Yellow estuary rice characteristic quality factor identification method, system and equipment

Abstract

The invention discloses a Yellow River estuary rice characteristic quality factor identification method, system and equipment, and belongs to the technical field of agricultural product detection and evaluation. The method for identifying the characteristic quality factors of the Yellow estuary rice is technically characterized in that dimension reduction processing is carried out on characteristic quality index change characteristic data to be screened through a principal component analysis method, and typical characteristic quality evaluation factors of the Yellow estuary rice are determined. And through weight analysis, constructing a Yellow River estuary rice characteristic quality factor identification statistical model based on the standardized value and the weight value of each characteristic quality evaluation factor. And evaluating and calculating the characteristic quality factors of the Yellow River estuary rice by using a multi-factor fuzzy mathematics comprehensive evaluation method, and evaluating the comprehensive quality of the Yellow River estuary rice based on an evaluation result. By setting the optimal parameters and carrying out product identification and judgment through a comparison principle, the method is simple and direct, high in practicability and wide in applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural product detection and evaluation, and particularly relates to a method, system and device for identifying characteristic quality factors of rice in the Yellow River Estuary. Background Art

[0002] The production history of rice in the Yellow River Estuary is long. Its rice grains are plump, crystal clear, soft, oily, fragrant for a long time, and have a sweet taste. The main production area belongs to the newly silted areas on both sides of the Yellow River Estuary, located at 36°55′ - 38°10′ north latitude, bordering the Bohai Sea, warm and humid, with early spring and late autumn frost. Although it is hot in summer, there is little severe heat. The accumulated temperature above or equal to 10°C per year is 3500°C, having significant advantages in location planting and variety breeding competition advantages.

[0003] Identifying characteristic quality components of agricultural products is one of the important technical means to protect and utilize the quality resources of edible agricultural products and improve the added value of products. The characteristic quality components of agricultural products include aspects such as sensory quality, which is the sum of quality indicators felt by the human sensory organs. Appearance factors include size, shape, glossiness, transparency, color, etc. The accuracy and sensitivity of characteristic quality identification restrict the identification ability of quality resources. Currently, in terms of processing characteristics, appearance performance, nutritional value, cooking and eating taste, and the types and contents of flavor substances of rice, there are differences due to different varieties and origins, and there is also a certain correlation between various quality measurement indicators. For example, Feng Yingying et al. focused on various japonica rice varieties in southern Northeast China, explored the differences and correlation analysis on 13 quality evaluation dimensions such as appearance, processing, nutrition, and taste, and also carried out comprehensive evaluation using principal component and cluster analysis. The team of Zhu Dawei focused on studying high-quality rice in China, analyzed its appearance, taste quality and related physical and chemical properties, and interpreted the relationship between taste value and physical and chemical indicators. The results of Sun Xuchao et al. showed that there are significant differences in the taste experience of japonica rice from different origins, and at the same time sorted out the internal relationship between various physical and chemical properties.

[0004] At the detection technology level, the headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) technology performs excellently, can quickly and accurately identify and quantify volatile components in samples, effectively distinguish the differences in volatiles between different samples, and is now widely used in the research field of rice volatile substances. For example, Liu Min et al. found through GC-MS detection that the volatile compounds contained in different varieties of rice are mostly hydrocarbons and aldehydes, and the contents are significantly different among varieties. The Lim team successfully distinguished rice from South Korea and China by means of HS-SPME-GC-MS combined with a partial least squares discriminant model, and locked hexanal, 1-hexanol and 10 hydrocarbon compounds as the characteristic biological components for distinguishing rice from the two countries. Geographical indication rice has distinct regional characteristics. Due to different climate and soil conditions, it has formed its own unique style in terms of quality and flavor.

[0005] Due to the uncertainty of the main characteristic quality factors of the rice from the Yellow River Estuary, as well as the independent influence and relative influence degree of the nutritional quality and regional characteristics are not yet clear. Under the special production conditions of the Yellow River Estuary, it is necessary to further explore and identify the main characteristic quality factors of the rice from the Yellow River Estuary, so as to provide theoretical and practical references for establishing new inspection and testing methods and protecting food safety, which is of great significance for improving the high-quality production of rice in the lower reaches of the Yellow River Delta. Summary of the Invention

[0006] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide a method for evaluating the quality of the rice from the Yellow River Estuary, which can clarify the quality indicators of the rice from the Yellow River Estuary, simplify the evaluation factors of the rice from the Yellow River Estuary, and determine the representative quality indicators of the rice from the Yellow River Estuary. It aims to evaluate the quality of the rice from the Yellow River Estuary through fewer parameters, and realizes the research on the identification of the characteristic component quality in the same kind of quality of the rice from the Yellow River Estuary.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A method for identifying the characteristic quality factors of the rice from the Yellow River Estuary, comprising the following steps:

[0009] S1: Collect multi-source data of the rice from the Yellow River Estuary, analyze the change characteristics of each characteristic quality index to be screened, including sensory quality, physical and chemical nutritional quality, taste quality and flavor substances, and perform dimensionality reduction processing on the change characteristic data of the characteristic quality index to be screened through the principal components analysis (PCA) method to determine the typical characteristic quality evaluation factors of the rice from the Yellow River Estuary;

[0010] S2: Standardize each characteristic quality evaluation factor to obtain the corresponding standardized value; calculate and analyze the corresponding weight value of each characteristic quality evaluation factor according to the standardized value to obtain the corresponding weight value of each characteristic quality evaluation factor, and construct a statistical model for identifying the characteristic quality factors of the rice from the Yellow River Estuary;

[0011] S3: Based on the standardized value and the weight value, use the multi-factor fuzzy mathematics comprehensive evaluation method to evaluate and calculate the characteristic quality factors of the rice from the Yellow River Estuary, and evaluate the comprehensive quality of the rice from the Yellow River Estuary based on the evaluation result.

[0012] Preferably, each index of the sensory quality, physical and chemical nutritional quality, taste quality and flavor substances in step S1 is a combination of multi-dimensional indexes, including but not limited to the combination of sensory evaluation indexes, physical and chemical nutritional indexes, taste quality indexes and flavor substances in multiple dimensions.

[0013] Preferably, the sensory quality indicators include: grain length, length-width ratio, and chalky grain rate. Further, the evaluation is carried out according to NY / T 2334-2013 "Determination of head rice rate, grain shape, chalky grain rate, chalkiness degree and transparency of rice - Image method".

[0014] Preferably, the physicochemical and nutritional quality indicators include: protein, amylose, sucrose, glucose, maltose, fructose, Ca, Mg, K, Na, Zn, Se. Further, the protein evaluation is carried out according to the First Method of GB 5009.5-2016; the amylose evaluation is carried out according to NY / T 2639-2014; the evaluations of sucrose, glucose, maltose, and fructose are carried out according to NY / T 3902-2021; the trace element evaluation is carried out according to GB / T 13885-2017 "National Food Safety Standard - Determination of multiple elements in foods".

[0015] Preferably, the eating quality indicators include: gel consistency and RVA pasting properties (peak viscosity, final viscosity, minimum viscosity, breakdown value, setback value, pasting temperature, peak time). Further, the eating quality evaluation is carried out according to GB / T 13885-2017 "National Food Safety Standard - Determination of multiple elements in foods".

[0016] Preferably, the flavor substances include: 2-acetyl-1-pyrroline, vanillin, 6-methyl-5-hepten-2-one, 2,3-butanediol, and 6-methyl-5-hepten-2-one.

[0017] Preferably, the basis for screening the number of principal components in the principal component analysis in step S1 is the cumulative variance contribution rate and eigenvalue. Cumulative variance contribution rate: If the cumulative variance contribution rate of the current m principal components reaches more than 75%, the first m principal components can be retained; Eigenvalue: Generally, the principal components with eigenvalues greater than or equal to 1 are selected.

[0018] The cumulative variance contribution rate refers to the proportion of the variance explained by a certain principal component in the total variance. The larger the value, the stronger the ability of the principal component to synthesize the information of the original variables. Its calculation formula is:

[0019]

[0020] Correspondingly, the proportion of the total variance explained by the first m principal components determined in the principal component screening in the total variance is called the cumulative variance contribution rate. Its formula is:

[0021]

[0022] The eigenvalue is an important indicator to measure the influence of the principal component, representing how much information of the original scalar can be explained on average by introducing this principal component. It is used to test whether the variable correlation coefficient matrix is an identity matrix. Let the variable correlation coefficient matrix be R, and the statistic of the Bartlett spherical test is:

[0023]

[0024] Where: p is the number of original variables;

[0025] n is the sample size;

[0026] ln(|R|) is the natural logarithm of the determinant of the correlation coefficient matrix.

[0027] Preferably, the typical characteristic quality evaluation factors determined in step S1 for the Yellow River Estuary rice are: length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol.

[0028] Preferably, the standardization process is performed on each characteristic quality evaluation factor in step S2 to obtain the standardized values corresponding to each characteristic evaluation index, including: performing dimensionless processing on the original data of the characteristic quality index by using the range standardization method to convert it into standardized data between 0 and 1.

[0029] Calculating the weight values corresponding to each characteristic evaluation index based on the standardized values, and obtaining the weight values corresponding to each characteristic evaluation index includes the following steps: calculating the proportion values corresponding to each characteristic evaluation index based on the standardized values; calculating the information entropy corresponding to each characteristic evaluation index based on the proportion values; calculating the weight values corresponding to each characteristic evaluation index based on the information entropy.

[0030] The second technical object of the present invention is to provide a system for identifying characteristic quality factors of the Yellow River Estuary rice, and the identification system includes:

[0031] A data acquisition module, which is used to perform data acquisition based on the real-time database of rice characteristic quality and the data acquisition module, and perform standardization processing and correction on the characteristic quality factors such as length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol to obtain the corresponding acquisition results and standard results;

[0032] Performing dimensionless processing on the original data of the typical characteristic quality evaluation indexes of the Yellow River Estuary rice screened in step S1 by using the range standardization method to convert it into standardized data between 0 and 1, and the calculation formula for the standardized data of the typical characteristic quality index is: C i =(C - C min) / R,

[0033] In the formula: C i is the standardized data of the i-th characteristic quality index of the Yellow River Estuary rice;

[0034] C is the measured value of the i-th characteristic quality index of the Yellow River Estuary rice;

[0035] R = C max - C min is the range, C max and C min are respectively the maximum and minimum values of the measured values of the i-th characteristic quality index of different Yellow River Estuary rices. By calculating the dimensionless data of each characteristic quality index through the standardization method, the standardized data vector of each characteristic quality index is obtained.

[0036] The calculation formula for the weight value corresponding to each characteristic quality evaluation factor is:

[0037]

[0038] where i = 0, 1, 2,..., n; W i is the weight value ratio corresponding to each characteristic quality evaluation factor.

[0039] The model construction module is used to construct a model based on the collection result, the standard result and preset parameters to obtain a corresponding recognition model for the characteristic quality factors of the Yellow River Estuary rice; extract the product vector, the omics component matrix and the predetermined parameter vector respectively, and construct a recognition statistical model for the characteristic quality components:

[0040]

[0041] where y is the comprehensive quality evaluation vector of the Yellow River Estuary rice, S i is the weight value of the i-th characteristic quality index, C i is the standardized data of the i-th characteristic quality index.

[0042] The comparative analysis module is used to evaluate and calculate the characteristic quality factors of the Yellow River Estuary rice, and evaluate the comprehensive quality of the Yellow River Estuary rice based on the evaluation result.

[0043] The third technical object of this application is to provide an electronic device for identifying the characteristic quality factors of the Yellow River Estuary rice, including: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the foregoing method for identifying and simulating the characteristic quality factors of the Yellow River Estuary rice.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] The method for identifying characteristic quality factors of estuary rice of the present invention, through principal component analysis (principalcomponents Perform dimensionality reduction on the characteristic data of the quality indicators of the to-be-screened features of the Yellow River Estuary rice through principal component analysis (PCA), and determine the typical characteristic quality evaluation factors of the Yellow River Estuary rice. Through weight analysis, construct an evaluation model for the nutritional quality and taste quality of rice, which has a wide range of applicability. Based on the standardized values and weight values, use the multi-factor fuzzy mathematics comprehensive evaluation method to evaluate and calculate the characteristic quality factors of the Yellow River Estuary rice, and evaluate the comprehensive quality of the Yellow River Estuary rice based on the evaluation results. By setting optimal parameters and making judgments through comparison principles, it is simple, direct, and highly practical. Brief Description of the Drawings

[0046] Figure 1 It is a schematic flowchart of the method for identifying the characteristic quality factors of the Yellow River Estuary rice of the present invention;

[0047] Figure 2 It is a schematic diagram of the identification system of the method for identifying the characteristic quality factors of the Yellow River Estuary rice of the present invention;

[0048] Figure 3 It is a structural diagram of a computer device in the method for identifying the characteristic quality factors of the Yellow River Estuary rice of the present invention. Detailed Embodiments

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Embodiment 1 A method for identifying the characteristic quality factors of the Yellow River Estuary rice

[0051] Step S1: Collect multi-source data of the Yellow River Estuary rice, analyze the change characteristics of each to-be-screened characteristic quality indicator of its sensory quality, physicochemical nutritional quality and taste quality, and perform dimensionality reduction on the characteristic data of the to-be-screened characteristic quality indicators through principal component analysis (PCA) to determine the typical characteristic quality evaluation factors of the Yellow River Estuary rice.

[0052] The sensory quality indicators include: grain length, length-width ratio, and chalky grain rate; the physicochemical nutritional quality indicators include: protein, amylose, sucrose, glucose, maltose, fructose, Ca, Mg, K, Na, Zn, Se;

[0053] The taste quality indicators include: gel consistency and RVA pasting characteristics. The RVA pasting characteristics include peak viscosity, final viscosity, minimum viscosity, breakdown value, setback value, pasting temperature, and peak time;

[0054] The flavor substances include: 2-acetyl-1-pyrroline, vanillin, 6-methyl-5-hepten-2-one, 2,3-butanediol, and 6-methyl-5-hepten-2-one.

[0055] The main statistics in principal component analysis include variance contribution rate, eigenvalue, and goodness-of-fit test in principal component analysis: The basis for screening the number of principal components is the cumulative variance contribution rate and eigenvalue. Cumulative variance contribution rate: When the cumulative variance contribution rate of the current m principal components reaches more than 75%, the first m principal components can be retained; Eigenvalue: Generally, the principal components with eigenvalues greater than or equal to 1 are selected.

[0056] The cumulative variance contribution rate refers to the proportion of the variance explained by a certain principal component in the total variance. The larger the value, the stronger the ability of the principal component to synthesize the information of the original variables. Its calculation formula is:

[0057]

[0058] Correspondingly, the proportion of the total variance explained by the first m principal components determined in principal component screening in the total variance is called the cumulative variance contribution rate. Its formula is:

[0059]

[0060] The eigenvalue is an important indicator to measure the influence of the principal component, representing how much information of the original scalar can be explained on average by introducing this principal component. It is used to test whether the variable correlation coefficient matrix is an identity matrix. Let the variable correlation coefficient matrix be R, and the statistic of Bartlett's spherical test is:

[0061]

[0062] In the formula: p is the number of original variables;

[0063] n is the sample size;

[0064] ln(|R|) is the natural logarithm of the determinant of the correlation coefficient matrix.

[0065] First, the sensory quality, physicochemical and nutritional quality, taste quality, and flavor substances of 50 samples of Yellow River Estuary rice collected from Tingluo Town, Huanghekou Town, Kenli District, Dongying City, Shandong Province were dimensionally reduced. The data was simplified, and evaluation factors for the typical characteristic quality of Yellow River Estuary rice were selected: grain length, length-width ratio, chalky grain rate, protein, amylose, sucrose, glucose, maltose, fructose, Ca, Mg, K, Na, Zn, Se, gel consistency, and RVA pasting properties (peak viscosity, final viscosity, minimum viscosity, breakdown value, setback value, pasting temperature, and peak time), 2-acetyl-1-pyrroline, vanillin, 6-methyl-5-hepten-2-one, 2,3-butanediol, and 6-methyl-5-hepten-2-one, a total of 27 indicators.

[0066] According to the extraction principle of eigenvalue ≥ 1, through principal component analysis, the 27 indicators of grain length, length-width ratio, chalky grain rate, protein, amylose, sucrose, glucose, maltose, fructose, Ca, Mg, K, Na, Zn, Se, gel consistency, and RVA pasting properties (peak viscosity, final viscosity, minimum viscosity, breakdown value, setback value, pasting temperature, and peak time), 2-acetyl-1-pyrroline, vanillin, 6-methyl-5-hepten-2-one, 2,3-butanediol, and 6-methyl-5-hepten-2-one were dimensionally reduced. The cumulative variance contribution rate of 13 characteristic factors including length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol was 86.3 > 85%. The variance contribution rates are shown in Table 1 below: Variance contribution rates of typical characteristic quality evaluation factors of Yellow River Estuary rice.

[0067] Table 1 Variance contribution rates of typical characteristic quality evaluation factors of Yellow River Estuary rice

[0068]

[0069]

[0070] The results prove that the principal components extracted by this analysis are reasonable, and the extracted principal components can better represent the original data. Therefore, 13 characteristic factors including length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol were selected as the typical characteristic quality evaluation factors of Yellow River Estuary rice.

[0071] S2: Standardize each characteristic quality evaluation factor to obtain the corresponding standardized value; calculate and analyze the weight value corresponding to each characteristic quality evaluation factor according to the standardized value to obtain the weight value corresponding to each characteristic quality evaluation factor, and construct an identification statistical model for the characteristic quality factors of Yellow River Estuary rice.

[0072] The original data of the typical characteristic quality evaluation indexes of the Yellow River Estuary rice screened in step S1 is dimensionless processed by the range normalization method, and converted into standardized data between 0 and 1. The calculation formula for the standardized data of the typical characteristic quality indexes is: C i =(C - C min ) / R,

[0073] where: C i is the standardized data of the i-th characteristic quality index of the Yellow River Estuary rice;

[0074] C is the measured value of the i-th characteristic quality index of the Yellow River Estuary rice;

[0075] R = C max - C min is the range, and C max and C min are respectively the maximum and minimum values of the measured values of the i-th characteristic quality index of different Yellow River Estuary rice. The dimensionless data of each characteristic quality index is calculated by the standardization method to obtain the standardized data vector of each characteristic quality index.

[0076] The calculation formula for the weight value corresponding to each characteristic quality evaluation factor is:

[0077]

[0078] where i = 0, 1, 2,..., n; W i is the weight value ratio corresponding to each characteristic quality evaluation factor.

[0079] The product vector, omics component matrix and predetermined parameter vector are respectively extracted to construct a characteristic quality component recognition statistical model:

[0080]

[0081] where y is the comprehensive quality evaluation vector of the Yellow River Estuary rice, S i is the weight value of the i-th characteristic quality index, and C i is the standardized data of the i-th characteristic quality index.

[0082] S3: Based on the standardized value and the weight value, the multi-factor fuzzy mathematics comprehensive evaluation method is used to evaluate and calculate the characteristic quality factors of the Yellow River Estuary rice. Based on the evaluation results, the comprehensive quality evaluation of 30 Yellow River Estuary rice samples and 5 commercially available ordinary rice samples collected from Tingluo Town, Huanghekou Town, Kenli District, Dongying City, Shandong Province is carried out. The comprehensive evaluation results are shown in Table 2:

[0083] Table 2 Comprehensive Score Table of Some Rice Samples

[0084]

[0085]

[0086]

[0087] The higher the numerical value of the evaluation result, the better the comprehensive quality of the Yellow River Estuary rice. When the comprehensive evaluation result is lower than five points, it indicates that the rice is not from the Yellow River Estuary. The evaluation result proves that the screening of the typical characteristic quality evaluation factors of the Yellow River Estuary rice is reasonable. When applied to the comprehensive evaluation of rice, it can identify and judge non-Yellow River Estuary rice.

[0088] Example 2 An identification system and device for the characteristic quality factors of the Yellow River Estuary rice

[0089] (1) The system includes: a data acquisition module, which is used to collect data based on the real-time database of the characteristic quality of rice and the data acquisition module, and perform standardization processing and correction on 13 characteristic factors including length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol to obtain the corresponding acquisition results and standard results;

[0090] A model construction module, which is used to construct a model based on the acquisition results, the standard results, and a preset simulation software to obtain the corresponding identification model of the characteristic quality factors of the Yellow River Estuary rice;

[0091] A comparative analysis module, which is used to evaluate and calculate the characteristic quality factors of the Yellow River Estuary rice and evaluate the comprehensive quality of the Yellow River Estuary rice based on the evaluation results.

[0092] (2) A computer-readable storage medium is used to store a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned average method for identifying the characteristic quality factors of the Yellow River Estuary rice. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0093] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for identifying characteristic quality factors of rice in the Yellow River Estuary, characterized in that It includes the following steps: S1: Collect multi-source data of the rice from the Yellow River Estuary, analyze the change characteristics of each index of its sensory quality, physical and chemical nutritional quality, eating quality, and flavor substances, and perform dimensionality reduction processing on the change characteristic data of the characteristic quality indexes to be screened through the principal component analysis method, and determine the typical characteristic quality evaluation factors of the rice from the Yellow River Estuary; Each index of the sensory quality, physical and chemical nutritional quality, eating quality, and flavor substances is a combination of multi-dimensional indexes, including but not limited to the multi-dimensional combination of sensory evaluation indexes, physical and chemical nutritional indexes, eating quality, and flavor substance indexes; S2: Perform standardization processing on each characteristic quality evaluation factor to obtain the standardized value corresponding to each characteristic quality evaluation factor; calculate and analyze the weight value corresponding to each characteristic quality evaluation factor according to the standardized value to obtain the weight value corresponding to each characteristic quality evaluation factor, and construct a recognition statistical model of the characteristic quality factors of the rice from the Yellow River Estuary: S3: Based on the standardized value and the weight value, use the multi-factor fuzzy mathematics comprehensive evaluation method to evaluate and calculate the characteristic quality factors of the rice from the Yellow River Estuary, and evaluate the comprehensive quality of the rice from the Yellow River Estuary based on the evaluation result.

2. A method for identifying characteristic quality factors of the Yellow River Estuary rice according to claim 1, characterized in that The sensory quality indexes described in step S1 include: grain length, length-width ratio, and chalky grain rate; the physical and chemical nutritional quality indexes include: protein, amylose, sucrose, glucose, maltose, fructose, Ca, Mg, K, Na, Zn, Se; the eating quality indexes include: gel consistency and RVA pasting properties, and the RVA pasting properties include peak viscosity, final viscosity, minimum viscosity, breakdown value, setback value, pasting temperature, and peak time; the flavor substances include: 2-acetyl-1-pyrroline, vanillin, 6-methyl-5-hepten-2-one, 2,3-butanediol, and 6-methyl-5-hepten-2-one.

3. A method for identifying the characteristic quality factors of the Yellow River Estuary rice according to claim 1, characterized in that The basis for screening the number of principal components in the principal component analysis method described in step S1 is: cumulative variance contribution rate and eigenvalue. If the cumulative variance contribution rate of the current m principal components reaches more than 75%, the first m principal components can be retained; The eigenvalue selects the principal component with an eigenvalue greater than or equal to 1; The cumulative variance contribution rate refers to the proportion of the variance that a certain principal component can explain in the total variance, and the calculation formula is: The proportion of the total variance that can be explained by the first m principal components determined in the principal component screening in the total variance is called the cumulative variance contribution rate, and its formula is: The eigenvalue represents how much original scalar information can be explained on average by introducing this principal component, and is used to test whether the variable correlation coefficient matrix is an identity matrix. Let the variable correlation coefficient matrix be R, and the statistic of the Bartlett spherical test is: In the formula: p is the number of original variables; n is the sample size; ln(|R|) is the natural logarithm of the determinant of the correlation coefficient matrix.

4. A method for identifying the characteristic quality factors of the Yellow River Estuary rice according to any one of claims 1 to 3, characterized in that The typical characteristic quality evaluation factors of the rice from the Yellow River Estuary determined in step S1 are: length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol.

5. A method for identifying characteristic quality factors of the Yellow River Estuary rice according to claim 1, characterized in that: Standardize each characteristic quality evaluation factor described in step S2 to obtain the standardized values corresponding to each characteristic evaluation index: Use the range standardization method to dimensionless process the original data of the characteristic quality indexes of the Yellow River Estuary rice and convert it into standardized data between 0 and 1.

6. A method for identifying the characteristic quality factors of the Yellow River Estuary rice according to claim 1 or 5, characterized in that Calculate the weight values corresponding to each characteristic evaluation index based on the standardized values described in step S2. Obtaining the weight values corresponding to each characteristic evaluation index includes: calculating the proportion values corresponding to each characteristic evaluation index based on the standardized values; calculating the information entropy corresponding to each characteristic evaluation index based on the proportion values; calculating the weight values corresponding to each characteristic evaluation index based on the information entropy.

7. A method for identifying the characteristic quality factors of the Yellow River Estuary rice according to claim 1, characterized in that Construct a recognition statistical model for the characteristic quality factors of the Yellow River Estuary rice in step S2: Extract the product vector, omics component matrix, and predetermined parameter vector respectively to construct a recognition statistical model for the characteristic quality factors of the Yellow River Estuary rice: In the formula: Y is the comprehensive quality evaluation vector of the Yellow River Estuary rice; S i is the weight value of the i-th characteristic quality index; C i is the standardized data of the i-th characteristic quality index.

8. A recognition system for characteristic quality factors of Yellow River Estuary rice, characterized in that: The recognition system includes: a data collection module, a model construction module, and a comparative analysis module.

9. The characteristic quality factor recognition system for the Yellow River Estuary rice according to claim 8, characterized in that: The data collection module is used to collect data based on the real-time database of the rice characteristic quality and the data collection module, and perform standardization processing and correction on the characteristic quality factors such as grain length, length-width ratio, protein, amylose, maltose, fructose, Ca, Mg, K, Zn, Se, gel consistency, 2-acetyl-1-pyrroline, vanillin, and 2,3-butanediol to obtain the corresponding collection results and standard results; The model construction module is used to construct a model based on the collection results, the standard results, and a preset simulation software to obtain a recognition model for the characteristic quality factors of the Yellow River Estuary rice; The comparative analysis module is used to evaluate and calculate the characteristic quality factors of the Yellow River Estuary rice and evaluate the comprehensive quality of the Yellow River Estuary rice based on the evaluation results.

10. An electronic device for a recognition system of characteristic quality factors of Yellow River Estuary rice, characterized in that: It includes a memory and a processor. The memory is used to store computer programs; the processor is used to execute the computer program of the recognition system for the characteristic quality factors of the estuary rice to implement the steps of the recognition and simulation analysis method for the characteristic quality factors of the Yellow River Estuary rice described above.

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