CN106092960A - A kind of quick correction near infrared gear also detects the method for chemical composition in agricultural product - Google Patents
A kind of quick correction near infrared gear also detects the method for chemical composition in agricultural product Download PDFInfo
- Publication number
- CN106092960A CN106092960A CN201610604333.4A CN201610604333A CN106092960A CN 106092960 A CN106092960 A CN 106092960A CN 201610604333 A CN201610604333 A CN 201610604333A CN 106092960 A CN106092960 A CN 106092960A
- Authority
- CN
- China
- Prior art keywords
- near infrared
- agricultural product
- data
- verification
- standard
- 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
- 239000000126 substance Substances 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000012937 correction Methods 0.000 title claims abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 36
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 35
- 238000012795 verification Methods 0.000 claims abstract description 31
- 238000013507 mapping Methods 0.000 claims abstract description 16
- 230000003595 spectral effect Effects 0.000 claims description 8
- 239000003242 anti bacterial agent Substances 0.000 claims description 2
- 229940088710 antibiotic agent Drugs 0.000 claims description 2
- 238000001228 spectrum Methods 0.000 description 7
- 244000105624 Arachis hypogaea Species 0.000 description 6
- 235000020232 peanut Nutrition 0.000 description 6
- 235000004252 protein component Nutrition 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 235000017060 Arachis glabrata Nutrition 0.000 description 2
- 235000010777 Arachis hypogaea Nutrition 0.000 description 2
- 235000018262 Arachis monticola Nutrition 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 210000000582 semen Anatomy 0.000 description 2
- 240000002791 Brassica napus Species 0.000 description 1
- 235000004977 Brassica sinapistrum Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000005064 physico chemical analysis method Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000004611 spectroscopical analysis Methods 0.000 description 1
- 238000010561 standard procedure 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
- 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
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
- G01N21/278—Constitution of standards
Abstract
The present invention relates to a kind of quick correction near infrared spectrum detection equipment and detect the method for chemical composition in agricultural product, the method standard near infrared detection equipment carries out near-infrared data acquisition to agricultural product standard sample, chemically detect the chemical composition data of agricultural product standard sample, according to standard near infrared spectrum data storehouse and standard chemical compositional data storehouse Criterion mapping model；Gather the near infrared spectrum data of agricultural product verification sample with the actual near infrared detection equipment of detection agricultural product and chemically detect the chemical composition data of agricultural product verification sample, comparing with Standard Map model and obtain verification mapping model, the near infrared spectrum data of fresh sample obtains chemical composition data by verification mapping model.By verification mapping model, solve place the most relatively far apart and use new near infrared detection equipment quickly to check the chemical composition data the most quickly detecting agricultural product.
Description
Technical field
The invention belongs to detection of agricultural products field, particularly relate to a kind of quickly correction near infrared spectrum detection equipment and detect
The method of chemical composition in agricultural product.
Background technology
The method quickly being detected agricultural production chemical composition by near-infrared increasingly obtains researcher and agricultural production consumer
Concern, the differentiation of the most the most key is near infrared detection equipment causes the inaccurate of testing result, often occurs a kind of new
Detection unit type all can cause the situation that data with existing storehouse is difficult by, there is presently no preferable solution.
The patent of Publication No. CN102305772A disclose a kind of for food and quality of agricultural product detect based on something lost
Pass the characteristic wavelength of near-infrared spectrum screening technique of kernel partial least squares, utilize physico-chemical analysis method to measure all samples to be tested
Concentration of component value to be measured after divide calibration set and the forecast set of sample；Utilize genetic algorithm to pretreated calibration set spectrum
Data point carries out global search, according to validation-cross root-mean-square error value minimum in kernel partial least squares interactive verification process
Determine the final characteristic variable number participating in modeling, and characteristic variable genetic algorithm screened reformulates new data
Matrix is as the input of model；Using the calibration set sample concentration of component to be measured matrix that records as the standard output of model, set up
Optimal correction analysis model.
The patent of Publication No. CN102879353A discloses and a kind of detects the method for content of protein components in Semen arachidis hypogaeae, bag
Include 1) the Standard for Peanuts product of known content of protein components are carried out near infrared spectrum scanning, it is thus achieved that described known protein component
The Standard for Peanuts product of content, at all spectral informations of near-infrared wavelength, obtain the calculating meansigma methods of calibration set sample spectrum；2)
To described step 1) gained calibration set sample spectrum carries out pretreatment；3) by described step 2) pretreated calibration set sample light
Information data in spectrum carries out principal component analysis, characteristic information extraction data；4) with the protein component of described Standard for Peanuts product
The chemical measurements of content is corrected value, using described step 3) gained characteristic information data is as independent variable, and described corrected value is made
For dependent variable, set up the calibration model between described independent variable and described dependent variable with Chemical Measurement Multivariate Correction algorithm；5)
By described step 1) the Standard for Peanuts product of described known content of protein components replace with peanut sample to be measured, repeating said steps
1) to step 3), by described step 3) gained characteristic information data input described step 4) calibration model, obtain described to be measured
Content of protein components in peanut sample.
The patent of Publication No. CN101907564A discloses the near infrared spectrum quickly side of detection of a kind of rapeseed quality
Method: first take different in moisture and fat the some parts of Semen Brassicae campestris sample, a part as calibration set, another part as forecast set,
Remainder is as testing sample, and the water content of employing National Standard Method mensuration all samples and fat content are as measured value；Use again
Near infrared detection instrument acquisition correction collection sample of the present invention, forecast set sample and the spectrum information that diffuses of testing sample, use school
Relation between the spectroscopic data just collected and its water content and fat content measured value sets up calibration model；By the spectrum number of forecast set
Be predicted according to being brought in calibration model, obtain the sample moisture content of forecast set and fat content predict the outcome and analyze with
The difference of its measured value, chooses precision of prediction and reaches the calibration model of requirement；Finally model parameter is imported in microprocessor,
The spectral information of testing sample is called in the calibration model chosen simultaneously, testing sample water content and fat content are counted
Calculating, the water content obtained and fat content predictive value are testing sample water content and determination of fat result.
But the method for being capable of quickly detecting in prior art does not the most solve different detecting instrument to be caused
There is the problem that data base is difficult by, it is impossible to realize utilizing new instrument that sample is used for quickly detecting.
Summary of the invention
For solving above-mentioned technical problem, the invention provides a kind of quickly correction near infrared spectrum detection equipment and detect agriculture
The method of chemical composition in product, it is characterised in that the method comprises the steps:
Step A: with standard near infrared detection equipment, n agricultural product standard sample is carried out near-infrared data acquisition, obtain
Standard near infrared spectrum data storehouse；
Step B: the chemically chemical composition data of n agricultural product standard sample in detecting step A, forms standard
Chemical composition data storehouse；
Step C: set up according to the standard near infrared spectrum data storehouse of step A and the standard chemical compositional data storehouse of step B
Standard Map model；
Step D: gather the near infrared light of m agricultural product verification sample with the actual near infrared detection equipment of detection agricultural product
Modal data, forms verification near infrared spectrum data storehouse；
Step E: the chemically chemical composition data of m agricultural product verification sample in detecting step D, forms verification
Chemical composition data storehouse；
Step F: verification near infrared spectrum data storehouse and verification chemical composition data storehouse are compared with Standard Map model
Right, it is thus achieved that the near infrared spectrum that actual near infrared detection equipment is gathered and the verification mapping model of chemical composition relation；
Step G: gather the near infrared spectrum data of agricultural product fresh sample with actual near infrared detection equipment, reflected by verification
Penetrate model and be calculated the chemical composition data of agricultural product fresh sample；
Wherein the agricultural product in step A, step D and step G belong to same on botany；
Wherein n and m is integer, and n ＞ 50 ＞ m ＞ 5.
Preferably, in said method, standard near infrared detection equipment is stable full spectral coverage infrared spectrum instrument.
Preferably, in said method, actual near infrared detection equipment is continuous portable infrared spectrograph, single-point type hands
Hold the spectral measuring devices of spectrogrph or other forms.
Preferably, in said method, Standard Map model and verification mapping model can apply appointing of data construct model
Meaning method builds, and is constantly verified by standard chemical compositional data and verification chemical composition data in building process,
Finally realize Standard Map model and verification mapping model is capable of actual near infrared detection equipment and standard near infrared detection
At the relatively accurate corresponding relation calculated between chemical composition content between equipment.
Preferably, in said method, agricultural product are veterinary antibiotics or grain etc., and agricultural product can also replace with food and exist
Said method is detected.
Present invention also offers a kind of quick correction near infrared spectrum detection equipment and detect chemical composition in agricultural product
System, it is characterised in that this system includes data input pin, Cloud Server and data output end, it is characterised in that data input pin
Having standardized agricultural products near infrared spectrum data input interface and standardized agricultural products chemical data input interface, same data input
End or another data database also have verification near infrared spectrum data input structure and verification chemistry agricultural product data input knot
Structure, Cloud Server includes Standard Map model and verification mapping model, and data output end has agricultural product chemical data outfan
Mouthful, data input pin is bi-directionally connected with Cloud Server, and Cloud Server is connected with data output end.
Beneficial effect:
1, the Standard Map model of specific agricultural product is set up, then in conjunction with the near infrared spectrum of other near infrared detection instruments
Data and chemical composition data set up verification mapping model, it is possible to realize quickly correcting and fast new near infrared detection equipment
Speed detection chemical composition.
2, by verification mapping model, solve place the most relatively far apart and use new near infrared detection equipment fast
Speed inspection the most quickly detects the chemical composition data of agricultural product.
3, standard near infrared spectrum detection equipment and actual near infrared spectrum detection equipment can arbitrarily select, the most in fact
Border near infrared detection equipment, in the case of needs quickly detection agricultural product chemical composition, it is thus only necessary to the chemistry of a small amount of sample
Detection can be realized as the rapid verification of new equipment and comes into operation.
4, Standard Map model is embedded in high in the clouds, verification spectra database and verification chemical composition data storehouse and is transferred to high in the clouds
After, it becomes possible to realize setting up new verification mapping model, then the near infrared spectrum data that actual sample detects is transferred to verification
Mapping model can obtain the chemical composition data of sample.
Accompanying drawing explanation
Fig. 1 is that block diagram set up by Standard Map model；
Fig. 2 sets up block diagram for verification mapping model；
Fig. 3 is that actual sample chemical composition detects block diagram.
Detailed description of the invention
Embodiment 1
Step A: with stable full spectral coverage infrared spectrum instrument (as standard near infrared detection equipment) to 150 Fructus Cucumidis sativi marks
Quasi-sample carries out near-infrared data acquisition, obtains Fructus Cucumidis sativi standard near infrared spectrum data storehouse；
Step B: the chemically chemical composition data of 150 Fructus Cucumidis sativi standard sample in detecting step A, forms Fructus Cucumidis sativi
Standard chemical compositional data storehouse；
Step C: according to Fructus Cucumidis sativi standard near infrared spectrum data storehouse and the Fructus Cucumidis sativi standard chemical component number of step B of step A
Fructus Cucumidis sativi Standard Map model is set up according to storehouse；
Step D: gather 20 with the hand-held spectrogrph of single-point type (as the actual near infrared detection equipment of detection agricultural product)
The near infrared spectrum data of Fructus Cucumidis sativi verification sample, forms Fructus Cucumidis sativi verification near infrared spectrum data storehouse；
Step E: the chemically chemical composition data of 20 Fructus Cucumidis sativi verification samples in detecting step D, forms Fructus Cucumidis sativi
Verification chemical composition data storehouse；
Step F: Fructus Cucumidis sativi is verified near infrared spectrum data storehouse and Fructus Cucumidis sativi verification chemical composition data storehouse and reflects with Fructus Cucumidis sativi standard
Penetrate model to compare, it is thus achieved that verification mapping model；
Step G: by the near infrared spectrum data of single-point type hand-held spectrometer collection Fructus Cucumidis sativi fresh sample, map mould by verification
Type is calculated the chemical composition data of Fructus Cucumidis sativi fresh sample.
Embodiment 2
Step A: with stable full spectral coverage infrared spectrum instrument (as standard near infrared detection equipment) to 120 Fructus Lycopersici esculenti
Standard sample carries out near-infrared data acquisition, obtains Fructus Lycopersici esculenti standard near infrared spectrum data storehouse；
Step B: the chemically chemical composition data of 120 Fructus Lycopersici esculenti standard sample in detecting step A, forms west
Red Fructus Kaki standard chemical compositional data storehouse；
Step C: study according to the Fructus Lycopersici esculenti standard near infrared spectrum data storehouse of step A and the Fructus Lycopersici esculenti standardization of step B
Fructus Lycopersici esculenti Standard Map model is set up in divided data storehouse；
Step D: adopt with continuous portable infrared spectrograph (as the actual near infrared detection equipment of detection agricultural product)
Collect the near infrared spectrum data of 10 Fructus Lycopersici esculenti verification samples, form Fructus Lycopersici esculenti verification near infrared spectrum data storehouse；
Step E: the chemically chemical composition data of 10 Fructus Lycopersici esculenti verification samples in detecting step D, forms west
Red Fructus Kaki verification chemical composition data storehouse；
Step F: Fructus Lycopersici esculenti is verified near infrared spectrum data storehouse and Fructus Lycopersici esculenti verification chemical composition data storehouse and Fructus Lycopersici esculenti
Standard Map model is compared, it is thus achieved that verification mapping model；
Step G: gather the near infrared spectrum data of Fructus Lycopersici esculenti fresh sample with continuous portable infrared spectrograph, by school
Test mapping model and be calculated the chemical composition data of Fructus Lycopersici esculenti fresh sample.
Claims (5)
1. a quick correction near infrared spectrum detects equipment and detects the method for chemical composition in agricultural product, it is characterised in that should
Method comprises the steps:
Step A: with standard near infrared detection equipment, n agricultural product standard sample is carried out near-infrared data acquisition, obtain standard
Near infrared spectrum data storehouse；
Step B: the chemically chemical composition data of n agricultural product standard sample in detecting step A, forms standard chemical
Compositional data storehouse；
Step C: according to standard near infrared spectrum data storehouse and the standard chemical compositional data storehouse Criterion of step B of step A
Mapping model；
Step D: gather the near infrared spectrum number of m agricultural product verification sample with the actual near infrared detection equipment of detection agricultural product
According to, form verification near infrared spectrum data storehouse；
Step E: the chemically chemical composition data of m agricultural product verification sample in detecting step D, forms verification chemistry
Compositional data storehouse；
Step F: compared with Standard Map model in verification near infrared spectrum data storehouse and verification chemical composition data storehouse, obtain
Obtain near infrared spectrum and the verification mapping model of chemical composition relation that actual near infrared detection equipment is gathered；
Step G: gather the near infrared spectrum data of agricultural product fresh sample with actual near infrared detection equipment, maps mould by verification
Type is calculated the chemical composition data of agricultural product fresh sample；
Wherein the agricultural product in step A, step D and step G belong to same on botany；
Wherein n and m is integer, and n ＞ 50 ＞ m ＞ 5.
Method the most according to claim 1, its Plays near infrared detection equipment is stable full spectral coverage infrared spectrometer
Device.
Method the most according to claim 1, actual near infrared detection equipment be continuous portable infrared spectrograph,
The hand-held spectrogrph of single-point type or the spectral measuring devices of other forms.
Method the most according to claim 1, wherein agricultural product are veterinary antibiotics or grain.
5. a quick correction near infrared spectrum detects equipment and detects the system of chemical composition in agricultural product, it is characterised in that should
System includes data input pin, Cloud Server and data output end, it is characterised in that it is near that data input pin has standardized agricultural products
Ir data input interface and standardized agricultural products chemical data input interface, same data input pin or another Data Data
Storehouse also has verification near infrared spectrum data input structure and verification chemistry agricultural product data input structure, and Cloud Server includes mark
Quasi-mapping model and verification mapping model, data output end has agricultural product chemical data output port, data input pin and cloud
Server is bi-directionally connected, and Cloud Server is connected with data output end.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610604333.4A CN106092960A (en) | 2016-07-28 | 2016-07-28 | A kind of quick correction near infrared gear also detects the method for chemical composition in agricultural product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610604333.4A CN106092960A (en) | 2016-07-28 | 2016-07-28 | A kind of quick correction near infrared gear also detects the method for chemical composition in agricultural product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106092960A true CN106092960A (en) | 2016-11-09 |
Family
ID=57478185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610604333.4A Pending CN106092960A (en) | 2016-07-28 | 2016-07-28 | A kind of quick correction near infrared gear also detects the method for chemical composition in agricultural product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106092960A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106872397A (en) * | 2016-12-29 | 2017-06-20 | 深圳市芭田生态工程股份有限公司 | A kind of method based on existing calibration model quick detection agricultural product chemical constituent |
| CN106970042A (en) * | 2017-04-12 | 2017-07-21 | 防城港出入境检验检疫局综合技术服务中心（广西国际旅行卫生保健中心防城港分中心） | A kind of carragheen impurity, moisture detection method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101865828A (en) * | 2010-05-31 | 2010-10-20 | 湖南大学 | Method for maintaining predication capability of spectrum correction model of complex system |
| CN102103079A (en) * | 2010-12-31 | 2011-06-22 | 聚光科技(杭州)股份有限公司 | Spectrum analysis method |
| US8119991B2 (en) * | 2004-08-11 | 2012-02-21 | Jordan Valley Semiconductors Ltd. | Method and apparatus for accurate calibration of VUV reflectometer |
| CN105699304A (en) * | 2016-01-28 | 2016-06-22 | 深圳市芭田生态工程股份有限公司 | Method for acquiring matter information represented by spectral information |
-
2016
- 2016-07-28 CN CN201610604333.4A patent/CN106092960A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8119991B2 (en) * | 2004-08-11 | 2012-02-21 | Jordan Valley Semiconductors Ltd. | Method and apparatus for accurate calibration of VUV reflectometer |
| CN101865828A (en) * | 2010-05-31 | 2010-10-20 | 湖南大学 | Method for maintaining predication capability of spectrum correction model of complex system |
| CN102103079A (en) * | 2010-12-31 | 2011-06-22 | 聚光科技(杭州)股份有限公司 | Spectrum analysis method |
| CN105699304A (en) * | 2016-01-28 | 2016-06-22 | 深圳市芭田生态工程股份有限公司 | Method for acquiring matter information represented by spectral information |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106872397A (en) * | 2016-12-29 | 2017-06-20 | 深圳市芭田生态工程股份有限公司 | A kind of method based on existing calibration model quick detection agricultural product chemical constituent |
| CN106970042A (en) * | 2017-04-12 | 2017-07-21 | 防城港出入境检验检疫局综合技术服务中心（广西国际旅行卫生保健中心防城港分中心） | A kind of carragheen impurity, moisture detection method |
| CN106970042B (en) * | 2017-04-12 | 2020-07-28 | 防城港出入境检验检疫局综合技术服务中心（广西国际旅行卫生保健中心防城港分中心） | Method for detecting impurity and moisture content of carrageenin |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102879353B (en) | The method of content of protein components near infrared detection peanut | |
| CN102590129B (en) | Method for detecting content of amino acid in peanuts by near infrared method | |
| CN103575689B (en) | Method for rapidly detecting amylose content in rice by near infrared spectrum and visible light analyzer | |
| CN103854305B (en) | A kind of Model Transfer method based on multi-scale Modeling | |
| CN103196838B (en) | Hyperspectral remote sensing monitoring method for coastal estuary eutrophication | |
| CN102323267A (en) | System and method used for rapidly evaluating freshness of raw meat products | |
| CN103808688B (en) | The quality conformance of near infrared spectroscopy Fast nondestructive evaluation final drug | |
| CN106680238B (en) | Method based on infrared spectrum analysis material component content | |
| CN101936895A (en) | Near infrared spectroscopy analysis rapid detection method of rice freshness | |
| CN107817223A (en) | The construction method of quick nondestructive real-time estimate oil property model and its application | |
| CN111965140B (en) | Wavelength point recombination method based on characteristic peak | |
| Wei et al. | Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing | |
| CN106596456A (en) | Changeable moving window based selection method of near infrared spectral region of solution | |
| CN105486658A (en) | Near-infrared physical property parameter measuring method without measuring point temperature compensation | |
| CN109211829A (en) | A method of moisture content in the near infrared spectroscopy measurement rice based on SiPLS | |
| CN109738380A (en) | A kind of high-spectrum remote-sensing judgment method of soil salinization degree | |
| CN105300911A (en) | Apparatus and method for testing materials | |
| CN104807777A (en) | Rapid detection method for areca-nut water content based on near infrared spectrum analysis technology | |
| CN109932319A (en) | A kind of orchard soil quick-acting potassium content acquisition methods, system and device | |
| CN106990056A (en) | A kind of total soil nitrogen spectrum appraising model calibration samples collection construction method | |
| US11093869B2 (en) | Analytical system with iterative method of analyzing data in web-based data processor with results display designed for non-experts | |
| CN108169168A (en) | Test and analyze rice grain protein content mathematical model and construction method and application | |
| CN105223140A (en) | The method for quickly identifying of homology material | |
| CN112098357A (en) | Strawberry sensory quality grade evaluation method based on near infrared spectrum | |
| CN104849234A (en) | Assay method for analyzing contents of principal components of imidacloprid based on near-infrared spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161109 |