CN1544921A - Non-destructive method for determining oil content in single peanut seed - Google Patents
Non-destructive method for determining oil content in single peanut seed Download PDFInfo
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Abstract
The invention is a nondestructive method of determining the oil content in a single peanut seed, based on Fourier transform near-infrared spectrometry and combined with chemical metrology method, adopting diffuse reflection determining mode, and using single seed of multigene peanut as sample to establish a multielement recurrent mathematical model, and then forecasting the oil content in the unknown sample by this model. It is nondestructive without any preprocessing of the sample and does not damage activity and tissue structure of the seed. It has simple operation, fast determining speed and higher determining accuracy. It is applied to high-oil quality peanut breeding, seed quality resources evaluation and research on genetic law.
Description
Technical field
The present invention relates to the quantitative analysis tech field of peanut seed quality component, specifically be meant a kind ofly use the Fourier transform near infrared spectrum analytical technology, in conjunction with the method for the nondestructively measuring peanut single seed oleaginousness of modern chemistry metrology method.
Background technology
The conventional chemical method of measuring the peanut seed oleaginousness generally adopts the Soxhlet method.The sampling mode of this assay method and sample pretreating method are destructive, also have time and effort consuming, loaded down with trivial details, the more high shortcoming of cost of determination of mensuration program.In peanut quality breeding and genetic research, very need analytical approach real-time fast, simple to operate, that do not destroy sample.Be in the peanut quality breeding of purpose to improve oleaginousness, press for the analytical technology that detects to the segregating generation non-destructive peanut single seed oleaginousness in early days, make the breeder select the high oiliness shape in that variation is from generation to generation directed, thus make high oil base because of added up and can with high yield, excellent genes integration such as disease-resistant.With doubting, the non-destructive analysis technology has not become the bottleneck of peanut high-oil breeding research, seriously hinders the raising of breeding efficiency.
Over past ten years, because the combination of near-infrared spectrum technique, Chemical Measurement and computer software technology, near-infrared spectrum technique is developed on the attributional analysis of natural complex material rapidly, range of application more and more widely, and is and fast, easy and simple to handle and to characteristics such as the non-destructives of sample and come into one's own day by day with its finding speed.Though it is a lot of to use the report of near-infrared spectrum technique anal yzing agricul products quality component, not seeing up to now has the report of using near-infrared spectrum technique nondestructively measuring peanut single seed oleaginousness.
Summary of the invention
Purpose of the present invention is exactly in order to solve above-mentioned the deficiencies in the prior art part, and a kind of method of real-time fast, simple to operate, nondestructively measuring peanut single seed oleaginousness is provided.
The method of a kind of nondestructively measuring peanut single seed oleaginousness of the present invention is characterized in that it comprises the steps:
The first step selects representational peanut single seed as the standard model collection of setting up the multiple regression forecasting model;
Second step was used the diffuse reflection spectrum of each peanut single seed sample in ft-nir spectrometer and the light transmitting fiber solid probe bioassay standard sample sets;
The 3rd step was adopted the oleaginousness of each peanut single seed sample in the remaining method bioassay standard of the Soxhlet sample sets, as with sample sets near infrared spectrum chemical score one to one;
The 4th step adopted multivariate calibration methods to set up and optimized the multiple regression forecasting model, by the more various coefficient of determination (R that may make up down forecast model
2) and root-mean-square deviation (RMSECV), choose R
2The big as far as possible and as far as possible little combination of RMSECV, RMSECV and R
2Determine in such a way:
Wherein, Differ
iBe the poor of the chemical score of i sample and predicted value, M is a sample number, y
iBe the chemical score of i sample, y
mMean value for M sample predicted value;
The 5th step was imported forecast model with spectrum, thereby determines the oleaginousness of unknown sample according to the near infrared spectrum of the method collection unknown sample in second step.
In order to realize the present invention better, another inventive point of the present invention is to solve the problem of seeking representative sample difficulty with the substep modeling, can save the time and the expense of modeling.Peanut seed is the natural organic substance of complex chemical composition, can't resemble the sample of the various concentration gradients of random artificial preparation the pure chemistry material, can only from wide variety of materials, screen representational sample, and adopting the conventional chemical method to measure peanut single seed oleaginousness time and effort consuming, workload is very big and effect is low.The concrete steps of substep modeling are: select 50～60 duplicate samples materials at random earlier, set up a basic model earlier by above-mentioned 1～4 step; Use basic model and from wide variety of materials, seek and screen 50～60 parts in representational sample, measure its near infrared spectrum and oleaginousness chemical score, it is joined in the basic model as modeling sample, and 4 method is carried out modeling and optimization set by step, obtains first Optimization Model; Repeat above-mentioned steps again, obtain second, the 3rd and more Optimization Model, up to obtaining satisfied forecast model.
Principle of the present invention is, based on Fourier transform near-infrared diffuse reflectance technology, in conjunction with modern chemistry metrology method, adopt the diffuse reflection measuring mode, with the peanut single seed of several genes type as sample background, set up the multiple regression forecasting mathematical model by chemometrics method, again by the oleaginousness of model determination unknown sample.
The present invention is applicable to that the non-destructive that can be used for segregating generation single seed oleaginousness detects, and also is applicable to the evaluation utilization of rule research of peanut oil content character inheritance and germ plasm resource to improve the quality breeding research that the peanut oleaginousness is a purpose.
The present invention compared with prior art has following advantage and beneficial effect:
1. the present invention is a kind of nondestructive analytical approach, is determination object with complete peanut single seed, but single seed goes out its oleaginousness without any need for pre-service with regard to fast detecting, and the vigor and the institutional framework of seed are not had any damage.
2. the inventive method is simple to operate, cost of determination is low, and finding speed is fast, and therefore one minute only consuming time of sample can handle a large amount of samples in a short time.
3. the mensuration accuracy of the inventive method is higher, can satisfy the requirement of peanut high-oil breeding.
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 is the structural representation of the diffuse reflection spectrum determinator of the embodiment of the invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is done detailed description further.
As shown in Figure 1, the flow process of the embodiment of the invention is as follows:
The selection of first step modeling sample collection
Adopt the method for substep modeling to seek representative sample, select the representational different genotype peanut of some single seed as the standard model collection of setting up model, the quantity of sample is to be no less than 150 parts for well, and present embodiment is selected altogether and collected 230 samples as the standard model collection.Peanut single seed material source belongs to the pearl beans type in 200 different cultivars or strain in classification; Material source also comprises different year and the season and the different place of production.The amplitude of variation of sample sets oleaginousness is 30.80%～66.89%, and the concentration gradient space distribution is reasonable.Dry peanut seed under field conditions (factors), the water cut of seed remains on below 8%.The said sample representativeness of the present invention is meant the variation of variation, time (time and season) and space (source, the place of production) distribution of sample genetic background.The representational quality of sample sets has a significant impact stability, the adaptability of forecast model.The material type of sample sets is The more the better, and the oleaginousness amplitude of variation is the bigger the better, and the space distribution of concentration gradient is even more good more.
The mensuration of the second step peanut single seed near infrared spectrum
The VECTOR 22/N type ft-nir spectrometer that present embodiment uses German Brooker company to produce, instrument parameter is set to: the scanning spectrum district is 4000～10000cm
-1, scanning times is 64 times, resolution is 8cm
-1, be background with the white pottery porcelain.The mensuration of sample near infrared spectrum is to finish on peanut single seed diffuse reflection spectrum determinator.
The collection of peanut single seed near infrared spectrum utilizes the light transmitting fiber solid probe, carries out in the diffuse reflection mode, can finish on the diffuse reflection spectrum determinator.The structure of the custom-designed diffuse reflection spectrum determinator that present embodiment provides as shown in Figure 2, vertical fixing has fixed support 1 on its base 10, the platform 8 that 2 geometrical clamps 3 is installed on fixed support 1 successively and can moves horizontally up and down, platform 8 is manually controlled by knob 9, the head that connects the solid probe 4 of light transmitting fiber 2 is installed on the fixed support 1 by geometrical clamp 3 vertically downward, on platform 8, white pottery porcelain 7 is installed over against solid probe 4, be fixed with sample packing ring 6 on the white pottery porcelain 7, place single seed sample 5 in it.
Dress quadrat method: on moveable platform, lay the white pottery porcelain background of surface smoothing, seed is placed on it, below seed, can place sizeable packing ring, prevent that seed from rolling; The cotyledon back side of seed upwards.
Assay method: utilize platform to move up and down to make middle part, the seed cotyledon back side to contact with vertical solid probe head, the instruction spectrometer begins scanning, promptly finish scanning in 1 minute, obtain the diffuse reflection near infrared spectrum of sample, scan period does not move or vibrating example and solid probe.
The mensuration of all samples near infrared spectrum is all carried out in strict accordance with above-mentioned dress quadrat method and assay method.
The mensuration of the 3rd step peanut single seed oleaginousness
Adopt the oleaginousness of each sample in the conventional chemical method bioassay standard sample sets, as with sample sets near infrared spectrum chemical score one to one.Adopt the remaining method of Soxhlet to measure the single seeded oleaginousness of peanut, with the ether extracting, the concrete operations step is with reference to standard GB 2909-82 in YG-2 type fat extractor.Strict control error at measurment, the standard deviation of measurement result are controlled at about 1% for well.
The foundation and the optimization of the 4th step mathematical prediction model
The multivariate calibration methods of setting up the mathematical prediction model employing can be offset minimum binary algorithm (PLS), principal component regression method (PCR), contrary least square method (ILS) and multiple linear regression method (MLR) etc.The most ripe, most widely used with partial least square method (PLS) at present.Adopt offset minimum binary algorithm (PLS) to set up the multiple regression forecasting mathematical model.The OPUS4.2 version quantitative analysis software that present embodiment is used German Brooker company carries out the correction and the optimization of multivariate regression model, can adopt other business-like quantitative analysis softwares of the same type to finish equally.
Standard model is concentrated the near infrared spectrum and the corresponding one by one input OPUS of the oleaginousness chemical score quantitative analysis software of each sample.Automatic searching that utilization is provided with in OPUS software and optimizational function are sought the top condition of setting up model, by the more various coefficient of determination (R that may make up down forecast model
2) and root-mean-square deviation (RMSECV), choose R
2The big as far as possible and as far as possible little combination of RMSECV.Adopt internal chiasma to confirm mathematical prediction model is verified.Internal chiasma confirms to be meant that rejecting modeling sample successively concentrates (or a plurality of) sample, comes the content of the disallowable sample of modeling and forecasting with remaining sample, and the difference of more disallowable sample predicted value and chemical score is judged the forecasting accuracy of institute's established model thus.
RMSECV and R
2Determine by following mode:
Wherein: Differ
iRepresent the poor of the chemical score of i sample and predicted value, M is a sample number, y
iBe the chemical score of i sample, y
mMean value for M sample predicted value.
Owing to error in conventional chemical analysis and near infrared ray, unavoidably occurs, when modeling, need carry out significance test on the statistics to the sample near infrared spectrum, this function is all arranged in the commercial software.When F checked, it was 0.99 that the unusual threshold value of check (F probable value) is set, and the chemical score of each modeling sample and the error between the predicted value and average error are compared, and obtained corresponding F value and F probable value.Through the check of F value, reject error significant abnormal sample.In addition, the number of sample sets is not The more the better.According to the principle of " fewer but better ", modeling sample is selected and optimized.Guaranteeing under the prerequisite that sample sets oleaginousness luffing is big, concentration gradient is evenly distributed, rejecting, avoiding space distribution the cavity to occur simultaneously as far as possible at the excessive intensive sample of space distribution.
Through testing repeatedly relatively, the top condition that peanut single seed oleaginousness multiple regression forecasting model is set up in the present embodiment acquisition is: best major component dimension is 15, and optimized spectrum district scope is 10000～6098cm
2With 4601～4247cm
2, the optimal spectrum preprocess method is first order derivative+straight-line decline (carries out first order derivative earlier and handle, carry out straight-line decline again and handle).The forecast model of Jian Liing has very high quality therefrom, the coefficient of determination (R of model
2) be 0.9785, error at measurment (RMSECV) is 1.16%, is close with the error at measurment 1.15% of the remaining method of Soxhlet.
The 5th step application model is measured unknown sample
Set up after the mathematical prediction model, just can measure the oleaginousness of unknown sample (complete peanut single seed).Repeat the near infrared spectrum of second step collection unknown sample, spectrum is imported forecast model, computing machine provides the oleaginousness of unknown sample immediately.
The oleaginousness of 30 peanut single seeds of model determination sample of setting up with present embodiment adopts the remaining method of Soxhlet to measure its oleaginousness as a comparison simultaneously, and measurement result is listed in table 1.By the result as seen, the measurement result of two kinds of methods is very close, and absolute error and relative error are all less, shows that the inventive method has higher mensuration accuracy.As a kind of non-destructive and assay method fast, practical measurement result is undoubtedly acceptable.
This example of table 1. is measured the effect of peanut single seed oleaginousness
| Sample number into spectrum | The inventive method % | The remaining method % of Soxhlet | Absolute error % | Relative error % |
| ????1# | ????46.20 | ????45.61 | ????-0.59 | ????-1.29 |
| ????2# | ????46.44 | ????47.65 | ????1.21 | ????2.54 |
| ????3# | ????46.96 | ????47.18 | ????0.22 | ????0.46 |
| ????4# | ????47.33 | ????47.96 | ????0.62 | ????1.30 |
| ????5# | ????47.50 | ????45.55 | ????-1.95 | ????-4.29 |
| ????6# | ????48.80 | ????49.32 | ????0.52 | ????1.05 |
| ????7# | ????49.25 | ????49.92 | ????0.67 | ????1.34 |
| ????8# | ????49.53 | ????50.86 | ????1.33 | ????2.62 |
| ????9# | ????49.56 | ????51.24 | ????1.68 | ????3.28 |
| ????10# | ????49.66 | ????51.02 | ????1.36 | ????2.66 |
| ????11# | ????49.88 | ????50.51 | ????0.63 | ????1.24 |
| ????12# | ????50.12 | ????51.97 | ????1.85 | ????3.56 |
| ????13# | ????50.28 | ????50.58 | ????0.30 | ????0.59 |
| ????14# | ????51.28 | ????52.00 | ????0.71 | ????1.37 |
| ????15# | ????51.41 | ????54.01 | ????2.60 | ????4.81 |
| ????16# | ????52.21 | ????51.62 | ????-0.59 | ????-1.13 |
| ????17# | ????52.80 | ????55.61 | ????2.82 | ????5.07 |
| ????18# | ????52.80 | ????53.67 | ????0.87 | ????1.63 |
| ????19# | ????53.19 | ????55.63 | ????2.43 | ????4.38 |
| ????20# | ????53.57 | ????55.03 | ????1.46 | ????2.65 |
| ????21# | ????53.66 | ????53.73 | ????0.07 | ????0.14 |
| ????22# | ????53.70 | ????56.33 | ????2.64 | ????4.68 |
| ????23# | ????54.68 | ????56.76 | ????2.07 | ????3.65 |
| ????24# | ????55.79 | ????56.85 | ????1.07 | ????1.88 |
| ????25# | ????44.07 | ????42.82 | ????-1.25 | ????-2.92 |
| ????26# | ????43.56 | ????46.04 | ????2.48 | ????5.38 |
| ????27# | ????41.86 | ????44.19 | ????2.33 | ????5.28 |
| ??28# | ????39.60 | ????42.00 | ????2.40 | ????5.71 |
| ??29# | ????35.49 | ????38.43 | ????2.94 | ????7.65 |
| ??30# | ????38.11 | ????37.52 | ????-0.58 | ????-1.56 |
As mentioned above, can realize the present invention preferably.
Claims (9)
1. the method for a nondestructively measuring peanut single seed oleaginousness is characterized in that it comprises the steps:
The first step selects representational peanut single seed as the standard model collection of setting up the multiple regression forecasting model;
Second step was used the diffuse reflection spectrum of each peanut single seed sample in ft-nir spectrometer and the light transmitting fiber solid probe bioassay standard sample sets;
The 3rd step was adopted the oleaginousness of each peanut single seed sample in the remaining method bioassay standard of the Soxhlet sample sets, as with sample sets near infrared spectrum chemical score one to one;
The 4th step adopted multivariate calibration methods to set up and optimized the multiple regression forecasting model, by the more various coefficient of determination R that may make up down forecast model
2With root-mean-square deviation RMSECV, choose R
2The big as far as possible and as far as possible little combination of RMSECV, RMSECV and R
2Determine in such a way:
Wherein, Differ
iBe the poor of the chemical score of i sample and predicted value, M is a sample number, y
iBe the chemical score of i sample, y
mMean value for M sample predicted value;
The 5th step was imported forecast model with spectrum, thereby determines the oleaginousness of unknown sample according to the near infrared spectrum of the method collection unknown sample in second step.
2. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1, it is characterized in that, the substep modeling is adopted in the single seeded selection of described representational peanut, selects 50～60 duplicate samples materials at random earlier, sets up a basic model; Use basic model and from wide variety of materials, seek and screen 50～60 parts in representational sample, measure its near infrared spectrum and oleaginousness chemical score, it is joined in the basic model as modeling sample, carry out modeling and optimization, obtain first Optimization Model by the method in the 4th step; Repeat above-mentioned steps again, obtain second, the 3rd and more Optimization Model, up to obtaining satisfied forecast model.
3. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1, it is characterized in that, the mensuration of described diffuse reflection spectrum adopts as lower device: a vertical fixing has support bracket fastened base, the platform that geometrical clamp is installed on fixed support successively and can moves horizontally up and down, platform is by knob controlling, the head that connects fibre-optic solid probe is installed on the fixed support by geometrical clamp vertically downward, on platform, the white pottery porcelain is installed over against solid probe, be fixed with the sample packing ring on the white pottery porcelain, place the single seed sample in it.
4. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 3 is characterized in that described solid probe head directly contacts the middle part at the seed cotyledon back side.
5. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1 is characterized in that, when measuring diffuse reflection spectrum, the cotyledon back side that the single seeded dress quadrat method of described peanut is a seed upwards.
6. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1 is characterized in that, the optimized spectrum district scope of setting up the multiple regression forecasting model is 10000～6098cm
2With 4601～4247cm
2
7. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1 is characterized in that, the optimal spectrum preprocess method of setting up the multiple regression forecasting model is to carry out first order derivative earlier to handle, and carries out straight-line decline again and handles.
8. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1 is characterized in that, described unknown sample is complete peanut single seed.
9. the method for a kind of nondestructively measuring peanut single seed oleaginousness according to claim 1 is characterized in that, described multivariate calibration methods comprises offset minimum binary algorithm, principal component regression method, contrary least square method, multiple linear regression method.
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| WO2010108312A1 (en) * | 2009-03-23 | 2010-09-30 | 山东省花生研究所 | A breeding method of peanut with high content of oleic acid and high yield |
| CN102072883A (en) * | 2010-07-07 | 2011-05-25 | 北京农业智能装备技术研究中心 | Device and method for detecting comprehensive quality of crop seeds |
| CN102128807A (en) * | 2010-12-24 | 2011-07-20 | 江苏大学 | Method for quickly detecting concentration of droplet on crop leaf |
| CN102177842A (en) * | 2010-12-06 | 2011-09-14 | 山东省花生研究所 | Breeding method for once measuring quality of peanuts selected by multiple indexes |
| CN101887018B (en) * | 2009-05-13 | 2012-01-25 | 山东省花生研究所 | Method for nondestructively measuring main fatty acid content of peanut seeds |
| CN102590129A (en) * | 2012-01-11 | 2012-07-18 | 中国农业科学院农产品加工研究所 | Method for detecting content of amino acid in peanuts by near infrared method |
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| CN101887018B (en) * | 2009-05-13 | 2012-01-25 | 山东省花生研究所 | Method for nondestructively measuring main fatty acid content of peanut seeds |
| CN102072883B (en) * | 2010-07-07 | 2013-03-13 | 北京农业智能装备技术研究中心 | Device and method for detecting comprehensive quality of crop seeds |
| CN102072883A (en) * | 2010-07-07 | 2011-05-25 | 北京农业智能装备技术研究中心 | Device and method for detecting comprehensive quality of crop seeds |
| CN107583878B (en) * | 2010-09-20 | 2020-03-06 | 辛根塔有限公司 | Improved method for obtaining substantially pure hybrid cereal seeds and machine therefor |
| CN107583878A (en) * | 2010-09-20 | 2018-01-16 | 辛根塔有限公司 | For obtaining the improved method of substantially pure hybrid cereal seed and the machine for it |
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| CN102759515A (en) * | 2012-04-26 | 2012-10-31 | 江苏大学 | Method for rapidly determining oil contents of agricultural products by using mid-infrared spectrometer based on horizontal attenuated total reflection (ATR) |
| CN105758843A (en) * | 2016-04-19 | 2016-07-13 | 长江大学 | Oil crop seed oil content measurement method based on laser-induced breakdown spectroscopy |
| CN105758843B (en) * | 2016-04-19 | 2018-11-27 | 长江大学 | A kind of oil crop seeds oil-containing quantity measuring method based on laser induced breakdown spectroscopy |
| CN106092961A (en) * | 2016-07-28 | 2016-11-09 | 深圳市芭田生态工程股份有限公司 | A kind of method of chemical composition in quick detection agricultural product |
| CN108801973A (en) * | 2018-06-28 | 2018-11-13 | 中国农业科学院农产品加工研究所 | Utilize the near-infrared method of main component in simple grain detection fitting detection peanut |
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| CN110231303A (en) * | 2019-06-10 | 2019-09-13 | 江南大学 | A kind of method of the odd sub- seed content of ashes of quick measurement |
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| Publication number | Publication date |
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| CN100419409C (en) | 2008-09-17 |
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