing method includes smoothing, the conversion of derivative, standard normal variable, multiplicative scatter correction and Orthogonal Signal Correction Analyze.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that described correction sample
The choosing method of this collection includes Kennard-Stone method, and i.e. based on sample spectra Euclidean distance selects calibration samples collection；Or
Spectrum is first carried out principal component analysis by person, carries out Kennard-Stone method choice according to principal component scores as characteristic variable
Calibration samples collection.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that described polynary time
Reduction method includes multiple linear regression analysis method and multiple nonlinear regression method, and wherein multiple linear regression analysis method includes main constituent
Returning and partial least square method, multiple nonlinear regression method includes artificial neural network and support vector regression.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that described characteristic wave
Long system of selection include correlation coefficient process, method of gradual regression, without information variable method of elimination and genetic algorithm.
Chemical Mixed Fertilizer main component harmless quantitative detection method the most according to claim 1, it is characterised in that step 4) and step
Rapid 5) also including the checking of calibration model between, concrete grammar is as follows:
Choose multiple forecast set samples of known component content, substitute into after the near infrared spectrum of forecast set sample is carried out pretreatment
In institute's positive model for school building, calculate the total nitrogen of forecast set sample, total phosphorus and total potassium content respectively, use correlation coefficient (R), pre-respectively
Survey root-mean-square error (RMSEP) and the forecast of regression model performance set up is carried out as standard by relation analysis error (RPD)
Evaluate.
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| CN109708973A (en) * | 2018-12-19 | 2019-05-03 | 武汉大学 | Materials chemistry-mechanical parameter real-time tracing test macro, method and mechanical parameter optimization system, method in a kind of photopolymerization reaction |
| CN109696407A (en) * | 2019-01-22 | 2019-04-30 | 中国农业大学 | A kind of coco bran matrix available nitrogen spectral method of detection based on characteristic wavelength |
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| CN112086137A (en) * | 2020-08-18 | 2020-12-15 | 山东金璋隆祥智能科技有限责任公司 | Method for quantitatively analyzing sorbose content in fermentation liquor |
| CN112945899A (en) * | 2021-01-29 | 2021-06-11 | 燕山大学 | Method for identifying polyglutamic acid compound fertilizer |
| CN112945899B (en) * | 2021-01-29 | 2022-10-14 | 燕山大学 | Method for identifying polyglutamic acid compound fertilizer |
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