CN108680515A - A kind of simple grain amylose in rice Quantitative Analysis Model structure and its detection method - Google Patents
A kind of simple grain amylose in rice Quantitative Analysis Model structure and its detection method Download PDFInfo
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Abstract
The invention discloses a kind of simple grain amylose in rice Quantitative Analysis Model construction methods, include the following steps：S1, the discrepant simple grain rice sample several pieces of amylose content are collected, be dried, calibration set is used as after equilibrium water conten；S2, acquisition correction concentrate the near infrared spectrum of each simple grain rice sample；S3, respectively by each simple grain rice sample treatment of calibration set at rice flour, obtain the amylose content reference value of each simple grain rice sample, build calibration set reference value matrix；The structure of S4, near-infrared simple grain amylose model：The near infrared spectrum that S2 is obtained screens spectrum range, obtains calibration set spectrum matrix, and reference value matrix described in the spectrum matrix and S3 is carried out recurrence association analysis, obtains the Near-Infrared Quantitative Analysis model of simple grain content of amylose in rice.
Description
Technical field
The present invention relates to a kind of detection methods of single grain crop component content, and in particular to a kind of simple grain rice straight chain shallow lake
Powder Quantitative Analysis Model is built and its detection method.
Background technology
In order to shorten breeding cycle, accelerate breeding process, rice breeding is required to detect in the early generation of cultivating seeds
Even sub-elect the seed for the specific trait for meeting breeder's needs.In rice quality breeding, amylose is that rice is most heavy
One of index of quality wanted, the content of amylose determine the edible quality and nutritional quality of rice, thus receive breeder with
Consumer pays close attention to.The detection method of traditional amylose such as iodine colour developing photometry and iodine affinity measuring method (including peace times is dripped
Determine method and potentiometric titration) etc., exist destroy seed, consumption reagent, cumbersome, time-consuming and laborious defect in various degree,
It cannot be satisfied to morning for the Fast nondestructive evaluation of seed and sorting needs.Near-infrared spectral analysis technology be it is a kind of it is quick, lossless,
Easy, environmental protection analytical technology, is widely used to the every field such as agricultural, biology, medicine, food, chemical industry.Simple grain near-infrared
Detection technique (Single kernel near-infrared spectroscopy, SK-NIRS) refers in simple grain crop level
Near-infrared spectral analysis technology, is expected to realize the Fast nondestructive evaluation of simple grain content of amylose in rice, and in combination with it is certain from
Dynamic makeup is set, and sorting is realized, to meet the demand that breeder's early generation selection and breeding suitably meets the rice varieties of quality requirements.
However, near-infrared is a kind of secondary analysis technology, when analysis, needs chemical method as reference, and reference method
Precision largely effect on the analysis result of near-infrared.In existing literature report, chemical reference value is mainly mixed by seediness grain
The average chemical of sample is worth to.But there are larger limitations for this method：Even if due to being same kind, different size, life
Its chemical score ingredient of the seed of elongate member is also not quite similar.So the chemical score obtained by this method is only used for detection kind
The content of ingredient can not obtain the accurate reference value of single grain, it is more difficult to build accurate single grain near-infrared analysis model.
L.E.Agelet etc. points out, the acquisition mode and modeling method of grain characters and size, the type of near infrared spectrometer and spectrum,
And the measurement of reference value is vital for establishing an accurate near-infrared model.And single grain chemistry reference value
Measurement is to influence a key factor of model foundation success or not：If the error of single grain chemistry reference value is larger, then mould
The error of type prediction can also become larger.For the problem that this simple grain detection reference method precision is inadequate, some domestic and international experts adopt
Different optimization methods is taken.Such as J.G.Wu improves amylose reference technology, using half method as reference method, realizes
The detection of simple grain content of amylose in rice；For another example Armstrong, P.R etc. are averaged to a plurality of single grain spectrum, analysis
The mixed reference value of these seeds builds the constituent analysis model of near-infrared simple grain crop according to averaged spectrum and reference value.
However, for former approach, that half rice for being free from embryo of half method detection, this with acquired it is complete
Simple grain rice spectrum be not exclusively corresponding, thus influence analysis result；And for later approach, due to eventually for
The spectrum of modeling is the spectrum after each seed spectrum arithmetic average, however each quality grain, of different sizes, is caused each
Seed differs to the contribution of the chemical score of final calculated aggregate sample, this and averaged spectrum nor completely corresponding, thus
Also prediction result can be influenced to a certain degree.Therefore, amylose reference method is only improved, enables to detect simple grain water
The Fast nondestructive evaluation that near-infrared method accurately realizes simple grain content of amylose in rice just may be used in rice.
The present invention acquires the diffusing transmission spectrum of sample, and to Ministry of Agriculture standard iodine colorimetry (NY/T when near-infrared models
2639) it is improved, sample size is reduced to 10mg by 50mg, while volumetric flask is replaced with centrifuge tube, to realize simple grain rice
The chemical detection of amylose, it is later again on the basis of the method, spectrum is associated with chemical score, build simple grain amylose
Near-infrared amylose content detection model, it is intended to provide a kind of quick, lossless, easy, accurate inspection for rice quality breeding
Survey means promote breeding technique development to accelerate breeding process.
Invention content
Technical problem to be solved by the present invention lies in providing, a kind of near-infrared of simple grain content of amylose in rice is fixed
Measure the construction method and amylose Sparklet testing method of analysis model.
The present invention is to solve above-mentioned technical problem by the following technical programs：
A kind of simple grain amylose in rice Quantitative Analysis Model construction method, includes the following steps：
S1, the discrepant simple grain rice sample several pieces of amylose content are collected, be dried, conduct after equilibrium water conten
Calibration set；
S2, acquisition correction concentrate the near infrared spectrum of each simple grain rice sample；
S3, respectively by each simple grain rice sample treatment of calibration set at rice flour, obtain each simple grain rice sample
Amylose content reference value, build calibration set reference value matrix；
The structure of S4, near-infrared simple grain amylose model：The near infrared spectrum that S2 is obtained screens spectrum range, obtains
Reference value matrix described in the spectrum matrix and S3 is carried out recurrence association analysis, obtains simple grain rice by calibration set spectrum matrix
The Near-Infrared Quantitative Analysis model of amylose content.
Further, each simple grain rice sample goes mouldy, for the full complete, disease-free spot of appearance, nothing without worm in the step S1
The simple grain rice of erosion.
Further, the near infrared spectrum of each simple grain rice sample is each simple grain rice sample point in the step S2
Not in the average value of at least one set of near infrared spectrum of tow sides acquisition.
Further, the near infrared spectrum is near-infrared diffusing transmission spectrum.
Further, in the step S3, the rice flour is coarse rice powder or milled rice flour.
Further, the measurement of calibration set rice flour amylose content reference value uses the iodine after improvement in the step S3
Colorimetric method, iodine colorimetry after the improvement the specific steps are：
(1) making of standard curve：Amylose standard sample several pieces are chosen, following steps operation is carried out, is marked
Directrix curve：
A) it is gelatinized：Every part of sample weighs suitable sample powder, and sample is placed in container of the capacity not less than 2ml,
Absolute ethyl alcohol, which will be added, gently to pat container by sample dispersion, it is ensured that sample powder is fully dispersed, and 1mol/L is then added
NaOH is put into 95-100 DEG C of water-bath and heats later, during which takes out shaken several times, and after heating 15-20 minutes, sample is taken out
It is cooling, water is added later and shakes up, wherein sample powder and absolute ethyl alcohol, the w/v of 1mol/L NaOH solutions, water
10mg:100μl:900μl:1ml；
B) it develops the color：Liquid after gelatinization pick and place capacity not less than 5ml container in, sequentially add water, 1mol/LHAc,
0.2%I2-KI dyeing liquors, are added water again after shaking up, continue to shake up, and colour developing measures the absorbance under 620nm after 5-20 minutes,
Every part of sample at least repeats above-mentioned process color twice, using the average value of the absorbance of replication as the extinction of the sample
Degree, wherein the water, HAc, I of liquid, addition for the first time after the gelatinization2The body of-KI dyeing liquors and second of the water being added
Product is than being 40 μ l:860μl:40μl:60:μl:3000μl；
C) standard curve is calculated：The amylose content of according to standard sample and the absorbance of standard sample and quality
Ratio fit standard curve；
(2) detection of calibration set amylose content, comprises the following steps：
D) it is gelatinized：Every part of calibration set simple grain rice sample is sieved with 100 mesh sieve by shelling, after milling processing, in 75-85 DEG C of baking
Drying to constant weight in case, obtains required rice flour.Later the step of same step a)；
E) it develops the color：The same step b) of process；
F) calibration set amylose content is calculated：According to the standard curve obtained in step c), according to calibration set sample
The amylose content of absorbance and the ratio calculation calibration set sample of its quality.
Further, the container is centrifuge tube.
Further, further include that pre-treatment step, the pretreatment step are carried out to the spectrum that S2 is obtained in the step S4
Suddenly it is：First derivative processing is carried out to the spectrum that S2 is obtained, then smooth 17 points of points intercept spectral region 11146.8cm-1-
9812.3cm-1、9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1Spectrum, obtain pretreated correction
Light harvesting spectrum matrix.
Further, fixed using Partial Least Squares structure simple grain content of amylose in rice near-infrared in the step S4
Analysis model is measured, number of principal components is set as 14 in used Partial Least Squares.
The present invention also protects a kind of simple grain amylose in rice detection method, includes the following steps：
1) select several appearances it is full it is complete, disease-free spot, nothing is gone mouldy, the simple grain rice without worm-eaten is as collection to be measured, and divide
The average value for each simple grain rice tow sides at least one set near infrared spectrum data not acquired；
2) the near infrared spectrum data average value obtained to step 1) carries out pretreatment and spectral region interception, utilizes right
It is required that a kind of model calculating of 1 to 9 any simple grain amylose in rice Quantitative Analysis Model construction method structure is to be measured
Collect the amylose content of each sample.
Further, the step that near infrared spectrum data average value carries out pretreatment in the step 2) and spectral region intercepts
Suddenly it is：First derivative processing is carried out to the spectrum of acquisition, then smooth 17 points of points intercept spectral region 11146.8cm-1-
9812.3cm-1、9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1Spectrum.
The present invention also protects a kind of single grain rice automatic sorting device, the sorting unit use as claim 1 to
10 any methods carry out automatic sorting to single grain rice.
In modeling, mainly have to the evaluation index of built quantitative model following several：The coefficient of determination (Coefficient
of Determination,R2), cross validation root-mean-square error (Root Mean Standard Error of Cross
Validation,RMSECV)；In external certificate, have to the evaluation index of prediction result following several：Prediction related coefficient
(coefficient of correlation, R), predicted root mean square error (Root Mean Standard Error of
Prediction,RMSEP)。
Detailed algorithm is shown as the following formula：
(1) coefficient of determination (R2)
In formula, yi,actualFor the reference value of i-th of calibration set sample；yi,predictedFor the close red of i-th calibration set sample
External model predicted value；yi,actualFor the average value of all calibration set sample reference values；N is calibration set sample number.R2It is used to comment
The models fitting effect that valence is established by correction.Under the premise of concentration range is identical, R2Closer to 1, indicate that predicted value is closer
Reference value, i.e. accuracy are higher；If R2Equal to 1, then it represents that fitting completely；If R2For negative value, then it represents that models fitting effect pole
Difference.In addition, R2Size and distribution relationship to be measured it is very big, for the to be measured of widely dispersed, it is possible to go out
Existing R2Close to 1, but the situation that its accuracy is poor.
(2) cross validation root-mean-square error (RMSECV)
In formula, yi,actualFor the reference value of i-th of sample in calibration set；yi,predictedFor calibration set cross-validation process
In i-th of sample model predication value；N is the sample number of calibration set；RMSECV is smaller, shows that model is pre- to the sample of calibration set
It is better to survey effect.
(3) prediction related coefficient (R)
In formula, yi,actualFor the reference value of i-th of verification collection sample；yi,predictedCollect the close red of sample for i-th of verification
External model predicted value；yi,actualFor the average value of all verification collection sample reference values；M verifications integrate as sample number.Predict phase relation
Number R indicates predicted value closer to reference value, i.e. accuracy is higher closer to 1.
(4) predicted root mean square error (RMSECP)
In formula, yi,actualThe reference value of i-th of sample is concentrated for verification；yi,predictedFor verification i-th of sample of concentration
Model predication value；M is the sample number of verification collection；RMSEP values are smaller, show that the predictive ability of institute's established model is stronger, prediction result
It is more accurate.
According to the method for the present invention, it can design, establish a set of near-infrared non-destructive testing automatic sorting device, according to being detected
The difference of component sorts single grain crop to be measured.
The present invention compared with prior art the advantages of be：Simple grain content of amylose in rice is realized using near infrared technology
Fast nondestructive evaluation；On this basis, it in near-infrared analysis, by improveing iodine colorimetry, obtains accurate straight chain and forms sediment
Powder reference value, thus constructed near-infrared model is more accurate compared to report in the past.It therefore, can be real using the method for the present invention
Now to quick, lossless, the accurate detection of simple grain content of amylose in rice.
Description of the drawings
Fig. 1 is the original spectrum acquired in embodiment 1；
Fig. 2 is the standard curve drawn according to 2639 methods of NY/T in embodiment 1；
Fig. 3 is the standard curve drawn according to the iodine colorimetry of improvement in embodiment 1；
Fig. 4 is the predicted value and reference value that near-infrared simple grain amylose in rice model collects verification in sample in embodiment 1
Scatter plot.
Specific implementation mode
It elaborates below to the embodiment of the present invention, the present embodiment is carried out lower based on the technical solution of the present invention
Implement, gives detailed embodiment and specific operating process, but protection scope of the present invention is not limited to following implementation
Example.
A kind of simple grain amylose in rice Quantitative Analysis Model construction method of embodiment
The specific detecting step of the present embodiment is as follows：
S1, sample collection
The present embodiment collects the rice varieties of different germplasm origins, including rice rice variety " 9311 ", japonica rice " force fortune round-grained rice
No. 7 " mutant after by heavy ion irradiation and other japonica rice varieties parts up to a hundred, it therefrom screens surface disease-free spot, go mouldy, worm
Full, complete, the ripe simple grain rice paddy seed of phenomena such as erosion totally 160 randomly chooses wherein 120, as calibration set, uses
In modeling.
The near-infrared diffusing transmission spectrum of S2, acquisition correction collection sample；
Spectra collection is Germany's Bruker companies MPA type ft-nir spectrometers using instrument, equipped with integral
7.0 data process＆analysis software of ball, PbS detectors and OPUS.
Detection window fixes the aluminium flake for having diameter 2mm apertures among a diameter 30mm, is scanned and is joined using specimen cup diffusing transmission
Number, spectral scanning range 5793cm-1-12489cm-1, resolution ratio 16cm-1, which is scanned, scanning times
64 times, as background spectrum.After background scans, rice sample is lain on aluminium flake, rice front, the back side respectively acquire 1 light
Averaged spectrum is calculated after spectrum, as the sample spectra.The original spectrum collected is as shown in Figure 1.
S3, simple grain rice craft decladding is sieved with 100 mesh sieve later to collected using mortar and pestle grinds, is prepared into
Rice flour.The amylose content reference value of calibration set sample is measured using the iodine colorimetry after improvement, and according to calibration set sample
Amylose content reference value build calibration set sample reference value matrix, every capable generation in the calibration set sample reference value matrix
The amylose content reference value of one sample of table, different rows represent the amylose content reference value of different samples.
Iodine colorimetry after improvement the specific steps are：
(1) making of standard curve：4 parts of suitable amylose standard sample is chosen, amylose content is respectively
1.5%, 10.4%, 16.2% and 26.5%, every part is repeated 2 times, and carries out following steps operation, obtains standard curve：
A) it is gelatinized：Every part of sample weighs the powder of 10mg or so, after recording its quality, is placed in centrifuge tube (centrifuge tube
Capacity is not less than 2ml), add 100 μ l absolute ethyl alcohols by sample dispersion, gently pat centrifuge tube, it is ensured that after powder is fully dispersed, adds
The 1mol/L NaOH of 900 μ l, are put into 95-100 DEG C of water-bath and heat later, during which take out shaken several times, heating time 15-
After twenty minutes, sample is taken out and is cooled down, later plus 1ml water shakes up.
B) it develops the color：Liquid after gelatinization takes 40 μ l to be put into new centrifuge tube (centrifuge tube capacity is not less than 5ml), adds successively
860 μ l of water add the 1mol/L HAc of 40 μ l, then add the 0.2%I of 60 μ l2- KI dyeing liquors add water 3ml, shake up after rocking, develop the color
After ten minutes, with the absorbance under spectrophotometric determination 620nm.Every part of sample is arranged in development step for 2 times or more and repeats, with
Absorbance of the average value of the absorbance of 2 repetitions as the sample.
C) standard curve is calculated：The amylose content of according to standard sample and the absorbance of standard sample and quality
Ratio fit standard curve.
(2) detection of calibration set sample amylose content, comprises the following steps：
D) it is gelatinized：Every part of calibration set simple grain rice sample is processed into rice flour by shelling, milling, sieving, later the step of
Same step a)；
E) it develops the color：The same step b) of process；
F) calibration set amylose content is calculated：According to the standard curve obtained in step c), according to calibration set sample
The amylose content of absorbance and the ratio calculation calibration set sample of its quality.
This iodine colorimetric method and its difference, which essentially consist in, has used water-bath to replace metal bath, to meet most of experiment
The needs of room, while the dosage of sample solution to be developed the color and corresponding reagent is increased in development step, and 2 repetitions are set, to protect
Demonstrate,prove the precision of development step.Have and traditional iodine colorimetry (NY/T 2639) phase to amylose to verify this iodine colorimetry
When accuracy of detection 10mg rice flour is weighed, using NY/T using this method to above-mentioned 4 parts of amylose standard samples respectively
Method described in 2639 weighs 50mg rice flour, carries out amylose content detection (being repeated 1 times), the standard that two methods are established
Curve distinguishes as shown in Figures 2 and 3, the coefficient of determination r of absorbance/quality and amylose true value2Respectively 0.9987 He
0.9972.It is 0.9950 (not shown) that two methods, which survey the correlation coefficient r between absorbance/gravimetric value of 4 parts of standard specimens,
It can be seen that the iodine colorimetry of improvement is feasible similarly for amylose detection.Wherein, r2Refer to two methods (tradition side
Method and improvement iodine colorimetry) coefficient of determination of standard curve is established, r refers to the correlation between two results that two methods are surveyed
Coefficient (correlation coefficient).
As seen from the figure, method precision described in the standard curve constructed by modification method and NY/T 2639 is close, illustrates institute
The iodine colorimetric method of the improvement of use can accurately detect the amylose chemical score of 10mg samples.
The amylose reference Data-Statistics such as table 1 of calibration set and verification collection after being measured using the iodine colorimetry of improvement.By
Table is it is found that the two has the average value being closer to, lower standard error and standard deviation.The amylose content range of calibration set
(1.35%-26.74%) is more than the amylose range (1.63%-24.45%) of verification collection, illustrates that calibration set can be preferably
Represent verification collection and most of rice varieties.
The statistical result for the iodine colorimetry detection simple grain content of amylose in rice that table 1 is improved
| Type | Calibration set sample | Verification collection sample |
| Sample size | 120 | 40 |
| Amylose content range | 1.35-26.74 | 1.63-24.45 |
| Amylose content average value | 13.54 | 13.46 |
| Standard error | 0.61 | 1.09 |
| Standard deviation | 6.71 | 6.91 |
The structure of S4, near-infrared simple grain amylose model：The original of calibration set is handled using suitable preprocess method
Spectrum, and spectrum range is screened, obtain pretreated spectrum matrix；And according to the spectrum matrix of above-mentioned acquisition, and correction
Collect sample reference value matrix, regression model is built using Partial Least Squares.Model construction is in matlab 2015b softwares (The
Mathworks, Natick, MA, USA) on realize.
The number of principal components selected when different preprocess methods, different spectral region and Partial Least Squares Regression is not
Together, the performance of final institute's established model is different.By comparing the combination of different preprocess method and different spectral regions, sieve
Select optimal calibration model.Preprocess method is from without pretreatment, first derivative (17 points of acquiescence is smooth), standard normal variable transformation
(SNV), first derivative+SNV is converted screens totally in 4 kinds of methods；The screening technique of spectral region is the data to full spectral region
(such as the spectrum of this full spectral region has 870 spectrum points to impartial 10 sections of the interception of point, 870 data of correspondence, then in 10 sections
Every section of data for including 87 spectrum points), spectral region intercept situation such as table 2.
Exhaustive wherein arbitrary 1 section, 2 sections, 3 sections, 4 sections of combination, amount to the combination of 210 kinds of spectral regions, therefrom screen most
Excellent spectral region.Preprocess method combines for total 840 kinds with spectral region, and partial least square model is established to this 840 kinds combinations,
Within selection wherein number of principal components 20, R2Highest, RMSECV minimum model is as best model, corresponding pretreatment side
Method, spectral region and number of principal components are optimal offset minimum binary modeling parameters.The part comparison result of model such as 3 institute of table
Show.
2 spectra number of table and corresponding spectral region
Modeling performance under 3 different pretreatments method of table and different spectral regions
As shown in Table 3, in preprocess method first derivative, spectral region 11146.8cm-1-9812.3cm-1、9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1When (the 3rd, 4,6,8 section in corresponding table 3), number of principal components are 14, built
The cross validation results of model are best, R2For 0.8563, RMSECV 2.55.Therefore the model is selected to be tested for subsequent
Card, prediction.
In order to verify the estimated performance of model, in the mutant library described in step S1, select again surface disease-free spot,
Go mouldy, worm-eaten phenomena such as full, complete, ripe simple grain rice paddy seed 40, as verification collect sample.These samples are adopted
Spectrum is acquired with the identical methods of same step S2, identical method acquires reference value with step S3, with identical light in step S4
Spectrum pretreatment (i.e. first derivative, spectral region 11146.8cm-1-9812.3cm-1、9133.5cm-1-8470.1cm-1With
7791.3cm-1-7127.9cm-1) processing verification collection original spectrum, it uses later after the model that builds is to pretreatment in step S4
Spectrum predicted that number of principal components is set as 14.Model prediction matlab 2015b softwares (The Mathworks,
Natick, MA, USA) on realize.The scatter plot of its predicted value and reference value is as shown in Figure 4.
From fig. 4, it can be seen that model has higher external certificate coefficient R2(0.9511) and lower RMSEP
(2.1135), the p value of the paired t-test of verification collection predicted value and reference value is 0.926>0.05, the result is better than J.G.Wu etc.
Simple grain rice (the R that (Field Crops Research, 2004) is reported2=0.66, RMSEP=4.69) amylose it is close
Infrared detections illustrate that the sample of verification collection can be effectively predicted in model, thus can also be extended to other similar to germplasm
On simple grain rice.
When predicting unknown rice paddy seed sample, simple grain to be measured is acquired using the identical methods of same step S2
Rice spectrum, identical Pretreated spectra (i.e. first derivative, spectral region 11146.8cm in step S4-1-9812.3cm-1、
9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1) original spectrum of these simple grain rice is handled, and use step
The model built in rapid S4 predicts pretreated spectrum acquired results are that the amylose of simple grain rice to be measured contains
Amount.
It is important to note that the detection method of the present invention is not limited to the above embodiments the application scenarios, into one
Step ground, can also design, establish the near-infrared amylose content automatic sorting device of the single grain rice of a set of heterogeneity,
Software in device can integrate this method design near-infrared model and to single grain rice spectra collection to be measured and prediction, with full
The Fast nondestructive evaluation of the single grain rice of different amylose contents and sorting are needed in sufficient breeding and Grain Trade.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (12)
1. a kind of simple grain amylose in rice Quantitative Analysis Model construction method, which is characterized in that include the following steps：
S1, the discrepant simple grain rice sample several pieces of amylose content are collected, be dried, as correction after equilibrium water conten
Collection；
S2, acquisition correction concentrate the near infrared spectrum of each simple grain rice sample；
S3, respectively by each simple grain rice sample treatment of calibration set at rice flour, obtain the straight of each simple grain rice sample
Chain content of starch reference value builds calibration set reference value matrix；
The structure of S4, near-infrared simple grain amylose model：The near infrared spectrum that S2 is obtained screens spectrum range, is corrected
Reference value matrix described in the spectrum matrix and S3 is carried out recurrence association analysis, obtains simple grain rice straight chain by light harvesting spectrum matrix
The Near-Infrared Quantitative Analysis model of content of starch.
2. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
In the step S1 each simple grain rice sample be the full complete, disease-free spot of appearance, without go mouldy, the simple grain rice without worm-eaten.
3. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
The near infrared spectrum of each simple grain rice sample is that each simple grain rice sample is acquired in tow sides respectively in the step S2
At least one set of near infrared spectrum average value.
4. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1 or 3, feature exist
In the near infrared spectrum is near-infrared diffusing transmission spectrum.
5. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
In the step S3, the rice flour is coarse rice powder or milled rice flour.
6. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
The measurement of calibration set rice flour amylose content reference value is using the iodine colorimetry after improvement in the step S3, after the improvement
Iodine colorimetry the specific steps are：
(1) making of standard curve：Amylose standard sample several pieces are chosen, following steps operation is carried out, it is bent to obtain standard
Line：
A) it is gelatinized：Every part of sample weighs suitable sample powder, and sample is placed in container of the capacity not less than 2ml, is added
Sample dispersion will gently be patted container by absolute ethyl alcohol, it is ensured that and sample powder is fully dispersed, and 1mol/L NaOH are then added, it
It is put into 95-100 DEG C of water-bath and heats afterwards, during which take out shaken several times, after heating 15-20 minutes, sample is taken out and is cooled down, it
Water is added afterwards to shake up, wherein sample powder and absolute ethyl alcohol, the w/v 10mg of 1mol/L NaOH solutions, water:100μ
l:900μl:1ml；
B) it develops the color：Liquid after gelatinization picks and places in container of the capacity not less than 5ml, sequentially adds water, 1mol/LHAc, 0.2%
I2-KI dyeing liquors, are added water again after shaking up, continue to shake up, the absorbance after colour developing 5-20 minutes under measurement 620nm, every part
Sample at least repeats above-mentioned process color twice, using the average value of the absorbance of replication as the absorbance of the sample,
In, the water, HAc, I of liquid, addition for the first time after the gelatinization2The volume ratio of-KI dyeing liquors and second of the water being added
For 40 μ l:860μl:40μl:60:μl:3000μl；
C) standard curve is calculated：The ratio of the amylose content of according to standard sample and the absorbance of standard sample and quality
Fit standard curve；
(2) detection of calibration set amylose content, comprises the following steps：
D) it is gelatinized：Every part of calibration set simple grain rice sample is sieved with 100 mesh sieve by shelling, after milling processing, in 75-85 DEG C of baking oven
Drying to constant weight, obtains required rice flour.Later the step of same step a)；
E) it develops the color：The same step b) of process；
F) calibration set amylose content is calculated：According to the standard curve obtained in step c), according to the extinction of calibration set sample
The amylose content of degree and the ratio calculation calibration set sample of its quality.
7. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 6, which is characterized in that
The container is centrifuge tube.
8. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
Further include that pre-treatment step is carried out to the spectrum that S2 is obtained in the step S4, the pre-treatment step is：The light that S2 is obtained
Spectrum carries out first derivative processing, then smooth 17 points of points intercept spectral region 11146.8cm-1-9812.3cm-1、
9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1Spectrum, obtain pretreated calibration set spectrum matrix.
9. a kind of simple grain amylose in rice Quantitative Analysis Model construction method according to claim 1, which is characterized in that
Simple grain content of amylose in rice Near-Infrared Quantitative Analysis model is built using Partial Least Squares in the step S4, is used
Partial Least Squares in number of principal components be set as 14.
10. a kind of simple grain amylose in rice detection method, which is characterized in that include the following steps：
1) select several appearances it is full it is complete, disease-free spot, nothing is gone mouldy, the simple grain rice without worm-eaten is adopted respectively as collection to be measured
The average value of each simple grain rice tow sides at least one set near infrared spectrum data of collection；
2) the near infrared spectrum data average value obtained to step 1) carries out pretreatment and spectral region interception, utilizes claim
It is each that a kind of model of 1 to 9 any simple grain amylose in rice Quantitative Analysis Model construction method structure calculates collection to be measured
The amylose content of sample.
11. according to the method described in claim 10, it is characterized in that, in the step 2) near infrared spectrum data average value into
Row is pre-processed is with the step of spectral region interception：First derivative processing is carried out to the spectrum of acquisition, it is smooth to count 17 points, then
Intercept spectral region 11146.8cm-1-9812.3cm-1、9133.5cm-1-8470.1cm-1And 7791.3cm-1-7127.9cm-1's
Spectrum.
12. a kind of single grain rice automatic sorting device, which is characterized in that the sorting unit uses such as claims 1 to 10
Any method carries out automatic sorting to single grain rice.
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| CN113484270A (en) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | Construction and detection method of single-grain rice fat content quantitative analysis model |
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