A method for predicting brown rice rate based on hydrogen peroxide content in rice seedling stage
By measuring the hydrogen peroxide content in the rice seedling stage and constructing a quadratic polynomial regression model, the problem of prolonged breeding cycle in traditional methods was solved, early prediction of brown rice rate was achieved, and breeding efficiency and decision-making accuracy were improved.
Patent Information
- Application Number
- CN202510901714.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-01
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2045-07-01
AI Technical Summary
The traditional method requires waiting until the rice matures before determining the brown rice rate, which leads to delayed breeding decisions, extended breeding cycles, and the inability to make early predictions that affect breeding efficiency.
Based on the hydrogen peroxide content in the rice seedling stage, a quadratic polynomial regression model was constructed through Pearson correlation coefficient analysis to predict the brown rice rate at maturity, and the prediction results were obtained 2-3 months in advance.
It improves breeding efficiency and provides an early decision-making tool for rice quality breeding and cultivation management, with high prediction accuracy and small error.
Smart Images
Figure CN120404464B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of rice cultivation physiology and quality prediction, and in particular to a method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage. Background Art
[0002] The brown rice percentage is a core indicator of rice processing quality and has a significant impact on every link in the rice industry chain. This metric directly determines the economic value of rice. Varieties with a high brown rice percentage (above 75%) are more competitive in the market, generating higher profits for growers and processors. Brown rice percentage is also closely linked to processing efficiency. A high brown rice percentage means lower broken rice rates and lower energy consumption, significantly improving the economic returns of processors. From a nutritional perspective, brown rice retains most of its nutrients, and its yield directly impacts the nutritional value of the final product.
[0003] The traditional method for determining the brown rice rate relies on physical testing of mature rice grains. After the rice has completed its entire growth cycle (usually 120-150 days), the rice samples are harvested and hulled using a rice husker. The brown rice rate is then calculated according to the following formula:
[0004] . Although this method can accurately obtain the brown rice rate, it must wait until the rice is fully mature before measurement, resulting in a delay of 120-150 days in breeding decisions; due to the inability to make early predictions, breeders can only obtain data after maturity and then proceed to the next round of selection, significantly extending the breeding cycle.
[0005] In the field of plant physiology research, hydrogen peroxide, a reactive oxygen species, has been shown to regulate plant growth and development and responses to stress, but its association with rice processing quality has not been reported. Therefore, there is an urgent need to develop a method to predict the brown rice rate based on hydrogen peroxide content in rice seedlings. Summary of the Invention
[0006] The method presented in this paper, based on Pearson correlation coefficient analysis, found that hydrogen peroxide content in the rice seedling stage was highly significantly negatively correlated with the brown rice rate at the maturity stage (p=0.000003796). Therefore, the present invention further developed a method for predicting the brown rice rate based on 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, achieving prediction results 2-3 months earlier than traditional methods. This improves breeding efficiency and provides an important early decision-making tool for rice quality breeding and cultivation management.
[0007] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:
[0008] A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage comprises the following steps:
[0009] 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;
[0010] S2. After the rice is mature, hull the rice sample according to the formula:
[0011] ;
[0012] 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.
[0013] 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.
[0014] Furthermore, in S1, each sample needs to be measured at least at three different leaf parts, using 0.1 gram of sample for each part, and the average is taken as the final hydrogen peroxide content of the sample.
[0015] 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.
[0016] 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.
[0017] 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.
[0018] 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
[0019] 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.
[0020] Figure 2 Fitting plots for the linear regression model (red) and the quadratic polynomial regression model (blue). DETAILED DESCRIPTION
[0021] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present application will be further described in detail below with reference to the embodiments. Example
[0022] 1. This example uses 52 rice varieties. The names of the 52 rice varieties are shown in Table 1. The seeds of these 52 rice varieties were purchased from relevant domestic seed companies.
[0023] 2. Soak seeds of 52 rice varieties for 48 hours and germinate for 24 hours. After germination, the seeds were sown on May 23, 2023, in the greenhouse of the China National Rice Research Institute in Fuyang District, Hangzhou City, Zhejiang Province. The seeds were sown in black plastic buckets. The black plastic buckets were round 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 rim of the plastic bucket. Three rice seeds of the same variety were evenly sown in each plastic bucket. Three buckets were used for each variety, for a total of nine seeds of each variety.
[0024] Measure the hydrogen peroxide content in the leaves 14 days after sowing. The specific steps are as follows:
[0025] (1) Take rice leaf samples 14 days after sowing and store them in liquid nitrogen for a long time;
[0026] (2) Take 0.1 g of rice leaves stored in liquid nitrogen, grind them with a grinding rod, add 0.1 M PBS phosphate buffer solution (pH = 7.4), and grind them into a homogenous state. Then, centrifuge the homogenous solution in a refrigerated centrifuge for 10 minutes (speed 8000 rpm, 4°C);
[0027] (3) After centrifugation, take 1 ml of the supernatant and add 1 ml of 0.1% titanium tetrachloride solution (titanium tetrachloride dissolved in 20% sulfuric acid) to the supernatant. Mix well and let it stand for 5 minutes. Centrifuge for 10 minutes (8000 rpm), and retain the supernatant for testing.
[0028] (4) Measure the absorbance of the supernatant at 410 nm using a UV spectrophotometer;
[0029] (5) Prepare a standard curve and calculate the hydrogen peroxide content in the sample based on the standard curve.
[0030] For each rice variety, at least three different leaf parts must be measured, and 0.1 gram of sample is used for analysis of hydrogen peroxide content in each part. The hydrogen peroxide content measurements of different parts are averaged to obtain the final hydrogen peroxide content value of the sample.
[0031] After the rice matured, five yield indicators were measured: stem weight, leaf weight, ear weight, number of ears, and dry matter weight; and five rice quality indicators: brown rice rate, polished rice rate, whole polished rice rate, chalky grain rate, and chalkiness.
[0032] Take the aerial parts of the same variety from all three plastic buckets and separate the aerial parts of each bucket into leaves, sheaths, and ears. Then dry the material in an oven at 105°C for 120 minutes and then dry it at 80°C to constant weight.
[0033] According to the formula: , calculate the actual stem weight.
[0034] According to the formula: , calculate the actual leaf weight.
[0035] According to the formula: , calculate the actual ear weight.
[0036] Count the number of valid ears of the same variety in each plastic bucket (ears with an ear length ≥5 cm and a fruit set rate ≥10% are considered valid ears) according to the formula:
[0037] ,
[0038] Calculate the actual number of ears.
[0039] According to the formula:
[0040] , calculate the above-ground dry weight of each barrel,
[0041] According to the formula:
[0042] , calculate the actual dry matter weight.
[0043] After the rice matures, rice samples of the same variety planted in three plastic buckets are collected and mixed. 50 g of rice sample is weighed and placed in an Otake-FC2R rice huller. The standard hulling procedure is set according to the equipment manual to obtain brown rice. According to the formula:
[0044] , calculate the brown rice rate, repeat 3 times and take the average as the final brown rice rate of the sample.
[0045] All the brown rice samples removed from the hulling machine of Otake-FC2R were transferred to the Puyun 2299 rice polisher. The milling parameters were set (milling time 20 seconds) to remove the bran layer on the surface of the brown rice and prepare polished rice. According to the formula:
[0046] , calculate the polished rice rate, repeat 3 times and take the average as the final polished rice rate of the sample.
[0047] A random sample of 20g of polished rice prepared by the Puyun 2299 rice polisher was weighed and placed in a transparent tray. The sample was then scanned and analyzed using the rice appearance analysis system from Hangzhou Wanshen Testing Technology Co., Ltd. This automatically generated data on the polished rice percentage, chalky grain percentage, and chalkiness. After scanning and analyzing the sample, the system's accompanying software captured images. The analysis system then calculated the following:
[0048] , 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:
[0049] , calculate the chalky particle rate; the analysis system uses the formula:
[0050] 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.
[0051] 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.
[0052] 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 ).
[0053] 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.
[0054] The linear regression model equation is:
[0055] , 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.
[0056] The quadratic polynomial regression model equation is:
[0057] , 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.
[0058] 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 the middle), 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.
[0059] According to the equation of the linear regression model:
[0060] Calculating the predicted brown rice percentages for 52 rice varieties and comparing these predicted brown rice percentages with the actual measured brown rice percentages revealed that the average absolute difference between the predicted and actual values for all 52 varieties was only 1.9%. Detailed data is shown in Table 3.
[0061] According to the equation of the quadratic polynomial regression model:
[0062] Calculating the predicted brown rice percentages for 52 rice varieties and comparing these predicted brown rice percentages with the actual measured brown rice percentages revealed that the average absolute difference between the predicted and measured values for all 52 varieties was only 1.6%. Detailed data is shown in Table 3.
[0063] Therefore, the present invention adopts a quadratic polynomial regression model to predict the brown rice rate of rice. The method of the invention can quickly and accurately predict the brown rice rate by the hydrogen peroxide content in the rice seedling stage, advancing the prediction period from the late rice maturity stage to the rice seedling stage, and obtaining 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.
[0064] Table 1 Names of all 52 rice varieties, their hydrogen peroxide content and five yield indicators
[0065]
[0066] Table 2 Names of all 52 rice varieties and their five rice quality indicators
[0067]
[0068] Table 3 Comparison of linear regression model and quadratic polynomial regression model
[0069] .
Claims
1. A method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage, 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 using brown rice percentage as output data and hydrogen peroxide content as input data. , 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 microgram / gram; 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 rice seedling stage according to claim 1, characterized in that: 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. The method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage according to claim 1, 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. The method for predicting the brown rice rate of rice based on the hydrogen peroxide content in the rice seedling stage according to claim 1, 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.