CN102841072A - Method for identifying transgenic rice and non-transgenic rice based on NIR (Near Infrared Spectrum) - Google Patents
Method for identifying transgenic rice and non-transgenic rice based on NIR (Near Infrared Spectrum) Download PDFInfo
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Abstract
The invention discloses a method for identifying transgenic rice and non-transgenic rice based on NIR (Near Infrared Spectrum), which comprises the following steps of: (1) transmitting the NIR to the rice seed samples, and collecting the diffuse reflection spectrum information of all the rice samples; (2) respectively preprocessing the diffuse reflection spectrum information for all the rice seed samples, extracting the spectrum information in characteristic spectrum regions after preprocessing through a principal component analysis method, selecting principal components, and acquiring the scores of the principal components; (3) building a model by the principal component scores corresponding to the rice seed sample spectrum information as an input and the rice seed type set values corresponding to the rice seed samples as an output; and (4) acquiring the scores of the principal components of the spectrum of rice seeds to be detected, taking the scores into the model in the step (3), and obtaining the types of the rice seeds to be detected. The invention is high in identification precision, simple to operate, low in cost, and is capable of realizing quickly and losslessly identifying the transgenic rice and the non-transgenic rice.
Description
Technical field
The invention belongs to the detection range of genetically modified plants, relate in particular to the method for differentiating transgenic paddy rice and non-transgenic paddy rice based near infrared spectrum.
Background technology
Paddy rice is the first cereal crops of China, and annual production accounts for 38% of total output of grain, and its relations of production the grain security of country.Transgenic technology has broken through the restriction of paddy rice traditional breeding method, for the grain security that ensures China provides new approach.Along with the develop rapidly of paddy gene engineering, the achievement in research of transgenic paddy rice is striking.Over nearly 20 years, utilize transgenic technology successfully to develop the transgenic paddy rice of pest-resistant, disease-resistant, antiweed, degeneration-resistant border and high yield and high quality, obtained a large amount of objective traits heredity and expressed the transgenic paddy rice strain system stable, that economical character is good.
Along with a large amount of genetically modified crops progressively move towards market, the safety issue of the food of genetically modified crops and genetically modified crops processing also begins to receive people's attention.In essence, genetically modified crops do not have difference with the conventional crop varieties of breeding.Conventional breeding generally is to realize through sexual hybridization; Plant genetic engineering then be with Agrobacterium, particle gun, electricity swash, technology such as microinjection imports the external source recombinant DNA in the Plant Genome; Although theoretically; Genetically modified hereditary capacity and phenotype should calculate to a nicety more, and be safer on using, but it is still necessary that genetically modified crops are carried out security.
The transgene component detection method can be divided into qualitative detection method and detection by quantitative method.Genetically modified crops detection method commonly used at present has PCR detection method, chemistry to organize detection method, enzyme linked immunosorbent assay, exogenous origin gene integrator identification method, Westren hybrid method, biometric detection method etc.The detection method of some conventional genetically modified plants can not satisfy present needs quick, that accurately detect, and the needs that these methods have change film, hybridization, complex operation, expense height, and the detection that is not suitable for the batch sample that has has seriously limited its application.The development trend of transgenosis detection technique be easy and simple to handle, expense is lower, applicability is strong.
Application publication number is that the application for a patent for invention of CN 102081075A discloses a kind of method of differentiating transgenic paddy rice and non-transgenic paddy rice, comprising: (1) is measured under non-transgenic paddy rice C418 and the paddy rice qualifications to be detected the content of shikimic acid and galacturonic acid in artificial incubation growth 6 all leaves respectively; (2) if the content of shikimic acid of paddy rice to be detected and galacturonic acid is compared the shikimic acid of non-transgenic paddy rice and the content of galacturonic acid has conspicuousness to descend, paddy rice then to be detected is a transgenic paddy rice; Because the detection method of shikimic acid and galacturonic acid is comparatively complicated, cause this invention complex operation step, length consuming time.
Summary of the invention
The invention provides the method for differentiating transgenic paddy rice and non-transgenic paddy rice based near infrared spectrum, this method can be fast, can't harm, easy discriminating transgenic paddy rice and non-transgenic paddy rice.
A kind of method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice may further comprise the steps:
(1) be 4000～10000cm to rice paddy seed sample emission wave-number range
-1Near infrared spectrum, and gather the spectrum information that diffuses of all rice paddy seed samples;
(2) respectively the spectrum information that diffuses of all rice paddy seed samples is carried out pre-service, utilize PCA to extract the spectral signature information in the characteristic spectrum section, choose major component, and obtain the score of each major component;
(3) with corresponding each principal component scores of all rice paddy seed sample light spectrum informations as input, as output, set up model with the corresponding rice paddy seed type setting value of rice paddy seed sample;
(4) obtain each principal component scores of rice paddy seed spectrum to be measured according to the operation of step (1)～(2), carry it into model described in (3), obtain rice paddy seed type to be measured.
Diffuse is after near infrared light gets into testing sample inside; Through repeatedly reflect, refraction, diffraction, absorption; Be back to the light of detecting device after having taken place to interact with the testing sample interior molecules, thus the diffuse reflection spectrum information loads structure and the composition information of testing sample.Because the structure that transgenic paddy rice seed and non-transgenic rice paddy seed are inner and form difference to some extent; In conjunction with the spectral information treatment technology; Can extract differential information; And pass through the active data disposal route with the further outstanding demonstration of the tiny difference between spectrum picture information, and change discernible signal into, be used for the discriminating of transgenic paddy rice and non-transgenic paddy rice.
In the step (1), said rice paddy seed sample is transgenic paddy rice seed sample and non-transgenic rice paddy seed sample, and transgenic paddy rice is white trans-genetic hybrid rice of turning egg(s) or modulation control trans-genetic hybrid rice.
In the step (2),, need carry out pre-service to spectrum in order to remove the influence of factors such as high frequency random noise, baseline wander, sample be inhomogeneous.
Preprocessing procedures choose the extraction that affects pretreating effect and spectral effective information, through comparative analysis, preprocess method is preferably the standard normal converter technique.
Said characteristic spectrum section is meant and has the comparatively obviously section of difference in transgenic paddy rice seed and the non-transgenic rice paddy seed spectral information; Through spectrum section spectral information is carried out digitized processing, can realize the discriminating of transgenic paddy rice seed and non-transgenic rice paddy seed; It is 4000～10000cm that the section of characteristic spectrum described in the present invention is preferably wave-number range
-1Or 4000～8000cm
-1The spectrum section.
Utilize PCA to extract spectral signature information; The purpose of principal component analysis (PCA) (PCA) is with the spectroscopic data dimensionality reduction; Convert former variable to the linear combination of one group of orthogonal new variables; Eliminated overlapped information in the multivariate coexistence, simultaneously, new variables can characterize the data structure characteristic of former variable to greatest extent.Because the new variables quantity of PCA is few, uncorrelated each other, more helps the analysis to spectral information.Principal component analytical method can be gone up Unscrambler software (being made by U.S. CAMO) and realize.
Choosing with effective extraction of raw information of major component is closely related, when selecting major component, gets contribution rate of accumulative total >=85%, but that major component is chosen is too much unsuitable, otherwise can introduce unnecessary noise and cause over-fitting; When pre-service was carried out in the accepted standard normal transformation, through comparative analysis, number of principal components was preferably 4～8.
Step (2) is preferably: utilize the standard normal converter technique respectively the spectrogram that diffuses of all rice paddy seed samples to be carried out pre-service, utilizing PCA to extract pretreated wave-number range is 4000～8000cm
-1Spectral signature information in the spectral information is chosen 5 major components, and obtains the score of each major component.
Step (2) is preferably: utilize the standard normal converter technique respectively the spectrogram that diffuses of all rice paddy seed samples to be carried out pre-service, utilizing PCA to extract pretreated wave-number range is 4000～10000cm
-1Spectral signature information in the spectral information is chosen 6 major components, and obtains the score of each major component.
In the step (3), the rice paddy seed type that the rice paddy seed sample is corresponding is transgenosis and non-transgenic, in model, transgenosis can be set and not genetically modified setting value is respectively-1 and 1.
Said model is preferably partial least squares discriminant analysis (PLS-DA) model; The PLS-DA method is based on a kind of discriminant analysis method that PLS returns; Class members's information because of having considered that companion matrix provides with code form when structural factor; Therefore have distinguishing ability efficiently, can improve the degree of accuracy that the paddy rice type is differentiated.
With respect to prior art, beneficial effect of the present invention is:
(1) the present invention is simple to operate, time saving and energy saving, only need obtain the near infrared spectrum of rice paddy seed, can carry out the discriminating of transgenosis and non-transgenic paddy rice;
(2) high, the reliable results of identification precision of the present invention can realize quick, the harmless discriminating of transgenosis and non-transgenic paddy rice.
Description of drawings
Fig. 1 is that transgenic paddy rice and non-transgenic paddy rice predicted value and actual value regression figure are concentrated in embodiment 1 checking.
Embodiment
1, sets up model
(1) choose 80 transgenic paddy rice seeds and 40 non-transgenic rice paddy seeds respectively as the rice paddy seed sample, utilizing Nicolet Nexus870 (Thermo Corporation USA) Fourier transform near infrared spectrometer is 4000～10000cm to rice paddy seed sample emission wave-number range
-1Near infrared spectrum, utilize the spectrogram that diffuses of all rice paddy seed samples of OMNIC 6.0 software collections; The near infrared spectrometer scanning times is set 32 times, resolution 4cm
-1Room temperature is controlled at about 25 ℃ during collection, and it is stable that humidity keeps.
Above transgenic paddy rice is for changeing the tctp gene paddy rice and changeing the Osmi166 trans-genetic hybrid rice; Its acquisition methods can be: it is explant induction, cultured calli that transgenic line adopts the mature embryo of paddy rice respectively; Select embryo callus as transformation receptor; Agrobacterium tumefaciems EHA105 through comprising plant expression vector p1301-TCTP and p1301-mi166 is transformed in the rice callus, obtains transgenic paddy rice through a series of screening differentiation.The non-transgenic paddy rice is spent 11 paddy rice in being.
(2) utilize the standard normal converter technique respectively the spectrogram that diffuses of all rice paddy seed samples to be carried out pre-service, obtain pretreated spectral information; Utilizing each the pretreated wave-number range of PCA extraction in the Unscrambler software is 4000～10000cm
-1Spectral signature information in the spectral information, choosing number of principal components is 6, obtains the score (as shown in table 1) of each major component;
(3) with corresponding each principal component scores of all rice paddy seed sample light spectrum informations as input, as output, set up the PLS-DA model with the corresponding rice paddy seed type setting value of rice paddy seed sample; The rice paddy seed type setting value that the rice paddy seed sample is corresponding is provided with as follows: change the tctp gene paddy rice and be set at-1 with commentaries on classics mi166 trans-genetic hybrid rice, the non-transgenic paddy rice is set at 1.As space is limited, only wherein the data of 20 rice paddy seed samples are listed in this, see table 1.
Table 1 is used for the partial database of modelling
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y |
| 1 | 1.3530 | -0.7930 | 0.7700 | 1.0820 | 0.3880 | -0.1170 | 1 |
| 2 | 1.1700 | 0.5890 | 0.7120 | -0.8540 | 0.8270 | -0.5790 | 1 |
| 3 | 2.0450 | 1.2560 | -0.0391 | -0.9710 | 0.5480 | -0.7620 | 1 |
| 4 | -0.4370 | -0.1520 | 1.0850 | 0.4820 | 0.4580 | -0.3600 | 1 |
| 5 | -0.6610 | 0.1800 | 0.4670 | 0.6060 | 0.5070 | -0.5080 | 1 |
| 6 | -2.7930 | 0.7790 | -0.2370 | 0.5790 | 0.4760 | 0.1470 | 1 |
| 7 | 2.0430 | -0.8770 | -0.3850 | 0.8900 | 0.1430 | 0.2080 | 1 |
| 8 | -2.5240 | 0.3730 | -0.0229 | 0.3550 | 0.6040 | 0.3070 | 1 |
| 9 | -1.3080 | -0.1710 | 0.5250 | 0.1620 | 0.6420 | 0.0927 | 1 |
| 10 | -4.5830 | 1.6350 | -0.3410 | 1.0520 | -0.1640 | -0.2080 | 1 |
| 11 | -1.0420 | -0.1430 | -0.5020 | -0.8720 | -0.6650 | -0.7770 | -1 |
| 12 | -2.1080 | -0.8060 | 0.0816 | -0.9740 | -0.0098 | -0.5140 | -1 |
| 13 | 1.3600 | -2.1210 | -0.0541 | -0.0623 | -0.4610 | -0.2890 | -1 |
| 14 | -4.7990 | -0.3640 | 0.1410 | -0.7450 | -0.6880 | -0.3830 | -1 |
| 15 | 0.9920 | -1.4920 | -0.3650 | 1.3850 | -0.7650 | -0.1610 | -1 |
| 16 | -0.4150 | -0.2960 | 0.2750 | -1.3150 | -0.0383 | -0.7440 | -1 |
| 17 | 0.6530 | -1.8010 | 0.2070 | -0.8100 | -0.2300 | -0.3810 | -1 |
| 18 | -2.5350 | -1.3630 | 0.5500 | -0.6420 | 0.0111 | -0.0736 | -1 |
| 19 | -2.1330 | -1.1560 | 0.6900 | -0.5170 | -0.1900 | -0.7030 | -1 |
| 20 | 1.3300 | -3.3070 | 0.6430 | 0.1860 | -0.1150 | -0.5490 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed sample, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve.
2, utilize the rice paddy seed type of model prediction calibration set rice paddy seed sample
After setting up the PLS-DA model, with in 1 according to 6 principal component scores of rice paddy seed sample in the calibration set that step (1)～(2) obtain, bring into and set up good PLS-DA model in 1, obtain output valve (as shown in table 2); Through output valve, confirm the rice paddy seed type of rice paddy seed sample according to following principle: predicted value was transgenic paddy rice less than 0 o'clock; Predicted value was the non-transgenic paddy rice greater than 0 o'clock.As space is limited, only wherein the data of 20 rice paddy seed samples are listed in this, see table 2.
Rice paddy seed sample prediction rice paddy seed type and actual rice paddy seed type in table 2 calibration set
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y | S 1 | S 2 |
| 1 | 1.3530 | -0.7930 | 0.7700 | 1.0820 | 0.3880 | -0.1170 | 0.88 | 1 | 1 |
| 2 | 1.1700 | 0.5890 | 0.7120 | -0.8540 | 0.8270 | -0.5790 | 1.213 | 1 | 1 |
| 3 | 2.0450 | 1.2560 | -0.0391 | -0.9710 | 0.5480 | -0.7620 | 1.162 | 1 | 1 |
| 4 | -0.4370 | -0.1520 | 1.0850 | 0.4820 | 0.4580 | -0.3600 | 0.976 | 1 | 1 |
| 5 | -0.6610 | 0.1800 | 0.4670 | 0.6060 | 0.5070 | -0.5080 | 0.793 | 1 | 1 |
| 6 | -2.7930 | 0.7790 | -0.2370 | 0.5790 | 0.4760 | 0.1470 | 0.665 | 1 | 1 |
| 7 | 2.0430 | -0.8770 | -0.3850 | 0.8900 | 0.1430 | 0.2080 | 0.222 | 1 | 1 |
| 8 | -2.5240 | 0.3730 | -0.0229 | 0.3550 | 0.6040 | 0.3070 | 0.604 | 1 | 1 |
| 9 | -1.3080 | -0.1710 | 0.5250 | 0.1620 | 0.6420 | 0.0927 | 0.635 | 1 | 1 |
| 10 | -4.5830 | 1.6350 | -0.3410 | 1.0520 | -0.1640 | -0.2080 | 0.653 | 1 | 1 |
| 11 | -1.0420 | -0.1430 | -0.5020 | -0.8720 | -0.6650 | -0.7770 | -1.046 | -1 | -1 |
| 12 | -2.1080 | -0.8060 | 0.0816 | -0.9740 | -0.0098 | -0.5140 | -1.015 | -1 | -1 |
| 13 | 1.3600 | -2.1210 | -0.0541 | -0.0623 | -0.4610 | -0.2890 | -1.253 | -1 | -1 |
| 14 | -4.7990 | -0.3640 | 0.1410 | -0.7450 | -0.6880 | -0.3830 | -1.264 | -1 | -1 |
| 15 | 0.9920 | -1.4920 | -0.3650 | 1.3850 | -0.7650 | -0.1610 | -0.717 | -1 | -1 |
| 16 | -0.4150 | -0.2960 | 0.2750 | -1.3150 | -0.0383 | -0.7440 | -0.456 | -1 | -1 |
| 17 | 0.6530 | -1.8010 | 0.2070 | -0.8100 | -0.2300 | -0.3810 | -1.153 | -1 | -1 |
| 18 | -2.5350 | -1.3630 | 0.5500 | -0.6420 | 0.0111 | -0.0736 | -0.919 | -1 | -1 |
| 19 | -2.1330 | -1.1560 | 0.6900 | -0.5170 | -0.1900 | -0.7030 | -0.909 | -1 | -1 |
| 20 | 1.3300 | -3.3070 | 0.6430 | 0.1860 | -0.1150 | -0.5490 | -1.566 | -1 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed sample, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve, S
1Be model prediction rice paddy seed type, S
2Be actual rice paddy seed type; S
1And S
2In the row, 1 represents the non-transgenic paddy rice, and-1 represents transgenic paddy rice.
Can know through data analysis: calibration set coefficient of determination R
2 cBe 0.9183, calibration set root-mean-square error RMSECV is 0.2695, the PLS-DA model of foundation reaches 100% to the predictablity rate of calibration set rice paddy seed sample.
3, utilize the rice paddy seed type of model prediction checking collection rice paddy seed to be measured
Get 60 rice paddy seeds to be measured,, obtain 6 principal component scores of rice paddy seed to be measured, and carry it into and set up good PLS-DA model in 1, obtain model output valve Y according to step (1)～(2) in 1 as checking collection; Through output valve Y, confirm the type of rice paddy seed to be measured according to decision principle in 2.As space is limited, only with the data list of 25 rice paddy seeds to be measured at this, as shown in table 3.
Rice paddy seed prediction rice paddy seed type to be measured and actual rice paddy seed type are concentrated in table 3 checking
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y | S 1 | S 2 |
| 1 | -2.9310 | -0.3970 | -0.6650 | 0.5860 | -0.1070 | 0.3520 | 0.968 | 1 | 1 |
| 2 | -0.6670 | -0.2310 | 0.1920 | 0.0401 | 0.0825 | 0.2030 | 0.73 | 1 | 1 |
| 3 | -2.2690 | -1.0310 | 0.8860 | -0.3120 | -0.0803 | 0.0724 | 1.188 | 1 | 1 |
| 4 | -0.5720 | -0.3310 | 0.3430 | -0.1160 | -0.2660 | -0.0198 | 1.178 | 1 | 1 |
| 5 | -0.7930 | -0.4970 | 0.0681 | 0.2100 | -0.1360 | 0.5340 | 0.846 | 1 | 1 |
| 6 | 6.3560 | 0.4730 | -0.8840 | 0.2470 | -0.5010 | 0.1160 | 0.986 | 1 | 1 |
| 7 | -2.6240 | 0.2530 | -0.0890 | -0.3410 | 0.1510 | -0.3470 | 0.505 | 1 | 1 |
| 8 | -3.1970 | 0.5690 | -0.6570 | -0.2880 | -0.2240 | -0.6270 | 0.935 | 1 | 1 |
| 9 | -1.0460 | 1.7940 | -0.7750 | 0.2840 | -0.2470 | -0.2890 | 0.978 | 1 | 1 |
| 10 | -0.8210 | 1.0490 | -0.2000 | 0.4340 | -0.1160 | -0.1670 | 0.293 | 1 | 1 |
| 11 | -3.7120 | 0.7550 | -0.0027 | -0.1010 | -0.3900 | -0.4650 | 0.28 | 1 | 1 |
| 12 | 0.2270 | 0.6260 | 0.0185 | -0.3020 | 0.2860 | 0.5710 | 0.871 | 1 | 1 |
| 13 | -2.8670 | -0.9860 | 0.9000 | -0.4200 | 0.1780 | 0.3570 | -0.722 | -1 | -1 |
| 14 | -2.5370 | -0.0343 | 0.8240 | -0.5960 | -0.1770 | 0.2700 | -0.771 | -1 | -1 |
| 15 | 2.9340 | 1.1510 | 1.5360 | -0.7920 | -0.3610 | 0.0080 | -0.886 | -1 | -1 |
| 16 | 3.2440 | 1.3420 | 1.3470 | -1.1520 | -0.3610 | -0.3780 | -1.093 | -1 | -1 |
| 17 | -1.3670 | 0.9700 | -0.4390 | 0.0960 | 0.4480 | 0.0888 | -0.959 | -1 | -1 |
| 18 | 0.0870 | 0.3080 | 0.8900 | -0.0888 | 0.5510 | 0.1600 | -1.023 | -1 | -1 |
| 19 | -2.4050 | 0.3110 | -0.2150 | 0.1330 | -0.0730 | -0.3800 | -0.959 | -1 | -1 |
| 20 | 3.1820 | -0.0256 | 1.6990 | 0.8590 | 0.5650 | -0.5930 | -1.554 | -1 | -1 |
| 21 | 0.2270 | -2.7020 | -0.3470 | -0.1950 | 0.2150 | -0.6300 | -0.45 | -1 | -1 |
| 22 | -3.4210 | -0.4880 | -0.1550 | 0.2390 | 0.2590 | -0.3490 | -0.878 | -1 | -1 |
| 23 | -0.8670 | 0.3150 | 0.3190 | 0.0802 | -0.1740 | -0.7870 | -1.316 | -1 | -1 |
| 24 | -1.4390 | 0.1650 | 0.0678 | 0.4940 | -0.4470 | -1.2150 | -0.944 | -1 | -1 |
| 25 | -0.3240 | 1.0430 | 0.0300 | -0.2890 | -0.1670 | 0.4830 | -0.877 | -1 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed to be measured, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve, S
1Be model prediction rice paddy seed type, S
2Be actual rice paddy seed type; S
1And S
2In the row, 1 represents the non-transgenic paddy rice, and-1 represents transgenic paddy rice.
Can know through data analysis: checking collection coefficient of determination R
2 pBe 0.8979, checking collection root-mean-square error RMSEP is 0.2878, the PLS-DA model of foundation reaches 100% to the catchment predictablity rate of rice of checking.Checking concentrates transgenic paddy rice and non-transgenic paddy rice predicted value and actual value regression figure as shown in Figure 1.
Embodiment 2
1, sets up model
(1) with embodiment 1.
(2) utilize the standard normal converter technique respectively the spectrogram that diffuses of all rice paddy seed samples to be carried out pre-service, obtain pretreated spectral information; Utilizing each the pretreated wave-number range of PCA extraction in the Unscrambler software is 4000～8000cm
-1Spectral signature information in the spectrum section, choosing number of principal components is 5, obtains the score (as shown in table 4) of each major component;
(3) with corresponding each principal component scores of all rice paddy seed sample light spectrum informations as input, as output, set up the PLS-DA model with the corresponding rice paddy seed type setting value of rice paddy seed sample; The rice paddy seed type setting value that the rice paddy seed sample is corresponding is provided with as follows: change the tctp gene paddy rice and be set at-1 with commentaries on classics mi166 trans-genetic hybrid rice, the non-transgenic paddy rice is set at 1.As space is limited, only wherein the data of 20 rice paddy seed samples are listed in this, see table 4.
Table 4 is used for the partial database of modelling
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y |
| 1 | 1.5760 | -1.2890 | 0.2420 | 0.7620 | 0.1610 | 0.0249 | 1 |
| 2 | 0.7060 | 1.4910 | -0.1330 | 0.4010 | 0.2350 | -0.2130 | 1 |
| 3 | 2.2380 | 1.8980 | -0.1730 | 0.3890 | 0.0969 | -0.5140 | 1 |
| 4 | -1.4210 | -0.5080 | 0.3290 | 0.9520 | -0.0171 | -0.1600 | 1 |
| 5 | -1.3610 | -0.4000 | 0.2890 | 0.5180 | 0.0336 | -0.3000 | 1 |
| 6 | -3.8860 | -0.1460 | 0.0375 | 0.2370 | 0.1880 | -0.2860 | 1 |
| 7 | 3.0420 | -1.4200 | 0.1070 | -0.1660 | 0.1130 | 0.1830 | 1 |
| 8 | -3.5180 | -0.0658 | -0.0167 | 0.2420 | 0.3000 | 0.0238 | 1 |
| 9 | -2.0770 | 0.1050 | 0.1180 | -0.2780 | 0.2160 | 0.0316 | 1 |
| 10 | -6.2470 | 0.3450 | 0.8330 | 0.4870 | -0.5970 | -0.1830 | 1 |
| 11 | -1.0590 | 0.2430 | -0.6420 | -0.0786 | -0.2050 | 0.1910 | -1 |
| 12 | -3.0900 | 0.0187 | -0.5990 | 0.2100 | -0.0894 | 0.0323 | -1 |
| 13 | 2.2590 | -1.2940 | -0.2860 | -0.2610 | -0.1850 | 0.2650 | -1 |
| 14 | -6.5120 | 0.3220 | -0.6920 | -0.2590 | 0.1780 | 0.4370 | -1 |
| 15 | 2.2500 | -2.4760 | -0.0601 | -0.2320 | -0.3630 | 0.2610 | -1 |
| 16 | -0.9040 | 0.9580 | -0.5490 | -0.2850 | 0.0365 | -0.1090 | -1 |
| 17 | 1.1180 | -0.1560 | -0.3270 | -0.7240 | 0.2460 | -0.2080 | -1 |
| 18 | -3.4160 | -0.1470 | -0.2280 | -0.2390 | -0.0240 | -0.0630 | -1 |
| 19 | -3.2530 | -0.4290 | -0.2160 | -0.4260 | 0.0084 | -0.0983 | -1 |
| 20 | 2.0300 | -1.7920 | -0.1390 | -0.9330 | -0.0915 | -0.5240 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed sample, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve.
2, utilize the rice paddy seed type of model prediction calibration set rice paddy seed sample
After setting up the PLS-DA model, with in 1 according to 6 principal component scores of rice paddy seed sample in the calibration set that step (1)～(2) obtain, bring into and set up good PLS-DA model in 1, obtain output valve (as shown in table 5); Through output valve, confirm the rice paddy seed type of rice paddy seed sample according to following principle: predicted value was transgenic paddy rice less than 0 o'clock; Predicted value was the non-transgenic paddy rice greater than 0 o'clock.As space is limited, only wherein the data of 20 rice paddy seed samples are listed in this, see table 5.
Rice paddy seed sample prediction rice paddy seed type and actual rice paddy seed type in table 5 calibration set
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y | S 1 | S 2 |
| 1 | 1.5760 | -1.2890 | 0.2420 | 0.7620 | 0.1610 | 0.0249 | 1.069 | 1 | 1 |
| 2 | 0.7060 | 1.4910 | -0.1330 | 0.4010 | 0.2350 | -0.2130 | 1.235 | 1 | 1 |
| 3 | 2.2380 | 1.8980 | -0.1730 | 0.3890 | 0.0969 | -0.5140 | 1.336 | 1 | 1 |
| 4 | -1.4210 | -0.5080 | 0.3290 | 0.9520 | -0.0171 | -0.1600 | 1.159 | 1 | 1 |
| 5 | -1.3610 | -0.4000 | 0.2890 | 0.5180 | 0.0336 | -0.3000 | 0.895 | 1 | 1 |
| 6 | -3.8860 | -0.1460 | 0.0375 | 0.2370 | 0.1880 | -0.2860 | 0.29 | 1 | 1 |
| 7 | 3.0420 | -1.4200 | 0.1070 | -0.1660 | 0.1130 | 0.1830 | 0.275 | 1 | 1 |
| 8 | -3.5180 | -0.0658 | -0.0167 | 0.2420 | 0.3000 | 0.0238 | 0.379 | 1 | 1 |
| 9 | -2.0770 | 0.1050 | 0.1180 | -0.2780 | 0.2160 | 0.0316 | 0.369 | 1 | 1 |
| 10 | -6.2470 | 0.3450 | 0.8330 | 0.4870 | -0.5970 | -0.1830 | 0.938 | 1 | 1 |
| 11 | -1.0590 | 0.2430 | -0.6420 | -0.0786 | -0.2050 | 0.1910 | -0.951 | -1 | -1 |
| 12 | -3.0900 | 0.0187 | -0.5990 | 0.2100 | -0.0894 | 0.0323 | -0.862 | -1 | -1 |
| 13 | 2.2590 | -1.2940 | -0.2860 | -0.2610 | -0.1850 | 0.2650 | -0.727 | -1 | -1 |
| 14 | -6.5120 | 0.3220 | -0.6920 | -0.2590 | 0.1780 | 0.4370 | -1.306 | -1 | -1 |
| 15 | 2.2500 | -2.4760 | -0.0601 | -0.2320 | -0.3630 | 0.2610 | -0.951 | -1 | -1 |
| 16 | -0.9040 | 0.9580 | -0.5490 | -0.2850 | 0.0365 | -0.1090 | -0.438 | -1 | -1 |
| 17 | 1.1180 | -0.1560 | -0.3270 | -0.7240 | 0.2460 | -0.2080 | -0.395 | -1 | -1 |
| 18 | -3.4160 | -0.1470 | -0.2280 | -0.2390 | -0.0240 | -0.0630 | -0.608 | -1 | -1 |
| 19 | -3.2530 | -0.4290 | -0.2160 | -0.4260 | 0.0084 | -0.0983 | -0.773 | -1 | -1 |
| 20 | 2.0300 | -1.7920 | -0.1390 | -0.9330 | -0.0915 | -0.5240 | -1.068 | -1 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed sample, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve, S
1Be model prediction rice paddy seed type, S
2Be actual rice paddy seed type; S
1And S
2In the row, 1 represents the non-transgenic paddy rice, and-1 represents transgenic paddy rice.
Can know through data analysis: calibration set coefficient of determination R
2 cBe 0.8578, calibration set root-mean-square error RMSECV is 0.3555, the PLS-DA model of foundation reaches 100% to the predictablity rate of calibration set paddy rice sample.
3, utilize the rice paddy seed type of model prediction checking collection rice paddy seed to be measured
Get 60 rice paddy seeds to be measured,, obtain 6 principal component scores of rice paddy seed to be measured, and carry it into and set up good PLS-DA model, obtain model output valve Y according to step (1)～(3) in 1 as checking collection; Through output valve Y, confirm the type of rice paddy seed to be measured according to decision principle in 2.As space is limited, only the data of 25 rice paddy seeds to be measured are listed in this, see table 6.
Rice paddy seed prediction rice paddy seed type to be measured and actual rice paddy seed type are concentrated in table 6 checking
| NO | X 1 | X 2 | X 3 | X 4 | X 5 | X 6 | Y | S 1 | S 2 |
| 1 | -3.4170 | -1.0060 | -0.7280 | 0.6610 | 0.0677 | -0.2440 | 0.858 | 1 | 1 |
| 2 | -0.9130 | -0.0728 | -0.0108 | 0.1660 | -0.0995 | 0.0692 | 0.787 | 1 | 1 |
| 3 | -3.6300 | -0.3270 | -0.0092 | -0.0052 | -0.1700 | -0.1260 | 0.994 | 1 | 1 |
| 4 | -0.9640 | -0.0178 | -0.1990 | -0.2020 | -0.0221 | -0.0748 | 0.713 | 1 | 1 |
| 5 | -1.0860 | -0.5690 | -0.2700 | 0.6380 | -0.3260 | 0.0469 | 0.616 | 1 | 1 |
| 6 | 8.0500 | -0.3310 | -0.3900 | -0.0776 | -0.2580 | 0.0654 | 1.431 | 1 | 1 |
| 7 | -3.0820 | 0.2200 | 0.5230 | -0.1130 | -0.0029 | 0.4140 | 0.375 | 1 | 1 |
| 8 | -3.4340 | 0.1150 | 0.0832 | -0.3410 | -0.2630 | 0.2880 | 1.216 | 1 | 1 |
| 9 | 0.0422 | 1.4380 | -0.3540 | -0.0101 | -0.5670 | 0.4380 | 0.998 | 1 | 1 |
| 10 | -0.2760 | 0.9760 | -0.5780 | 0.1790 | -0.1710 | 0.3150 | 0.953 | 1 | 1 |
| 11 | -4.3810 | 0.9250 | -0.0026 | -0.9750 | -0.4250 | 0.2840 | 0.880 | 1 | 1 |
| 12 | 0.5170 | 0.8000 | 0.6040 | 0.7250 | 0.0724 | 0.1790 | 0.324 | 1 | 1 |
| 13 | -4.3450 | -0.3570 | 0.4730 | 0.3960 | 0.1150 | -0.0927 | -0.537 | -1 | -1 |
| 14 | -3.5570 | 0.6740 | 0.4930 | -0.0930 | 0.0196 | -0.3320 | -1.026 | -1 | -1 |
| 15 | 3.2640 | 2.4540 | 0.1720 | 0.1120 | -0.0863 | -0.3330 | -0.754 | -1 | -1 |
| 16 | 3.7650 | 2.4540 | 0.8320 | -1.1410 | 0.1980 | -0.5470 | -1.203 | -1 | -1 |
| 17 | -0.9060 | 0.8170 | 0.2280 | 1.0120 | 0.1660 | 0.5320 | -1.083 | -1 | -1 |
| 18 | -0.1260 | 1.1780 | 0.2870 | 0.9450 | 0.2910 | 0.4300 | -1.299 | -1 | -1 |
| 19 | -2.6800 | 0.2390 | -0.3510 | 0.1930 | 0.1640 | 0.4000 | -1.048 | -1 | -1 |
| 20 | 3.0620 | 1.2720 | -1.0030 | 0.1120 | 0.6080 | 0.3440 | -1.242 | -1 | -1 |
| 21 | -0.6180 | -3.1850 | -0.0892 | -0.3880 | 0.7230 | 0.4070 | 0.045 | 1 | 1 |
| 22 | -4.3620 | -0.6470 | -0.0510 | -0.2630 | 0.4750 | 0.3470 | -0.709 | -1 | -1 |
| 23 | -0.9680 | 0.6720 | -0.3570 | -0.7200 | 0.0012 | 0.5530 | -0.550 | -1 | -1 |
| 24 | -1.5620 | 0.3000 | -1.0520 | -1.3440 | 0.4130 | 0.6250 | -0.768 | -1 | -1 |
| 25 | 0.1140 | 1.1700 | 0.6600 | -0.1340 | 0.0222 | -0.3180 | -1.524 | -1 | -1 |
Wherein, NO is meant the sequence number of rice paddy seed to be measured, X
1, X
2, X
3, X
4, X
5, X
6Respectively corresponding each principal component scores; Y is an output valve, S
1Be model prediction rice paddy seed type, S
2Be actual rice paddy seed type; S
1And S
2In the row, 1 represents the non-transgenic paddy rice, and-1 represents transgenic paddy rice.
Can know through data analysis: checking collection coefficient of determination R
2 pBe 0.8344, checking collection root-mean-square error RMSEP is 0.3543, the PLS-DA model of foundation reaches 100% to the catchment predictablity rate of rice of checking.
Claims (7)
1. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice is characterized in that, may further comprise the steps:
(1) be 4000～10000cm to rice paddy seed sample emission wave-number range
-1Near infrared spectrum, and gather the spectrum information that diffuses of all rice paddy seed samples;
(2) respectively the spectrum information that diffuses of all rice paddy seed samples is carried out pre-service, utilize PCA to extract the spectral signature information in the pretreated characteristic spectrum section, choose major component, and obtain the score of each major component;
(3) with corresponding each principal component scores of all rice paddy seed sample light spectrum informations as input, as output, set up model with the corresponding rice paddy seed type setting value of rice paddy seed sample;
(4) obtain each principal component scores of rice paddy seed spectrum to be measured according to the operation of step (1)～(2), carry it into model described in (3), obtain rice paddy seed type to be measured.
2. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice as claimed in claim 1 is characterized in that in the step (2), preprocess method is the standard normal converter technique.
3. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice as claimed in claim 1 is characterized in that in the step (2), said characteristic spectrum section is that wave-number range is 4000～10000cm
-1The spectrum section.
4. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice as claimed in claim 1 is characterized in that in the step (2), said characteristic spectrum section is that wave-number range is 4000～8000cm
-1The spectrum section.
5. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice as claimed in claim 1 is characterized in that in the step (2), number of principal components is 4～8.
6. like claim 3 or 4 described methods, it is characterized in that in the step (2), number of principal components is 5 based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice.
7. the method based near infrared spectrum discriminating transgenic paddy rice and non-transgenic paddy rice as claimed in claim 1 is characterized in that in the step (3), said model is the partial least squares discriminant analysis model.
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