WO2022080412A1 - Procédé de prévision de rendement de blé - Google Patents

Procédé de prévision de rendement de blé Download PDF

Info

Publication number
WO2022080412A1
WO2022080412A1 PCT/JP2021/037901 JP2021037901W WO2022080412A1 WO 2022080412 A1 WO2022080412 A1 WO 2022080412A1 JP 2021037901 W JP2021037901 W JP 2021037901W WO 2022080412 A1 WO2022080412 A1 WO 2022080412A1
Authority
WO
WIPO (PCT)
Prior art keywords
components
yield
prediction model
yield prediction
tables
Prior art date
Application number
PCT/JP2021/037901
Other languages
English (en)
Japanese (ja)
Inventor
春香 前田
輝久 藤松
圭二 遠藤
Original Assignee
花王株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 花王株式会社 filed Critical 花王株式会社
Publication of WO2022080412A1 publication Critical patent/WO2022080412A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N5/00—Analysing materials by weighing, e.g. weighing small particles separated from a gas or liquid

Definitions

  • the present invention relates to a method for predicting the yield of wheat at an early stage.
  • Wheat is an important grain called the three major grains along with corn and rice, and is widely eaten in Japan and around the world. It is widely cultivated as one of the important grains, and techniques for increasing the yield are being developed.
  • the growing period of wheat varies slightly depending on the variety and cultivation conditions, but it usually takes a long period of 3 to 6 months from sowing to harvesting. Therefore, in the development of a technique for increasing the yield of wheat, it takes a lot of time for cultivation to evaluate the yield, and therefore, a method for predicting the yield at an early stage has been required. In addition, if the yield can be predicted at an early stage in the actual production situation, the producer can easily determine whether or not to introduce a costly additional technique in order to secure a stable yield.
  • Non-Patent Document 1 a model for predicting production at the national level is constructed using meteorological data such as cumulative precipitation and cumulative average temperature before sowing.
  • Non-Patent Documents 2 and 3 disclose an attempt to evaluate growth and yield from meteorological conditions.
  • Non-Patent Document 4 discloses a technique of estimating NDVI (normalized difference vegetation index) from an image of a field and simply predicting the yield in each field. Further, in Patent Document 1, an attempt is made to measure the amount of nitrogen in the leaf blade, the color of the leaf, or the amount of chlorophyll, evaluate the growth and yield, and determine the amount of fertilizer applied.
  • NDVI normalized difference vegetation index
  • Non-Patent Document 1 is a model for predicting the yield of wheat using meteorological data predicted before sowing, but the predictable unit is the national unit, and the predictors and yields for each individual are used. It is not suitable for evaluation when you want to correspond.
  • the methods of Non-Patent Documents 2, 3 and 4 and Patent Document 1 can be said to be non-destructive and simple measurement, but the prediction time is after the panicle formation stage, that is, after half of the growth period has passed. Become. Furthermore, since the prediction is made on a field-by-field basis, it is not a technique for predicting the yield at the individual level.
  • Non-Patent Document 5 in this report, the predictability evaluation of the model called cross-validation, which is performed when constructing a normal predictive model, is not performed, and it cannot be said that the verification is sufficient. In addition, it is invasive and is not suitable for evaluation when it is desired to correspond the predictor of each individual with the yield.
  • Patent Document 1 Japanese Unexamined Patent Publication No. 2000-300077
  • Non-Patent Document 2 Toyohisa Minoda, Journal of the Crop Science Society of Japan, 2010, Vol. 79, No. 1, p. 62-68, "Effects of meteorological conditions on the yield of wheat” Norin 61 "in upland field test fields in Saitama Prefecture”
  • Non-Patent Document 3 Marletto, V. et al., Agricultural and Forest Meteorology, 2007, vol. 147, p.
  • Non-Patent Document 4 Junichi Ikeda et al., Journal of Japanese Society of Soil Science and Technology, 2001, Vol. 72, No. 6, p. 786-789, "Prediction of wheat yield by image data of high resolution satellite IKONOS"
  • Non-Patent Document 5 Dan, Z. et al., Scientific Reports, 2016, 6, 21732
  • the present invention is a method for predicting the yield of wheat, which obtains analysis data of one or more components from a leaf sample collected from wheat and predicts the yield of wheat by using the correlation between the data and the yield of wheat. offer.
  • the present invention relates to providing a method for accurately predicting the yield of wheat at an early stage.
  • the present inventors have found that the metabolites contained in the leaves have a component whose abundance correlates with the yield, and as early as about one month after sowing. It was found that the final yield can be evaluated at the individual level by collecting a part from the expanded leaves of wheat and analyzing the components contained in the leaves.
  • the yield of wheat can be predicted at an early stage. As a result, for example, it becomes easy to determine the introduction of additional techniques for ensuring the yield, and it is possible to greatly improve the efficiency of the development of the techniques for increasing the yield.
  • wheat is an annual plant of the Gramineae family and means all plants belonging to the genus Wheat (scientific name: Triticum). There are a total of 18 species belonging to the genus Wheat. Theoudar, T.I. Monococcum, T.I. Dicoccoides, T.I. Dicoccum, T.I. Pyromdale, T.I. Orientale, T.I. Durum, T.I. Turgidom, T.I. Polonicum, T.I. Persicum, T.I. Aestivum, T.I. Spelled, T.I. Compactum, T.I. Sphaerococcum, T.I. Maha, T.I. Vavilovii, T.I.
  • Timopheevi can be mentioned.
  • T.I. Aestivum is mentioned, and its varieties include Kitahonami, Yumechi, Kitanokaori, Tsurukichi, Kitasachiho, Haruyo Koi, Harukirari, Haruyutaka, Norin 61, etc.
  • other representative species include T.I. Dicoccum is mentioned, and its varieties include Abruzzo, Molise, Umbria and the like.
  • T. Examples of durum varieties include setodur. As described above, the varieties included in the genus Wheat are diverse, but the present invention is not limited thereto.
  • the growth stages from the germination to the flowering stage of wheat are the germination stage (around 7 days after sowing), the 5 leaf age stage (around 15 days after sowing), the stalk standing stage (around 30 days after sowing), and the panicle formation stage (around 30 days after sowing). It is divided into a flagging stage (around 50 days after sowing), a heading stage (around 60 days after sowing), and a flowering stage (around 65 days after sowing).
  • the leaf sample of wheat may be collected from the sowing stage to the heading stage when the leaves can be collected, and the collection time is preferably from the 5th leaf age to the heading stage, more preferably.
  • Examples thereof include 5 leaf age to stop leaf stage after sowing, preferably 20 days to stop leaf stage after sowing, and more preferably 20 days to young ear formation stage after sowing. It is preferable that the number of days before and after each of the above growth stages is within 5 days.
  • the time for collecting wheat leaves is 7 days or more after sowing, preferably 15 days or more, more preferably 20 days or more, and preferably 60 days or more, more preferably 50 days or more after sowing. More preferably, it may be before the 40th day. Further, it may be 7 to 60 days after sowing, preferably 15 to 50 days, and more preferably 20 to 40 days. For example, it is preferable to collect leaves from wheat 27 days ⁇ 3 to 5 days after sowing.
  • the number of days after sowing represents the number of days when spring wheat is cultivated.
  • the number of days after sowing of each growth stage is different from the above number of days, but those skilled in the art can understand the number of days after sowing of each growth stage in consideration of the cultivation conditions of autumn wheat.
  • Leaf samples may be taken at the time corresponding to.
  • the yield means the parenchyma of wheat per individual or the number of grains per individual.
  • the mass is not particularly limited, but a dry mass is preferable.
  • the actual amount of dried wheat per individual is particularly preferable.
  • the actual amount of dried child in the present invention means the mass measured in a state where the water content contained in the grain after drying at 90 ° C. for 72 hours is reduced to 7% or less. It is preferable that the actual amount of the dryer is measured only by a balance with a calibration function such as an electronic balance.
  • the collection site of the leaf sample is not particularly limited, but for example, about 4 to 7 leaves may be cut from the root of the strain and collected.
  • the analysis data of the components to be obtained include high performance liquid chromatography (HPLC), gas chromatography (GC), ion chromatography, mass spectrometry (MS), near infrared spectrometric analysis (NIR), and Fourier conversion.
  • HPLC high performance liquid chromatography
  • GC gas chromatography
  • MS mass spectrometry
  • NMR near infrared spectrometric analysis
  • FT-IR Infrared spectroscopic analysis
  • NMR nuclear magnetic resonance analysis
  • FT-NMR Fourier transform nuclear magnetic resonance analysis
  • ICP-MS inductively coupled plasma mass spectrometer
  • liquid chromatograph examples thereof include data analyzed / measured using a combined LC / MS or other instrument analysis means, preferably mass spectrometric data, and more preferably mass spectrometric data by LC / MS.
  • the mass spectrometric data include precision mass (“m / z value”), ionic strength, holding time, and the like, but precision mass information is preferable.
  • the leaf sample In order to apply the leaf sample to the above-mentioned instrumental analysis means, it is appropriately pretreated according to the analysis means, but usually, the collected leaves are wrapped in aluminum foil and immediately frozen in liquid nitrogen to stop the metabolic reaction. After being freeze-dried and dried, it is subjected to an extraction operation. Extraction is performed by pulverizing the freeze-dried leaf sample using a bead crusher or the like, adding an extraction solvent, and stirring the mixture.
  • the extraction solvent used here include methanol, ethanol, butanol, acetonitrile, chloroform, ethyl acetate, hexane, acetone, isopropanol, water and the like, and a mixture thereof.
  • an 80v / v% aqueous methanol solution to which an internal standard substance is added is preferably used.
  • examples of the components in the leaves to be analyzed include wheat metabolites separated and detected by LC / MS.
  • the components provided by mass spectrometry have a precise mass (m / z) of 104 to 1178. More preferably, 1,450 components listed in Tables 1a to 1j below, which are defined by the precise mass (m / z value) provided by mass spectrometry, can be mentioned.
  • Tables 1a to 1j which are defined by the precise mass (m / z value) provided by mass spectrometry
  • the 1,450 components are selectively extracted from the metabolites of wheat, and the selection method is as shown in the examples in detail. ) Is cultivated, 2) 4 to 7 leaves are collected about 1 month after sowing to obtain leaf samples, 3) components are extracted using 80v / v% methanol, and then 4) LC / MS. Analysis is performed to obtain molecular ion information (precision mass, m / z) and structural information derived from fragments, 5) peaks derived from components are extracted, and then each peak is aligned between each sample. Analysis data of 1,450 components is acquired by removing body peaks, correcting peak intensity between samples, and removing noise.
  • the method of correcting the peak intensity between the samples is not particularly limited, and examples thereof include correction using the popled QC method.
  • a sample called poored QC is prepared by mixing a certain amount from all the samples in the same batch, and the popled QC is analyzed at a constant frequency (about once every 5 to 9 times) between each sample.
  • the estimated value "what will happen to each peak intensity if it is assumed that the QC sample was analyzed when each sample was analyzed" is calculated, and the process of correcting with that value is performed. It corrects the sensitivity between each sample.
  • the data correction method does not significantly affect the correlation with yield and the performance of the prediction model.
  • the component to be analyzed in the present invention is a component having a significant correlation with the yield (p ⁇ 0.05) and an absolute value of the correlation coefficient
  • the components to be analyzed in the present invention are components having a significant correlation with yield (p ⁇ 0.05) and an absolute value of the correlation coefficient
  • the components of 1,450 are defined by the precise mass obtained by mass spectrometry, but the composition formula of the compound can be estimated from these precise mass data. Further, the partial structure information of the compound can be obtained from the MS / MS data acquired at the same time as the analysis. Therefore, the target component can be estimated from the composition formula and the partial structure information, and the one that can be compared with the reagent can be identified.
  • No. 12 is pyroglutamic acid of composition formula C 5 H 7 NO 3
  • No. 20 is L-asparagine of composition formula C 4 H 8 N 2 O 3
  • No. 48 is composition formula C 12 H. 16
  • No. 132 is cloxatin G of composition formula C 12 H 20 O 2
  • No. 184 is kukurublutamic acid of composition formula C 12 H 20 O 3
  • No. 210 is composition formula C 13 H 18 O 3
  • No. 346 Is a composition formula C 16 H 12 O 4
  • No. 355 is the composition formula C 12 H 14 O 7
  • No. 386 is the composition formula C 14 H 20 N 4 O 2 , No. 386.
  • preferred examples of the components to be analyzed in the present invention include pyroglutamic acid, L-asparagine, cloxatin G, cucurbic acid, 5-aminoimidazole ribonucleotide and the like.
  • the above 1,450 components preferably components having a significant correlation with the yield (p ⁇ 0.05) and an absolute value of the correlation coefficient
  • the abundance of components preferably significant (p ⁇ 0.05) and having an absolute value of the correlation coefficient
  • Peak area can also be measured for the wheat leaf sample to be predicted, and the yield can be predicted from the correlation between the known yield and the measured peak area.
  • the yield can be predicted by using a plurality of the analysis data of the above 1,450 components and collating with the yield prediction model constructed by using the multivariate analysis method. That is, a wheat leaf sample after a lapse of a predetermined period from sowing is collected, an analysis sample is obtained, the analysis sample is subjected to instrumental analysis to obtain instrumental analysis data, and the instrumental analysis data is collated with a yield prediction model. Therefore, the yield of the wheat can be predicted.
  • the yield prediction model can be constructed by performing regression analysis using the peak area value of the corrected component analysis data with each precise mass as the explanatory variable and the yield value as the objective variable.
  • Regression analysis methods include, for example, principal component regression analysis, PLS (Partial least squares projection to latent structures) regression analysis, OPLS (Orthogonal projections to latent structures) regression analysis, generalized linear regression analysis, bagging, and support vector machines. Examples include multivariate regression analysis methods such as machine learning / regression analysis methods such as random forest and neural network regression analysis. Of these, it is preferable to use the PLS method, the OPLS method which is an improved version of the PLS method, or the machine learning / regression analysis method.
  • the OPLS method has the same predictability as the PLS method, but is superior in that it is easier to visualize for interpretation for the purpose of this time.
  • Both the PLS method and the OPLS method are methods in which information is aggregated from high-dimensional data, replaced with a small number of latent variables, and the objective variable is expressed using the latent variables. It is important to properly select the number of latent variables, and cross-validation is often used to determine the number of latent variables. That is, the data for model construction is divided into several groups, one group is used for model verification, and the other group is used for model construction to estimate the prediction error, and this work is repeated while exchanging the groups to obtain the prediction error. The number of latent variables with the smallest total is chosen.
  • the evaluation of the prediction model is mainly judged by two indicators.
  • R 2 which represents prediction accuracy
  • Q 2 which represents predictability
  • R 2 is the square of the correlation coefficient between the measured value of the data used for constructing the prediction model and the predicted value calculated by the model, and the closer it is to 1, the higher the prediction accuracy.
  • Q2 is the result of the above cross validation, and represents the square of the correlation coefficient between the actually measured value and the predicted value which is the result of repeated model validation.
  • a list of the top 500 VIP values is shown in Tables 5a to 5g below.
  • Model construction using VIP value as an index (2-1) Model using analysis data of components up to the top 500 VIP values Select all components up to the top 500 VIP values and select the 500 per data.
  • the number of components used for prediction is preferably as small as possible, for example, 10 or less, preferably 5 or less, more preferably 3 or less, and most preferably 3 or less, in the case of simple prediction. It is one. Further, when it is desired to improve the accuracy, it is preferable that the number of components is large, for example, 11 or more, preferably 20 or more, more preferably 50 or more, still more preferably 90 or more, and most preferably 150. More than one. When predicting with a small number of components, it is preferable to use a component having a higher VIP value or a component having a higher correlation coefficient for prediction.
  • the component having a higher VIP value is, for example, at least one component selected from the top 500 VIP values, preferably at least five components selected from the top 500 VIP values, and more preferably a VIP value. At least 10 components selected from the top 500, more preferably at least 1 component selected from the top 10 VIP values, and even more preferably at least 5 selected from the top 10 VIP values. It is a component of, more preferably at least 8 components selected from the top 10 VIP values, still more preferably at least 9 components selected from the top 10 VIP values, and even more preferably a VIP value.
  • the top 500 components is, for example, at least one component selected from the top 500 VIP values, preferably at least five components selected from the top 500 VIP values, and more preferably a VIP value. At least 10 components selected from the top 500, more preferably at least 1 component selected from the top 10 VIP values, and even more preferably at least 5 selected from the top 10 VIP values. It is a component of, more preferably at least 8 components selected from the top 10 VIP values, still more preferably at least 9 components
  • a wheat yield prediction method in which analysis data of one or more components is acquired from a leaf sample collected from wheat, and the yield of wheat is predicted by using the correlation between the data and the yield of wheat.
  • the component is at least one selected from the components having a precise mass (m / z) of 104 to 1178 provided by mass spectrometry.
  • the component is at least one selected from the components shown in Tables 1a to 1i defined by the precision mass (m / z) provided by mass spectrometry, ⁇ 1> or ⁇ 2>.
  • the method described in. ⁇ 5> The components are the component Nos. No. 1 shown in Tables 1a to 1j.
  • the method according to ⁇ 4> which is one or more of the above.
  • the components are the component Nos. No. 1 shown in Tables 1a to 1j. 1, 12, 17, 20, 34, 48, 52, 58, 60, 61, 68, 71, 72, 74, 83, 84, 85, 90, 92, 101, 107, 109, 110, 115, 117, 118, 129, 132, 133, 136, 143, 160, 164, 170, 171, 172, 178, 179, 181, 184, 189, 191, 193, 196, 198, 203, 206, 210, 218, 235, 237, 249, 255, 257, 282, 283, 291 294, 297, 298, 310, 312, 315, 318, 330, 331, 345, 346, 348, 355, 360, 361, 362, 373, 384, 386, 388, 392, 395, 406, 408, 411, 421,
  • the component is the component No. 1 shown in Tables 1a to 1j. 12, 20, 34, 48, 58, 61, 85, 101, 109, 115, 132, 133, 172, 184, 193, 198, 206, 255, 282, 283, 312, 330, 331, 346, 348, 355, 361, 386, 449, 461, 482, 505, 542, 589, 662, 731, 760, 761, 783, 785, 787, 815, 824, 825, 874, 875, 886, 929, 934, 994, One or more selected from 1005, 1009, 1039, 1040, 1067, 1068, 1085, 1143, 1181, 1183, 1186, 1209, 1232, 1244, 1279, 1284, 1337, 1346, 1350 and 1355 ⁇ 4> The method described in.
  • ⁇ 8> The method according to ⁇ 4>, wherein the component comprises one or more selected from pyroglutamic acid, L-asparagine, cloxatin G, cucurbic acid and 5-aminoimidazole ribonucleotide.
  • the component comprises one or more selected from pyroglutamic acid, L-asparagine, cloxatin G, cucurbic acid and 5-aminoimidazole ribonucleotide.
  • ⁇ 9> The method according to any one of ⁇ 1> to ⁇ 8>, wherein the leaf sample is collected from wheat in the budding stage to the heading stage.
  • ⁇ 10> The method according to any one of ⁇ 1> to ⁇ 8>, wherein the leaf sample is collected from wheat in the 5th leaf instar to the panicle formation stage.
  • the analysis data is mass spectrometry data.
  • ⁇ 12> At least one of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1j of the component analysis data obtained from the leaf sample.
  • the yield prediction model uses at least 5 of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i.
  • the yield prediction model uses at least 10 out of the top 500 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i. The method described.
  • the yield prediction model uses at least one of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1i.
  • the yield prediction model uses at least 9 out of the top 10 VIP values calculated from the yield prediction model constructed using the component information shown in Tables 1a to 1j.
  • ⁇ 19> The method according to ⁇ 12>, wherein the yield prediction model uses the top 500 VIP values calculated from the yield prediction model constructed by using the component information shown in Tables 1a to 1j.
  • ⁇ 20> The method according to any one of ⁇ 12> to ⁇ 19>, wherein the yield prediction model is a model constructed by using the OPLS method.
  • ⁇ 21> The method according to any one of ⁇ 1> to ⁇ 20>, wherein the precision mass is measured with an accuracy of four or more digits after the decimal point.
  • Leaf sampling was performed during the daytime, 27 days after sowing (generally from 13:00 to 15:00). The growth stage of wheat at this time was slightly different depending on the individual, but the number of leaves per individual was about 12 to 15, which corresponded to the stalk standing stage at the growth stage. Leaf sampling was taken by cutting 4-7 leaves from the root of the plant. At the time of collection, the whole strain was collected without bias. The collected leaves were wrapped in aluminum foil and immediately frozen in liquid nitrogen to stop the metabolic reaction. The frozen sample was taken back to the laboratory while maintaining the frozen state, and dried by freeze-drying. This dried sample was subjected to the extraction operation described later.
  • LC / MS analysis is performed using an Agilent HPLC system (Infinity 1260 series) as a front and an AB SCIEX Q-TOFMS device (TripleTOF4600) as a detector. I did it. Separation columns in HPLC include a core-shell column Capcell core C18 (2.1 mm ID ⁇ 100 mm, particle total 2.7 ⁇ m) manufactured by Shiseido Co., Ltd. and a guard column (2.1 mm ID ⁇ 5 mm, A particle meter (2.7 ⁇ m) was used, and the column temperature was set to 40 ° C. The autosampler was kept at 5 ° C during the analysis. The analytical sample was injected with 5 ⁇ L.
  • A: 0.1 v / v% formic acid aqueous solution and B: 0.1 v / v% formic acid acetonitrile solution were used as eluents.
  • the gradient elution condition was maintained at 1v / v% B (99v / v% A) for 0 to 0.1 minutes, and 1v / v% B to 99.5v / v for 0.1 to 13 minutes.
  • the ratio of eluent B was increased to% B and maintained at 99.5 v / v% B from 13.01 minutes to 16 minutes.
  • the flow velocity was 0.5 mL / min.
  • the ionization mode was set to the positive mode, and ESI was used as the ionization method.
  • the elution ions are scanned by TOFMS for 0.1 seconds, 10 high-intensity ions are selected, and each of them is subjected to MS / MS for 0.05 seconds while repeating the cycle.
  • Molecular ion information (precision mass, m / z) obtained by TOFMS scanning and structural information derived from fragments generated by MS / MS scanning were acquired.
  • the mass measurement range was set to m / z 100-1,250 for TOFMS and m / z 50-1,250 for MS / MS.
  • Peak finding option is a peak corresponding to a retention time of 0.5 to 16 minutes, 20 scans of Subtraction offset in the item of “Enhance Peak Finding”, 5 ppm of Minimum spectral peak width, and 5 ppm of Minimum spectral peak. .. Factor was set to 1.2, Minimum RT peak width was set to 10 scans, Noise threshold was set to 5, and the Assign charge state in the "More" item was checked. As a result, peak information of 31,649 was obtained.
  • an alignment process was performed to align the detected peaks between the analyzed samples.
  • the alignment processing conditions (“Alightment & Filtering”)
  • the Retition time tradition in the item of “Alignment” was set to 0.20 minutes and the Mass tolerance was set to 10.0 ppm.
  • the Integrity threshold in the "Filtering” item was set to 10
  • the Retention time filtering was checked, the Move peaks in ⁇ 3 samples were set, and the Maximum number of peaks was set to 50,000.
  • the retention time was corrected using the lidocaine peak in the item of "Internal standard”.
  • the isotope peak was removed. Since the isotope peak is automatically recognized by the software at the time of peak extraction and labeled as "isotopic" on the peak list, the corresponding peak was deleted by sorting by "isotopic". As a result, the peak decreased to 25,895 peaks.
  • a sample called poored QC was prepared by mixing a certain amount from all the samples, and the polled QC was analyzed once every 6 times. From all these QC analysis results, an estimated value "what will happen to each peak intensity if it is assumed that the QC sample was analyzed when each sample was analyzed" is calculated, and the process of correcting with that value is performed. This was done and the sensitivity between each sample in the same batch was corrected. For this treatment, free software (LOWESS-Normalization-Tool) provided by RIKEN was used.
  • Correlation analysis was performed using the analysis data of 1,450 components in the leaves of 23 individuals and the corresponding yield data (real amount of dried matter), that is, the matrix data of 23 ⁇ 1,450.
  • the p-value was calculated by the simple correlation coefficient r and the uncorrelated test between the analysis data of each component and the yield data.
  • Tables 4a-4s The results are shown in Tables 4a-4s.
  • "component No.” in the table is for convenience, in which 1,450 components are arranged in order of mass and numbered from the one with the smallest mass number.
  • the analysis result includes information on the holding time as well as the mass information. It has been shown that mass spectrometric data can be compared and analyzed between mass spectrometric samples. Therefore, the information on the holding time was removed, and only the precise mass information was described.
  • Model construction / evaluation A multivariate analysis method was used to construct a yield prediction model using analysis data of two or more components, and SIMCA ver. 14 (Umetrics) was used.
  • regression analysis was performed using the peak area value of the corrected component analysis data with each precise mass as the explanatory variable and the yield value as the objective variable.
  • Regression analysis was performed by the OPLS method, which is an improved version of the PLS method.
  • the evaluation method of the prediction model is mainly judged by two indexes.
  • R 2 which represents prediction accuracy
  • Q 2 which represents predictability
  • R 2 is the square of the correlation coefficient between the measured value of the data used for constructing the prediction model and the predicted value calculated by the model, and the closer it is to 1, the higher the prediction accuracy.
  • Q 2 is the result of the cross validation, and represents the square of the correlation coefficient between the actually measured value and the predicted value which is the result of repeated model validation. From a prediction point of view, the model is considered to have good predictability if at least Q2 > 0.50 (Triba, M.N. et al., Mol. BioSyst. 2015, 11, 13-19. .), Q2 > 0.50 was used as the standard for model evaluation. Since R 2 > Q 2 is always satisfied, Q 2 > 0.50 satisfies R 2 > 0.50 at the same time.
  • VIP Value calculation In the model constructed in 8-1, VIP (Variable Impact in) The degree of contribution to the model performance given to each component called the projection (variable importance in projection) value is calculated. The larger the VIP value, the greater the contribution to the model, and it also correlates with the absolute value of the correlation coefficient. A list of the top 500 VIP values is shown in Tables 5a-5g.
  • Model construction using the VIP value as an index A model was constructed with a plurality of components based on the VIP value ranking (Tables 5a to 5g), which is the contribution of each component to the model constructed in 8-1.
  • the model performance standard is set to Q2 > 0.50 for convenience.
  • Model using analysis data of components with VIP values up to the top 500 Select all components up to the top 500 VIP values and have the peak area value and yield value of the analysis data of the 500 components per data.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Physics & Mathematics (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Botany (AREA)
  • Ecology (AREA)
  • Forests & Forestry (AREA)
  • Environmental Sciences (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

L'invention fournit un procédé destiné à prévoir de manière précoce et selon une précision satisfaisante un rendement de blé. Plus précisément, l'invention concerne un procédé de prévision de rendement de blé selon lequel des données d'analyse relatives à au moins un composant sont acquises à partir d'un échantillon de feuille prélevé sur le blé, et le rendement de blé est prévu à l'aide d'une corrélation entre ces données et le rendement de blé.
PCT/JP2021/037901 2020-10-13 2021-10-13 Procédé de prévision de rendement de blé WO2022080412A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2020172821 2020-10-13
JP2020-172821 2020-10-13

Publications (1)

Publication Number Publication Date
WO2022080412A1 true WO2022080412A1 (fr) 2022-04-21

Family

ID=81209180

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2021/037901 WO2022080412A1 (fr) 2020-10-13 2021-10-13 Procédé de prévision de rendement de blé

Country Status (2)

Country Link
JP (1) JP2022064329A (fr)
WO (1) WO2022080412A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012510062A (ja) * 2008-11-25 2012-04-26 ザ・ジョージ・ワシントン・ユニバーシティ 赤外レーザアブレーションによる三次元の分子画像のエレクトロスプレーイオン化質量分析
US20150168419A1 (en) * 2010-11-16 2015-06-18 University College Cork - National University Of Ireland, Cork Prediction of a small-for-gestational age (sga) infant
CN107356569A (zh) * 2017-06-06 2017-11-17 河南农业大学 基于叶绿素荧光预测小麦籽粒产量的方法及其模型的构建方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012510062A (ja) * 2008-11-25 2012-04-26 ザ・ジョージ・ワシントン・ユニバーシティ 赤外レーザアブレーションによる三次元の分子画像のエレクトロスプレーイオン化質量分析
US20150168419A1 (en) * 2010-11-16 2015-06-18 University College Cork - National University Of Ireland, Cork Prediction of a small-for-gestational age (sga) infant
CN107356569A (zh) * 2017-06-06 2017-11-17 河南农业大学 基于叶绿素荧光预测小麦籽粒产量的方法及其模型的构建方法

Also Published As

Publication number Publication date
JP2022064329A (ja) 2022-04-25

Similar Documents

Publication Publication Date Title
Couture et al. Spectroscopic determination of ecologically relevant plant secondary metabolites
Bucher et al. Inter-and intraspecific variation in stomatal pore area index along elevational gradients and its relation to leaf functional traits
WO2022080411A1 (fr) Procédé de prévision de rendement de riz
CN102478563B (zh) 一种用于研究转基因和非转基因水稻代谢差异的方法
Liu et al. Long-term agricultural effects on the authentication accuracy of organic, green, and conventional rice using isotopic and elemental chemometric analyses
Zhu et al. 13 C isotope fractionation during rhizosphere respiration of C 3 and C 4 plants
Wadood et al. Geographical traceability of wheat and its products using multielement light stable isotopes coupled with chemometrics
WO2020213672A1 (fr) Procédé de prévision de rendement en soja
Griepentrog et al. Influence of plant growth form, habitat and season on leaf-wax n-alkane hydrogen-isotopic signatures in equatorial East Africa
CN108535375B (zh) 一种基于液质联用代谢组学分析玛咖标志性代谢产物的方法
Montero-Vargas et al. Metabolic phenotyping for the classification of coffee trees and the exploration of selection markers
Vassiliadis et al. A simple LC–MS method for the quantitation of alkaloids in endophyte-infected perennial ryegrass
Chivasa et al. Phenology-based discrimination of maize (Zea mays L.) varieties using multitemporal hyperspectral data
Murphy et al. Analysing Sr isotopes in low‐Sr samples such as single insects with inductively coupled plasma tandem mass spectrometry using N2O as a reaction gas for in‐line Rb separation
Roscher et al. Functional groups differ in trait means, but not in trait plasticity to species richness in local grassland communities
Roscher et al. Functional composition has stronger impact than species richness on carbon gain and allocation in experimental grasslands
Monreal et al. Labile organic matter in soil solution: II. Separation and identification of metabolites from plant‐microbial communication in soil solutions of wheat rhizospheres
WO2022080412A1 (fr) Procédé de prévision de rendement de blé
Joergensen et al. Evaluation of organic sunflower fertilization using δ 15 N values
JP7244338B2 (ja) ダイズの収量予測方法
CN111220734A (zh) 一种利用敏感代谢物评价潜伏缺素期作物营养状态的方法
WO2022080410A1 (fr) Procédé de prévision de rendement de maïs
Bohley et al. C4-like photosynthesis and the effects of leaf senescence on C4-like physiology in Sesuvium sesuvioides (Aizoaceae)
Steinbauer et al. Foliar quality of co-occurring mallee eucalypts: balance of primary and secondary metabolites reflects past growing conditions
Mandrone et al. Metabolomic study of Dactylis glomerata growing on Aeolian archipelago (Italy)

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21880150

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21880150

Country of ref document: EP

Kind code of ref document: A1