EP3488234A1 - Procédé de prédiction de la capacité de germination d'une graine de maïs par résonance magnétique nucléaire - Google Patents

Procédé de prédiction de la capacité de germination d'une graine de maïs par résonance magnétique nucléaire

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Publication number
EP3488234A1
EP3488234A1 EP17739615.7A EP17739615A EP3488234A1 EP 3488234 A1 EP3488234 A1 EP 3488234A1 EP 17739615 A EP17739615 A EP 17739615A EP 3488234 A1 EP3488234 A1 EP 3488234A1
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EP
European Patent Office
Prior art keywords
seed
nmr
germinating
seeds
lot
Prior art date
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EP17739615.7A
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German (de)
English (en)
Inventor
Kareine Vachelard
Nicolas HESLOT
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Vilmorin SA
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Vilmorin SA
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Publication of EP3488234A1 publication Critical patent/EP3488234A1/fr
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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01C—PLANTING; SOWING; FERTILISING
    • A01C1/00—Apparatus, or methods of use thereof, for testing or treating seed, roots, or the like, prior to sowing or planting
    • A01C1/02—Germinating apparatus; Determining germination capacity of seeds or the like
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/28—Details of apparatus provided for in groups G01R33/44 - G01R33/64
    • G01R33/30—Sample handling arrangements, e.g. sample cells, spinning mechanisms
    • G01R33/307—Sample handling arrangements, e.g. sample cells, spinning mechanisms specially adapted for moving the sample relative to the MR system, e.g. spinning mechanisms, flow cells or means for positioning the sample inside a spectrometer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/448—Relaxometry, i.e. quantification of relaxation times or spin density
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/46—NMR spectroscopy
    • G01R33/465—NMR spectroscopy applied to biological material, e.g. in vitro testing

Definitions

  • the present invention relates to a non-destructive method for predicting the germinating profile of maize seeds, using low field Nuclear Magnetic Resonance. Furthermore, the invention relates to a method for improving the germination ability of a maize seed lot, and a method for predicting the germination ability of a maize seed lot.
  • Estimation of the germination ability of a seed lot may be performed for a variety of reasons. Mainly, it may be applied to determine germination ability of a seed lot or distinguish and select seeds that are able to develop into normal seedling under favorable conditions.
  • the germination ability of a commercial seed lot is critical. For maize, it should be above 90% for a seed lot to be commercialized.
  • this value is measured by the official methods of the International Seed Testing Association (ISTA) protocols.
  • the seeds are placed in moistened filter paper of a defined quality under controlled humidity and temperature conditions over a specific period of time. After this time, the seeds that have germinated are counted manually by trained expert people.
  • the main disadvantages related to the use of such tests are their time, space and electrical energy consuming. It further needs manual handling and specific training and it is destructive.
  • Low-field NMR relaxometry is a nondestructive method, which is used to investigate water contents, mobility and interactions in matrix systems, such as plant organs. Deconvolution of the multiexponential curves obtained from longitudinal (Tl) and transverse (T2) relaxations allow identifying groups and proportions of water protons that share environments, interactions or molecular exchange rates. NMR relaxation time measurements have been used to characterize the water status of certain plant seeds during germination. In all of the studies already performed, NMR relaxation time measurements have identified three components of the water proton system.
  • the germination ability of soybean and wheat seeds are linked to storage condition (humidity and temperature) and NMR can be used to follow or detect seed affected by unappropriate storage conditions.
  • Poulinquen D. et al. have reviewed the use of NMR on seed characterization (Poulinquen D. et al., Utilisation de la RMN en analyse de semences, communication Malawi actes, colloque biologie et qualite des semences, Angers decembre 98, presses de l'universite d'Angers (France) p 35-42), leading to the conclusion that a best knowledge of molecular dynamics and physiological and pathological modification may help to the nondestructive evaluation of physical and germinative quality of seed.
  • the structure and the composition of the seed vary so much (for example, the proportion embryo/albumen, or the composition in water, starch, etc.) that NMR experiments with one plant species could not automatically be extrapolated to other plant species.
  • NMR Nuclear Magnetic Resonance
  • step (b) predicting the germinating profile of the seed based on the measurement(s) of step (a), using an appropriate mathematical model.
  • said one or more low field NMR parameter(s) is/are selected among the low field NMR parameter(s) which predicts the germinating profile. More specifically, said one or more low field NMR parameter(s) is/are advantageously selected among those which present a correlation coefficient with the germination ability of the seed, comprised between [-1; -0.6] and [0.6; 1].
  • said one or more low field NMR parameter(s) is/are selected among the following:
  • said one or more low field NMR parameter(s) is/are selected among the following:
  • the measurement(s) of step a) may be further completed by measurement(s) selected among visible spectroscopy, X ray 2D and 3D, fluorescence, and multi and hyperspectral spectroscopy.
  • the measurements can be combined and used in step b) for the prediction of a single germinating profile, or the prediction of the germinating profile can be performed multiple times using each data from the different methods.
  • measurement(s) of Near Infrared spectroscopy is/are performed on the seed before or after the step a) and the germinating profile is predicted based on the combined measurements ofNMR parameter(s) and RS parameter(s).
  • the seeds are calibrated by size before the measurements.
  • the method for improving the germination ability of a maize seed lot comprises
  • Another aspect of the present disclosure relates to a method for predicting the germination ability of a maize seed lot, comprising the steps of (i) obtaining a random sample of seeds from said seed lot, (ii) applying the above-described predicting method to said random sample of seeds, thereby obtaining a predicted germination ability value, and (iii) assessing the germination ability of the seed lot based on the predicted germination ability values of the random sample of seeds.
  • the seed lot with low predicted germination ability is further analyzed to determine its germination ability by other non-NMR technique.
  • said other non-NMR technique includes NIR spectroscopy analysis of each seed of the random sample.
  • Another aspect of the present disclosure relates to an apparatus for carrying out the methods as defined above.
  • the apparatus of the present disclosure may comprise:
  • a detector for detecting at least the NMR signal sequence having interacted with the seed or seed lot
  • a data processing device for determining one or more NMR parameter(s), and more preferably one or more NMR parameter(s) selected among the following:
  • the apparatus comprises:
  • an individualization of seed system (2) 2) optionally, an individualization of seed system (2), 3) an NMR device for detecting one or more NMR signal sequence(s) having interacted with an individualized seed or seed lot (3),
  • a first aspect of the present disclosure relates to a method for predicting the germinating profile of a maize seed, said method comprising:
  • NMR Nuclear Magnetic Resonance
  • step (b) predicting the germinating profile of the seed, based on the measurement(s) of step (a), using an appropriate mathematical model.
  • Germination refers to the first stage in the development of a plant from a seed, in particular the time at which the radicle breaks through the seed coat. Germination of the seed in a laboratory test is defined as the emergence and development of a seedling to a stage where the aspect of its essential structure indicates whether or not it is able to develop further into a satisfactory plant under favorable conditions in the field.
  • the term "germinating profile" of a seed refers to the classification of a seed, whether or not, it is a germinating seed, i.e. it will germinate and develop further into a satisfactory plant under favorable conditions in the field, or a non- germinating seed, i.e. it will not be able to germinate or it will not develop further into a satisfactory plant under favorable conditions in the field, as defined by the ISTA rules.
  • the method enables to determine the probability for a seed to have a certain germinating profile. More specifically, the method may allow to predict whether a seed is germinating or non-germinating as defined by the ISTA rules. The prediction is therefore not certain but associated to a specific probability, based on the mathematical method and the chosen threshold. It has been surprisingly found out that the measurement of a number of low field NMR parameters correlates with high significance with the germinating profile of a seed and/or the germination ability of a seed lot.
  • the method therefore provides one essential step of measuring one or more of such low field NMR parameter(s) on a maize seed, especially 'lI-NMR parameters.
  • the one or more low field NMR parameter(s) for use in the above defined method is/are selected among the low field NMR parameters which can correlate with the germinating or non-germinating profile, as determined by the ISTA protocols, with a correlation coefficient comprised between [-1; -0.6] and [0.6; 1].
  • said one or more low field NMR parameter(s) are selected among the following:
  • said one or more low-field NMR parameter(s) are selected among the following:
  • the skilled person will know how to measure these NMR parameters, which measurement methods can be classical NMR sequences such as those described for example in Singh K. and B. Blumich. NMR spectroscopy with compact instruments. Trends in Analytical Chemistry. Jan. 1, 2016; or, B. Blumich “Essential NMR” for Engineers, 2005, Springer Berlin Heidelberg publisher.
  • the parameter T2 represents the spin-spin relaxation times or transversal relaxation time.
  • the parameter T2(l) represents the spin-spin relaxation times of the protons having short relaxation time.
  • the parameter T2(2) represents the spin-spin relaxation times of the protons having long relaxation time.
  • the parameter FID Free Induction Decay
  • the parameter Ampl represents the amplitude of T2.
  • the parameter A(l) represents the amplitude of T2(l).
  • the parameter A(2) represents the amplitude of T2(2).
  • P represents the mass of the maize seed or of the sample of seeds.
  • the step of measuring one or more low field NMR parameter(s) will include:
  • a radio frequency pulse to a maize seed or a maize seed lot in the presence of an external low field magnetic field, for example a 20MHz field,
  • the conditions for acquiring the Tl and/or T2 NMR measurement(s) or other NMR parameters such as FID (Free Induction Decay) from the maize seed can be standard.
  • the T2 measurements are obtained using the sequence as defined by Carr-Purcell-Meiboom-Gil (Snarr J. E. M. et H. Van As, 1992, Biophys. J. 63: 1654-1658).
  • the Tl, T2, FID and other NMR parameter measurements are obtained as described in detail in Examples 1 and 2.
  • the method of the present disclosure may then predict the germinating profile based on the obtained low field NMR parameter values, as obtained from a maize seed, using an appropriate mathematical model.
  • the model may be based on a binary feature (germinating or non germinating), which model can be calibrated with a calibration dataset (i.e. control seeds with known NMR parameters values and germinating profile).
  • J is a binary response variable ("germinating profile")
  • X is a matrix containing predictors variables: variables accounting for the experimental design, NMR, NIRS variables...
  • a is a vector of effects for those variables and ⁇ is a residual
  • Predictions y hat from this model are probability of germinating (between 0 and 1).
  • a threshold c is preferably set such that if ⁇ C for a given seed, it is predicted to germinate, otherwise it is predicted not to germinate.
  • this threshold is set to 0.5. It can be modified to increase the detection power of non-germinating seed, at the cost of a higher false positive rate.
  • This mathematical model is e.g. a Lasso regression, which is an analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
  • Lasso regression is an analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
  • a key feature of the Lasso is that it performs automatic feature selection such that the obtained model can be very sparse (with few coefficients in a different from 0).
  • the binary feature for the germinating profile is "germinating" or “non-germinating” seed in real seed storage conditions of humidity and temperature, preferably according to ISTA rules.
  • the above prediction method further comprises a measurements of Near Infrared spectroscopy performed on the seed before or after the step of measurements of said one or more NMR parameter(s).
  • a method for predicting the germinating profile of a maize seed comprising:
  • NMR Nuclear Magnetic Resonance
  • NIRS Near infra-red spectroscopy
  • steps (a) and (b) is not essential.
  • said Near infra-red spectroscopy parameter(s) correspond to the measurement obtained on a maize seed at one or more of the following wavelengths: 1422 nm, 1896 nm, 1898nm, 1176nm, 1184nm, 1188 nm, 1678 nm, 1940 nm, 1944 nm, and 1968 nm.
  • near infra-red wavelengths used for the measurement are those of the area 1 to 4 in the table 14, or wavelengths with the higher effect as shown in figures 7 and 9.
  • the wavelengths with the higher effect as shown in figures 7 and 9 also include VIS.
  • said one or more low-field NMR parameter(s) is/are selected among the following:
  • the NMR measurements of step a) of any of the above methods are further completed by measurement(s) selected among visible spectroscopy, fluorescence, 2D & 3D Xray, multi and hyperspectral imaging etc... Obviously more than one of these techniques can be used in addition to NMR measurements.
  • the method for predicting the germinating profile of a maize seed comprises the following steps:
  • NMR Nuclear Magnetic Resonance
  • step (b) predicting the germinating profile of the seed, based on the measurement(s) of step (a), using an appropriate mathematical model.
  • step (c) predicting the germination ability of the maize seed lot, based on the measurements of step (b), using an appropriate mathematical model.
  • the term "germination ability" of the maize seed lot means the germination level (percentage) of seed lot, under favorable conditions, according to the ISTA protocols.
  • This method for predicting the germination ability of a maize seed lot can represent a complement to official ISTA protocols, leading to reliable and faster results.
  • a previous step of seed sampling from the seed lot is needed and defined by the ISTA rules.
  • the seed lot germination ability is usually an estimation rate predicted based on the real germination observed on the seed sample according to ISTA rules.
  • the seed lot germination ability is predicted based on the predicted germination ability of the random sample.
  • this method can thus be performed before the official one, for predicting seed lot certification.
  • the ISTA protocol may be further used for confirming of infirming the first result as ISTA protocol is internationally used and even compulsory for seed lot certification (finished product stage).
  • the size of the random sample will depend on the required accuracy of the results. It will generally include between 10 and 500 seeds, for example between 20 and 200 seeds. For example, the measurements may be performed on 25, 100 or 200 maize seeds.
  • the prediction of the germination ability is based on the measurements of low-field NMR parameters, optionally combined with NIRS parameters, for each seed of the random sample and the prediction of their germinating profile as described in the previous sections.
  • the prediction is based on the measurement(s) of low-field NMR parameter(s), on the maize seed lot as a whole.
  • the method is the same as the one performed on an individualized seed except that the low field NMR measurement is obtained on the random sample of maize seed lot, as a whole (in one measurement step).
  • the one or more low field NMR parameter(s) for use in the above defined method is/are selected among the low field NMR parameters which can correlate with the germination ability of a maize seed lot as determined by the ISTA protocols, with a correlation coefficient comprised between [-1; -0.6] and [0.6; 1].
  • said one or more low- field NMR parameter(s) is/are selected among the following: (i) T2,
  • said low-field NMR parameter(s) is/are selected among the following:
  • a suitable mathematical model is applied to obtain a germination ability value of the sample of the maize seed lot, which correlates with the low-field NMR parameter(s) (optionally combined with NIRS parameter(s)) obtained on said sample of maize seed lot.
  • Such germination ability value is also an estimate of the maize seed lot, as obtained from a random sample.
  • the seed lot with low predicted germination ability is further analyzed to determine its germination ability by other non-NMR technique.
  • Determination of germination ability can be also done at any step of the seed process, including, for example, immediately following accidental damage on seed, or on aged seed lots, or on stored seeds.
  • a method for improving the germination ability of a maize seed lot comprises
  • the step (i) of the above method is performed as described in the previous sections.
  • the sorting step (ii) may be manual, semi-automatic or automatic.
  • the term "improving the germination ability” means that the maize seed lot obtained after the method has a higher germination ability than the maize seed lot obtained before carrying out the method.
  • the method is applied to a maize seed lot with an initial germination ability which is below 0.90 (90%).
  • the method is adjusted so that the predicted germination ability is increased to a level at least higher than 0.90 (90%), preferably, at least higher than 0.95% (95%).
  • the low field NMR parameter(s) is/are selected among either (i) the T2 parameter, (ii) a combination of T2 and FID parameters, or (iii) all the NMR parameters without T2 parameter.
  • the method for improving the germination ability of a maize seed lot comprises the following steps:
  • step (iii) predicting the germinating profile of each seed, using an appropriate mathematical model combining the measurements of step (i) and (ii), and (iv) sorting the seeds by, either, retaining in the seed lot the seeds which are predicted as germinating seed, or, discarding in the seed lot the seeds which are predicted as non-germinating seed,
  • the method for improving the germination ability of a maize seed lot comprises the following steps:
  • step (iii) predicting the germinating profile of each seed, using an appropriate mathematical model combining the measurements of step (i) and (ii), and
  • J is a binary response variable ("germinating profile")
  • X is a matrix containing predictors variables: variables accounting for the experimental design, NMR, NIRS variables...
  • a is a vector of effects for those variables and ⁇ is a residual
  • a threshold c is preferably set such that if ⁇ C for a given seed, it is predicted to germinate, otherwise it is predicted not to germinate. By default, this threshold is set to 0,5. It can be modified to increase the detection power of non-germinating seed, at the cost of a higher false positive rate. Because predictors variables in X are highly dimensional and correlated, for optimal predictive power, penalized regression methods are preferable to estimate a . A first computation was done with the R glmnet (generalized linear models) package, with a « regularized logistic regression » (Friedman J.
  • This mathematical model is advantageously a Lasso regression, which is an analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
  • a key feature of the Lasso is that it performs automatic feature selection such that the obtained model can be very sparse (with few coefficients in a different from 0).
  • one or more (preferably all) of the following wavelengths of NIRS are used: 1422 nm, 1896 nm, 1898nm, 1176nm, 1 184nm, 1188 nm, 1678 nm, 1940 nm, 1944 nm, 1968 nm. .
  • near infra-red wavelengths used for the measurement are those of the area 1 to 4 in the table 14, or wavelengths with the higher effect as shown in figure 7 and 9.
  • the wavelengths with the higher effect as shown in figures 7 and 9 also include VIS. It is also possible to use visible spectroscopy in addition to NIRS and/or NMR.
  • the invention relates also to an apparatus for carrying out the methods for predicting the germinating profile of a maize seed or for predicting the germination ability of a maize seed lot, as previously described.
  • a device For predicting the germinating profile of a seed lot, a device can be implemented on the seed process with a random deviation of seeds sample from a seed lot, this seed sample being or not subsampled and used for NMR measurements and germination prediction of the global seed lot.
  • the invention relates also to an apparatus for carrying out the method for improving the germination ability of a maize seed lot, as previously described.
  • the apparatus according to the methods of the present disclosure typically includes
  • a data processing device for determining the NMR parameter(s), and more preferably those selected among the following:
  • the apparatus according to the methods of the present disclosure includes
  • a radio-frequency receive coil for receiving the NMR signals emitted by the seed or seed lot
  • a data processing device configured to determine the NMR parameter(s), and more preferably those selected among the following:
  • the data processing device further includes a seed germinating profile output, providing for each seed, a result of its germinating profile, whether germinating or non-germinating.
  • the apparatus may further include one or more of the following:
  • a seed feeding system upstream of the apparatus, for feeding the seed, one by one, into the apparatus,
  • a separator downstream of the apparatus, the separator having a control input connected to the seed germinating profile output, the separator being arranged to separate the seed predicted as germinating seed from the seed predicted as non- germinating seed.
  • Such apparatus may therefore include:
  • Individualization can be achieved by an individualization system comprising a hopper supplied with corn kernels by a vibrating plate.
  • the hopper drives the corn kernels in a system with two endless screws having an increasing pitch. These screws enable speed of the corn kernels to be increased and corn kernels to be separated from each other so as to be individualized.
  • Individualized kernels can laid onto the support surface of a conveyor and two guiding elements can position the corn kernel in the median part of the conveyor.
  • such apparatus includes:
  • an individualization system comprising a hopper supplied with corn kernels by a vibrating plate (2),
  • an NIRS device (4) optionally, an NIRS device (4), 5) a separator for sorting the seed depending on the detected NMR or NMR and NIRS signals (5).
  • a NIRS device suitable for measuring NIRS parameters from seed is commercialized by Polytec (NIR spectrometer on diode array technology), but also by Zeiss or Specim. More than one spectrometer can be used simultaneously to cover the NIR spectra.
  • the apparatus may include a system for orienting the seed with respect to its embryo for NIRS measurement.
  • the system for orienting the seed may comprise at least one laser device arranged to lighten the corn kernel with a laser line, and a plurality of orientation imaging devices configured to acquire respective two- dimension orientation images of the corn kernel along different viewing directions.
  • the orientation of the corn kernel with respect to the support surface may then be determined based on the structural features of the corn kernel measured on each of the two-dimension orientation images.
  • a data processing device (6) may be connected to the NMR device, the NIRS device and the separator.
  • FIG. 6 An embodiment of such apparatus is represented in Figure 6. Such specific embodiment of apparatus is useful in the method for improving the germination ability of a maize seed lot.
  • NIRS values also correlate with the germinating profile of a maize seed. Such NIRS values may be used alone or in combination with NMR parameter values in methods for predicting the germinating profile of a maize seed using NIRS.
  • a method for predicting the germinating profile of a maize seed comprising:
  • step (b) predicting the germinating profile of the seed based on the measurement of step (a), using an appropriate mathematical model.
  • J is a binary response variable ("germinating profile")
  • X is a matrix containing predictors variables: variables accounting for the experimental design, NMR, NIRS variables...
  • a is a vector of effects for those variables and ⁇ is a residual
  • a threshold c is preferably set such that if ⁇ C for a given seed, it is predicted to germinate, otherwise it is predicted not to germinate. By default, this threshold is set to 0,5. It can be modified to increase the detection power of non-germinating seed, at the cost of a higher false positive rate.
  • This mathematical model is advantageously a Lasso regression, which is an analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
  • a key feature of the Lasso is that it performs automatic feature selection such that the obtained model can be very sparse (with few coefficients in a different from 0).
  • NIRS values are measured at one or more of the following wave-lengths: 1422 nm, 1896 nm, 1898nm, 1176nm, 1184nm, 1188 nm, 1678 nm, 1940 nm, 1944 nm, 1968 nm.
  • near infra-red wavelengths used for the measurement are those of the area 1 to 4 in the table 14, or wavelengths with the higher effect as shown in figure 7 and 9.
  • the wavelengths with the higher effect as shown in figures 7 and 9 also include VIS.
  • a suitable mathematical model is applied to determine whether a seed is germinating or non-germinating based on the NIRS values.
  • a method for improving the germination ability of a maize seed lot comprises the following steps:
  • NIRS Near infra-red spectroscopy
  • the invention relates also to an apparatus for carrying out the method for improving the germination ability of a maize seed lot, as described above.
  • Such apparatus may include:
  • Figure 1 Correlation between the germination ability (% germination) and the low- field NMR parameters, for the same seed lot at a different step in the seed process: (a) T2, (b) T2(l), (c) FID/P, (d) Ampl/P and (e) A(l)/P.
  • Figure 2 Correlation between the germination ability (% germination) and the low- field NMR parameters for different seed varieties: (a) FID/P, (b) A(l)/P.
  • Figure 3 illustrates for each wavelength the correlation with the germination ability. Relevant wavelengths for the model are those with a correlation coefficient different from zero.
  • Figure 4 Comparison of germination ability of seed lots after sorting seed according to the germinating profile predicted on different NMR parameters compared to germination ability before (without sorting).
  • Figure 5 Comparison of germination ability of seed lots after sorting seed according to the germination profile predicted on all NMR parameters, with the same mathematical model and a different threshold compared to germination ability before (without sorting).
  • Figure 6 Embodiment of an apparatus according to the invention combining NMR and NIRS parameters detection.
  • Figure 7 This figure illustrates for each wavelength the relevance to predict the germination ability, relevant wavelengths for the model are those with a value (effect) different from zero
  • FIG. 8 This figure illustrates for each wavelength the correlation between repeated measures
  • Figure 9 This figure illustrates for each wavelength the relevance to predict the germination ability, the thousand kernel weight (TKW) has been include in the model, relevant wavelengths for the model are those with a value (effect) different from zero.
  • Figure 10 This figure illustrates for each parameter (NMR and NIRS and VIS spectroscopy) used in example 7, the relevance to predict the germination ability, relevant parameters are those with a value (effect) different from zero.
  • signal average means FID.
  • Example 1 Identification of an indicator of the germination ability The experiment was conducted on the same seed lot of maize.
  • Lot A correspond to freshly shelled stage
  • Lot B corresponds to filling truck after having been shelled
  • Lot C is truck sample on its arrival on new factory (after transfer).
  • the germination ability was measured by a conventional protocol on these lots, according to ISTA rules, the average values of the 5 lots are respectively 96,6% for lot A, 92,4% for lot B and 90,6% for lot C.
  • the low-field NMR measurements were carried out on a Minispec mq 20 of the Bruker type running at 20MHz.
  • the bi-exponential relaxation decay curves are measured in the same experimental conditions than T2 to discriminate between the different protons families (A(l) and A(2)) having respectively short or long relaxation time (T2(l) and T2(2)).
  • the amplitudes and relaxation times of the different components were extracted from bi- exponential decay curves using the apparatus software.
  • the FID corresponds to the signal intensity recovered after a 90° excitation pulse.
  • P is the weight of the sample (g)
  • FID/P, Ampl/P, A(l)/P and A(2)/P are respectively FID, Ampl, A(l) and A(2) value divided by the sample weight.
  • This example shows the possibility of sampling kernel seed lot and of predicting the germination ability of the seed lot based on certain low- field NMR parameters and interpretation on such sample.
  • the experiment was conducted on 11 lots of 384 maize kernels each. Each lot corresponds to kernels from different varieties, and these lots are at bulk shelled level i.e no sized.
  • the bi-exponential relaxation decay curves are measured in the same experimental conditions than T2 to discriminate between the different protons families (A(l) and A(2)) having respectively short or long relaxation time (T2(l) and T2(2)).
  • the amplitudes and relaxation times of the different components were extracted from bi- exponential decay curves using the apparatus software.
  • the FID corresponds to the signal intensity recovered after a 90° excitation pulse.
  • T2(l) and T2 (2) are expressed in ms, FID, Ampl, A(l) and A(2) in AU arbitrary unit, and expressed per g of sample.
  • a germination ability is calculated by taking an average of the values attributed for the 384 seeds which is then expressed as a percentage. This percentage is referred as the calculated germination ability (FGcal).
  • a correlation is sought between the NMR parameter and FGcal: The correlation coefficient is determined in order to know if there is a link between the 2 variables and how strong such link is. The closer the correlation coefficient is to 1 or -1, the stronger the link is between the 2 variables.
  • Figure 2 further illustrates the correlation between germination ability of ten maize seed lots and the obtained NMR parameters values. This correlation makes it possible to classify the lots based on the NMR parameters values. For example, a lot that obtains an NMR value for FID/P greater than 210 UA/g, is assumed to have a germination ability greater than 90% and therefore can be marketed in accordance with ISTA rules.
  • EXAMPLE 3 Improvement of the germination ability of a maize seed lot a- Mathematical model
  • the statistical analysis model used is:
  • J is a binary response variable ("germinating profile")
  • X is a matrix containing predictors variables: variables accounting for the experimental design, NMR, NIRS variables...
  • a is a vector of effects for those variables and ⁇ is a residual
  • Predictions y hat from this model are probability of germinating (between 0 and 1).
  • a threshold c has to be set such that if y M ⁇ C for a given seed, it is predicted to germinate, otherwise it is predicted not to germinate. By default, this threshold is set to 0,5. It can be modified to increase the detection power of non-germinating seed, at the cost of a higher false positive rate.
  • This model is a Lasso regression, which is an analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
  • a key feature of the Lasso is that it performs automatic feature selection such that the obtained model can be very sparse (with few coefficients in a different from 0).
  • This model was used for cross validation with the set of data to predict the germinating profile of one lot.
  • the threshold value is equal to 0,5.
  • Tables 3 to 7 corresponds to the sorting based on T2 NMR parameter.
  • Table 4 corresponds to the sorting based on T2 & FID parameters.
  • Table 5 is the sorting based on all NMR parameters except T2.
  • Tables 6 and 7 corresponds to the sorting made with all NMR parameters but with different threshold (see also Figures 4 and 5).
  • % identified is the ratio of seed identified as non-germinating seeds
  • % false positive is the number of germinating seed identified as non-germinating seed
  • germination rate before is the germination rate of the lot without sorting
  • germination rate after if the germination rate after elimination of the seed identified as non- germinating
  • ⁇ case is the effective number of non-germinating seed in the lot.
  • Table 3 prediction of germinating vs. non-germinating seeds with the model according to the invention.
  • the model is constructed on the T2 value (threshold 0,5).
  • this threshold should be adapted to discard more seeds and still improve the germination ability of the lot.
  • Table 4 Prediction of germinating vs. non-germinating seeds with the model of the invention.
  • the model is constructed on the T2 and FID values (threshold 0,5)
  • Table 5 Prediction of germinating vs. non-germinating seeds with the model of the invention.
  • the model is constructed with all the values without T2 (threshold 0,5).
  • Table 6 Prediction of germinating vs. non-germinating seeds with the model of the invention including all the NMR values (threshold 0.5).
  • Table 7 Prediction of germinating vs. non-germinating seeds with the model of the invention including all the NMR values (threshold (0,3).
  • the rate of germinating seeds has been increased until 18 %. This shows the efficiency of the method to increase germination ability of a seed lot. The results show that 90% of germination or above can be reached when decreasing the threshold (see also Figure 5).
  • EXAMPLE 4 identification of germination ability by Near Infrared Spectroscopy (NIRS)
  • the Foss 6500 module is used for NIRS analysis with a mono seed ring cup.
  • the seed is manually introduced in the ring cup with a proper orientation according to the embryo.
  • Analysis is done for wavelength from 800 to 2500 nm with a step of 2nm, the analysis is of 1 min for each sample.
  • Table 8 describes the germination rate improvement based on NIRS data model combining all relevant 482 wavelengths.
  • Table 9 shows how that the same model can be improved by injecting 3 extra lots data.
  • Table 10 shows the germination rate improvement when using only 10 out of 482 wavelengths (chosen amongst best ones). Legends of the tables are identical to those of example 3.
  • Table 8 Prediction of germinating vs non-germinating seeds with a model including all NIR spectra for the 482 wavelength according to the invention.
  • the model is constructed on whole wavelength values, these values are regularized by a lasso type penalization and a threshold of 0.5.
  • Germination ability has been improved for 6 of the 11 seeds lots. This analysis has been completed by measurements performed on 3 seed lots with a very low germination ability (Table 9).
  • Table 9 Prediction of germinating vs non-germinating seeds with a model including all NIR spectra for the 482 wavelength to the invention, the model is constructed on all wavelength values, these values are regularized by a lasso type penalization and a threshold of 0.5.
  • the model is improved (probably by the asset of data more important in number and in diversity) and the ability of the method to enable germination rate improvement of lots is confirmed: 8 of the 11 lots have been improved and the mean of improvement reaches 9,75%.
  • the following table 10 shows the improvement of germination ability using the following 10 best wave-lengths 1422 nm, 1896 nm, 1898nm, 1176nm, 1184nm, 1188 nm, 1678 nm, 1940 nm, 1944 nm, 1968 nm. 4 lots have been improved.
  • NIR spectra 10 best wavelengths
  • Table 10 prediction of germinating and non-germinating seeds with the model of the invention, the model being constructed on the 10 wavelength with the best correlation with germination ability (fig. 3).
  • the model is constructed on 10 wavelength values, these values are regularized by a lasso type penalization and a threshold of 0.5.
  • EXAMPLE 5 combination of the use of NMR and NIRS for predicting and improving germination ability of a seed lot.
  • Table 11 Improvement of the germination ability of a seed lot comprising, prediction of germinating vs. non-germinating seeds with the model of the invention on NMR values and discarding non germinating seed followed by prediction on remaining seed by the model of the invention on NIRS values and discarding non germinating seeds (0.5 threshold used for both predictions).
  • Table 12 Improvement of the germination ability of a seed lot comprising, prediction of germinating vs. non-germinating seeds with the model of the invention on NIRS values and discarding non germinating seed followed by prediction on remaining seed by the model of the invention on NMR values and discarding non germinating seeds.
  • NMR and NIRS measurements are done and the sorting is done according to the method on an estimation being based on the combination of both results. "# cases" number of non-germinating seeds.
  • Table 13 NIRS and NMR measurements are done sequentially, but the model includes correlation and sorting on both parameters. 0,5 threshold used. The results of Table 13 shows that, if the sorting is based on a model combining the values of NIRS and NMR measurements, the germination ability and prediction is further improved.
  • EXAMPLE 6 identification of germination ability by NIR (Near Infrared Spectroscopy) and VIS (visible) Spectroscopy.
  • the Foss 6500 module offers a larger range of spectra from 400 nm to 2500 nm this spectra include visible light and NIR.
  • the most relevant wavelength identified are listed in Table 14. These wavelength define some area of interest on the spectra scanned by the experiment, area 2 and 3 are common to the experiment of example 4.
  • the experiment 6 performed on seed analyzed 2 to 10 weeks from the harvest confirm results of example 4 done on seed 1 year on average after harvest. This experiment 6 shows a new zone in the lower part of the NIR spectrum (700- 2500 nm) at the neighborhood of the visible spectra.
  • Table 14 the most relevant wavelength for predicting germination ability obtained for the example 5, line 1 and example 4, line 2.
  • the third line are the zones defined as relevant by the experiment (1 to 4) a) repeatability of relevant wavelength
  • Table 15 Prediction of germinating vs non-germinating seeds with a model using NIRS data.
  • the model is constructed on whole wavelength values, these values are regularized by a lasso type penalization and a threshold of 0.2.
  • Germination ability has been improved for 20 of the 29 seeds lots.
  • EXAMPLE 7 combination of the use of NIR and VIS spectroscopy and NMR for predicting and improving germination ability of a seed lot,
  • Table 16 Improvement of the germination ability of a seed lot comprising, prediction of germinating vs. non-germinating seeds with the model of the invention on NIR and VIS values and discarding non germinating seed followed by prediction on remaining seed by the model of the invention on NMR values and discarding non germinating seeds (0.5 threshold used for both predictions).
  • the figure 10 illustrates for each parameter (NMR and NIRS and VIS spectroscopy) used in the example, the relevance to predict the germination ability, relevant parameters are those with a value (effect) different from zero.
  • signal average means FID.

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Abstract

La présente invention concerne un procédé permettant de prédire le profil de germination d'une graine de maïs, le procédé consistant à : a) mesurer un ou plusieurs paramètres de résonance magnétique nucléaire (RMN) à champ faible sur ladite graine de maïs, et b) prédire le profil de germination de la graine en fonction de la valeur mesurée à l'étape (a), à l'aide d'un modèle mathématique approprié.
EP17739615.7A 2016-07-20 2017-07-20 Procédé de prédiction de la capacité de germination d'une graine de maïs par résonance magnétique nucléaire Withdrawn EP3488234A1 (fr)

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CN112098451A (zh) * 2020-09-16 2020-12-18 沈阳农业大学 一种基于低场核磁共振技术对水稻种子活力检测方法
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