CN106442397B - A kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio - Google Patents
A kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio Download PDFInfo
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- 235000009566 rice Nutrition 0.000 claims abstract description 68
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- 238000002329 infrared spectrum Methods 0.000 claims abstract description 24
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- 241000209140 Triticum Species 0.000 description 2
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- 239000010903 husk Substances 0.000 description 2
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- 102000004169 proteins and genes Human genes 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
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- 238000005516 engineering process Methods 0.000 description 1
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Abstract
The present invention provides a kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio, specially, the near infrared spectrum for repeatedly measuring the seed of rice paddy seed in near-infrared spectroscopy, kind shell, brown rice, obtains seed, kind shell and brown rice near-infrared averaged spectrum；Further according to formula: seed averaged spectrum=K1 × Brown Rice averaged spectrum+K2 × rice seed shell averaged spectrum calculates brown rice, plants the fit-spectra of shell and obtain regression coefficient K1, K2；According to K1, K2 coefficient magnitude, the influence for being overlapped information can be deducted, the near-infrared spectroscopy of optimization is established using fit-spectra；Compared with prior art, the influence of the non-intrinsically safe redundant information of part interference model in rice paddy seed near-infrared spectroscopy can be removed according to K1, K2 coefficient magnitude using fit-spectra, thus the near-infrared spectroscopy that the method for the present invention is established has preferably analysis and estimated performance.
Description
Technical field
The present invention relates to the near-infrared spectroscopy analysis technical fields of rice paddy seed, specifically a kind of to be based on spectrum
The paddy near-infrared spectroscopy optimization method that ratio deducts.
Background technique
Rice is gramineae plant, seed, that is, paddy, is one of main cereal crops in the world, belongs to direct economy
Crop.Paddy can integrally be divided into two parts: rice husk and brown rice, and wherein rice is the staple food in East Asia and Southeast Asia population, nutrition at
Divide more, comprising: starch, protein, vitamin etc., it can be also used for the traditional industries such as wine brewing；Rice husk then can be used as feeding
Material etc..Near-infrared (NIR) spectrum is absorption spectra of the substance in 780-2526nm wavelength, because its with lossless, quick, multicomponent,
Free of contamination analysis characteristic is agriculturally having been widely used.Currently, the cereal crops such as rice, wheat, soybean, corn
All kinds of near-infrared spectroscopies to varying degrees in various fields, such as corresponding protein, starch, fat, water
Equal near-infrared spectroscopies are divided to play very important effect in fields such as feedstuff industry, grain and oil industry, breeding industries.
But the near-infrared spectroscopy of rice paddy seed analysis difficulty is higher, this is primarily due to relative to wheat, greatly
Beans, rice paddy seed is containing kind of two different pieces of shell and brown rice, and kind shell, brown rice content of chemical substances difference are larger, only with
General chemometrics method is more difficult to establish accurate near-infrared spectroscopy.So not having also for rice paddy seed at present
There are satisfactory near infrared spectra quantitative models.In this invention, we design and have invented a kind of new rice seed
Sub- NIR processing and analysis method establish a kind of pair of water by the near infrared cheracteristics of analyzing rice kind shell and brown rice part
Rice near-infrared model optimization method.
Summary of the invention
The purpose of the present invention is to solve the near-infrared spectroscopy of rice paddy seed in the prior art analysis difficulty is high, pre-
It is above-mentioned to solve to provide a kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio for the true defect of indeterminacy
Problem.
The present invention is achieved through the following technical solutions above-mentioned technical purpose:
A kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio, comprising the following steps:
1) calculating of near-infrared averaged spectrum
The near infrared spectrum for repeatedly measuring the seed of rice paddy seed in near-infrared spectroscopy, kind shell, brown rice, is planted
Son, kind shell and brown rice near-infrared averaged spectrum；
2) fit-spectra calculates
According to formula:
Seed averaged spectrum=K1 × Brown Rice averaged spectrum+K2 × rice seed shell averaged spectrum, to seed average light
Spectrum carries out linear regression fit, thus obtains COEFFICIENT K 1, K2, so that its fitting result and the standard deviation of seed averaged spectrum are most
It is small；
3) foundation of near-infrared spectroscopy
According to K1, K2 coefficient magnitude, deducting rice paddy seed spectrum in near-infrared spectroscopy includes kind of shell or brown rice part
Coincidence information influence to get arrive formula:
The fit-spectra of brown rice=(seed averaged spectrum-K2 × kind of shell averaged spectrum)/K1,
Or
Fit-spectra=(seed averaged spectrum-K1 × brown rice averaged spectrum)/K2 of kind shell.
After obtaining fit-spectra, near-infrared spectroscopy is established hereby based on fit-spectra；
Preferably, the step 1) includes:
11) judgement influences the major part of near-infrared spectroscopy
First judge the main information of the model foundation is present in which position of rice, according still further to spectral composition Proportional coefficient K 1
The duplicate message for influencing model accuracy is removed with K2；
12) near infrared spectrum of measurement kind shell and brown rice
The rice paddy seed sample populations for establishing near-infrared spectroscopy are first chosen, every sample of group is then subjected to kind of a shell
It is separated with brown rice, multiple near infrared ray then is carried out to kind of shell and brown rice respectively, acquires the average light of kind of shell and brown rice
Spectrum；
Preferably, the step 2) includes:
According to the averaged spectrum of the kind shell and brown rice that are acquired in step 12), it is fitting object with seed spectrum, acquires kind of a shell
With the composition ratio COEFFICIENT K 1 of brown rice, K2.
Preferably, establish the method for rice paddy seed moisture near-infrared spectroscopy the following steps are included:
1) the rice paddy seed gradation of moisture is constructed
Modeling collection are as follows: seed is individually positioned in room temperature, 20 DEG C, 30 DEG C, 40 DEG C of 4 temperature values, 11%, 43%, 75%,
97% totally 4 humidity values, place the different time and establish the corresponding gradation of moisture, totally 38 points；
Forecast set are as follows: seed is respectively placed in 20 DEG C, 25 DEG C, 30 DEG C, 35 DEG C of temperature and 11%, 43%, 75% humidity
Lower 5 months seeds, totally 12 points；
2) it establishes, analyzing rice seed moisture content near-infrared spectroscopy
The near-infrared diffusing reflection spectrum of the rice paddy seed of acquisition modeling collection and forecast set, wave-number range 12000-
4000cm-1, resolution ratio 16cm-1；Remove 12000~10500cm-1Wave-number range selects 10500~4000cm-1Wave-number range,
To modeling collection equalization processing under establish rice paddy seed moisture partial least square model, after to forecast set carry out forecast analysis；
3) judgement influences the main portions of water model
By the comparison of the water content to each position of seed, show that the main portions for influencing rice paddy seed water model are rough
Rice, answers the spectrum ratio of proportional deduction kind shell；
4) spectral composition ratio K1, K2 is calculated
The seed for taking modeling to collect, is repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, acquires rice paddy seed
Averaged spectrum；Gently peel off kind of a shell, press from both sides out Brown Rice with tweezers, after kind shell is restored as former state, to keep its form consistent, repeatedly
Measurement near-infrared diffusing reflection spectrum 15 times, acquires the averaged spectrum of kind of a shell, brown rice；Time of brown rice spectrum, kind shell spectrum is calculated again
Return COEFFICIENT K 1 and K2；
5) practical near infrared spectrum is replaced with fit-spectra, reanalyses water model
Using K1, K2 as coefficient, by the seed spectrum of modeling collection all in water model and forecast set all in accordance with following formula
Processing, it may be assumed that
Brown rice fit-spectra=(averaged spectrum-K2 of seed × kind of shell averaged spectrum)/K1
Then the brown rice averaged spectrum that actual measurement is replaced with the brown rice fit-spectra acquired, is established based on fit-spectra
New near-infrared spectroscopy.
Compared with the prior art, the present invention has the following beneficial effects:
Rice paddy seed spectrum is actually made of rice seed shell and brown rice spectrum two parts, corresponding in different rice paddy seeds
Composition ratio COEFFICIENT K 1 and K2 is different；Part in rice paddy seed near-infrared spectroscopy can be removed according to K1, K2 coefficient magnitude
The influence of the non-intrinsically safe redundant information of interference model, so as to promote the near-infrared about certain constituent analysis in rice paddy seed
The prediction accuracy of spectral model.
Detailed description of the invention
Fig. 1 is in a kind of paddy near-infrared spectroscopy optimization method embodiment 1 deducted based on spectrum ratio of the present invention
According to the modeling result (a) of the seed moisture content model of actual measurement near infrared spectrum and prediction result (b)；
Fig. 2 is the influence that disturbing factor (kind shell) is proportionally deducted in the embodiment of the present invention 1, obtains kissing with averaged spectrum
Close good fit-spectra；
Fig. 3 is the modeling result (a) and prediction result (b) in the embodiment of the present invention 1 based on fit-spectra.
Specific embodiment
The effect of to make to structure feature of the invention and being reached, has a better understanding and awareness, to preferable
Examples and drawings cooperation detailed description, is described as follows:
The present invention is a kind of composition ratio by measuring and analyzing paddy brown rice and kind shell spectrum near infrared spectrum
The method for removing optimization paddy near-infrared spectroscopy.The following steps are included:
Step 1. judgement influences the major part of near-infrared spectroscopy
Before information according to spectral composition ratio removal interference model, it would be desirable to first judge the main of the model analysis
Information is present in which position of rice.Such as: for establishing rice paddy seed water model, moisture is primarily present in rice paddy seed
Among brown rice part, then, we need to remove the spectrum that the interference model from kind of shell is established according to certain spectrum ratio and believe
Breath.
Step 2. measurement kind shell and brown rice near infrared spectrum
Since brown rice is different with the kind composition ratio of shell spectrum in its near infrared spectrum of different rice paddy seeds, so we
It is sample that the rice paddy seed for establishing near-infrared spectroscopy, which must be chosen,.It is more that near infrared spectrum is repeatedly measured to sample populations
Secondary, number is more, and averaged spectrum is more stable, and it is more accurate to establish model.When measurement, sample populations are carefully peeled off, are paid attention to as far as possible
Keep sample state consistency, after respectively to brown rice, that kind shell is repeatedly measured near infrared spectrum is multiple, be thus averaging spectrum.
Step 3. calculates kind of a shell, brown rice composition ratio COEFFICIENT K 1, K2
The modeling wave-number range of suitable near-infrared spectroscopy is selected, is fitting object with seed spectrum, by following public affairs
Formula:
Seed near infrared spectrum=K1 × brown rice near infrared spectrum+K2 × kind of shell near infrared spectrum (1)
It is fitted seed near infrared spectrum by linear regression method, so that the standard deviation of its fitting result and seed spectrum
(RMSE) minimum, it is hereby achieved that brown rice and kind shell spectral composition Proportional coefficient K 1, K2.
Step 4. establishes near-infrared spectroscopy according to the fit-spectra of seed
It is used for above-mentioned K1, K2 coefficient to establish near-infrared spectroscopy；That is: it is replaced by the fit-spectra that formula (1) pushes away
Thus the near infrared spectrum of actual measurement establishes new NIR Spectroscopy Analysis Model.
Such as: during optimization rice paddy seed water model, brown rice, the composition ratio COEFFICIENT K 1 for planting shell, K2 are calculated first,
Major part due to influencing its model is brown rice, so obtaining according to formula (1):
The fit-spectra of brown rice=(seed averaged spectrum-K2 × kind of shell averaged spectrum)/K1 (2)
That is: the influence that kind shell in paddy water model is overlapped information is deducted, corresponding seed moisture content is then re-established
Near-infrared spectroscopy.
Embodiment 1
Establish rice paddy seed moisture near-infrared spectroscopy, specific steps are as follows:
Step 1. constructs the rice paddy seed gradation of moisture
In the present embodiment, the method is used to analyze 9311 conventional Rice group seed near infrared spectrum water models by us,
The modeling collection of averaging model are as follows: rice paddy seed is individually positioned in room temperature, 20 DEG C, 30 DEG C, 40 DEG C of 4 temperature values, 11%,
43%, 75%, 97% totally 4 humidity values, place the different time and establish the corresponding gradation of moisture, totally 38 points.Different temperatures,
Different time, different humidity construction method can effectively avoid the variable of non-moisture inside model identical with moisture variation generation
Variation tendency, influence the subsequent analysis of model.
Forecast set is then that 9311 seeds are respectively placed in 20 DEG C, 25 DEG C, 30 DEG C, 35 DEG C of temperature and 11%, 43%, 75%
The lower 5 months seeds of humidity, totally 12 points.
Although this model number is less relative to the near-infrared spectroscopy of other crops, due to moisture with it is close red
External spectrum correlation is stronger, and meets molecule so variable number is less using 9311 seed of conventional Rice of the same race in this model
The number requirement of calibration set in spectrum Multivariate Correction quantitative analysis general rule (GBT 29858-2013).
Step 2. foundation, analyzing rice seed moisture content model
Use the near-infrared diffusing reflection spectrum of German Brooker MPA near infrared spectrometer acquisition rice paddy seed, wave-number range
For 12000-4000cm-1, resolution ratio 16cm-1.Remove 12000~10500cm-1Wave-number range (this partial spectrum noise is big), choosing
Select 10500~4000cm-1Wave-number range establishes rice under equalization processing to modeling collection using Unscramble9.5 software
Seed moisture content partial least square model, after to forecast set carry out forecast analysis；As a result as shown in Fig. 1 (a), Fig. 1 (b).
Step 3. judgement influences the main portions of water model
Oven drying method is used to measure 9311 seed moisture contents as 11.758%, brown rice water content is 12.733%, and kind shell is aqueous
Amount is larger for brown rice specific gravity in 7.937% seed, and kind shell gas permeability is preferable, moisture evaporation fast speed, so it is considered that
The main portions for influencing rice paddy seed water model are brown rice, it should deduct the spectrum ratio of kind of shell.
Step 4. calculates spectral composition ratio K1, K2
The seed for taking modeling to collect, is repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, acquires rice paddy seed
Averaged spectrum；Gently peel off kind of a shell, press from both sides out Brown Rice with tweezers, after kind shell is restored as former state, to keep its form consistent, then
It is the same with seed, it is repeatedly measured near-infrared diffusing reflection spectrum 15 times, acquires the averaged spectrum of kind of a shell, brown rice.Pass through linear regression
The composition ratio COEFFICIENT K 1 and K2 that the method for fitting calculates brown rice spectrum, plants shell spectrum.In this example, according to fit-spectra and kind
The standard deviation of sub-light spectrum is minimum, thus obtains K1=0.428, K2=0.633.
Step 5. replaces the seed near infrared spectrum of actual measurement with fit-spectra, re-establishes and analyze new moisture mould
Type
Using K1, K2 as coefficient, by the seed spectrum of all modeling collection and forecast set all in accordance with following formula (2) processing, it may be assumed that
Each group brown rice fit-spectra in the water model=(averaged spectrum-K2 of each group seed × moisture mould in water model
Each group seed kind shell averaged spectrum in type)/K1；
Then actual measurement spectrum is not replaced with above-mentioned brown rice fit-spectra, establishes the near-infrared spectroscopy of moisture, and not
It deducts the near-infrared spectroscopy before being overlapped information to compare, as shown in Fig. 3 (a), Fig. 3 (b), as a result are as follows: deduct kind of a shell letter
The near-infrared spectroscopy of breath significantly improves in predictive ability, and wherein RMSEP reduces by 0.357, and mean absolute deviation reduces
0.2085, such as table 1.
In addition, it has been observed that the variable residual, information of fit-spectra model is preceding 4 compared with based on original measurement spectrum
A principal component is less, and the less available higher prediction accuracy of principal component, and as shown in table 2, this illustrates us originally
The inventive method of patent can promote seed near-infrared analysis and predictive ability really.
In conclusion it is proposed that this new method deducted based on spectrum ratio, can optimize paddy near infrared light
Spectrum model, and by specific example, illustrate that our method successfully optimizes rice paddy seed water model really, improves pre-
The accuracy for surveying seed moisture content content, thus demonstrates the validity of the method.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and what is described in the above embodiment and the description is only the present invention
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement is both fallen in the range of claimed invention.The present invention claims protection scope by appended claims and its
Equivalent defines.
Claims (4)
1. a kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio, it is characterised in that: including following step
It is rapid:
1) calculating of near-infrared averaged spectrum
The near infrared spectrum for repeatedly measuring the seed of rice paddy seed in near-infrared spectroscopy, kind shell, brown rice, obtains seed, kind
Shell and brown rice near-infrared averaged spectrum；
2) fit-spectra calculates
According to formula:
Seed averaged spectrum=K1 × Brown Rice averaged spectrum+K2 × rice seed shell averaged spectrum, to seed averaged spectrum into
Thus row linear regression fit obtains COEFFICIENT K 1, K2, so that its fitting result and the standard deviation of seed averaged spectrum are minimum；
3) foundation of near-infrared spectroscopy
According to K1, K2 coefficient magnitude, the weight that rice paddy seed spectrum in near-infrared spectroscopy includes kind of shell or brown rice part is deducted
The influence of information is closed to get formula is arrived:
The fit-spectra of brown rice=(seed averaged spectrum-K2 × kind of shell averaged spectrum)/K1,
Or
Fit-spectra=(seed averaged spectrum-K1 × brown rice averaged spectrum)/K2 of kind shell；
After obtaining fit-spectra, near-infrared spectroscopy is established hereby based on fit-spectra.
2. a kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio according to claim 1,
Be characterized in that: the step 1) includes:
11) judgement influences the major part of near-infrared spectroscopy
First judge the main information of the model foundation is present in which position of rice, according still further to spectral composition Proportional coefficient K 1 and K2
Removal influences the duplicate message of model accuracy；
12) near infrared spectrum of measurement kind shell and brown rice
It first chooses and establishes the rice paddy seed sample populations of near-infrared spectroscopy, every sample of group is then subjected to kind of shell and rough
Rice separation, then carries out multiple near infrared ray to kind of shell and brown rice respectively, acquires the averaged spectrum of kind of shell and brown rice.
3. a kind of paddy near-infrared spectroscopy optimization method deducted based on spectrum ratio according to claim 2,
Be characterized in that: the step 2) includes:
According to the averaged spectrum of the kind shell and brown rice that are acquired in step 12), it is fitting object with seed spectrum, acquires kind of shell and rough
The composition ratio COEFFICIENT K 1 of rice, K2.
4. a kind of method for establishing rice paddy seed moisture near-infrared spectroscopy, it is characterised in that: based on described in claim 1
Optimization method；The following steps are included:
1) the rice paddy seed gradation of moisture is constructed
Modeling collection are as follows: seed is individually positioned in room temperature, 20 DEG C, 30 DEG C, 40 DEG C of 4 temperature values, 11%, 43%, 75%, 97%
Totally 4 humidity values place the different time and establish the corresponding gradation of moisture, totally 38 points；
Forecast set are as follows: seed is respectively placed in 20 DEG C, 25 DEG C, 30 DEG C, 35 DEG C of temperature and 11%, 43%, 75% humidity lower 5
Month seed, totally 12 points；
2) it establishes, analyzing rice seed moisture content near-infrared spectroscopy
The near-infrared diffusing reflection spectrum of the rice paddy seed of acquisition modeling collection and forecast set, wave-number range 12000-4000cm-1, point
Resolution 16cm-1；Remove 12000~10500cm-1Wave-number range selects 10500~4000cm-1Wave-number range exists to modeling collection
Equalization processing under establish rice paddy seed moisture partial least square model, after to forecast set carry out forecast analysis；
3) judgement influences the main portions of water model
By the comparison of the water content to each position of seed, show that the main portions for influencing rice paddy seed water model are brown rice,
Answer the spectrum ratio of proportional deduction kind shell；
4) spectral composition Proportional coefficient K 1, K2 are calculated
The seed for taking modeling to collect, is repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, acquires being averaged for rice paddy seed
Spectrum；Gently peel off kind of a shell, press from both sides out Brown Rice with tweezers, after kind shell is restored as former state, to keep its form consistent, be repeatedly measured
Near-infrared diffusing reflection spectrum 15 times, acquire the averaged spectrum of kind of a shell, brown rice；The ratio of components for calculating brown rice spectrum again, planting shell spectrum
Example COEFFICIENT K 1 and K2；
5) practical near infrared spectrum is replaced with fit-spectra, reanalyses water model
Using K1, K2 as coefficient, by the seed spectrum of all modeling collection and forecast set all in accordance with following formula manipulation, it may be assumed that
Brown rice fit-spectra=(averaged spectrum-K2 of each group seed × kind of shell averaged spectrum in water model)/K1
Then former actually measured brown rice spectrum is replaced with the brown rice fit-spectra acquired, because having deducted incoherent kind of shell spectrum
Information, it is possible thereby to establish the near-infrared spectroscopy that prediction more accurately optimizes.
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| CN108169168A (en) * | 2017-12-19 | 2018-06-15 | 信阳师范学院 | Test and analyze rice grain protein content mathematical model and construction method and application |
| CN109580493A (en) * | 2018-11-16 | 2019-04-05 | 长江大学 | A kind of method of quick detection to section Chinese wax batch seed quality |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0875643A (en) * | 1994-09-09 | 1996-03-22 | Iseki & Co Ltd | Estimating method of component value of hulled rice with mixed unhulled rice |
| CN102179375A (en) * | 2011-03-09 | 2011-09-14 | 中国科学院合肥物质科学研究院 | Nondestructive detecting and screening method based on near-infrared for crop single-grain components |
| CN102960096A (en) * | 2012-11-13 | 2013-03-13 | 中国科学院合肥物质科学研究院 | Rice single seed vigor nondestructive testing screening method based on near-infrared spectrum |
| CN103344602A (en) * | 2013-07-04 | 2013-10-09 | 中国科学院合肥物质科学研究院 | Nondestructive testing method for rice idioplasm authenticity based on near infrared spectrum |
| CN105181643A (en) * | 2015-10-12 | 2015-12-23 | 华中农业大学 | Near-infrared inspection method for rice quality and application thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06103379B2 (en) * | 1985-10-26 | 1994-12-14 | 三田工業株式会社 | Wire drive optical system device |
-
2016
- 2016-09-05 CN CN201610802820.1A patent/CN106442397B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0875643A (en) * | 1994-09-09 | 1996-03-22 | Iseki & Co Ltd | Estimating method of component value of hulled rice with mixed unhulled rice |
| CN102179375A (en) * | 2011-03-09 | 2011-09-14 | 中国科学院合肥物质科学研究院 | Nondestructive detecting and screening method based on near-infrared for crop single-grain components |
| CN102960096A (en) * | 2012-11-13 | 2013-03-13 | 中国科学院合肥物质科学研究院 | Rice single seed vigor nondestructive testing screening method based on near-infrared spectrum |
| CN103344602A (en) * | 2013-07-04 | 2013-10-09 | 中国科学院合肥物质科学研究院 | Nondestructive testing method for rice idioplasm authenticity based on near infrared spectrum |
| 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 (2)
| Title |
|---|
| "Effect of g-irradiation on rice seed vigor assessed by near-infrared spectroscopy";Le Song 等;《Journal of Stored Products Research》;20150409;第62卷;第46-51页 * |
| "基于近红外光谱的单粒水稻种子活力快速无损检测";宋乐 等;《粮食储藏》;20151231;第44卷;第20-23页 * |
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