CN106442397A - Rice near infrared spectrum model optimizing method based on spectrum proportional deduction - Google Patents
Rice near infrared spectrum model optimizing method based on spectrum proportional deduction Download PDFInfo
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- 238000001228 spectrum Methods 0.000 title claims abstract description 126
- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 89
- 235000009566 rice Nutrition 0.000 title claims abstract description 69
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 25
- 240000007594 Oryza sativa Species 0.000 title claims description 66
- 241000209094 Oryza Species 0.000 claims abstract 23
- 235000021329 brown rice Nutrition 0.000 claims description 60
- 238000004497 NIR spectroscopy Methods 0.000 claims description 46
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 22
- 241000196324 Embryophyta Species 0.000 claims description 15
- 238000005457 optimization Methods 0.000 claims description 9
- 230000003595 spectral effect Effects 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 8
- 238000012417 linear regression Methods 0.000 claims description 4
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 11
- 230000002452 interceptive effect Effects 0.000 abstract 1
- 210000000582 semen Anatomy 0.000 description 4
- 235000013339 cereals Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 229920002472 Starch Polymers 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000010903 husk Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 235000018102 proteins Nutrition 0.000 description 2
- 102000004169 proteins and genes Human genes 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 235000019698 starch Nutrition 0.000 description 2
- 239000008107 starch Substances 0.000 description 2
- 244000025254 Cannabis sativa Species 0.000 description 1
- 244000046052 Phaseolus vulgaris Species 0.000 description 1
- 235000010627 Phaseolus vulgaris Nutrition 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 239000003925 fat Substances 0.000 description 1
- 235000019197 fats Nutrition 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 238000000643 oven drying Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
- 150000003722 vitamin derivatives Chemical class 0.000 description 1
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- 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
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- 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
- G01N21/3554—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
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- 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
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Abstract
The invention provides a rice near infrared spectrum model optimizing method based on spectrum proportional deduction. The method specifically includes the steps of measuring near infrared spectrums of seeds, seed shells and coarse rice of rice seeds in a near infrared spectrum model multiple times to obtain the near infrared average spectrum of the seeds, seed shells and coarse rice; calculating a fitting spectrum of coarse rice and seed shells and obtaining regression coefficients K1 and K2 according to the formula: the seed average spectrum=K1*rice coarse rice average spectrum+K2*rice seed shell average spectrum; deducting influences of overlapping information according to the values of the coefficients K1 and K2, and establishing an optimized near infrared spectrum model through the fitting spectrum. Compared with the prior art, the influences of inessential redundant information of part of interfering models in the near infrared spectrum model of rice seeds can be removed according to the values of the coefficients K1 and K2 through the fitting spectrum, and therefore the near infrared spectrum model established through the method has better analysis and prediction performance.
Description
Technical field
The present invention relates to agricultural technology field, plant shell spectrum and brown rice spectrum in rice paddy seed near infrared spectrum by calculating
Proportion of composing, the spectral information of the interference model from kind of shell for the deduction, thus optimize corresponding near-infrared spectroscopy.
Background technology
Oryza sativa L. is grass, and institute's knot reality is Oryza glutinosa, is one of main cereal crops in the world, belongs to direct economy
Crop.Oryza glutinosa integrally can be divided into two parts：Rice husk and brown rice, wherein rice are East Asia and the staple food of Southeast Asia population, and nutrition becomes
Divide more, including：Starch, protein, vitamin etc., it can be also used for the traditional industries such as wine brewing；Rice husk then can be used as raising
Material etc..Near-infrared (NIR) spectrum is the absorption spectra in 780-2526nm wavelength for the material, because its have lossless, quick, multicomponent,
Free of contamination analysis characteristic, is agriculturally obtaining a wide range of applications.At present, the cereal crops such as Oryza sativa L., Semen Tritici aestivi, Semen sojae atricolor, Semen Maydiss
All kinds of near-infrared spectroscopies be all used for varying degrees in various fields, such as corresponding protein, starch, fat, water
The near-infrared spectroscopy that grades has played 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, and this is primarily due to respect to Semen Tritici aestivi, big
Bean, rice paddy seed contains kind of shell and two different pieces of brown rice, and it is larger to plant shell, brown rice content of chemical substances difference, only with
General chemometrics method is more difficult to set up accurate near-infrared spectroscopy.So, at present rice paddy seed is not also had
There are gratifying near infrared spectra quantitative models.In this invention, we design and have invented a kind of new rice seed
Sub- NIR is processed and analysis method, by the near infrared cheracteristics of analyzing rice kind shell and brown rice part, establish a kind of to water
Rice near-infrared model optimization method.
Content of the invention
The invention aims to the near-infrared spectroscopy analysis difficulty of rice paddy seed is high, pre- in solution prior art
The true defect of indeterminacy, provide a kind of based on spectrum ratio deduction Oryza glutinosa near-infrared spectroscopy optimization method solve above-mentioned
Problem.
The present invention is achieved through the following technical solutions above-mentioned technical purpose：
A kind of Oryza glutinosa near-infrared spectroscopy optimization method based on spectrum ratio deduction, comprises the following steps：
1) calculating of near-infrared averaged spectrum
Repeatedly measure the seed of rice paddy seed in near-infrared spectroscopy, plant shell, the near infrared spectrum of brown rice, 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 carry out linear regression fit, thus obtain COEFFICIENT K 1, K2 so that the standard deviation of its fitting result and seed averaged spectrum
Little；
3) foundation of near-infrared spectroscopy
According to K1, K2 coefficient magnitude, deduct rice paddy seed spectrum in near-infrared spectroscopy and comprise kind of shell or brown rice part
Coincidence information impact, that is, obtain formula：
The fit-spectra of brown rice=(seed averaged spectrum-K2 × kind of shell averaged spectrum)/K1,
Or
Plant fit-spectra=(seed averaged spectrum-K1 × brown rice averaged spectrum)/K2 of shell.
After obtaining fit-spectra, it is hereby based on fit-spectra and sets up near-infrared spectroscopy；
Preferably, described step 1) include：
11) judge the major part of impact near-infrared spectroscopy
First judge the main information that this model is set up is present in which position of Oryza sativa L., according still further to spectral composition Proportional coefficient K 1
Remove the duplicate message of impact model accuracy with K2；
12) near infrared spectrum of measurement kind shell and brown rice
First choose the rice paddy seed sample populations setting up near-infrared spectroscopy, then every sample of colony is carried out kind of a shell
Separate with brown rice, then respectively multiple near infrared ray is carried out to kind of shell and brown rice, try to achieve the average light of kind of shell and brown rice
Spectrum；
Preferably, described step 2) include：
According to step 12) in the averaged spectrum planting shell and brown rice tried to achieve, with seed spectrum for matching object, try to achieve kind of a shell
Proportion of composing COEFFICIENT K 1, K2 with brown rice.
Preferably, the method setting up rice paddy seed moisture near-infrared spectroscopy comprises the following steps：
1) build the rice paddy seed gradation of moisture
Modeling collection is：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 times and set up the corresponding gradation of moisture, totally 38 points；
Forecast set is：Seed is respectively placed in 20 DEG C, 25 DEG C, 30 DEG C, 35 DEG C of temperature and 11%, 43%, 75% humidity
The seed of lower 5 months, totally 12 points；
2) foundation, analyzing rice seed moisture content near-infrared spectroscopy
The near-infrared diffusing reflection spectrum of the rice paddy seed of collection modeling collection and forecast set, wave-number range is 12000-
4000cm-1, resolution 16cm-1；Remove 12000～10500cm-1Wave-number range, selects 10500～4000cm-1Wave-number range,
Under equalization is processed, rice paddy seed moisture partial least square model is set up to modeling collection, after forecast set is predicted analyze；
3) main portions of impact water model are judged
By the comparison of the water content to each position of seed, show that the main portions of impact rice paddy seed water model are rough
Rice, answers the spectrum ratio of proportional deduction kind shell；
4) calculate spectral composition ratio K1, K2
Take the seed of modeling collection, be repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, try to achieve rice paddy seed
Averaged spectrum；Gently strip off kind shell, presss from both sides out Brown Rice with tweezers, after will plant shell and recover former state, keep its form consistent, repeatedly
Mensure near-infrared diffusing reflection spectrum 15 times, tries to achieve the averaged spectrum of kind of a shell, brown rice；Calculate brown rice spectrum again, plant returning of shell spectrum
Return COEFFICIENT K 1 and K2；
5) replace actual near infrared spectrum with fit-spectra, reanalyse water model
With K1, K2 as coefficient, by the seed spectrum of modeling collection all in water model and forecast set all according to equation below
Process, that is,：
Brown rice fit-spectra=(averaged spectrum K2 of seed × kind of shell averaged spectrum)/K1
Then replace the brown rice averaged spectrum of actual measurement with the brown rice fit-spectra tried to achieve, set up based on fit-spectra
New near-infrared spectroscopy.
The present invention compared with prior art, has the advantages that：
Rice paddy seed spectrum is actually made up of rice seed shell and brown rice spectrum two parts, corresponding in different rice paddy seeds
Proportion of composing COEFFICIENT K 1 is different with K2；Part in rice paddy seed near-infrared spectroscopy can be removed according to K1, K2 coefficient magnitude
The impact of the non-intrinsically safe redundant information of interference model, such that it is able to lift the near-infrared with regard to certain component analysis in rice paddy seed
The prediction accuracy of spectral model.
Brief description
Fig. 1 is in a kind of Oryza glutinosa near-infrared spectroscopy optimization method embodiment 1 based on spectrum ratio deduction of the present invention
Measure the modeling result (a) of the seed moisture content model of near infrared spectrum according to reality and predict the outcome (b)；
Fig. 2 is the impact proportionally deducting interference factor (kind shell) in the embodiment of the present invention 1, obtains kissing with averaged spectrum
Close good fit-spectra；
Fig. 3 for the modeling result (a) based on fit-spectra in the embodiment of the present invention 1 and predicts the outcome (b).
Specific embodiment
Effect for making the architectural feature to the present invention and reached has a better understanding and awareness, in order to preferably
Embodiment and accompanying drawing cooperation detailed description, are described as follows：
The present invention is a kind of by measuring and analyzing Oryza glutinosa brown rice and the proportion of composing planting shell spectrum near infrared spectrum
The method going to optimize Oryza glutinosa near-infrared spectroscopy.Comprise the following steps：
Step 1. judges the major part of impact near-infrared spectroscopy
Remove the information of interference model according to spectral composition ratio in the past it would be desirable to first judge the main of this model analysis
Information is present in which position of Oryza sativa L..Such as：For setting up rice paddy seed water model, in rice paddy seed, moisture is primarily present in
Among brown rice part, then, we need to remove the spectrum letter of the interference model foundation from kind of shell according to certain spectrum ratio
Breath.
Step 2. measurement kind shell and brown rice near infrared spectrum
Because in its near infrared spectrum of different rice paddy seeds, brown rice is different with the proportion of composing planting shell spectrum, so we
Must choose for the rice paddy seed setting up near-infrared spectroscopy is sample.Near infrared spectrum is repeatedly measured to sample populations many
Secondary, number of times is more, and averaged spectrum is more stable, sets up model more accurate.During measurement, careful for sample populations strip off notes trying one's best
Keep sample state consistency, after respectively to brown rice, plant shell and be repeatedly measured near infrared spectrum repeatedly, be thus averaging spectrum.
Step 3. calculates kind of a shell, brown rice proportion of composing COEFFICIENT K 1, K2
Select the modeling wave-number range of suitable near-infrared spectroscopy, with seed spectrum for matching object, by following public affairs
Formula：
Seed near infrared spectrum=K1 × brown rice near infrared spectrum+K2 × kind of shell near infrared spectrum (1)
By linear regression method matching seed near infrared spectrum 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. sets up near-infrared spectroscopy according to the fit-spectra of seed
Above-mentioned K1, K2 coefficient is used for setting up near-infrared spectroscopy；I.e.：Replaced by the fit-spectra that formula (1) pushes away
The near infrared spectrum of actual measurement, thus sets up new NIR Spectroscopy Analysis Model.
Such as：During optimizing rice paddy seed water model, calculate brown rice, the proportion of composing COEFFICIENT K 1 planting shell, K2 first,
Major part due to affecting 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)
I.e.：Plant the impact of shell coincidence information in deduction Oryza glutinosa water model, then re-establish corresponding seed moisture content
Near-infrared spectroscopy.
Embodiment 1
Set up rice paddy seed moisture near-infrared spectroscopy, concretely comprise the following steps：
Step 1. builds the rice paddy seed gradation of moisture
In the present embodiment, the method is used for analyzing 9311 conventional Rice colony seed near infrared spectrum water models by we,
The modeling of averaging model collects：Rice paddy seed is individually positioned in room temperature, 20 DEG C, 30 DEG C, 40 DEG C of 4 temperature values, 11%,
43%th, 75%, 97% totally 4 humidity values, place the different times and set up the corresponding gradation of moisture, totally 38 points.Different temperatures,
The variable that different time, the construction method of different humidity can be prevented effectively from the internal non-moisture of model is identical with moisture change generation
Variation tendency, impact model subsequent analysis.
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 humidity seed of lower 5 months, totally 12 points.
Although with respect to the near-infrared spectroscopy of other crops, this model number is less, due to moisture with closely red
External spectrum dependency is stronger, and uses conventional Rice 9311 seed of the same race in this model, so variable number is less, meets molecule
In spectrum Multivariate Correction quantitative analyses general rule (GBT 29858-2013), the number of calibration set requires.
Step 2. is set up, analyzing rice seed moisture content model
Gather the near-infrared diffusing reflection spectrum of rice paddy seed, wave-number range using German Brooker MPA near infrared spectrometer
For 12000-4000cm-1, resolution 16cm-1.Remove 12000～10500cm-1Wave-number range (this partial spectrum noise is big), choosing
Select 10500～4000cm-1Wave-number range, sets up Oryza sativa L. to modeling collection using Unscramble9.5 software under equalization is processed
Seed moisture content partial least square model, after forecast set is predicted analyze；Shown in result such as Fig. 1 (a), Fig. 1 (b).
Step 3. judges the main portions of impact water model
Recording 9311 seed moisture contents using oven drying method is 11.758%, and brown rice water content is 12.733%, plants shell aqueous
Measure larger for brown rice proportion in 7.937% seed, and plant shell breathability preferably, moisture evaporation speed, so it is considered that
The main portions of impact rice paddy seed water model are brown rice it should deduction plants the spectrum ratio of shell.
Step 4. calculates spectral composition ratio K1, K2
Take the seed of modeling collection, be repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, try to achieve rice paddy seed
Averaged spectrum；Gently strip off kind shell, presss from both sides out Brown Rice with tweezers, after will plant shell and recover former state, keep its form consistent, then
The same with seed, it is repeatedly measured near-infrared diffusing reflection spectrum 15 times, try to achieve the averaged spectrum of kind of a shell, brown rice.By linear regression
The method of matching calculates brown rice spectrum, the proportion of composing COEFFICIENT K 1 planting shell spectrum and K2.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. fit-spectra replaces the seed near infrared spectrum of actual measurement, re-establishes and analyze new moisture mould
Type
With K1, K2 as coefficient, the seed spectrum of all modeling collection and forecast set is all processed according to equation below (2), that is,：
Each group brown rice fit-spectra in 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 do not replace actual measure spectrum with above-mentioned brown rice fit-spectra, set up the near-infrared spectroscopy of moisture, and not
Near-infrared spectroscopy before deduction coincidence information is contrasted, and such as shown in Fig. 3 (a), Fig. 3 (b), result is：Deduction kind 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 found that：Compared with based on original measurement spectrum, the variable residual, information of fit-spectra model is front 4
Individual main constituent is less, and less main constituent can obtain higher prediction accuracy, and as shown in table 2, this illustrates us originally
The inventive method of patent can lift seed near-infrared analysis and predictive ability really.
In sum, it is proposed that this new method based on spectrum ratio deduction, Oryza glutinosa near infrared light can be optimized
Spectrum model, and pass through instantiation, illustrate that our method successfully optimizes rice paddy seed water model really, improve pre-
Survey the accuracy of seed moisture content content, thus demonstrate the effectiveness of the method.
Ultimate principle, principal character and the advantages of the present invention of the present invention have been shown and described above.The technology of the industry
The simply present invention it should be appreciated that the present invention is not restricted to the described embodiments, described in above-described embodiment and description for the personnel
Principle, without departing from the spirit and scope of the present invention the present invention also have various changes and modifications, these change and
Improvement both falls within the range of claimed invention.The protection domain of application claims by appending claims and its
Equivalent defines.
Claims (4)
1. a kind of based on spectrum ratio deduction Oryza glutinosa near-infrared spectroscopy optimization method it is characterised in that：Walk including following
Suddenly：
1) calculating of near-infrared averaged spectrum
Repeatedly measure the seed of rice paddy seed in near-infrared spectroscopy, plant shell, the near infrared spectrum of brown rice, obtain 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, enters to seed averaged spectrum
Row linear regression fit, thus obtains COEFFICIENT K 1, K2 so that the standard deviation of its fitting result and seed averaged spectrum is minimum；
3) foundation of near-infrared spectroscopy
According to K1, K2 coefficient magnitude, deduct rice paddy seed spectrum in near-infrared spectroscopy and comprise kind of shell or the weight of brown rice part
The impact of conjunction information, that is, obtain formula：
The fit-spectra of brown rice=(seed averaged spectrum-K2 × kind of shell averaged spectrum)/K1,
Or
Plant fit-spectra=(seed averaged spectrum-K1 × brown rice averaged spectrum)/K2 of shell.
After obtaining fit-spectra, it is hereby based on fit-spectra and sets up near-infrared spectroscopy.
2. a kind of Oryza glutinosa near-infrared spectroscopy optimization method based on spectrum ratio deduction according to claim 1, its
It is characterised by：Described step 1) include：
11) judge the major part of impact near-infrared spectroscopy
First judge the main information that this model is set up is present in which position of Oryza sativa L., according still further to spectral composition Proportional coefficient K 1 and K2
Remove the duplicate message of impact model accuracy；
12) near infrared spectrum of measurement kind shell and brown rice
First choose and set up the rice paddy seed sample populations of near-infrared spectroscopy, then every sample of colony is carried out kind of shell and rough
Rice separates, and then carries out multiple near infrared ray to kind of shell and brown rice respectively, tries to achieve the averaged spectrum of kind of shell and brown rice.
3. a kind of Oryza glutinosa near-infrared spectroscopy optimization method based on spectrum ratio deduction according to claim 2, its
It is characterised by：Described step 2) include：
According to step 12) in the averaged spectrum planting shell and brown rice tried to achieve, with seed spectrum for matching object, try to achieve kind of shell and rough
The proportion of composing COEFFICIENT K 1 of rice, K2.
4. a kind of Oryza glutinosa near-infrared spectroscopy optimization method based on spectrum ratio deduction according to claim 1, its
It is characterised by：The method setting up rice paddy seed moisture near-infrared spectroscopy, comprises the following steps：
1) build the rice paddy seed gradation of moisture
Modeling collection is：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 times and set up the corresponding gradation of moisture, totally 38 points；
Forecast set is：Seed is respectively placed in 20 DEG C, 25 DEG C, 30 DEG C, 35 DEG C of temperature and lower 5 of 11%, 43%, 75% humidity
The seed of the moon, totally 12 points；
2) foundation, analyzing rice seed moisture content near-infrared spectroscopy
The near-infrared diffusing reflection spectrum of the rice paddy seed of collection modeling collection and forecast set, wave-number range is 12000-4000cm-1, point
Resolution 16cm-1；Remove 12000～10500cm-1Wave-number range, selects 10500～4000cm-1Wave-number range, exists to modeling collection
Equalization sets up rice paddy seed moisture partial least square model under processing, after forecast set is predicted analyze；
3) main portions of impact water model are judged
By the comparison of the water content to each position of seed, show that the main portions of impact 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
Take the seed of modeling collection, be repeatedly measured rice paddy seed near infrared spectrum 15 times by rocking, try to achieve the average of rice paddy seed
Spectrum；Gently strip off kind shell, presss from both sides out Brown Rice with tweezers, after will plant shell and recover former state, keep its form consistent, be repeatedly measured
Near-infrared diffusing reflection spectrum 15 times, tries to achieve the averaged spectrum of kind of a shell, brown rice；Calculate brown rice spectrum, the ratio of components of kind shell spectrum again
Example COEFFICIENT K 1 and K2；
5) replace actual near infrared spectrum with fit-spectra, reanalyse water model
With K1, K2 as coefficient, the seed spectrum of all modeling collection and forecast set is all processed according to equation below, that is,：
Brown rice fit-spectra=(averaged spectrum K2 of each group seed × kind of shell averaged spectrum in water model)/K1
Then replace former actually measured brown rice spectrum with the brown rice fit-spectra tried to achieve, because having deducted incoherent kind of shell spectrum
Information, thus can set up the near-infrared spectroscopy that prediction more accurately optimizes.
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