Wheat starch content change prediction method and system based on carbon dioxide concentration increase

Abstract

The invention discloses a wheat starch content change prediction method and system based on carbon dioxide concentration increase, and relates to the technical field of wheat starch content prediction. Farmland carbon dioxide concentration gradients are set, and farmlands with different carbon dioxide concentrations correspond to different test fields; building a plant physiological database based on different test fields; a segmented Piecewise SEM regression model is adopted to analyze the regression relation between the amylose content and the total starch content of the winter wheat grains and the carbon dioxide concentration; and predicting the evolution trend of the amylose content and the total starch content of the winter wheat according to the carbon dioxide concentration change in the future by utilizing a segmented Piecewise SEM regression model and a regression relationship. Simulation prediction of the amylose content and the total starch content of the wheat grains is carried out based on the actually measured physiological indexes of the leaves and the grains after flowering, accurate planting and classification of wheat can be guided, the requirements of different wheat processing industries are met, and the toughness of an agricultural system to cope with climatic changes is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of wheat starch content prediction, and more specifically to a method and system for predicting changes in wheat starch content based on increased carbon dioxide concentration. Background Art

[0002] The content of amylose and total starch in wheat grains not only directly determines the yield of wheat, but also plays a key role in its processing quality and edible quality. The content of amylose affects the gelatinization properties and gel-forming ability of wheat flour, as well as the taste and digestibility of food; the total starch content is related to the application value of wheat in the food industry, feed industry and other fields.

[0003] Studies have shown that changes in carbon dioxide concentrations affect the carbon metabolism pathway of wheat, and thus affect the synthesis and accumulation of starch. High concentrations of carbon dioxide may promote wheat photosynthesis, provide more carbon sources for starch synthesis, and affect the activity of enzymes related to starch synthesis, thereby changing the synthesis and accumulation of amylose and total starch.

[0004] Although it has been recognized that carbon dioxide concentration affects the starch content of wheat, the wheat growth environment is complex. In addition to carbon dioxide concentration, factors such as temperature, light, and soil fertility also affect starch content. Existing studies often explore the role of carbon dioxide concentration in isolation, without fully considering the coupling effect between various environmental factors and carbon dioxide concentration, resulting in the established prediction system being out of touch with the actual growth environment and limited practicality.

[0005] Against the backdrop of global climate change, carbon dioxide concentrations continue to rise. How to quantitatively evaluate changes in the content of amylose and total starch in wheat grains has become a key issue in ensuring food security and promoting sustainable agricultural development. Summary of the invention

[0006] In view of this, the present invention discloses a method and system for predicting changes in wheat starch content based on increased carbon dioxide concentration, in order to solve the problems in the background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: A method for predicting changes in wheat starch content based on elevated carbon dioxide concentration comprises the following steps: S1. Data collection and preprocessing: according to the climate change scenario, the carbon dioxide concentration gradient of the farmland is set. Farmlands with different carbon dioxide concentrations correspond to different test fields. Plant samples are collected from different test fields, physiological and biochemical indicators are measured, and a database of grain starch synthesis responding to CO2 concentration is constructed; S2. The segmented SEM regression model was used for analysis. The segmented SEM regression model was established based on the measured data. The explained variables were the content of amylose and total starch in the grains, and the mediating variables were the dynamic physiological and biochemical indicators of leaves and grains after flowering. The regression relationship between the content of amylose and total starch in winter wheat grains and the carbon dioxide concentration was analyzed by segmented stepwise regression. A regression model was constructed in each CO2 concentration interval. Secondly, the path coefficient was estimated. In each interval, the segmented regression model was: in: Y: explained variable (amylose or total starch); :intercept; : slope; X: predictor variable (CO2 concentration); e : residual; The parameters of these segmented regressions, including the intercept and slope, were estimated based on the measured indicator data, the total effect of each path was calculated, and a significance test was performed. Model fit assessment was performed to determine the overall fit of the model in each interval (adjusted R 2 ) and the significance of the path coefficient ( p <0.05), check whether the relationship between variables in the segmented regression is reasonable. For example, under high CO2 concentration, SPS enzyme activity increases, leaf sucrose content increases, and the two intermediate variables are positively correlated.

[0008] S3. The piecewise SEM regression model and regression relationship were used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration.

[0009] Optionally, the carbon dioxide concentration gradient is ACO2, ACO2+100ppm, ACO2+200ppm.

[0010] Optionally, the process of data collection of plant physiological database is as follows: in different carbon dioxide concentration gradient test areas, four single-stem wheat ears and flag leaves were cut at the wheat booting stage, 11 days after flowering, and 18 days after flowering, respectively. One plant sample was stored in liquid nitrogen and brought back to the laboratory and placed in a -80℃ refrigerator for enzyme activity testing, and one was placed in an oven at 75℃ to dry to constant weight, and ground into powder to measure plant physiological indicators. The soluble sugar and sucrose content of leaves and grains were determined by anthrone colorimetry, and the activities of sucrose phosphate synthase in leaves at the booting stage, ADPG-pyrophosphorylase in grains 11 days after flowering, and bound starch synthase in grains 18 days after flowering were determined using sucrose phosphate synthase activity detection kits, adenosine diphosphate glucose pyrophosphorylase activity detection kits, and bound starch synthase activity detection kits, respectively; the total starch content and amylose content at maturity were determined using starch kits.

[0011] Optionally, it is characterized in that the indicators in the plant physiological database include soluble sugar content, sucrose content, sucrose phosphate synthase content in leaves at the booting stage, and sucrose, soluble sugar, ADPG-pyrophosphorylase content and granule-bound starch synthase content in grains 11 days after flowering.

[0012] A system for predicting changes in wheat starch content based on elevated carbon dioxide concentration, comprising: Experimental field construction module: used to set the carbon dioxide concentration gradient of farmland. Farmlands with different carbon dioxide concentrations correspond to different experimental fields. Basic database module: used to build plant physiological database based on different experimental fields; Regression calculation module: used to analyze the regression relationship between the content of amylose and total starch in regional winter wheat grains and carbon dioxide concentration using the segmented Piecewise SEM regression model.

[0013] Future wheat starch and component content prediction module: used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration using the piecewise SEM regression model and regression relationship.

[0014] It can be seen from the above technical scheme that compared with the prior art, the present invention provides a method and system for predicting changes in wheat starch content based on increased carbon dioxide concentration. Relying on an open CO2 concentration increase platform, it provides a carbon dioxide concentration gradient increase scenario that is closest to the natural state and the actual wheat growth state. Based on the measured physiological indicators of leaves and grains after flowering, simulation predictions of the amylose and total starch content of wheat grains are carried out, which can guide the precise planting and classification of wheat, meet the needs of different wheat processing industries, and enhance the resilience of the agricultural system to climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0016] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 It is the internal flow chart of the system of the present invention; Figure 3 is a graph showing the relationship between the change in carbon dioxide concentration and the content of amylose and total starch; Figure 4 is a fitted plot of observed and predicted values. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The embodiment of the present invention discloses a method for predicting changes in wheat starch content based on increased carbon dioxide concentration, comprising the following steps: S1. Set the carbon dioxide concentration gradient in the farmland, and different carbon dioxide concentration farmlands correspond to different test fields. Collect plant samples based on different test fields, measure the physiological and biochemical indicators of leaves and grains, and build a database of changes in grain starch response to CO2 concentration; S2. The segmented Piecewise SEM regression model was used to analyze the regression relationship between the amylose and total starch content of winter wheat grains and the carbon dioxide concentration in the region; S3. The piecewise SEM regression model and regression relationship were used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration.

[0019] Furthermore, in S1, the process of data collection of plant physiological database is as follows: In the experimental area with different carbon dioxide concentration gradients, wheat plants were marked, and four single-stem wheat ears and flag leaves were cut at the wheat booting stage, 11 days after flowering, and 18 days after flowering. One plant sample was stored in liquid nitrogen and brought back to the laboratory and placed in a -80℃ refrigerator for enzyme activity testing, and one was placed in an oven at 75℃ to dry to constant weight, and ground into powder to measure plant physiological indicators. The soluble sugar and sucrose content of leaves and grains were determined by anthrone colorimetry, and the activities of sucrose phosphate synthase in leaves, ADPG-pyrophosphorylase in grains, and granule-bound starch synthase were determined by sucrose phosphate synthase (SPS) activity detection kit (BC0605, Solarbio), adenosine diphosphate glucose pyrophosphorylase (AGP) activity detection kit (BC0435, Solarbio), and bound starch synthase (GBSS) activity detection kit (BC0435, Solarbio).

[0020] Furthermore, in S2, the model definition defines the measurement model of the explained variables (amylose and total starch) in each CO2 concentration gradient interval. The predictor variable is set as the CO2 concentration gradient, and the mediating variables affecting the content of amylose and total starch are the sucrose and soluble sugar content of leaves and grains, as well as the leaf SPS enzyme, grain AGPase and GBSS enzyme. A regression model is constructed in each CO2 concentration interval. Secondly, the path coefficient is estimated. In each interval, the piecewise linear regression relationship is: ①f(x) 叶片可溶性糖 =129.18443-0.03603X1-0.06855X2 Where: f(x) 叶片可溶性糖 is the explained variable X1 is the predicted variable CO2 concentration X2 is the SPS enzyme content in leaves at the booting stage ②f(x) SPS酶 =519.2718+0.227X f(x) SPS酶 is the explained variable X is the predicted variable CO2 concentration ③f(x) 叶片蔗糖 =-71.11559-0.06575X1+0.19867X2 in: f(x) 叶片蔗糖 is the explained variable X1 is the predicted variable CO2 concentration X2 is the SPS enzyme content in leaves at the booting stage ④f(x) 籽粒蔗糖=63.47447+0.55171X1+0.07334X2 in: f(x) 籽粒蔗糖 is the explained variable X1 is the sucrose content of leaves X2 is the predicted variable CO2 concentration ⑤f(x) AGP酶 =38.3554+0.2634X in: f(x) AGP酶 is the explained variable X1 is the sucrose content of the grain ⑥f(x) 籽粒可溶性糖 =-57.4406-0.7752X1+1.3893X2-0.2938X3+0.5968X4 in: f(x) 籽粒可溶性糖 is the explained variable X1 is the sucrose content of leaves X2 is the soluble sugar content of the grain X3 is the predicted variable CO2 concentration X4 is ADPG enzyme content ⑦f(x) GBSS酶 =7.46137-0.04441X in: f(x) GBSS酶 is the explained variable X is the soluble sugar content of the grain ⑧f(x) 直链淀粉含量 =118.95547+3.83595X1+0.03834X2-0.20064X3 in: f(x) 直链淀粉含量 is the explained variable X1 is the GBSS enzyme content X2 is the predicted variable CO2 concentration X3 is the soluble sugar content of the grain ⑨f(x) 总淀粉含量 =514.602+1.231X in: f(x) 总淀粉含量 is the explained variable X is the amylose content The parameters of these segmented regressions were estimated based on the measured indicator data, the total effect of each path was calculated, and significance tests were performed ( Figure 3Then the model fit was evaluated (Table 1) to determine the overall fit of the model in each interval (adjusted R 2 ) and the significance of the path coefficient ( p <0.05), to check whether the relationship between variables in the segmented regression is reasonable.

[0021] Table 1. Evaluation results of the starch component simulation model Furthermore, in S3, the segmented Piecewise SEM regression model and regression relationship were used to predict the evolution trend of the amylose content and total starch content of winter wheat under the future changes in carbon dioxide concentration. The fitting diagram of the observed value and the predicted value is shown in Figure 4 .

[0022] This embodiment also discloses a wheat starch content change prediction system based on increased carbon dioxide concentration, comprising: Experimental field construction module: used to set the carbon dioxide concentration gradient of farmland. Farmlands with different carbon dioxide concentrations correspond to different experimental fields. Basic database module: used to build plant physiological and biochemical databases based on different experimental fields; Regression calculation module: used to analyze the regression relationship between the content of amylose and total starch in regional winter wheat grains and carbon dioxide concentration using the piecewise SEM regression model; Future wheat starch and component content prediction module: used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration using the piecewise SEM regression model and regression relationship.

[0023] Specifically, Figure 1 As shown, the system mainly includes the following modules: Module 1 is the basic database, which embeds the starch and component content data of winter wheat under the increasing carbon dioxide concentration gradient in my country's A winter wheat area, and can realize the query of amylose and total starch content data under different carbon dioxide concentration gradients. It includes 9 indicators such as leaf soluble sugar, sucrose, sucrose phosphate synthase (SPS) at the wheat booting stage, 11 days after flowering and 18 days after flowering, as well as grain sucrose, soluble sugar, pyrophosphorylase (AGP), plant granule-bound starch synthase (GBSS) and grain amylose content and total starch content at maturity.

[0024] Module 2 is an algorithm module. For the scenarios of carbon dioxide concentration increase of 100ppm and 200ppm, the segmented piecewise SEM (also known as confirmatory path analysis) is used to expand the traditional SEM by introducing a flexible mathematical framework that can contain a variety of model structures, distributions and assumptions to build a prediction model for the amylose content and total starch content of winter wheat. This module provides two data output functions: the evolution characteristics of the amylose content and total starch content of winter wheat in region A-a.

[0025] Module 3 is the application of wheat starch and component content prediction in the future. The soluble sugar and sucrose content of leaves and grains at the booting stage, wheat booting stage, 11 days after flowering and 18 days after flowering are input to predict the straight chain and total starch content of mature wheat grains for every 100 ppm increase in carbon dioxide concentration.

[0026] The workflow of each module is as follows, see Figure 2 : Module 1: Basic Dataset: Step 1: Set the carbon dioxide concentration gradient, atmospheric CO2 concentration, ACO2+100ppm, ACO2+200ppm Step 2: Physiological database: In the three CO2 concentration gradient test areas, four single-stem wheat ears and flag leaves were cut at the wheat booting stage, 11 days after anthesis, and 18 days after anthesis, and nine indicators including soluble sugar content, sucrose content, and sucrose phosphate synthase content in leaves at the booting stage, sucrose content in grains 11 days after anthesis, soluble sugar content, pyrophosphorylase content, and plant granule-bound starch synthase in grains 18 days after anthesis, as well as amylose content and total starch content in grains at maturity were measured.

[0027] Module 2: Algorithm Module Step 3: See Figure 3 , Piecewise SEM (also known as confirmatory path analysis) extends traditional SEM by introducing a flexible mathematical framework that can include a variety of model structures, distributions, and assumptions. The present invention uses a piecewise structural equation model to analyze the regression relationship between the amylose and total starch content of winter wheat grains in region A and carbon dioxide concentration.

[0028] Module 3: Applied Forecasting Step 4: Using the constructed model and output data, predict the evolution trend of the amylose content and total starch content of winter wheat in Region A under the influence of future changes in carbon dioxide concentration: in the future, for every 100 ppm increase in carbon dioxide, the amylose content and total starch content of winter wheat in Region A are expected to increase by 9.71% and 1.81% respectively.

[0029] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0030] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting changes in wheat starch content based on elevated carbon dioxide concentration, characterized in that: The following steps are involved: Set the carbon dioxide concentration gradient of the farmland, and farmlands with different carbon dioxide concentrations correspond to different test fields; Construct a plant physiological database based on different experimental fields; The segmented Piecewise SEM regression model was used to analyze the regression relationship between the amylose and total starch content of winter wheat grains and the carbon dioxide concentration. The piecewise SEM regression model and regression relationship were used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration.

2. A method for predicting changes in wheat starch content based on elevated carbon dioxide concentration according to claim 1, characterized in that: The carbon dioxide concentration gradient is ACO2, ACO2+100ppm, ACO2+200ppm.

3. A method for predicting changes in wheat starch content based on elevated carbon dioxide concentration according to claim 1, characterized in that: The process of data collection of plant physiological database is as follows: in the experimental areas with different carbon dioxide concentration gradients, four single-stem wheat ears and flag leaves were cut at the wheat booting stage, 11 days after anthesis and 18 days after anthesis, respectively. One plant sample was stored in liquid nitrogen and brought back to the laboratory and placed in a -80℃ refrigerator for enzyme activity testing, and the other was placed in an oven at 75℃ to dry to constant weight, and then ground into powder to measure plant physiological indicators; the soluble sugar and sucrose contents of leaves and grains were determined by anthrone colorimetry, and the activities of sucrose phosphate synthase in leaves, ADPG-pyrophosphorylase in grains and granule-bound starch synthase were determined using sucrose phosphate synthase activity detection kit, adenosine diphosphate glucose pyrophosphorylase activity detection kit and bound starch synthase activity detection kit, respectively.

4. A method for predicting changes in wheat starch content based on elevated carbon dioxide concentration according to claim 1, characterized in that: The indicators in the plant physiological database include soluble sugar, sucrose, and sucrose phosphate synthase in leaves at the booting stage, sucrose in grains 11 days after anthesis, and soluble sugar, ADPG-pyrophosphorylase, and granule-bound starch synthase in grains 18 days after anthesis.

5. A wheat starch content change prediction system based on increased carbon dioxide concentration, characterized in that: include: Experimental field construction module: used to set the carbon dioxide concentration gradient of farmland. Farmlands with different carbon dioxide concentrations correspond to different experimental fields. Basic database module: used to build plant physiological database based on different experimental fields; Regression calculation module: used to analyze the regression relationship between the content of amylose and total starch in regional winter wheat grains and carbon dioxide concentration using the piecewise SEM regression model; Future wheat starch and component content prediction module: used to predict the evolution trend of the amylose content and total starch content of winter wheat under future changes in carbon dioxide concentration using the piecewise SEM regression model and regression relationship.

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