CN105181606B - The method that cane sugar content is distributed in peanut is detected based on high light spectrum image-forming technology - Google Patents
The method that cane sugar content is distributed in peanut is detected based on high light spectrum image-forming technology Download PDFInfo
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Abstract
The present invention provides based on high light spectrum image-forming technology detect peanut in cane sugar content be distributed method, including：Spectrum picture of the peanut sample in characteristic wave strong point is acquired, by characteristic wave strong point by pretreated spectral reflectance value, input peanut cane sugar content distribution quantitative model obtains the distribution of peanut sample cane sugar content.The present invention also provides the methods for establishing cane sugar content distribution quantitative model in peanut, including acquiring peanut high spectrum image, and measure its cane sugar content using conventional method；High spectrum image is deleted by image rectification and background, extracts averaged spectrum；Using averaged spectrum after pretreatment as independent variable, using cane sugar content as dependent variable, the regression model of all band cane sugar content is established, on this basis using regression coefficient, determines characteristic wavelength, establish and verify the quantitative model.The present invention is fast and convenient, efficient, does not destroy sample, without using any chemical reagents, measurement result is accurate, realizes the visualization of peanut cane sugar content.
Description
Technical field
The method of cane sugar content in peanut is detected the present invention relates to a kind of, specifically, being related to based on high light spectrum image-forming skill
The method that cane sugar content is distributed in art detection peanut.
Background technology
Various nutrients ingredient, such as protein, fat and carbohydrate are included in peanut.In carbohydrate, sugarcane
Sugar is one of sugar most wanted, and the sweet taste of raw peanut is mainly derived from sucrose；The peculiar taste of toast earthnut be after sucrose hydrolysis with
What free amino acid was generated in peanut baking process by Maillard reaction.Therefore, the direct shadow of the height of cane sugar content in peanut
Ring the flavouring quality of (Groundnut products).The method that tradition measures sucrose in peanut includes：High performance liquid chromatography and acid-hydrolysis method, but
These methods are slow there are analyze speed, complex for operation step, of high cost destructive strong, the shortcomings of using reagent contamination environment.Cause
This is badly in need of finding one kind quickly, and nondestructive method provides foundation for the measure of peanut cane sugar content.
High light spectrum image-forming technology combines spectroscopy and imaging technique, is emerging quick a, lossless detection method.It is high
A series of 3 d image data block that spectrum picture is made of continuous band images has the figure under some specific wavelength
As information, and for some specific pixel in plane again with the spectral information under different wave length.Its principle is to utilize peanut
The groups such as CH, OH determine the quantitative pass between spectrum and cane sugar content in the spectral absorption characteristics of near infrared spectrum in sucrose
System, so as to predict cane sugar content and distribution in peanut.
Chinese patent CN102621077A discloses high spectrum reflection image capturing system and the maize seed based on the system
Sub- purity lossless detection method；Chinese patent CN1995987 discloses the lossless inspection of agricultural and animal products based on hyper-spectral image technique
Survey method and device；Chinese patent CN 103636315A disclose a kind of percentage of seedgermination on-line checking dress based on EO-1 hyperion
It puts and method.Above invention detects product index using hyper-spectral image technique, avoids the limitation of conventional method.But it studies
In terms of being concentrated mainly on seed purity, through retrieval, up to the present, peanut is detected with high light spectrum image-forming technology not yet both at home and abroad
The report of cane sugar content distribution.
Invention content
In order to solve the problems in the existing technology, the object of the present invention is to provide detected based on high light spectrum image-forming technology
The method that cane sugar content is distributed in peanut.
In order to realize the object of the invention, first aspect present invention is to provide one kind and establishes peanut based on high light spectrum image-forming technology
The method of middle cane sugar content distribution quantitative model, this method include the following steps：
1.1 collect representative peanut sample, and scanning each pixel in acquisition peanut sample with bloom spectrometer exists
Image information under each wavelength obtains the original EO-1 hyperion 3-D view of peanut sample；
Preferably, the wave-length coverage of the bloom spectrometer scanning is 900-1700nm, and scan mode is scanned for line；
The original EO-1 hyperion 3-D view of 1.2 pairs of peanut samples be corrected and background delete after, extract peanut sample
Product image averaging spectrum；
Preferably, the correction is to referring to the original EO-1 hyperion 3-D view I to the peanut samplerawCarry out black and white school
Just, specific method is that the Standard adjustable board for being 99% to reflectivity is acquired, and obtains complete white uncalibrated image Iwhite, Ran Houguan
Head of covering the lens acquires, and obtains completely black uncalibrated image Idark, image I after correcting is calculated according to following formulanorm：
Preferably, the background delete the specific steps are：Using principal component analysis, determine the boundary of background and raw material, delete
Except background, peanut sample image is obtained；
1.3 pairs of peanut sample image averaging spectrum carry out standard normal variable transformation combination and trend are gone to pre-process；
1.4 detect the cane sugar content of the peanut sample using conventional method, obtain the cane sugar content of peanut sample；
Preferably, the cane sugar content method of the detection peanut sample is carried out according to GB/T 22221-2008.
1.5 using the peanut sample as calibration set, using the pretreated averaged spectrum of the calibration set as change certainly
Amount, using the cane sugar content of the peanut sample as dependent variable, by Partial Least Squares establish the calibration set independent variable and because
The Partial Least Squares regression model of variable；It is verified using leaving-one method；The calibration set Partial Least Squares is returned
Model is verified；
1.6, according to the regression coefficient of the calibration set Partial Least Squares regression model, select to the regression model tribute
The wavelength for offering rate maximum absolute value is characterized wavelength；And pass through Partial Least Squares and establish cane sugar content distribution in calibration set peanut
Quantitative model；It is verified using leaving-one method；The quantitative model is verified；
The leaving-one method refers to leave and take a peanut sample every time as verification, and remaining n-1 peanut sample foundation is tested
Model of a syndrome verifies this peanut sample left and taken with the verification model, above-mentioned process is repeated, until to all
Peanut sample is all verified.
Quantitative model described in above-mentioned steps 1.6 represents the cane sugar content of the calibration set peanut sample and the characteristic wavelength
The quantitative relationship of the spectral reflectance value at place；
Preferably, the characteristic wavelength is respectively：964nm、1017nm、1067nm、1170nm、1200nm、1233nm、
1293nm、1420nm、1457nm、1634nm；
Preferably, cane sugar content distribution quantitative model is as follows in the peanut established：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm
+8.918R1200nm–21.432R1233nm–47.142R1293nm+16.914R1420nm
–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、
R1293nm、R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm,
Pass through pretreated spectral reflectance value at 1200nm, 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
The present invention, which carries out the peanut sample image averaging spectrum standard normal variable transformation, can eliminate peanut pellets
Size, surface are strafed and influence of the change in optical path length to spectrum；After Trend Algorithm is gone to be typically applied to standard normal variable processing
Spectrum, for eliminating the baseline drift of slow reflectance spectrum.
Characteristic wavelength is chosen excessive or very few all unsuitable；If characteristic wavelength is chosen excessively, increase computation complexity；It is if special
It is very few to levy wavelength selection, then can reduce testing result accuracy.
The purpose that the present invention is verified is to ensure that the quantitative model Stability and veracity.Usually, after empirical tests
If the quantitative model established is accurate, stablizes, cane sugar content in detection peanut can be used for be distributed；If that establishes is described quantitative
Model accuracy and stability are bad, then need to establish the regression model or the quantitative model according to above-mentioned steps again.
Specifically, by calculating the coefficient R of the calibration setcalWith the coefficient R of leaving-one methodcvAnd calibration set
Standard deviation S EC and the standard deviation S ECV of leaving-one method judge the regression model and the quantitative model accuracy and steady
It is qualitative.Usually, as related coefficient (RcalOr Rcv) >=0.8 during standard deviation (SEC or SECV)≤2, shows the recurrence mould
Type or quantitative model accuracy height, stability are good.
The present invention calculates related coefficient (R using following formula (1)calOr Rcv)；Formula (2) calculate standard deviation (SEC or
SECV)。
In formula (1), xiFor i-th of sample bloom spectral method predicted value,It is the average value of predicted value；yiFor i-th of sample
The measured value of conventional method,It is the average value of measured value；N is the number of the sample value of two variables.If sample is correction
Collection, then R is Rcal；It is verified if it is leaving-one method, R Rcv。
In formula (2), xiFor the predicted value of calibration set the i-th sample bloom spectral method, yiFor calibration set the i-th sample conventional method
Measured value, n be calibration set sample number.If xiFor the predicted value of the i-th sample bloom spectral method in leaving-one method verification process,
That then formula (2) represents is SECV.
Second aspect of the present invention is to provide application of the above-mentioned quantitative model in peanut is detected in cane sugar content distribution.
Third aspect present invention is to provide detects the method that cane sugar content is distributed in peanut, institute based on high light spectrum image-forming technology
The method of stating includes：
1) spectrum picture of the peanut sample to be measured at following features wavelength is acquired：964nm、1017nm、1067nm、
1170nm、1200nm、1233nm、1293nm、1420nm、1457nm、1634nm；
2) the characteristic wave strong point is distributed by cane sugar content in pretreated spectral reflectance value input peanut quantitative
Model obtains peanut sample cane sugar content distribution to be measured；Cane sugar content distribution quantitative model is as follows in the peanut：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm
+8.918R1200nm–21.432R1233nm–47.142R1293nm+16.914R1420nm
–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、
R1293nm、R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm,
Pass through pretreated spectral reflectance value at 1200nm, 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
The method that the step 1) acquires the spectrum picture of peanut sample characteristic wave strong point to be measured is established with above-mentioned in peanut
The method that spectrum picture is obtained in the method for cane sugar content distribution quantitative model is identical.
It is such as indicated without special, pretreatment of the present invention refers to that standard normal variable transformation combines and trend is gone to pre-process.
Specifically, the method that the step 1) acquires the spectrum picture of peanut sample characteristic wave strong point to be measured includes following step
Suddenly：
1.1 obtain each image information of the pixel under each wavelength in peanut sample to be measured with the scanning of bloom spectrometer, obtain
To the original EO-1 hyperion 3-D view of peanut sample to be measured；
Preferably, the wave-length coverage of the bloom spectrometer scanning is 900-1700nm, and scan mode is scanned for line；
The original EO-1 hyperion 3-D view of 1.2 pairs of peanut samples to be measured be corrected and background delete after, extraction is treated
Survey peanut sample image averaging spectrum；
Preferably, the correction refers to the original EO-1 hyperion 3-D view I to the peanut samplerawCarry out black and white correction；
Specific method is that the Standard adjustable board for being 99% to reflectivity is acquired, and obtains complete white uncalibrated image Iwhite, it is then shut off
Camera lens acquires, and obtains completely black uncalibrated image Idark, image I after correcting is calculated according to following formulanorm：
Preferably, the background delete the specific steps are：Using principal component analysis, determine the boundary of background and raw material, delete
Except background, peanut sample image is obtained；
1.3 pairs of peanut sample image averaging spectrum to be measured carry out standard normal variable transformation combination and trend are gone to locate in advance
Reason.
The present invention has collected the regional main breed peanut of the main cultivation in China, such as：White sand 1016, rich spends No. 1, Lu Hua at seaflower No. 1
No. 11 etc., any pretreatment need not be carried out to the peanut sample of collection, while acquire high spectrum image and measure cane sugar content,
And the regression model of spectrum sucrose information and cane sugar content in image is established using Partial Least Squares, it utilizes back on this basis
Coefficient is returned to select characteristic wavelength, research is associated to peanut characteristic wavelength and cane sugar content using Partial Least Squares, is determined
Quantitative relationship between the two, i.e. quantitative model measure the high spectrum image of unknown sample, will be under each pixel on image
Characteristic wavelength brings quantitative model into, calculates cane sugar content, obtains the detailed sucrose spatial distribution map of pixel class resolution ratio, realizes
The spatial visualization of sucrose information distribution.Compared with prior art, the present invention has the following advantages and beneficial effect:
1st, the present invention realizes the quick peanut cane sugar content that measures by characteristic wave bands and is distributed, and improves picking rate, contracts
Short high-spectral data analysis time, detection efficiency is improved, and realize the spatial visualization of peanut sucrose information distribution, be
It is really achieved quick nondestructive on-line checking and provides theoretical foundation.
2nd, because in addition to containing sucrose, also containing other substances such as fat, protein, moisture in sample peanut.These other
Substance be affected to spectrum, severe jamming cane sugar content detection accuracy.For interference is overcome to improve accuracy, this hair
The bright characteristic wavelength selected from peanut collection of illustrative plates using scientific method and cane sugar content is closely related, has filled up high light spectrum image-forming skill
Art detects the blank of peanut cane sugar content.
3rd, peanut sample need not carry out any pretreatment, and no destructiveness, without using any reagent, environmental protection, operation is fast
Fast interference that is simple, avoiding human factor, measurement result is more efficient, objective.
4th, the present invention is clear and definite and perfect to establish the analytical procedure for measuring peanut cane sugar content on the basis of numerous studies；
The good and bad degree of model is obtained by comparing different pretreatments, different modeling methods, the best pretreatment for determining spectrum is mark
Quasi- normal variate transformation, which combines, goes trend；Best modeled method is Partial Least Squares.
5th, by collecting main difference, the difference of kind planted regional main breed, overcome area of national peanut over nearly 2 years
With the difference of time, the method for the present invention is enable to cover national most kinds, it is applied widely.
Description of the drawings
Fig. 1 is based on high light spectrum image-forming technology for embodiment 1 and detects cane sugar content location mode flow chart in peanut；
Fig. 2 is the averaged spectrum (not preprocessed) that embodiment 1 extracts peanut high spectrum image；
Fig. 3 is that the calibration model of 1 essential characteristic wavelength of embodiment verifies the relational graph of measured value and reference value with leaving-one method；
Fig. 4 is the peanut cane sugar content distribution map of 2 to be measured 6 peanut varieties of embodiment.
" Color " represents to represent peanut difference cane sugar content with different colors in Fig. 4；" Amplitude " represents peanut
The range of middle cane sugar content；Cane sugar content is higher from " 0 to 10 " number bigger expression peanut.
Specific embodiment
The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention..
Experimental method used in following embodiment is conventional method unless otherwise specified.
Material used in following embodiment, reagent etc., are commercially available unless otherwise specified.
Unless otherwise specified, bloom spectrometer Imspector N17E used in following embodiment；Inductor TE-cooled
InGaAs photodiode array；Light source 10W Halogen sidereflector.Bloom spectrometer is opened at 20 DEG C, in advance
Hot 10min, set acquisition parameter, wherein the time for exposure be 5.8s, picking rate 8mm/s, field range：200mm, spectrum wave
Long range 900-1700nm, resolution ratio 3nm, scan mode are scanned for line.
The correction of high spectrum image in following embodiment, background are deleted and the extraction of spectrum is by Umbio companies of Sweden
It is completed in the image analysis software Evince 2.4 of sale.
The chemo metric software That the analyzing and processing of spectroscopic data is sold by CAMO companies of Norway in following embodiment
It is completed in Unscrambler 9.7.
The mathematical software that cane sugar content spatial visualization is sold by MathWorks companies of the U.S. in following embodiment
It is completed in MatlabR2014b.
Embodiment 1
The present embodiment provides a kind of sides that cane sugar content distribution quantitative model in peanut is established based on high light spectrum image-forming technology
Method, this method include the following steps：
1.1 collect main cultivation 80 kinds of peanut sample in the Zhu Zai provinces of China in 2013 and 2014, are selected from each kind
30 complete shelled peanuts with bloom spectrometer are scanned and obtain image letter of each pixel under each wavelength in peanut sample simultaneously
Breath repeats 3 times, takes the average value of the high spectrum image of 3 scanning.Obtain the original EO-1 hyperion 3-D view of peanut sample.Often
Before secondary scanning, the complete white uncalibrated image I of first acquisitionwhiteWith completely black uncalibrated image Idark。
The original EO-1 hyperion 3-D view of 1.2 pairs of above-mentioned peanut samples be corrected and background delete after, extract peanut sample
Product image averaging spectrum；
The correction refers to the original EO-1 hyperion 3-D view I to the peanut samplerawCarry out black and white correction；Specific root
Image I after correcting is calculated according to following formulanorm：
The background deletion is using principal component analysis, in 1 (PC of principal component1) and 2 (PC of principal component2) under determine background with
Then peanut sample deletes background, determine peanut sample as target area, by same 30 peanuts of kind as a whole, therefrom
Extract same kind peanut sample averaged spectrum.
1.3 pairs of above-mentioned different cultivars peanut sample image averaging spectrum carry out standard normal variable transformation combination and go trend pre-
Processing.
1.4 measure the cane sugar content of peanut sample, each kind replication using standard GB/T/T 22221-2008
Three times, it is averaged.This step is measured using Agilent companies of U.S. 1200LC liquid chromatographs.
1.5 using above-mentioned 80 peanut varieties as calibration set, and cane sugar content statistics is shown in Table 1, with described in the calibration set
Pretreated peanut sample image averaging spectrum (specifically referring to by pretreated spectral reflectance value) is independent variable, with institute
The cane sugar content of peanut sample is stated as dependent variable, the inclined of the calibration set independent variable and dependent variable is established by Partial Least Squares
Least square method regression model (all band).
Then it is verified using leaving-one method, the coefficient R of calibration set is calculated using formula (1)calIt is built with leaving-one method
The coefficient R of vertical verification modelcv, tested using what the standard deviation S EC and leaving-one method of formula (2) calculating calibration set were established
The standard deviation S ECV of model of a syndrome, is shown in Table 1.
In formula (1), xiFor i-th of sample bloom spectral method predicted value,It is the average value of predicted value；yiFor i-th of sample
The measured value of conventional method,It is the average value of measured value；N is the number of the sample value of two variables.If sample is correction
Collection, then R is Rcal；It is verified if it is leaving-one method, R Rcv。
In formula (2), xiFor the predicted value of calibration set the i-th sample bloom spectral method, yiFor calibration set the i-th sample conventional method
Measured value, n be calibration set sample number.If xiFor the predicted value of the i-th sample bloom spectral method in leaving-one method verification process,
That then formula represents is SECV.
The cane sugar content statistics and model parameter of 1 peanut calibration set of table and leaving-one method verification model
1.6 using regression coefficient method (representing that wavelength influences cane sugar content the parameter of size), and regression coefficient absolute value is got over
It is bigger to show that the wavelength influences cane sugar content greatly, selects to be characterized wavelength to 10 points of model contribution rate maximum absolute value, point
It is not：964nm, 1017nm, 1067nm, 1170nm, 1200nm, 1233nm, 1293nm, 1420nm, 1457nm, 1634nm are built
Cane sugar content distribution quantitative model in vertical peanut calculates the coefficient R of calibration set using formula (1) methodcal, formula (2) meter
The error SEC of calibration set is calculated, is shown in Table 2, cane sugar content distribution quantitative model is as follows in the peanut：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm
+8.918R1200nm–21.432R1233nm–47.142R1293nm+16.914R1420nm
–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、
R1293nm、R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm,
Pass through pretreated spectral reflectance value at 1200nm, 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
The model of foundation is verified using leaving-one method, calculates leaving-one method verification mould respectively using above-mentioned formula (1) (2)
The coefficient R of typecvWith standard deviation S ECV, it is shown in Table 2.
2 feature based wavelength peanut cane sugar content calibration set of table and leaving-one method verification model parameter
Selection characteristic wavelength can represent most information, cane sugar content point in the peanut established using the method for the present invention
Cloth quantitative model is detected cane sugar content distribution in peanut, and testing result is measured with national standard GB/T 22221-2008 and spent
The testing result of sucrose is in highly relevant in raw sample, and can simplify the operational analysis time, improves arithmetic speed.
Embodiment 2
The method that cane sugar content is distributed in peanut, this method are detected based on high light spectrum image-forming technology the present embodiment provides a kind of
Include the following steps：
1) spectrum picture of the peanut sample to be measured at following features wavelength is acquired：964nm、1017nm、1067nm、
1170nm、1200nm、1233nm、1293nm、1420nm、1457nm、1634nm；
Detailed process：6 peanut varieties separately are taken, obtain peanut sample with bloom spectrometer according to the same manner as in Example 1
Original EO-1 hyperion 3-D view；And then extract peanut sample image averaging spectrum with method same as Example 1；Then again
6 kind peanut sample image averaging spectrum are carried out with standard normal variable transformation combination goes trend to pre-process；It is final to obtain
The spectrum picture of 6 peanut varieties samples at features described above wavelength.
2) cane sugar content in the spectral reflectance value input peanut at features described above wavelength is distributed quantitative model, obtained to be measured
Peanut sample cane sugar content is distributed, and the results are shown in Figure 4；Cane sugar content distribution quantitative model is as follows in the peanut：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm
+8.918R1200nm–21.432R1233nm–47.142R1293nm+16.914R1420nm
–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、
R1293nm、R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm,
Pass through pretreated spectral reflectance value at 1200nm, 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
Comparative example 1
This comparative example provides a kind of side that cane sugar content distribution quantitative model in peanut is established based on high light spectrum image-forming technology
Method is different from the wavelength of the Partial Least Squares regression model for differing only in selection of embodiment 1.This comparative example chooses ten
Wavelength, respectively 994nm, 1037nm, 1090nm, 1183nm, 1280nm, 1353nm, 1444nm, 1524nm, 1614nm,
1675nm, and establish cane sugar content in peanut in method same as Example 1 based on this ten wavelength and be distributed quantitative model, it uses
Above-mentioned formula (1) (2) calculates the coefficient R of calibration setcalWith correction error SEC, it is shown in Table 3, cane sugar content in the peanut of foundation
It is as follows to be distributed quantitative model：
Ysucrose=7.522+31.38R994nm–100.505R1037nm+12.894R1090nm–22.618R1183nm
–76.511R1280nm+10.793R1353nm–21.024R1444nm–8.556R1524nm
–23.582R1614nm–21.505R1675nm
The model of foundation is assessed and verified using leaving-one method, leaving-one method verification is calculated using above-mentioned formula (1) (2)
The coefficient R of modelcvWith standard error SECV, it is shown in Table 3.
Table 3 is based on other wavelength peanut cane sugar content calibration sets and leaving-one method verification model parameter
Comparative example 2
This comparative example provides a kind of side that cane sugar content distribution quantitative model in peanut is established based on high light spectrum image-forming technology
Method is different from the wavelength of the Partial Least Squares regression model for differing only in selection of embodiment 1.This comparative example chooses ten
Wavelength, respectively 954nm, 1037nm, 1083nm, 1183nm, 1217nm, 1270nm, 1337nm, 1400nm, 1477nm,
1678nm, and establish cane sugar content in peanut in method same as Example 1 based on this ten wavelength and be distributed quantitative model, it uses
Above-mentioned formula (1) (2) calculates the coefficient R of calibration setcalWith correction error SEC, it is shown in Table 4, cane sugar content in the peanut of foundation
It is as follows to be distributed quantitative model：
Yfat=3.233+4.275R954nm-77.879R1037nm-51.787R1083nm+10.291R1183nm
–24.822R1217nm–48.489R1270nm–21.153R1337nm+51.54R1400nm
+18.887R1477nm–41.757R1678nm
The model of foundation is assessed and verified using leaving-one method, leaving-one method verification is calculated using above-mentioned formula (1) (2)
The coefficient R of modelcvWith standard error SECV, it is shown in Table 4.
Table 4 is based on other wavelength peanut cane sugar content calibration sets and leaving-one method verification model parameter
By from the point of view of the result of embodiment 1-2 and comparative example 1-2, the selection of characteristic wavelength to measuring cane sugar content in peanut,
Important, the model that the characteristic wavelength that the present invention selects is established, related coefficient is high, and error is low, can be used for measuring flower
Cane sugar content in life.
Although above the present invention is described in detail with a general description of the specific embodiments,
On the basis of the present invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Cause
This, these modifications or improvements, belong to the scope of protection of present invention without departing from theon the basis of the spirit of the present invention.
Claims (9)
1. a kind of method that the distribution quantitative model of cane sugar content in peanut is established based on high light spectrum image-forming technology, this method include with
Lower step：
1.1 collect representative peanut sample, with each pixel in bloom spectrometer scanning acquisition peanut sample in each wave
Image information under long, obtains the original EO-1 hyperion 3-D view of peanut sample；
The original EO-1 hyperion 3-D view of 1.2 pairs of peanut samples be corrected and background delete after, extract peanut sample figure
As averaged spectrum；
1.3 pairs of peanut sample image averaging spectrum carry out standard normal variable transformation combination and trend are gone to pre-process；
1.4 detect the cane sugar content of the peanut sample using conventional method, obtain the cane sugar content of peanut sample；
1.5 using the peanut sample as calibration set, using averaged spectrum pretreated described in the calibration set as independent variable, with
The cane sugar content of the peanut sample is dependent variable, and the calibration set independent variable and dependent variable are established by Partial Least Squares
Partial Least Squares regression model；It is verified using leaving-one method；
1.6, according to the regression coefficient of the calibration set Partial Least Squares regression model, select to the regression model contribution rate
The wavelength of maximum absolute value is characterized wavelength；And pass through Partial Least Squares to establish in calibration set peanut cane sugar content distribution quantitative
Model；It is verified using leaving-one method；
Characteristic wavelength described in step 1.6 is respectively：964nm、1017nm、1067nm、1170nm、1200nm、1233nm、
1293nm、1420nm、1457nm、1634nm；Cane sugar content distribution quantitative model is as follows in the peanut established：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm+8.918R1200nm–
21.432R1233nm–47.142R1293nm+16.914R1420nm–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、R1293nm、
R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm, 1200nm,
Pass through pretreated spectral reflectance value at 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
2. the method for cane sugar content distribution quantitative model in peanut is established according to claim 1, which is characterized in that the height
The wave-length coverage of spectrometer scanning is 900-1700nm, and scan mode is scanned for line.
3. the method for cane sugar content distribution quantitative model in peanut is established according to claim 1, which is characterized in that the school
Exactly to the original EO-1 hyperion 3-D view I of the peanut samplerawBlack and white correction is carried out, specific method is to be to reflectivity
99% Standard adjustable board is acquired, and obtains complete white uncalibrated image Iwhite, camera lens acquisition is then shut off, obtains completely black calibration
Image Idark, image I after correcting is calculated according to following formulanorm：
4. the method for cane sugar content distribution quantitative model in peanut is established according to claim 1, which is characterized in that the back of the body
Scape delete the specific steps are：Using principal component analysis, the boundary of background and peanut is determined, delete background, obtain peanut sample figure
Picture.
5. the method for cane sugar content distribution quantitative model in peanut is established according to claim 1, which is characterized in that step
The cane sugar content method that peanut sample is detected described in 1.4 is carried out according to GB/T22221-2008.
6. the method for cane sugar content distribution quantitative model in peanut is established according to claim 1, which is characterized in that described to stay
One method refers to leave and take a peanut sample every time as verification, and remaining n-1 peanut sample establishes verification model, is tested with described
Model of a syndrome verifies described this peanut sample left and taken, and repeats above-mentioned process, until to all peanut samples all
It is verified.
7. cane sugar content distribution quantitative model sugarcane in peanut is detected in the peanut that any one of claim 1-6 the methods are established
Application in sugared content distribution.
8. a kind of detect the method that cane sugar content is distributed in peanut based on high light spectrum image-forming technology, the method includes：
1) spectrum picture of the peanut sample to be measured at following features wavelength is acquired：964nm、1017nm、1067nm、1170nm、
1200nm、1233nm、1293nm、1420nm、1457nm、1634nm；
2) the characteristic wave strong point is inputted into the quantitative mould of cane sugar content distribution in peanut by pretreated spectral reflectance value
Type obtains peanut sample cane sugar content distribution to be measured；Cane sugar content distribution quantitative model is as follows in the peanut：
Ysucrose=8.119+12.657R964nm–30.076R1017nm+12.958R1067nm+11.679R1170nm+8.918R1200nm–
21.432R1233nm–47.142R1293nm+16.914R1420nm–30.055R1457nm–64.920R1634nm
Wherein, YsucroseFor the cane sugar content of peanut sample, R964nm、R1017nm、R1067nm、R1170nm、R1200nm、R1233nm、R1293nm、
R1420nm、R1457nm、R1634nmRespectively peanut sample characteristic wavelength 964nm, 1017nm, 1067nm, 1170nm, 1200nm,
Pass through pretreated spectral reflectance value at 1233nm, 1293nm, 1420nm, 1457nm, 1634nm.
9. the method that cane sugar content is distributed in peanut, feature are detected based on high light spectrum image-forming technology according to claim 8
It is, the method that the step 1) acquires the spectrum picture of peanut sample characteristic wave strong point to be measured includes the following steps：
1.1 obtain each image information of the pixel under each wavelength in peanut sample to be measured with the scanning of bloom spectrometer, are treated
Survey the original EO-1 hyperion 3-D view of peanut sample；The wave-length coverage of bloom spectrometer scanning is 900-1700nm, scanning side
Formula is scanned for line；
The original EO-1 hyperion 3-D view of 1.2 pairs of peanut samples to be measured be corrected and background delete after, extract flower to be measured
Raw sample image averaged spectrum；The correction refers to the original EO-1 hyperion 3-D view I to the peanut samplerawCarry out black and white
Correction；Specific method is that the Standard adjustable board for being 99% to reflectivity is acquired, and obtains complete white uncalibrated image Iwhite, then
Camera lens acquisition is closed, obtains completely black uncalibrated image Idark, image I after correcting is calculated according to following formulanorm：
The background delete the specific steps are：Using principal component analysis, the boundary of background and peanut is determined, delete background, obtain
Peanut sample image；
1.3 pairs of peanut sample image averaging spectrum to be measured carry out standard normal variable transformation combination and trend are gone to pre-process.
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