Method for predicting rice brown rice rate based on hydrogen peroxide content in rice seedling stage

Abstract

The invention relates to the technical field of rice cultivation physiology and quality prediction, and particularly discloses a method for predicting the rice brown rice rate based on the hydrogen peroxide content in the rice seedling stage, and the method comprises the following steps: collecting a leaf sample on the fourteenth day after rice sowing, and accurately determining the hydrogen peroxide content by adopting a titanium reagent method; after the rice is mature, measuring the actual brown rice rate through a standard shelling program; the method comprises the following steps: constructing a quadratic polynomial regression model of hydrogen peroxide content-brown rice rate by using more than 50 rice varieties with genetic background differences, wherein the equation is # imgabs0 #; and substituting the hydrogen peroxide content of a to-be-detected sample into the model to obtain a predicted value. According to the method, the prediction period is advanced from the late mature period of the rice to the seedling period of the rice, and compared with a traditional method, the prediction result is obtained 2-3 months earlier, so that the breeding efficiency is improved, and an important early decision-making tool is provided for rice quality breeding and cultivation management.

Description

Technical Field

[0001] The present invention relates to the technical field of rice cultivation physiology and quality prediction, and particularly relates to a method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice. Background Art

[0002] The brown rice rate of rice is a core index for measuring the processing quality of rice and has important impacts on all links of the industrial chain. This index directly determines the economic value of paddy rice. Varieties with a high brown rice rate (above 75%) are more competitive in the market and can bring higher returns to growers and processing enterprises. At the same time, the brown rice rate is closely related to the processing efficiency. A high brown rice rate means a lower broken rice rate and energy consumption, which can significantly improve the economic benefits of processing enterprises. From a nutritional perspective, brown rice retains most of the nutritional components of rice, and its yield directly affects the nutritional value of the final product.

[0003] Traditional methods for measuring the brown rice rate of rice rely on physical detection of paddy rice at maturity, that is, after the rice completes its entire growth cycle (usually 120 - 150 days), harvesting paddy rice samples, shelling them using a hulling machine, and then calculating the brown rice rate according to the formula: Although the brown rice rate can be accurately obtained using this method, this method must wait until the rice is fully mature for measurement, resulting in a delay of 120 - 150 days in breeding decisions; due to the inability to predict early, breeders can only obtain data after maturity and then make the next round of selections, significantly extending the breeding cycle.

[0004] In the field of plant physiology research, hydrogen peroxide, as a reactive oxygen molecule, has been proven to be involved in regulating plant growth and development and stress responses, but its association with the processing quality of rice has not been reported. Therefore, there is an urgent need to develop a method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice. Summary of the Invention

[0005] Based on Pearson correlation coefficient analysis, the method of the present invention found that the hydrogen peroxide content in the seedling stage of rice is extremely significantly negatively correlated with the brown rice rate at maturity (p = 0.000003796). Therefore, the present invention further constructs a method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice. This inventive method advances the prediction period from the late maturity stage of rice to the seedling stage of rice, obtaining the prediction result 2 - 3 months earlier than the traditional method, thereby improving the breeding efficiency and providing an important early decision-making tool for rice quality breeding and cultivation management.

[0006] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows: A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice, comprising the following steps: S1. Rice leaf samples were collected 14 days after sowing and the hydrogen peroxide content in the leaves was measured using the titanium reagent method; S2. After the rice is mature, hull the rice sample according to the formula: ; S3. Construct a quadratic polynomial regression model using the brown rice percentage as the output data and the hydrogen peroxide content as the input data. S4. Measure the hydrogen peroxide content of the rice leaves to be tested, substitute the content into the quadratic polynomial regression model, and predict the brown rice rate of the rice to be tested.

[0007] Furthermore, in S1, each sample needs to be measured at least at three different leaf parts, with 0.1 gram of sample used for each part, and the average value is taken as the final hydrogen peroxide content of the sample.

[0008] Furthermore, in S2, the brown rice rate determination requires mixing all the rice grains and randomly selecting ≥150 grains for hulling determination, repeating this three times and taking the average as the final brown rice rate of the sample.

[0009] Furthermore, in S3, the input and output data for constructing the quadratic polynomial regression model must include at least 50 rice varieties with different genotypes, and there are differences in hydrogen peroxide content and brown rice rate among these rice varieties.

[0010] Furthermore, in S3, the formula of the quadratic polynomial regression model is: , where 101 is the intercept of the equation, is the coefficient of hydrogen peroxide content, 0.0005647 is the coefficient of the square of hydrogen peroxide content, the unit of brown rice rate is %, and the unit of hydrogen peroxide content is μg / g.

[0011] Beneficial effects: The present invention provides a method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage. This method advances the prediction period from the late rice maturity stage to the rice seedling stage, and obtains the prediction results 2-3 months earlier than the traditional method, thereby improving breeding efficiency and providing an important early decision-making tool for rice quality breeding and cultivation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a heat map of the correlation coefficients between the hydrogen peroxide content in the rice seedling stage and five yield and five rice quality indices at the maturity stage.

[0013] Figure 2 Fitting plots for the linear regression model (red) and the quadratic polynomial regression model (blue). DETAILED DESCRIPTION

[0014] To enable those skilled in the art to better understand the technical solutions in the present invention, the following will further elaborate on the present application in conjunction with embodiments. Embodiment

[0015] 1. This embodiment was carried out using 52 rice varieties. The names of the 52 rice varieties are shown in Table 1. The seeds of these 52 rice varieties were obtained by purchasing from relevant domestic seed industry enterprises.

[0016] 2. The seeds of the 52 rice varieties were soaked for 48 hours and germinated for 24 hours. After the seeds germinated, they were sown in the greenhouse of the China National Rice Research Institute in Fuyang District, Hangzhou City, Zhejiang Province on May 23, 2023. The sowing was carried out in black plastic buckets. The black plastic buckets were circular plastic buckets with a diameter of 10 cm and a height of 15 cm. The height of the soil in each plastic bucket was flush with the edge of the plastic bucket. 3 rice seeds of the same variety were evenly sown in each plastic bucket. 3 buckets were sown for each variety, that is, a total of 9 seeds of the same variety were sown for each variety.

[0017] On the 14th day after sowing, the content of hydrogen peroxide in the leaves was measured. The specific operation steps are as follows: (1) Take the rice leaf samples on the 14th day after sowing and store them in liquid nitrogen for a long time; (2) Take 0.1 g of rice leaves stored in liquid nitrogen, grind and crush them with a grinding rod in a grinder, then add 0.1 M PBS phosphate buffer solution (pH = 7.4), and grind them into a homogenized state. Subsequently, centrifuge the homogenized solution in a refrigerated centrifuge for 10 minutes (rotation speed 8000 rpm, 4 °C); (3) After centrifugation, take 1 ml of the supernatant, add 1 ml of 0.1% titanium tetrachloride solution (titanium tetrachloride is dissolved in 20% sulfuric acid) to the supernatant, mix well and let it stand for 5 minutes, then centrifuge for 10 minutes (rotation speed 8000 rpm), and retain the supernatant for testing; (4) Use an ultraviolet spectrophotometer to measure the absorbance value of the supernatant at 410 nm; (5) Make a standard curve and obtain the hydrogen peroxide content in the sample according to the standard curve.

[0018] For each rice variety, at least 3 different leaf parts need to be measured. 0.1 g of the sample is used to analyze the hydrogen peroxide content for each part. The measured values of the hydrogen peroxide content of different parts are averaged to obtain the final hydrogen peroxide content value of the sample.

[0019] After the rice is mature, five yield indexes are measured respectively: stem weight, leaf weight, panicle weight, panicle number, dry matter weight, and five rice quality indexes: brown rice rate, milled rice rate, head rice rate, chalky grain rate, and chalkiness degree.

[0020] Take the above-ground parts of the same variety from all three plastic buckets, and separate the above-ground parts of each bucket into leaves, leaf sheaths, and panicles. Then dry the materials in an oven at 105 °C for 120 minutes, and then dry them at 80 °C until a constant weight is reached.

[0021] According to the formula: , calculate the actual stem weight.

[0022] According to the formula: , calculate the actual leaf weight.

[0023] According to the formula: , calculate the actual panicle weight.

[0024] Count the number of effective panicles of the same variety in each plastic bucket (panicles with a panicle length ≥ 5 cm and a seed-setting rate ≥ 10% are regarded as effective panicles). According to the formula: , calculate the actual number of panicles.

[0025] According to the formula: , calculate the dry weight of the above-ground parts of each bucket, and then according to the formula: , calculate the actual dry matter weight.

[0026] After the rice is mature, collect and mix the rice samples of the same variety planted in three plastic buckets. Weigh 50 g of the rice samples and place them in an Otake-FC2R type rice huller. Set the standard hulling procedure according to the equipment instruction manual to obtain brown rice. According to the formula: , calculate the brown rice rate, and repeat 3 times to take the average value as the final brown rice rate of this sample.

[0027] Transfer all the brown rice samples hulled from the Otake-FC2R type rice huller to a Puyun 2299 type rice polisher, set the milling parameters (milling time 20 seconds), remove the bran layer on the surface of the brown rice, and prepare polished rice. According to the formula: , calculate the milled rice rate, and repeat 3 times to take the average value as the final milled rice rate of this sample.

[0028] Randomly weigh 20 g from the polished rice samples prepared by the Puyun 2299 type rice polisher. Place the weighed polished rice samples in a transparent tray and use the rice appearance analysis system of Hangzhou Wanshen Detection Technology Co., Ltd. for scanning and analysis to automatically obtain the data of head rice rate, chalky grain rate, and chalkiness degree. After the rice appearance analysis system scans and analyzes, perform image acquisition according to the system supporting software. The analysis system calculates according to the formula: , calculate the whole polished rice rate; set the software detection threshold (chalky area ≥ 5% is judged as chalky grains), automatically identify the number of chalky rice grains, and analyze the system according to the formula: , calculate the chalky particle rate; the analysis system uses the formula: Repeat 3 times and take the average value as the final polished rice rate, final chalky kernel rate and final chalkiness data of the sample.

[0029] The hydrogen peroxide content and five yield indicators of 52 rice varieties: stem weight, leaf weight, ear weight, number of ears, and dry matter weight are shown in Table 1. The five rice quality indicators of 52 rice varieties: brown rice rate, polished rice rate, whole polished rice rate, chalky grain rate, and chalkiness are shown in Table 2.

[0030] 3. Pearson correlation analysis results showed that there was a significant negative correlation between the hydrogen peroxide content in the seedling stage and the brown rice rate in the mature stage. The statistical test results showed that the negative correlation reached an extremely significant level (p < 0.001), indicating that the higher the hydrogen peroxide content in the seedling stage, the lower the brown rice rate in the mature stage ( Figure 1 ).

[0031] 4. Based on the significant negative correlation between the hydrogen peroxide content in the seedling stage and the brown rice rate in the mature stage, a linear regression model and a quadratic polynomial regression model were constructed with the hydrogen peroxide content in rice leaves in the seedling stage as the independent variable and the brown rice rate in the mature stage as the dependent variable, respectively.

[0032] The linear regression model equation is: , where 83.464406 is the intercept of the equation, is the coefficient of hydrogen peroxide content, the unit of brown rice rate is %, and the unit of hydrogen peroxide content is μg / g.

[0033] The quadratic polynomial regression model equation is: , where 101 is the intercept of the equation, −0.2472 is the coefficient of hydrogen peroxide content, 0.0005647 is the coefficient of the square of hydrogen peroxide content, the unit of brown rice rate is %, and the unit of hydrogen peroxide content is μg / g.

[0034] The root mean square error (RMSE) of the quadratic polynomial regression model was 2.098, and the coefficient of determination (R²) was 0.48 ( Figure 2 The blue curve in the middle); the root mean square error (RMSE) of the linear regression model is 2.344, and the coefficient of determination (R²) is 0.35 ( Figure 2(the red straight line in it), the coefficient of determination of the quadratic polynomial regression model is higher than that of the linear regression model, while the root mean square error is lower than that of the linear regression model.

[0035] According to the equation of the linear regression model: , calculate the predicted brown rice rate of 52 rice varieties, and then compare the predicted brown rice rate with the actually measured brown rice rate. It is found that the average absolute difference between the predicted values and the actual values of all 52 varieties is only 1.9%. The detailed data are shown in Table 3.

[0036] According to the equation of the quadratic polynomial regression model: , calculate the predicted brown rice rate of 52 rice varieties, and then compare the predicted brown rice rate with the actually measured brown rice rate. It is found that the average absolute difference between the predicted values and the measured values of all 52 varieties is only 1.6%. The detailed data are shown in Table 3.

[0037] Therefore, the present invention uses a quadratic polynomial regression model to predict the brown rice rate of rice. The inventive method can quickly and accurately predict the brown rice rate through the hydrogen peroxide content at the seedling stage of rice, advance the prediction period from the late maturity stage of rice to the seedling stage of rice, obtain the prediction result 2-3 months earlier than the traditional method, thereby improving the breeding efficiency and providing an important early decision-making tool for rice quality breeding and cultivation management.

[0038] Table 1 Names of all 52 rice varieties and their hydrogen peroxide content and 5 yield indicators

[0039] Table 2 Names of all 52 rice varieties and their 5 rice quality indicators

[0040] Table 3 Comparison between the linear regression model and the quadratic polynomial regression model

Claims

1. A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice, characterized in that, The following steps are involved: S1. Rice leaf samples were collected 14 days after sowing and the hydrogen peroxide content in the leaves was measured using the titanium reagent method; S2. After the rice is mature, hull the rice sample according to the formula: ； S3. Construct a quadratic polynomial regression model with the brown rice rate as the output data and the hydrogen peroxide content as the input data , where 101 is the equation intercept, is the coefficient of the hydrogen peroxide content, 0.0005647 is the coefficient of the square of the hydrogen peroxide content, the unit of the brown rice rate is %, and the unit of the hydrogen peroxide content is μg / g; S4. Measure the hydrogen peroxide content of the rice leaves to be tested, substitute the content into the quadratic polynomial regression model, and predict the brown rice rate of the rice to be tested.

2. The method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice according to claim 1, wherein In S1, each sample needs to be measured at least at three different leaf parts, with 0.1 gram of sample used for each part, and the average value is taken as the final hydrogen peroxide content of the sample.

3. A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice, characterized in that, In S2, the brown rice rate is determined by mixing all the rice grains and randomly weighing ≥50 g of the rice for hulling. This is repeated three times and the average value is taken as the final brown rice rate of the sample.

4. A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the seedling stage of rice, characterized in that, In S3, the input and output data for constructing the quadratic polynomial regression model must include at least 50 rice varieties with different genotypes, and there are differences in hydrogen peroxide content and brown rice rate among these rice varieties.

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