CN106706553A - Method for quick and non-destructive determination of content of amylase in corn single grains - Google Patents
Method for quick and non-destructive determination of content of amylase in corn single grains Download PDFInfo
- Publication number
- CN106706553A CN106706553A CN201610152174.9A CN201610152174A CN106706553A CN 106706553 A CN106706553 A CN 106706553A CN 201610152174 A CN201610152174 A CN 201610152174A CN 106706553 A CN106706553 A CN 106706553A
- Authority
- CN
- China
- Prior art keywords
- sample
- model
- near infrared
- single grain
- content
- 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
- 240000008042 Zea mays Species 0.000 title claims abstract description 34
- 235000002017 Zea mays subsp mays Nutrition 0.000 title claims abstract description 34
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 title claims abstract description 32
- 235000005822 corn Nutrition 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000001066 destructive effect Effects 0.000 title abstract description 4
- 239000004382 Amylase Substances 0.000 title abstract 5
- 102000013142 Amylases Human genes 0.000 title abstract 5
- 108010065511 Amylases Proteins 0.000 title abstract 5
- 235000019418 amylase Nutrition 0.000 title abstract 5
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 20
- 239000000126 substance Substances 0.000 claims abstract description 20
- 239000000463 material Substances 0.000 claims abstract description 11
- 229920000856 Amylose Polymers 0.000 claims description 37
- 238000001228 spectrum Methods 0.000 claims description 23
- 238000001320 near-infrared absorption spectroscopy Methods 0.000 claims description 17
- 238000004458 analytical method Methods 0.000 claims description 7
- ZCYVEMRRCGMTRW-UHFFFAOYSA-N 7553-56-2 Chemical compound [I] ZCYVEMRRCGMTRW-UHFFFAOYSA-N 0.000 claims description 5
- 238000004737 colorimetric analysis Methods 0.000 claims description 5
- 238000002790 cross-validation Methods 0.000 claims description 5
- 229910052740 iodine Inorganic materials 0.000 claims description 5
- 239000011630 iodine Substances 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- 229920002472 Starch Polymers 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 210000001161 mammalian embryo Anatomy 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 3
- 235000019698 starch Nutrition 0.000 claims description 3
- 239000008107 starch Substances 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 2
- 230000009977 dual effect Effects 0.000 claims description 2
- 238000007427 paired t-test Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 238000005303 weighing Methods 0.000 claims 1
- 238000007781 pre-processing Methods 0.000 abstract 1
- 235000013339 cereals Nutrition 0.000 description 27
- 238000009395 breeding Methods 0.000 description 8
- 230000001488 breeding effect Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 8
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 description 4
- 229920001685 Amylomaize Polymers 0.000 description 4
- 238000004445 quantitative analysis Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000012216 screening Methods 0.000 description 3
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000005119 centrifugation Methods 0.000 description 2
- 230000007812 deficiency Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 235000009973 maize Nutrition 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000035479 physiological effects, processes and functions Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 229920002261 Corn starch Polymers 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- LSNNMFCWUKXFEE-UHFFFAOYSA-N Sulfurous acid Chemical compound OS(O)=O LSNNMFCWUKXFEE-UHFFFAOYSA-N 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229920000704 biodegradable plastic Polymers 0.000 description 1
- 238000009614 chemical analysis method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 239000008120 corn starch Substances 0.000 description 1
- 229940099112 cornstarch Drugs 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000000227 grinding Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 238000012628 principal component regression Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000006228 supernatant Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000003828 vacuum filtration Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 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
- G01N21/31—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
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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
The invention relates to a method for quick and non-destructive determination of content of amylase in corn single grains and application thereof. The method mainly comprises a step (1) of collecting corn single grain materials; a step (2) of collecting near infrared spectra of a sample; a step (3) of using a conventional chemical method to determine the content of amylase of the sample; a step (4) of preprocessing the obtained near infrared spectra and eliminating interference factors; a step (5) of establishing a calibration model between a chemical value of the content of amylase in the corn single grains and the near infrared spectra, and checking the model; a step (6) of externally verifying the model; and a step (7) of collecting near infrared spectra of a to-be-measured sample, and using the established near infrared model to perform quick and non-destructive determination of the content of amylase in the to-be-measured sample.
Description
Technical field
The present invention relates to corn single grain amylose content determination technical field, it is right to be realized using near-infrared spectrum technique
Quick, the Non-Destructive Testing of corn single grain amylose content.
Background technology
It is possible to fundamentally solve white pollution problems using amylomaize production biodegradable plastic,
Great revolution will be brought once for world's environmental protection cause.Carry out amylomaize breeding, development amylomaize life
Produce and processing has huge economic benefit and social benefit.Quickly and accurately determine amylose content of corn seeds for
Development amylomaize seed selection is very necessary, wherein determining the content of single grain amylose to corn quality breeding generation morning
The screening of material and the physiology of maize amylose and genetic research are particularly important.The amylose content of corn kernel is determined,
China is determined referring especially to the iodine colorimetry of GB7648-87, and the method is generally used for breeding generation high to be carried out directly with the seed of volume
The measure of chain content of starch, but the method wastes time and energy, high cost, and excellent seed can be destroyed, especially it is difficult to protecting
Amylose content on the premise of card seed sprouting ability to the single grain seed in early generation is measured.Therefore research one kind can
The method for determining with quick nondestructive and accurately corn single grain amylose content is early for corn quality breeding for material
Screening and maize amylose physiology and genetic research it is significant.
Near-infrared（Near Infrared, NIR）The wave-length coverage of light is about 780~2500nm, be between visual field with
Electromagnetic wave between infrared region, by the effect with the hydric group X-H keys of organic molecule in material, formed organic molecule times
Frequency and sum of fundamental frequencies absorption spectrum.The information characteristics such as position, the absorption intensity occurred according to these near infrared absorption frequency spectrums, with reference to mathematics
Statistics is to this into being allocated as qualitative and quantitative analysis.Compared with conventional analysis, this technology needs more chemometrics algorithms
With software engineering.With the deep and near infrared spectroscopy instrument manufacturing technology of development, the Chemical Measurement research of computer technology
Increasingly perfect, near infrared spectrum（Near Infrared Reflectance Spectroscopy, NIRS）Analytical technology is obtained
To developing by leaps and bounds.Due to it is quick, lossless, environmentally friendly the features such as and be widely used in agricultural product, food, chemistry, medicine, oil etc.
Field.In corn quality breeding process, carrying out quantitative analysis using near infrared technology can be reduced to a large amount of in segregating generation
The screening operation of sample, saves breeding material and time, and is Nondestructive Identification, the need for meeting modern breeding.
The content of the invention
The limitation on single grain amylose content determination is applied to overcome iodine colorimetry to be difficult to, and makes up its method
Time-consuming, high cost deficiency, the invention provides quick, non-destructive determination corn single grain amylose content a method,
Establish the Near-Infrared Quantitative Analysis model of corn single grain amylose content.The present invention is able to by the following technical programs
Realize：
Step one：Collect corn single grain material.Collect single grain material more than 100 parts, to ensure the stability of institute's established model,
Storeroom grain type, color etc. should differences.
Step 2：Collection sample near infrared spectrum.Before spectrum is gathered, all material should be balanced moisture solution.
Sample spectra, the nm of Spectral range 950~1650, the nm of resolution ratio 2 are gathered using near infrared grain quality analysis meter.Using single seed
Grain minute surface sample disc loads sample, and seed need to lie in a horizontal plane in sample disk center, without embryo side upward.Each sample repeats dress sample
3 times, dress sample scanning 2 times, preserve averaged spectrum every time.Instrument itself institute band with NIRS collect, storage, processing function it is soft
Part or other generally acknowledged statistical software treatment spectrograms, for example, can use multivariable chemometrics application software The
Unscrambler。
Step 3：With conventional chemistry determination sample amylose content.After the completion of spectra collection, each sample is soaked in
50 DEG C, 60h in the sulfurous acid solution of volume fraction 0.25%, then fine grinding, filtering, centrifugation, remove supernatant, add 0.2%NaOH
Solution, uses ddH after 4h2O is washed, and adds acetone, is stood, and centrifugation goes acetone, vacuum filtration to air-dry and obtain cornstarch.It is double
Wavelength iodine colorimetry determination sample amylose content, determines wavelength and is respectively 620nm and 510nm, sample according to chemical score from
Small that 1 composition checking collection is taken every 3 for model checking to longer spread, remaining sample composition calibration set is used to build
Mould.
Step 4：Step 2 gained near infrared spectrum is pre-processed, disturbing factor is eliminated.Original spectrum carries out pre- place
Reason method is including first derivative, second dervative, multiplicative scatter correction, normal orthogonal change of variable etc..These methods can individually make
With or multiple be used in combination, to reach optimal pretreating effect.
Step 5：The calibration model set up between the amylose content chemical score of corn single grain and near infrared spectrum is simultaneously
Inspection.The chemometrics method of the calibration model set up between NIRS spectrum and chemical score includes：PLS
（PLS）, multiple linear regression（MLR）, principal component regression（PCR）Deng.Sample chemical value is input into corresponding sample, and and spectrum
Data correspondence, quantitative point of corn single grain amylose content is set up with PLS or other chemometrics methods
Analysis model, with the coefficient of determination（R2）, cross validation standard deviation（RMSECV）Evaluation model is good and bad.The coefficient of determination is maximum, standard
The minimum model of difference, best results.
The coefficient of determination（R2）With cross validation standard deviation（RMSECV）：
；
Differ in formulaiThe chemical score of i-th sample and the difference of NIRS predicted values are represented, M is calibration set sample number, yiIt is i-th
The chemical score of individual sample, ymIt is the m average value of sample NIRS predicted values.
Step 6：External certificate is carried out to model.Can investigate model with checking collection sample quantify sample, with prediction standard
Difference（RMSEP）, and chemically measurement result and NIR predict the outcome and are compared, and check the significant difference of two methods
Property, difference is inapparent to illustrate that the model can replace conventional method.
Prediction standard is poor（RMSEP）：
Differ in formulaiThe chemical score of i-th sample and the difference of NIRS predicted values are represented, N is checking collection sample number.
Step 7：Gather the near infrared spectrum of testing sample.The spectra collection method of testing sample gathers light with when modeling
The method of spectrum, with the amylose content of the NIRS Quantitative Analysis Model quick detection testing samples built up.
The present invention has following beneficial effect：（1）The present invention is contained using near-infrared spectrometers determination sample amylose
Amount, has the advantages that fast analyze speed, not damaged, environmental protection.Instant invention overcomes conventional chemical analysis method is time-consuming, high cost and
The deficiency for determining single grain amylose content is difficult to use in, the quick nondestructive to corn single grain amylose content is realized
Analysis.（2）Corn single grain amylose content is analyzed using diffusing reflection near-infrared spectral analysis technology, with reference to methods such as PLS
The calibration model of sample amylose content and near infrared spectrum is set up, by predicting unknown sample, reliable results, ideal.Cause
This, can be promoted the technology, be applied to the single grain amylose content analysis link in corn quality breeding.
Brief description of the drawings
Fig. 1 is the near-infrared primary light spectrogram of corn single grain sample.
Fig. 2 is the related figure between checking collection sample amylose content NIRS predicted values and actual value.
Specific embodiment
Following examples are used to illustrate the present invention, but are not used in limitation the scope of the present invention.
The collection of the corn single grain atlas of near infrared spectra of embodiment 1
196 parts of corn single grain samples are collected, before spectrum is gathered, all material equilibrium water conten 60d at room temperature.Use
Perten companies DA7200 types near infrared spectrometer gathers sample spectra, the nm of Spectral range 950~1650, the nm of resolution ratio 2.Adopt
Sample is loaded with single grain minute surface sample disc, seed need to lie in a horizontal plane in sample disk center, without embryo side upward.Each sample weight
Reassemble sample 3 times, dress sample is scanned 2 times every time, preserves averaged spectrum（See Fig. 1）.
The corn single grain amylose content NIRS models of embodiment 2
The foundation of 2.1 models
After extraction obtains each sample total starch, each sample amylose content is determined with dual wavelength iodine colorimetry, determine wavelength point
Not Wei 620nm and 510nm, sample arranged from small to large according to chemical score, and 1 composition checking collection is taken every 3 for model
Checking, remaining sample composition calibration set is used to model.Using multivariable chemometrics application software The Unscrambler
（9.8 editions）Sample spectra to the collection of embodiment 1 carries out first derivative+normal orthogonal change of variable（SNV）Pretreated spectra, adopts
With PLS founding mathematical models and do cross validation.Modeling result shows, calibration set coefficient of determination R2=0.8970, cross validation
Standard deviation RMSECV=1.805.
The external certificate of 2.2 models
Checking collection sample amylose content is predicted with the model set up, is verified collection sample amylose content
NIRS predicted values figure related to chemical score（See Fig. 2）.Prediction coefficient of determination R2 cal=0.7859, reach the pole level of signifiance（P<
0.01）;Prediction standard deviation RMSEP=2.017.Paired t-test result shows between the NIRS predicted values of sample and chemical score
Without significant difference.Result above shows that built NIRS models are accurately and reliably for the measure of corn single grain amylose content
's.
Embodiment 3 predicts the amylose content of testing sample
Unknown corn single grain sample is scanned, compares the near infrared spectrum of unknown sample and calibration sample, with above
The amylose content of the model prediction unknown sample of foundation.
Claims (8)
1. a kind of method that utilization near-infrared spectrum technique determines corn single grain amylose content, it is characterised in that including
Following steps：
（1）Collect corn single grain material；
（2）Collection sample near infrared spectrum；
（3）With conventional chemistry determination sample amylose content；
（4）Gained near infrared spectrum is pre-processed, disturbing factor is eliminated；
（5）The calibration model set up between the amylose content chemical score of corn single grain and near infrared spectrum is simultaneously checked；
（6）External certificate is carried out to model；
（7）The near infrared spectrum of testing sample is gathered, quickly determining testing sample amylose with the near-infrared model set up contains
Amount.
2. the method for claim 1, it is characterised in that step（1）It is described, collect single grain material more than 100 parts, and
Storeroom grain type, color etc. should differences.
3. the method for claim 1, it is characterised in that step（2）It is described, adopted using near infrared grain quality analysis meter
Collection corn single grain sample near infrared spectrum, scans the nm of Spectral range 950~1650, the nm of resolution ratio 2, using single grain minute surface
Sample disc loads sample, and seed need to lie in a horizontal plane in sample disk center, and without embryo side upward, each sample repeats dress sample 3 times, often
Secondary dress sample is scanned 2 times, preserves averaged spectrum.
4. the method for claim 1, it is characterised in that step（3）It is described, dual wavelength iodine colorimetry determination sample straight chain
Content of starch, determines wavelength and is respectively 620nm and 510nm, and sample is arranged from small to large according to chemical score, and 1 is taken every 3
Individual composition checking collection is used to model for model checking, remaining sample composition calibration set.
5. the method for claim 1, it is characterised in that step（4）Described, the method for being pre-processed is led selected from single order
One or more in number, second dervative, multiplicative scatter correction, normal orthogonal change of variable.
6. the method for claim 1, it is characterised in that step（5）Described, calibration model is passed through using PLS
Internal chiasma inspection is set up, the specific algorithm of internal chiasma inspection：The 1st sample spectra is taken out in M sample spectra, M- is used
1 sample spectra sets up basic model, then the sample spectra of taking-up is used to check, and calculation error；By the 1st sample spectra
Put back to, take out another sample spectra, so repeat, circulate, until each spectrum is examined analysis；It is near by weighing sample
The coefficient of determination between infrared predicted value and chemical score（R2）With cross validation standard deviation（RMSECV）Metrics evaluation model performance,
Wherein R2Computing formula with RMSECV is as follows：
；
Differ in formulaiThe chemical score of i-th sample and the difference of NIRS predicted values are represented, M is calibration set sample number, yiIt is i-th
The chemical score of sample, ymIt is the m average value of sample NIRS predicted values.
7. the method for claim 1, it is characterised in that step（6）It is described, predict checking with the calibration model for having optimized
Collection sample, compares NIRS predicted values and chemical score content, with prediction mean square deviation（RMSEP）With paired t-test evaluation model,
RMSEP formula are as follows：
Differ in formulaiThe chemical score of i-th sample and the difference of NIRS predicted values are represented, N is checking collection sample number.
8. the near-infrared spectrum method as described in any one of claim 1~7 is in corn single grain amylose content is determined
Application.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610152174.9A CN106706553A (en) | 2016-03-17 | 2016-03-17 | Method for quick and non-destructive determination of content of amylase in corn single grains |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610152174.9A CN106706553A (en) | 2016-03-17 | 2016-03-17 | Method for quick and non-destructive determination of content of amylase in corn single grains |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106706553A true CN106706553A (en) | 2017-05-24 |
Family
ID=58939662
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610152174.9A Pending CN106706553A (en) | 2016-03-17 | 2016-03-17 | Method for quick and non-destructive determination of content of amylase in corn single grains |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106706553A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107356488A (en) * | 2017-06-21 | 2017-11-17 | 广西壮族自治区亚热带作物研究所 | The simple and fast assay method of amylose in a kind of grain |
| CN107515203A (en) * | 2017-07-19 | 2017-12-26 | 中国农业大学 | The research of near infrared technology quantitative analysis rice single grain amylose content |
| CN108680515A (en) * | 2018-08-27 | 2018-10-19 | 中国科学院合肥物质科学研究院 | A kind of simple grain amylose in rice Quantitative Analysis Model structure and its detection method |
| CN109916844A (en) * | 2019-04-15 | 2019-06-21 | 长江大学 | Method for rapidly determining resistant starch content of wheat grains |
| CN110530843A (en) * | 2019-08-22 | 2019-12-03 | 北京农业智能装备技术研究中心 | The detection method and system of content of starch in a kind of corn kernel |
| CN110596038A (en) * | 2019-09-27 | 2019-12-20 | 南京晶薯生物科技有限公司 | Method for rapidly determining starch content of sweet potatoes |
| CN110715918A (en) * | 2019-10-14 | 2020-01-21 | 北京农业智能装备技术研究中心 | Single-kernel corn starch content Raman hyperspectral classification method |
| CN111024649A (en) * | 2020-01-09 | 2020-04-17 | 山西省农业科学院农作物品种资源研究所 | Method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy |
| CN111122470A (en) * | 2019-12-27 | 2020-05-08 | 海南大学 | Method for detecting amylose content of single-grain wheat |
| CN111537467A (en) * | 2020-05-18 | 2020-08-14 | 河北省农林科学院粮油作物研究所 | Method for nondestructively measuring starch content of mung beans |
| CN112179871A (en) * | 2020-10-22 | 2021-01-05 | 南京农业大学 | Method for nondestructive detection of caprolactam content in sauce food |
| CN112683842A (en) * | 2020-11-23 | 2021-04-20 | 河南工业大学 | Method for measuring total starch content and starch direct-to-branch ratio of wheat in infrared spectrum interval |
| CN112683840A (en) * | 2020-10-29 | 2021-04-20 | 河南工业大学 | Method for rapidly and nondestructively measuring amylose content of single wheat grain by utilizing near infrared spectrum technology |
| CN113484270A (en) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | Construction and detection method of single-grain rice fat content quantitative analysis model |
| CN114018859A (en) * | 2021-10-13 | 2022-02-08 | 中国水稻研究所 | Method for rapidly and synchronously measuring apparent amylose, amylose and amylopectin contents of rice flour |
| CN114384041A (en) * | 2021-11-18 | 2022-04-22 | 河北农业大学 | Method for constructing near-infrared model of soluble sugar content of peanuts with different seed coat colors |
| CN114813463A (en) * | 2022-05-31 | 2022-07-29 | 中国林业科学研究院林产化学工业研究所 | Method for predicting basic density of papermaking wood chips by near infrared spectrum without moisture interference |
| CN114935555A (en) * | 2022-06-28 | 2022-08-23 | 中国农业科学院农产品加工研究所 | Rapid nondestructive testing method for flour water absorption |
| CN114965351A (en) * | 2020-12-17 | 2022-08-30 | 江苏省农业科学院 | Near-infrared image-based high-throughput rapid nondestructive quantitative analysis method for fusarium toxin in single grain |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680426A (en) * | 2012-04-28 | 2012-09-19 | 中国农业大学 | Method for rapidly determining starch gelatinization degree of steam-tabletting corn |
| CN103575689A (en) * | 2013-10-11 | 2014-02-12 | 西北农林科技大学 | Method for rapidly detecting amylose content in rice by near infrared spectrum and visible light analyzer |
| CN105181643A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Near-infrared inspection method for rice quality and application thereof |
-
2016
- 2016-03-17 CN CN201610152174.9A patent/CN106706553A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680426A (en) * | 2012-04-28 | 2012-09-19 | 中国农业大学 | Method for rapidly determining starch gelatinization degree of steam-tabletting corn |
| CN103575689A (en) * | 2013-10-11 | 2014-02-12 | 西北农林科技大学 | Method for rapidly detecting amylose content in rice by near infrared spectrum and visible light analyzer |
| CN105181643A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Near-infrared inspection method for rice quality and application thereof |
Non-Patent Citations (8)
| Title |
|---|
| SYAHIRA IBRAHIM ET AL.: "The Assessment of Amylose Content using Near Infrared Spectroscopy Analysis of Rice Grain Samples", 《IEEE CONFERENCE ON OPEN SYSTEMS》 * |
| 刘涛 等: "农作物品质的近红外光谱无损检测研究进展", 《食品与机械》 * |
| 吕慧 等: "基于近红外光谱技术的大米品质分析与种类鉴别", 《食品工业科技》 * |
| 周琼: "双波长分光光度法测定玉米微孔淀粉的直链淀粉、支链淀粉含量", 《光谱试验室》 * |
| 彭建 等: "小麦籽粒淀粉和直链淀粉含量的近红外漫反射光谱法快速检测", 《麦类作物学报》 * |
| 朱苏文 等: "玉米籽粒直链淀粉含量的近红外透射光谱无损检测", 《中国粮油学报》 * |
| 杨有仙 等: "直链淀粉含量测定方法研究进展", 《食品科学》 * |
| 樊明月 等: "近红外光谱技术结合偏最小二乘法快速测定砂仁中乙酸龙脑酯的含量", 《南京中医药大学学报》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107356488A (en) * | 2017-06-21 | 2017-11-17 | 广西壮族自治区亚热带作物研究所 | The simple and fast assay method of amylose in a kind of grain |
| CN107515203A (en) * | 2017-07-19 | 2017-12-26 | 中国农业大学 | The research of near infrared technology quantitative analysis rice single grain amylose content |
| CN108680515A (en) * | 2018-08-27 | 2018-10-19 | 中国科学院合肥物质科学研究院 | A kind of simple grain amylose in rice Quantitative Analysis Model structure and its detection method |
| CN109916844A (en) * | 2019-04-15 | 2019-06-21 | 长江大学 | Method for rapidly determining resistant starch content of wheat grains |
| CN110530843A (en) * | 2019-08-22 | 2019-12-03 | 北京农业智能装备技术研究中心 | The detection method and system of content of starch in a kind of corn kernel |
| CN110596038A (en) * | 2019-09-27 | 2019-12-20 | 南京晶薯生物科技有限公司 | Method for rapidly determining starch content of sweet potatoes |
| CN110715918A (en) * | 2019-10-14 | 2020-01-21 | 北京农业智能装备技术研究中心 | Single-kernel corn starch content Raman hyperspectral classification method |
| CN111122470A (en) * | 2019-12-27 | 2020-05-08 | 海南大学 | Method for detecting amylose content of single-grain wheat |
| CN111024649A (en) * | 2020-01-09 | 2020-04-17 | 山西省农业科学院农作物品种资源研究所 | Method for rapidly determining amylose and amylopectin in millet by near infrared spectroscopy |
| CN111537467A (en) * | 2020-05-18 | 2020-08-14 | 河北省农林科学院粮油作物研究所 | Method for nondestructively measuring starch content of mung beans |
| CN112179871A (en) * | 2020-10-22 | 2021-01-05 | 南京农业大学 | Method for nondestructive detection of caprolactam content in sauce food |
| CN112179871B (en) * | 2020-10-22 | 2022-10-18 | 南京农业大学 | Method for nondestructive detection of caprolactam content in sauce food |
| CN112683840A (en) * | 2020-10-29 | 2021-04-20 | 河南工业大学 | Method for rapidly and nondestructively measuring amylose content of single wheat grain by utilizing near infrared spectrum technology |
| CN112683842A (en) * | 2020-11-23 | 2021-04-20 | 河南工业大学 | Method for measuring total starch content and starch direct-to-branch ratio of wheat in infrared spectrum interval |
| CN114965351A (en) * | 2020-12-17 | 2022-08-30 | 江苏省农业科学院 | Near-infrared image-based high-throughput rapid nondestructive quantitative analysis method for fusarium toxin in single grain |
| CN113484270A (en) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | Construction and detection method of single-grain rice fat content quantitative analysis model |
| CN114018859A (en) * | 2021-10-13 | 2022-02-08 | 中国水稻研究所 | Method for rapidly and synchronously measuring apparent amylose, amylose and amylopectin contents of rice flour |
| CN114384041A (en) * | 2021-11-18 | 2022-04-22 | 河北农业大学 | Method for constructing near-infrared model of soluble sugar content of peanuts with different seed coat colors |
| CN114813463A (en) * | 2022-05-31 | 2022-07-29 | 中国林业科学研究院林产化学工业研究所 | Method for predicting basic density of papermaking wood chips by near infrared spectrum without moisture interference |
| CN114935555A (en) * | 2022-06-28 | 2022-08-23 | 中国农业科学院农产品加工研究所 | Rapid nondestructive testing method for flour water absorption |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106706553A (en) | Method for quick and non-destructive determination of content of amylase in corn single grains | |
| Zhang et al. | Determination of water content in corn stover silage using near-infrared spectroscopy | |
| CN101413885A (en) | Near-infrared spectrum method for rapidly quantifying honey quality | |
| CN108680515B (en) | Single-grain rice amylose quantitative analysis model construction and detection method thereof | |
| Huang et al. | Improved generalization of spectral models associated with Vis-NIR spectroscopy for determining the moisture content of different tea leaves | |
| CN113049530B (en) | Single-seed corn seed moisture content detection method based on near-infrared hyperspectrum | |
| Li et al. | Determination of soluble solid content in multi-origin ‘Fuji’apples by using FT-NIR spectroscopy and an origin discriminant strategy | |
| Tan et al. | Analysis of different hyperspectral variables for diagnosing leaf nitrogen accumulation in wheat | |
| CN107515203A (en) | The research of near infrared technology quantitative analysis rice single grain amylose content | |
| Xu et al. | A calibration transfer optimized single kernel near-infrared spectroscopic method | |
| CN104390927B (en) | The quick determination method of ash content in coal sample | |
| CN108613943B (en) | Near-infrared single-grain crop component detection method based on spectrum morphology transfer | |
| CN106932365A (en) | Method for detecting components of corn straws by using near-infrared instrument | |
| CN102393376A (en) | Support vector regression-based near infrared spectroscopy for detecting content of multiple components of fish ball | |
| CN106706554A (en) | Method for rapidly and nondestructively determining content of straight-chain starch of corn single-ear grains | |
| Wang et al. | Onsite nutritional diagnosis of tea plants using micro near-infrared spectrometer coupled with chemometrics | |
| CN105158195A (en) | Method for rapidly determining holocellulose content of pulping material based on near-infrared spectrum technology | |
| CN101701911A (en) | Method for detecting content of reducing sugar in fermentation materials | |
| CN109540837A (en) | The method that near-infrared quickly detects Boehmeria nivea leaves wood fibre cellulose content | |
| CN106018321A (en) | Establishment method for single corn seed protein detection model and application of establishment method | |
| CN108169168A (en) | Test and analyze rice grain protein content mathematical model and construction method and application | |
| CN102519903B (en) | Method for measuring whiteness value of Agaricus bisporus by using near infrared spectrum | |
| CN110376154A (en) | Fruit online test method and system based on spectrum correction | |
| Zhu et al. | Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine | |
| CN104316492A (en) | Method for near-infrared spectrum measurement of protein content in potato tuber |
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 |
Application publication date: 20170524 |
|
| WD01 | Invention patent application deemed withdrawn after publication |