Quantitative detection method of porcine reproductive and respiratory syndrome serum antibody

Abstract

The invention provides a quantitative detection method of a porcine reproductive and respiratory syndrome serum antibody, and belongs to the technical field of veterinary immunological detection.The method comprises the steps that S1, a serum sample is collected and pretreated; s2, antibody marker extraction and characteristic parameter extraction; s3, feature processing and antibody signal enhancement based on four-algorithm fusion; and S4, quantitative calibration and antibody concentration output. Through closed-loop design of multi-algorithm fusion, accurate quantitative detection of the porcine reproductive and respiratory syndrome serum antibody is realized.

Description

Technical Field

[0001] This invention relates to the field of veterinary immunology detection technology, and in particular to a quantitative detection method for serum antibodies against porcine reproductive and respiratory syndrome (PRRS). Background Technology

[0002] Porcine reproductive and respiratory syndrome (PRRS) is a highly contagious disease caused by the virus, severely impacting pig growth, development, and reproductive performance, resulting in significant economic losses for large-scale pig farms. Quantitative serum antibody testing is a core technical means to assess the effectiveness of PRRS vaccines and screen pigs with insufficient antibody levels, directly impacting the formulation of disease control strategies.

[0003] Existing methods for detecting serum antibodies against porcine reproductive and respiratory syndrome (PRRS) mainly include enzyme-linked immunosorbent assay (ELISA) and indirect fluorescent antibody assay, but they have the following shortcomings: First, the selection of feature parameters lacks specificity, easily retaining redundant features that are weakly correlated with antibody concentration, leading to significant detection interference. Second, the feature weight allocation is fixed, failing to consider the differences in the importance of different features to the detection results, and cannot adapt to the detection needs of complex samples. Third, the feature fusion method is singular, mostly using simple weighted summation without incorporating stabilization mechanisms such as feature mean compensation, making the fusion results susceptible to fluctuations in a single feature. Fourth, there is a lack of interactive optimization between algorithms, and error correction results are not fed back to the preprocessing stage, resulting in the inability to dynamically eliminate system errors, insufficient detection accuracy and stability, and difficulty in meeting the needs of large-scale pig farms for precise evaluation of immunization effects. Summary of the Invention

[0004] This invention provides a method for the quantitative detection of serum antibodies against porcine reproductive and respiratory syndrome (PRRS). Through a closed-loop design that integrates multiple algorithms, the method achieves accurate quantitative detection of PRRS serum antibodies.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A quantitative detection method for serum antibodies against porcine reproductive and respiratory syndrome (PRRS) includes the following steps: S1. Serum sample collection and pretreatment: Blood samples were collected from the anterior vena cava of pigs. After standing and centrifugation, the serum was separated, diluted with phosphate buffer, filtered, and the initial absorbance of the filtered serum was detected. The initial volume and the volume after dilution of the serum were recorded to obtain the pretreated serum sample. S2. Antibody biomarker extraction and characteristic parameter extraction: Porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent was added to the pretreated serum sample, and the antigen-antibody complex was formed by incubation. The complex was separated by high performance liquid chromatography, and three types of characteristic parameters, namely peak area, retention time and absorbance peak value, were extracted to construct a characteristic matrix. S3. Feature Processing and Antibody Signal Enhancement Based on Four-Algorithm Fusion: The feature matrix is ​​input into a feature screening algorithm based on mutual information to remove redundant features that are weakly correlated with antibody concentration, resulting in a screened feature matrix. The screened feature matrix is ​​then input into a weight allocation algorithm based on hierarchical analysis, which dynamically allocates feature weights based on error feedback to obtain a final weight vector. The screened feature matrix and the final weight vector are input into a feature fusion algorithm based on weighted fusion to obtain initial fusion feature values. The initial fusion feature values ​​are then input into an error correction algorithm based on adaptive iteration to calculate the error and feed it back to the weight allocation algorithm. After iterative correction, the final fusion feature values ​​are obtained. S4. Quantitative Calibration and Antibody Concentration Output: Based on the final fusion characteristic value obtained by processing a standard sample with a known antibody concentration through the above steps, a standard curve is constructed. The final fusion characteristic value of the sample to be tested is substituted into the standard curve to calculate the antibody concentration. Parallel tests are performed on the same sample to verify the detection accuracy and output the antibody concentration that meets the accuracy requirements.

[0006] In this specification, in step S3, the mutual information threshold of the feature screening algorithm based on mutual information is obtained by training with standard samples. The standard samples are porcine reproductive and respiratory syndrome (PRRS) serum standards with known antibody concentrations. The mutual information threshold is determined by calculating the average value of the mutual information values ​​between each feature parameter and the corresponding antibody concentration of all standard samples.

[0007] In this specification, in step S3, when constructing the judgment matrix based on the weight allocation algorithm of hierarchical analysis, the quantitative detection accuracy is taken as the target layer, the selected feature parameters are taken as the criterion layer, the importance of the feature parameters is compared pairwise to determine the elements of the judgment matrix, the maximum eigenvalue and eigenvector of the judgment matrix are calculated, the standard weight vector is obtained after normalization, and the rationality of the weight allocation is verified by consistency test.

[0008] In this specification, in step S3, the fusion coefficient of the feature fusion algorithm based on weighted fusion is determined by standard sample verification. The fusion coefficient is the value that maximizes the correlation between the fusion feature value and the standard antibody concentration. The fusion process combines the average value of the screened feature parameters for mean compensation.

[0009] In this specification, in step S3, the number of iterations and the convergence threshold are set based on the adaptive iterative error correction algorithm. The number of iterations is determined by training with standard samples, and the convergence threshold is the relative deviation threshold of the fused feature values. When the difference between the fused feature values ​​of two adjacent iterations is less than the convergence threshold or the number of iterations is reached, the iteration stops.

[0010] In this specification, in step S4, the standard curve is fitted by linear regression, and the slope and intercept of the standard curve are calculated by least squares method. The standard samples are five groups of serum standards for porcine reproductive and respiratory syndrome (PRRS) with different antibody concentrations.

[0011] In this instruction manual, in step S4, the number of parallel tests is 3. The relative standard deviation of the three test results is calculated. When the relative standard deviation is less than 5%, the antibody concentration is output as the final test result; otherwise, the test steps are repeated.

[0012] In this instruction manual, in step S2, the components of the porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent are N protein antigen and GP5 protein antigen.

[0013] In this instruction manual, in step S2, the detection parameters of the high performance liquid chromatograph are: column temperature 30℃, mobile phase is a mixed solution of methanol and water, mobile phase flow rate 1mL / min, and detection wavelength 254nm.

[0014] In this instruction manual, in step S1, the centrifugation speed is 3000 r / min, the centrifugation time is 15 minutes, the pH value of the phosphate buffer is 7.4, the volume ratio of serum to phosphate buffer is 1:5, and a 0.22 μm filter membrane is used for filtration.

[0015] In summary, the present invention has at least the following beneficial effects: Improve the effectiveness of feature parameters: By using a feature screening algorithm based on mutual information, redundant features that are weakly correlated with antibody concentration are accurately removed, reducing the impact of irrelevant interference on the detection results and providing a high-quality feature foundation for subsequent processing.

[0016] Achieving adaptive optimization of weights: The weight allocation algorithm based on hierarchical analysis, combined with error correction feedback, dynamically adjusts the feature weights, enabling important features to play a dominant role in the fusion calculation and adapting to different sample detection scenarios.

[0017] Enhance the stability of fusion features: Employ a fusion algorithm that combines weighted fusion with mean compensation to balance the contribution of key features with the stability of basic features, avoid deviations in fusion results caused by fluctuations in a single feature, and improve the accuracy of feature representation of antibody concentration.

[0018] Dynamic elimination of systematic errors: Through the bidirectional interaction of the adaptive iterative error correction algorithm and the weight allocation algorithm, the deviation between the fusion features and the true antibody level is gradually reduced. At the same time, the weight allocation is optimized by feedback, forming a closed-loop optimization mechanism, which significantly improves the accuracy and reliability of the detection results.

[0019] Adapted to the needs of large-scale farming: The testing process is standardized and easy to operate, and multiple samples can be processed in batches. The test results can directly support the evaluation of immunization effectiveness and supplementary immunization decisions, providing a scientific basis for the prevention and control of porcine reproductive and respiratory syndrome (PRRS) in large-scale pig farms. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the quantitative detection method for porcine reproductive and respiratory syndrome (PRRS) serum antibodies involved in this invention.

[0022] Figure 2 This is a schematic diagram of the overall detection process involved in this invention.

[0023] Figure 3 This is a schematic diagram of the four-algorithm fusion feature processing flow involved in this invention.

[0024] Figure 4 This is a schematic diagram of the quantitative calibration and accuracy verification process involved in this invention. Detailed Implementation

[0025] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0026] The following disclosure provides many different implementations or examples for carrying out different structures of the embodiments of the present invention. To simplify the disclosure of the embodiments of the present invention, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. Furthermore, reference numerals and / or reference letters may be repeated in different examples of the embodiments of the present invention; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or arrangements discussed.

[0027] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] like Figure 1 As shown in the figure, this embodiment provides a method for quantitative detection of serum antibodies against porcine reproductive and respiratory syndrome (PRRS), including the following steps: S1. Serum sample collection and pretreatment: Blood samples were collected from the anterior vena cava of pigs. After standing and centrifugation, the serum was separated, diluted with phosphate buffer, filtered, and the initial absorbance of the filtered serum was detected. The initial volume and the volume after dilution of the serum were recorded to obtain the pretreated serum sample. S2. Antibody biomarker extraction and characteristic parameter extraction: Porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent was added to the pretreated serum sample, and the antigen-antibody complex was formed by incubation. The complex was separated by high performance liquid chromatography, and three types of characteristic parameters, namely peak area, retention time and absorbance peak value, were extracted to construct a characteristic matrix. S3. Feature Processing and Antibody Signal Enhancement Based on Four-Algorithm Fusion: The feature matrix is ​​input into a feature screening algorithm based on mutual information to remove redundant features that are weakly correlated with antibody concentration, resulting in a screened feature matrix. The screened feature matrix is ​​then input into a weight allocation algorithm based on hierarchical analysis, which dynamically allocates feature weights based on error feedback to obtain a final weight vector. The screened feature matrix and the final weight vector are input into a feature fusion algorithm based on weighted fusion to obtain initial fusion feature values. The initial fusion feature values ​​are then input into an error correction algorithm based on adaptive iteration to calculate the error and feed it back to the weight allocation algorithm. After iterative correction, the final fusion feature values ​​are obtained. S4. Quantitative Calibration and Antibody Concentration Output: Based on the final fusion characteristic value obtained by processing a standard sample with a known antibody concentration through the above steps, a standard curve is constructed. The final fusion characteristic value of the sample to be tested is substituted into the standard curve to calculate the antibody concentration. Parallel tests are performed on the same sample to verify the detection accuracy and output the antibody concentration that meets the accuracy requirements.

[0029] In some embodiments, in step S3, the mutual information threshold of the feature screening algorithm based on mutual information is obtained by training with standard samples. The standard samples are porcine reproductive and respiratory syndrome (PRRS) serum standards with known antibody concentrations. The mutual information threshold is determined by calculating the average value of the mutual information values ​​between each feature parameter and the corresponding antibody concentration of all standard samples.

[0030] In some embodiments, in step S3, when constructing the judgment matrix based on the weight allocation algorithm of hierarchical analysis, the quantitative detection accuracy is taken as the target layer, the filtered feature parameters are taken as the criterion layer, the importance of the feature parameters is compared pairwise to determine the elements of the judgment matrix, the maximum eigenvalue and eigenvector of the judgment matrix are calculated, the standard weight vector is obtained after normalization, and the rationality of the weight allocation is verified by consistency test.

[0031] In some embodiments, in step S3, the fusion coefficient of the feature fusion algorithm based on weighted fusion is determined by standard sample verification. The fusion coefficient is the value that maximizes the correlation between the fusion feature value and the standard antibody concentration. The fusion process combines the average value of the screened feature parameters for mean compensation.

[0032] In some embodiments, in step S3, the number of iterations and the convergence threshold are set based on the adaptive iterative error correction algorithm. The number of iterations is determined by training with standard samples, and the convergence threshold is the relative deviation threshold of the fused feature values. When the difference between the fused feature values ​​of two adjacent iterations is less than the convergence threshold or the number of iterations is reached, the iteration stops.

[0033] In some embodiments, in step S4, the standard curve is fitted by a linear regression method, and the slope and intercept of the standard curve are calculated by the least squares method. The standard samples are five groups of serum standards for porcine reproductive and respiratory syndrome (PRRS) with different antibody concentrations.

[0034] In some embodiments, in step S4, the number of parallel tests is 3, the relative standard deviation of the three test results is calculated, and when the relative standard deviation is less than 5%, the antibody concentration is output as the final test result; otherwise, the test steps are repeated.

[0035] In some embodiments, in step S2, the components of the porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent are N protein antigen and GP5 protein antigen.

[0036] In some embodiments, in step S2, the detection parameters of the high-performance liquid chromatograph are: column temperature 30°C, mobile phase is a mixture of methanol and water, mobile phase flow rate 1 mL / min, and detection wavelength 254 nm.

[0037] In some embodiments, in step S1, the centrifugation speed is 3000 r / min, the centrifugation time is 15 minutes, the pH value of the phosphate buffer is 7.4, the volume ratio of serum to phosphate buffer is 1:5, and a 0.22 μm filter membrane is used for filtration.

[0038] The technical concept of this invention is as follows: This invention discloses a quantitative detection method for serum antibodies against porcine reproductive and respiratory syndrome (PRRS). The core of the method involves four steps: sample preprocessing, feature extraction, four-algorithm fusion feature processing, and quantitative calibration to achieve accurate antibody concentration determination. First, porcine serum samples are collected and preprocessed (dilution, filtration, etc.). Then, antigen-antibody complexes are separated using high-performance liquid chromatography (HPLC), and three characteristic parameters—peak area, retention time, and absorbance peak value—are extracted. Subsequently, a feature screening algorithm based on mutual information is used to remove redundant features. A weighted allocation algorithm based on hierarchical analysis is used to dynamically allocate feature weights. A weighted fusion algorithm is used to integrate effective features, and an adaptive iterative error correction algorithm is combined to eliminate errors and optimize weights. Finally, antibody concentration is quantitatively calculated through standard curve fitting, and accuracy is verified through parallel detection. The four algorithms form a closed-loop synergy through parameter transfer and feedback, solving problems such as inaccurate feature screening, fixed weights, and inability to dynamically correct errors in existing methods, thus achieving efficient and accurate quantitative detection of PRRS serum antibodies. The overall detection process is as follows: Figure 2 As shown.

[0039] S1. Serum Sample Collection and Preprocessing Blood samples were collected from the anterior vena cava of pigs aged 30 days and older, with 5 mL of blood collected from each pig, for a total of 100 samples. The blood samples were placed in anticoagulant-free centrifuge tubes and incubated at room temperature for 2 hours. Then, they were centrifuged at 3000 rpm for 15 minutes in a high-speed refrigerated centrifuge to separate the supernatant serum. The centrifuged serum samples were then diluted with phosphate-buffered saline (PBPS) at a volume ratio of 1:5 (pH 7.4) to reduce interference from contaminating proteins. The diluted serum samples were then filtered through a 0.22 μm filter to remove cell debris and large molecular particles. The absorbance of the filtered serum samples was measured at 280 nm using a UV spectrophotometer. This value reflects the initial total protein content in the serum. The volume of the sample after dilution is also recorded. (Unit: mL) and initial serum volume after centrifugation (Unit: mL). This step outputs the pre-processed serum sample and the initial absorbance value. Sample dilution volume and initial volume The above data will be directly used as input parameters for step S2, providing a sample basis with sufficient purity for the subsequent extraction of antibody markers.

[0040] S2. Antibody biomarker extraction and characteristic parameter extraction Take 2 mL of the pretreated serum sample from step S1 and add 50 μL of porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent to obtain a mixed sample. This binding reagent consists of N protein antigen and GP5 protein antigen, which have high affinity for the specific antibodies in PRRS serum. It was purchased from a biological reagent company, such as: 1. Porcine reproductive and respiratory syndrome virus (PRRSV) antibody ELISA detection kit (Lepusen, indirect method): Contains recombinant N protein-coated antigen, which can specifically bind to N protein antibodies in serum to form a complex, meeting the antigen component requirements of this protocol for binding reagents, and the stability of the complex meets the requirements of chromatographic separation.

[0041] 2. PRRSV Antibody ELISA Detection Kit (Nanjing Boyan, Recombinant N Protein Coated): The core antigen is recombinant N protein, which is consistent with the N protein antigen component of the binding reagent in this protocol. It can specifically capture porcine reproductive and respiratory syndrome (PRRS) antibodies in serum, and the resulting complex can be effectively separated by high performance liquid chromatography.

[0042] 3. Porcine reproductive and respiratory syndrome virus GP5 protein antibody ELISA kit (Shanghai Xinyu Biotechnology): Using GP5 protein as the core antigen, it can specifically bind to GP5 protein antibodies in serum, supplementing the binding function of GP5 protein antigen in this solution. The resulting antigen-antibody complex has a stable structure and is compatible with subsequent chromatographic detection procedures.

[0043] 4. VDPro PRRSV-VR / LV Antibody Detection Kit (King Pro): Contains N protein antigen component, can simultaneously identify American and European porcine reproductive and respiratory syndrome (PRRS) antibodies, the complex formed after specific binding has high purity, can be accurately separated by high performance liquid chromatography, and is suitable for large-scale sample testing scenarios.

[0044] The mixed sample was incubated at a constant temperature of 37°C for 30 minutes. This temperature is the optimal temperature for antigen-antibody binding, ensuring that serum porcine reproductive and respiratory syndrome (PRRS) antibodies, including N protein antibodies and GP5 protein antibodies, bind sufficiently and specifically to the binding reagent to form a stable antigen-antibody complex. The incubated mixture was then placed in a high-performance liquid chromatograph (HPLC). The column temperature was set to 30°C, the mobile phase was a 40:60 (v / v) mixture of methanol and water, the flow rate was 1 mL / min, and the detection wavelength was 254 nm. The chromatographic column separation process effectively separated the antigen-antibody complex from other proteins in the serum.

[0045] Three core characteristic parameters were extracted using the data analysis module built into the chromatograph. The first type is peak area characteristics, corresponding to the size of the peak area formed by the antigen-antibody complex in the chromatogram. Specifically, the peak area corresponding to the N protein antibody-antigen complex is... The peak area corresponding to the GP5 protein antibody-antigen complex is The peak area is positively correlated with the content of the complex. The second type is retention time characteristics, i.e., the time from injection into the column to elution of the complex. The retention time of the N protein antibody-antigen complex is... The retention time of the GP5 protein antibody-antigen complex is Retention time reflects the interaction strength between the complex and the column packing material, and is used to verify the specificity of the complex. The third type is the absorbance peak characteristic, which is the maximum absorbance value corresponding to the elution of the complex. The absorbance peak value corresponding to the N protein antibody-antigen complex is... The absorbance peak of the GP5 protein antibody-antigen complex is The peak absorbance is directly related to the concentration of the complex.

[0046] The above six feature parameters are arranged as follows , , , , , Arrange them in order to construct the feature matrix. This matrix, with dimensions of 1×6, comprehensively covers information related to the content, specificity, and concentration of antigen-antibody complexes. This step outputs a feature matrix. This matrix will serve as the core input data for step S3, providing a comprehensive feature foundation for subsequent algorithmic fusion processing.

[0047] S3. Feature Processing and Antibody Signal Enhancement Based on Four-Algorithm Fusion This step employs four mutually integrated and pairwise interactive algorithms to process the feature matrix output from step S2. The processing involves four algorithms: a feature selection algorithm based on mutual information, a weight allocation algorithm based on hierarchical analysis, a feature fusion algorithm based on weighted fusion, and an error correction algorithm based on adaptive iteration. These four algorithms form a closed-loop collaborative working mode through parameter passing and result feedback. The output of the feature selection algorithm serves as the input to the weight allocation and fusion calculation algorithms, while the output of the error correction algorithm is fed back to the weight allocation algorithm for dynamic adjustment. The final output is the enhanced antibody feature value. The core objective is to improve the specificity and accuracy of the feature parameters, providing highly reliable input data for subsequent quantitative calibration. The feature processing flow of the four algorithms is as follows: Figure 3 As shown.

[0048] 3.1 Algorithm 1: Feature Selection Algorithm Based on Mutual Information Model Construction: A feature selection model is constructed based on the mutual information theory in information theory. Mutual information is used to measure the correlation between two random variables. In this scheme, it is specifically reflected in the degree of correlation between each feature parameter and the concentration of porcine reproductive and respiratory syndrome (PRRS) antibodies. The higher the mutual information value, the stronger the characterization ability of the feature parameter on antibody concentration, and the higher its effectiveness in quantitative detection. Since the feature parameters are all discrete detection values, the information entropy is calculated using the discrete variable formula, that is, for the feature parameter... Its information entropy The calculation formula is: ; in Representing characteristic parameters The Each possible value Representing characteristic parameters The number of possible values, Representing characteristic parameters Pick The probability of a value is obtained by statistically analyzing the frequency of that feature parameter in a standard sample set. For two variables... and Joint information entropy The calculation formula is: ; in This indicates the concentration of antibodies against porcine reproductive and respiratory syndrome (PRRS). The first number representing antibody concentration Each possible value This indicates the number of possible values ​​for antibody concentration. Representing characteristic parameters Pick Value and antibody concentration are taken The joint probability of the values.

[0049] Model Training: The model was trained using a standard sample set with known antibody concentrations. The standard sample set consisted of five groups, sourced from authoritative, compliant channels with guaranteed sample traceability. All sources were required to ensure the accuracy and stability of the antibody concentrations in the standard samples, meeting quantitative detection calibration requirements. These sources included the National Veterinary Microbiology Culture Collection Center, various inspection institutes, laboratories, enterprises, universities, and research institutions. The antibody concentrations of the five groups were 10 ng / mL, 20 ng / mL, 40 ng / mL, 80 ng / mL, and 160 ng / mL, respectively. Steps S1 and S2 were performed on each group of standard samples to obtain the standard feature matrix corresponding to each group. ,in This indicates the number of standard sample groups, ranging from 1 to 5. The specific form is , , , , , subscript This indicates the parameters corresponding to the standard samples. The antibody concentration of each group of standard samples is also recorded. .

[0050] For each characteristic parameter of each set of standard samples, calculate its mutual information value with the corresponding antibody concentration. ,in The index represents the characteristic parameter, with values ​​ranging from 1 to 6, and the calculation formula is: ; in Indicates the first Group Standard Sample No. Information entropy of each feature parameter Indicates the first Information entropy of antibody concentration in the standard sample group Indicates the first Group Standard Sample No. The joint information entropy of each characteristic parameter and its corresponding antibody concentration.

[0051] Calculate the average of the mutual information values ​​of all feature parameters of all standard samples, and use it as the mutual information threshold. The calculation formula is: ; This threshold is used to determine the validity of the characteristic parameters of the sample to be tested. Only characteristic parameters with mutual information values ​​not lower than this threshold are considered to be significantly correlated with antibody concentration and are retained for subsequent processing.

[0052] Model application: The feature matrix output from step S2 Each feature parameter in Substituting into the mutual information calculation model, where The values ​​range from 1 to 6, corresponding to... , , , , , First, calculate the information entropy of each feature parameter of the sample to be tested. Then calculate the information entropy of the average antibody concentration of the standard samples. ,in The average antibody concentration of the five standard sample groups is calculated using the following formula: ; Then the test sample number is calculated. The combined information entropy of a feature parameter and the average antibody concentration of standard samples Finally, the test sample number was obtained. The mutual information values ​​of each feature parameter The calculation formula is: ; The mutual information value of each feature parameter With mutual information threshold When comparing, When, retain the feature parameter; when When this happens, the feature parameter is removed. Considering the actual effectiveness of the porcine reproductive and respiratory syndrome (PRRS) antibody detection features, after model training and screening, the retained feature parameters are: , , , The reason for this is that peak area characteristics directly reflect the content of antigen-antibody complexes, and absorbance peak characteristics directly reflect the concentration of the complexes; both are strongly correlated with antibody concentration. Retention time characteristics, however, are mainly used for specificity verification and have a weaker correlation with concentration, therefore they were excluded. A feature matrix was constructed after screening. Its dimensions are 1×4, and its specific form is as follows: , , , .

[0053] The core function of this algorithm is to eliminate feature parameters that are unrelated to or weakly correlated with porcine reproductive and respiratory syndrome (PRRS) antibody concentration, reducing the interference of redundant information in subsequent calculations, while also reducing data processing volume and improving overall detection efficiency. By accurately screening effective features, a high-quality data foundation is laid for subsequent weight allocation and fusion calculations, avoiding quantitative errors caused by irrelevant features.

[0054] 3.2 Algorithm 2: Weight Allocation Algorithm Based on Analytic Hierarchy Process (AHP) Model Construction: Considering the varying degrees of influence of each selected feature parameter on the quantitative detection results of porcine reproductive and respiratory syndrome (PRRS) antibodies, a weighted allocation model was constructed based on the analytic hierarchy process (AHP). This model uses quantitative detection accuracy as the target layer and the four selected feature parameters as the criteria layer. By comparing the importance of each criterion layer factor to the target layer pairwise, the weight of each feature parameter is determined, ensuring that more important feature parameters occupy a larger weight proportion in subsequent fusion calculations, thereby improving the relevance of the fusion results.

[0055] Model training: The feature matrices of five sets of standard samples were used as training data. The feature matrix corresponding to each set of standard samples was as follows: The specific form is , , , After constructing the hierarchical structure, a pairwise comparison method is used to construct the judgment matrix. The matrix has a dimension of 4×4, and the matrix elements are... Indicates the first The feature parameter relative to the first The importance of each feature parameter is categorized into 1 to 9 levels, where 1 indicates that both feature parameters are equally important, 3 indicates that the second most important feature parameter is of equal importance, and so on. The first feature parameter is compared to the first The 5th feature parameter is slightly more important; 5 indicates the 5th feature parameter. The feature parameter is compared to the first The 7th feature parameter is clearly important; 7 indicates that the 7th feature parameter is significantly important. The feature parameter is compared to the first The 9th characteristic parameter is highly important. The feature parameter is compared to the first The first feature parameter is extremely important, while 2, 4, 6, and 8 represent the intermediate importance between the above adjacent levels. The first feature parameter is compared to the first When a feature parameter is important, matrix elements It is the reciprocal of the corresponding importance level.

[0056] Based on the four characteristic parameters of this scheme, and combined with the biological characteristics of porcine reproductive and respiratory syndrome (PRRS) antibody detection, the peak area of ​​the N protein antibody-antigen complex is... Peak area of ​​GP5 protein antibody-antigen complex Directly reflects the amount of antibody binding, and is of the highest importance, with both being equally important; absorbance peak value and As a concentration-assisted characterization parameter, its importance is secondary and equally important, while peak area characteristics are significantly more important than absorbance peak characteristics. A judgment matrix is ​​constructed based on this. Specifically: , , , ; , , , ; , , , ; , , , ; Calculate the judgment matrix Maximum eigenvalue and the corresponding feature vector The calculation process involves first normalizing each column of the judgment matrix to obtain a normalized matrix. , of which elements Then, the average value of each row of the normalized matrix is ​​calculated to obtain the eigenvectors. Finally, through the formula Calculate the largest eigenvalue, where Representation matrix with vector The product result of the first Each element.

[0057] For eigenvectors Normalization is performed to obtain the standard weight vector. Each element And satisfy The rationality of the weight allocation is verified through a consistency check. First, the consistency index is calculated. Where 4 is the order of the judgment matrix; then find the random consistency index. When the order of the judgment matrix is ​​4, The value is set to 0.90; finally, the consistency ratio is calculated. ,when If the weight allocation meets the consistency requirement, then the judgment matrix needs to be readjusted; otherwise, the model's... After calculation, the... The weight allocation is effective.

[0058] Model application: The filtered feature matrix output from step S3.1 Substituting the trained weight allocation model, we first obtain the standard weight vector. As the initial weight vector To achieve interactive optimization between algorithms, an error correction algorithm is introduced to output the error value. The initial weight vector is dynamically adjusted using the following formula: ; in The final weight vector, its elements , , , Corresponding to middle , , , The weights of the four feature parameters; The weight adjustment coefficient is 0.3, which was determined through verification with multiple sets of standard samples. This value ensures that the weight adjustment range matches the error magnitude, avoiding over-adjustment or under-adjustment. This is the error value output by the error correction algorithm.

[0059] Adjusted weight vector Need to meet If the adjusted result does not meet the condition, normalization is performed again to ensure the rationality of the weight allocation. The core function of this algorithm is to allocate weights based on the differences in the importance of feature parameters, allowing key feature parameters to play a dominant role in the fusion calculation. At the same time, it dynamically adjusts the weights by receiving feedback from the error correction algorithm, achieving adaptive optimization of the weights and improving the accuracy of the fusion result in representing antibody concentration.

[0060] 3.3 Algorithm 3: Feature Fusion Algorithm Based on Weighted Fusion Model Construction: Based on the selected feature parameters and the weights output by the weight allocation algorithm, a weighted fusion model is constructed. Through linear weighting and mean compensation, effective features from multiple dimensions are integrated into a single comprehensive fusion feature value. This model highlights the contribution of important features while balancing the fundamental influence of each feature through mean compensation, avoiding excessive impact of single feature fluctuations on the results, and enhancing the specificity and stability of the antibody signal.

[0061] Model training: Feature matrix selected from five sets of standard samples and the corresponding standard weight vector A fusion computation model is constructed using training data. The training objective is to maximize the correlation coefficient between the fusion feature values ​​and the standard antibody concentration. The correlation coefficient measures the degree of linear correlation between two variables, and its value ranges from -1 to 1. The closer the value is to 1, the stronger the correlation.

[0062] Introducing fusion coefficient This coefficient is used to adjust the ratio of the weighted summation term to the mean compensation term. The value range is from 0 to 1. By iterating through all intervals of 0.1 between 0.1 and 1.0, different values ​​are calculated. The correlation coefficient between the fusion characteristic value and the standard antibody concentration was used to select the value with the highest correlation coefficient as the fusion coefficient. After training and verification, when At that time, the correlation coefficient reached its maximum value of 0.98, therefore it was determined that... At this point, the weighted summation term becomes dominant, while the mean compensation term plays a supporting stabilizing role.

[0063] Model application: The filtered feature matrix output from step S3.1 and the final weight vector output by step S3.2 Substituting into the fusion calculation model, the average value of the filtered feature parameters is first calculated. The average value is the arithmetic mean of the four characteristic parameters, and the calculation formula is: ; in Represents the feature matrix after filtering The first in Each of the following feature parameters corresponds to , , , .

[0064] Then the initial fusion feature values ​​are calculated. The calculation formula is: ; in This is the fusion coefficient with a value of 0.8. For the first The weights of each feature parameter, For the first Each filtered feature parameter This represents the average value of the filtered feature parameters.

[0065] The core function of this algorithm is to integrate effective features from multiple dimensions, highlighting the contribution of key features through weighted averaging and reducing the impact of abnormal fluctuations in individual features through mean compensation, thus forming a fusion feature value that comprehensively reflects the level of porcine reproductive and respiratory syndrome (PRRS) antibodies. Compared with single feature parameters, this fusion feature value has higher specificity and stability, effectively reducing the influence of interfering factors in serum samples and providing high-quality basic data for subsequent error correction.

[0066] 3.4 Algorithm 4: Error Correction Algorithm Based on Adaptive Iteration Model Construction: An adaptive iterative error correction model is constructed. The error is calculated by comparing the initial fused feature values ​​with the standard fused feature values. Iterative correction is then performed based on this error, and the error is fed back to the weight allocation algorithm, achieving interactive optimization between the two algorithms. This model gradually reduces the deviation between the fused feature values ​​and the true antibody level through multiple iterations, improving the accuracy of the feature values. Simultaneously, error feedback makes the weight allocation more aligned with actual detection scenarios.

[0067] Model training: using the initial fused feature values ​​of five sets of standard samples and the corresponding standard antibody concentration For training data, where By filtering the feature matrix of standard samples and standard weight vector Substituting these values ​​into the fusion calculation model yields the following: First, the average value of the initial fusion feature values ​​of the five sets of standard samples is calculated. This average value is used as the standard fusion feature value, and the calculation formula is: ; Set the number of iterations and iterative convergence threshold Number of iterations The number of iterations was determined to be 5 through training. This number ensures the correction effect while avoiding computational redundancy caused by excessive iteration; the iteration convergence threshold... The threshold is set to 0.01. This threshold is the relative deviation threshold of the fused feature values. When the difference between the fused feature values ​​of two adjacent iterations is less than this threshold, it indicates that the correction result has become stable and the iteration can be stopped.

[0068] Model application: First, calculate the initial fusion feature values. Eigenvalues ​​fused with standards initial error This error is a relative error, and the calculation formula is: ; The initial error As error value The weight allocation algorithm, fed back to step S3.2, is used to dynamically adjust the weight vector. This enables the interaction between the error correction algorithm and the weight allocation algorithm.

[0069] Then, an adaptive iterative correction process is initiated, with the number of iterations starting from 1 and counting to... . No. The correction formula for the next iteration is: ; in That is, the initial correction value is the initial fusion feature value. For the first The corrected fused feature values ​​after the next iteration For the first The error value of the next iteration is calculated using the following formula: ; In each iteration, the difference between two adjacent corrected fusion feature values ​​is calculated. ,when Or the number of iterations reaches When the convergence condition is met, the iteration stops. In this scheme, the number of iterations is set to 5. If the convergence condition is met in the first few iterations, the iteration can be stopped early. The final output is the [number of iterations]. The corrected fusion feature value after the next iteration is used as the final fusion feature value. .

[0070] The core function of this algorithm is to eliminate systematic and random errors in the initial fused feature values ​​through iterative correction, thereby improving the accuracy and reliability of the feature values. Simultaneously, by feeding the error back to the weight allocation algorithm, the two algorithms achieve bidirectional interactive optimization, enabling the weight allocation to be dynamically adjusted based on actual detection errors. This further improves the overall accuracy of feature processing and provides highly accurate input data for subsequent quantitative calibration.

[0071] S4. Quantitative Calibration and Antibody Concentration Output First, a standard curve is constructed using the final fusion feature values ​​of five sets of standard samples. and the corresponding standard antibody concentration For data points, where The final fusion feature values ​​for each standard sample after processing by the four algorithms in step S3 are given. The standard antibody concentrations for the five standard samples are 10 ng / mL, 20 ng / mL, 40 ng / mL, 80 ng / mL, and 160 ng / mL, respectively. A linear regression method is used to fit the standard curve equation. The linear regression method minimizes the sum of squared residuals between the data points and the fitted line using the least squares method to ensure the accuracy of the fitting results. The quantitative calibration and accuracy verification process is as follows: Figure 4 As shown.

[0072] Let the equation of the standard curve be: ; in The concentration of porcine reproductive and respiratory syndrome (PRRS) antibodies (unit: ng / mL). The slope of the standard curve. The standard curve intercept. These are the final fused feature values.

[0073] Calculate the slope using the least squares method. and intercept The calculation formulas are as follows: ; ; in The standard sample size is 5. For the first The final fusion feature value of the standard samples. For the first Antibody concentrations in the standard samples.

[0074] The final fused feature value output from step S3 Substituting into the standard curve equation, the concentration of porcine reproductive and respiratory syndrome (PRRS) antibodies in the serum sample to be tested was calculated. To verify the accuracy of the test results, three parallel tests were performed on the same sample, yielding three antibody concentration values. , , Calculate the average of the three test results. and relative standard deviation , where the average The calculation formula is: ; Relative standard deviation The calculation formula is: ; Relative standard deviation Used to measure the repeatability and stability of test results, when When the accuracy of the test results meets the requirements, the antibody concentration is output. As the final test result; when If the test results show significant fluctuations, steps S1 to S4 need to be repeated until the test results meet the accuracy requirements.

[0075] In one specific embodiment: Application scenarios A large-scale pig farm with 1000 fattening pigs aged 30-60 days needs to assess the immunization effect 21 days after completing porcine reproductive and respiratory syndrome (PRRS) vaccination, and select pigs with insufficient antibody levels for booster vaccination. This study selected 50 pigs as the testing subjects and used the aforementioned quantitative detection method to determine antibody concentration. The specific implementation process is as follows: S1. Serum Sample Collection and Preprocessing 1. Sample collection: 50 fattening pigs aged 30-60 days were selected. 5 mL of blood sample was collected from each pig via the anterior vena cava. The samples were numbered 1-50 and placed in 50 centrifuge tubes without anticoagulants.

[0076] 2. Serum separation: Incubate all centrifuge tubes at room temperature for 2 hours, then place them in a high-speed refrigerated centrifuge at 3000 rpm for 15 minutes. After centrifugation, the initial serum volume of samples 1-50 is... The values ​​were 2.2 mL, 2.3 mL, 2.1 mL...2.4 mL (average 2.25 mL).

[0077] 3. Dilution and Filtration: Add phosphate buffer (pH=7.4) to each serum sample at a volume ratio of 1:5. For example, add 11 mL of buffer to sample 1. The volume after dilution is... All samples were diluted to the corresponding volume. Six times the concentration. Diluted serum was filtered through a 0.22 μm filter membrane one by one to remove impurities.

[0078] 4. Absorbance detection: The absorbance value of the filtered serum was measured at a wavelength of 280 nm using a UV spectrophotometer. Samples 1-50 The values ​​are 0.32, 0.35, 0.31...0.33 (mean 0.33).

[0079] S2. Antibody biomarker extraction and characteristic parameter extraction 1. Antigen-antibody binding: Take 2 mL of each pretreated serum sample, add 50 μL of porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent (containing N protein antigen and GP5 protein antigen), and incubate in a 37℃ constant temperature incubator for 30 minutes to form an antigen-antibody complex.

[0080] 2. High-performance liquid chromatography (HPLC) detection: The incubated mixture was injected sequentially into the HPLC instrument. The column temperature was set to 30℃, the mobile phase was methanol-water (volume ratio 40:60), the flow rate was 1 mL / min, the detection wavelength was 254 nm, and the chromatogram was recorded.

[0081] 3. Feature Parameter Extraction: Six feature parameters are extracted using the chromatographic data analysis module. Taking sample 1 as an example, the specific values ​​are as follows: Peak area characteristics: (N protein antibody-antigen complex) (GP5 protein antibody-antigen complex); Retention time characteristics: (N protein antibody-antigen complex) (GP5 protein antibody-antigen complex); Absorbance peak characteristics: (N protein antibody-antigen complex) (GP5 protein antibody-antigen complex); Construct the feature matrix of sample 1 The remaining 49 samples had their feature parameters extracted in the same way to form their respective feature matrices. .

[0082] S3. Feature Processing and Antibody Signal Enhancement Based on Four-Algorithm Fusion Taking sample 1 as an example, the specific application process of the four algorithms is shown in detail: 3.1 Application of Feature Selection Algorithm (FS Algorithm) 1. Standard sample data: Antibody concentrations of five standard sample groups. , , , , The pre-screening feature parameters and mutual information values ​​have been obtained through training, and the mutual information threshold... .

[0083] 2. Mutual information calculation of the test samples: Calculate the mutual information values ​​of each feature parameter of sample 1. : , , , , , ; 3. Feature Filtering: Because and The mutual information value is lower than Eliminate and retain , , , Construct the filtered feature matrix .

[0084] 3.2 Application of Weight Allocation Algorithm (WA Algorithm) 1. Standard weight vector: The standard weight vector obtained during training. ,satisfy Consistency ratio .

[0085] 2. Error Feedback Adjustment: Receives the initial error output from subsequent error correction algorithms. Weighting adjustment coefficient Adjust the weights according to the formula: ; 3. Normalization Verification: The adjusted weight sum is 1.009. Renormalization yields the final weight vector. .

[0086] 3.3 Application of Feature Fusion Algorithm (FF Algorithm) 1. Calculation of the mean of features: The average value of the feature parameters after screening sample 1: ; 2. Initial fusion feature value calculation: fusion coefficient Substitute into the formula: ; 3.4 Application of Error Correction Algorithm (EC Algorithm) 1. Standard Fusion Feature Values: The initial fusion feature values ​​of the five standard sample groups are as follows: , , , , Standard fusion feature values: ; 2. Initial error calculation: ; 3. Iterative correction: setting , ; First iteration: , ; Second iteration: , (Error rises, continue iteration); 3rd iteration: , ; 4th iteration: , ; 5th iteration: , Stop iterating and finally fuse the feature values. (Note: If the error persists abnormally during actual testing, it is necessary to investigate sample processing or instrument problems. This example only demonstrates the calculation process.)

[0087] S4. Quantitative Calibration and Antibody Concentration Output 1. Standard curve construction: The final fusion feature values ​​of the five sets of standard samples are as follows: , , , , Substitute the values ​​into the least squares formula to calculate the slope and intercept: ; ; The equation of the standard curve is: ; 2. Antibody concentration calculation: Take sample 1 Substitute into the equation: ; 3. Accuracy Verification: Three parallel tests were performed on sample 1, yielding concentration values ​​of 1.50 ng / mL, 1.48 ng / mL, and 1.52 ng / mL, with an average value of: ; Relative standard deviation: ; The test results are valid, and the final antibody concentration for sample 1 is 1.50 ng / mL.

[0088] Implementation Results After the above steps were performed on 50 samples, the antibody concentration ranged from 1.2 to 8.6 ng / mL. Among them, the concentration of 3 samples was lower than 2.0 ng / mL (poor immunization effect). The pig farm gave these 3 pigs a booster immunization. The antibody concentration of the remaining 47 samples met the standard, and the immunization effect was good.

[0089] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0090] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0091] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0092] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0093] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0094] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0095] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, aspects of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0096] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0097] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0098] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A method for quantitative detection of serum antibodies against porcine reproductive and respiratory syndrome (PRRS), characterized in that, include: S1. Serum sample collection and preprocessing: Blood samples were collected from the anterior vena cava of pigs and preprocessed to obtain preprocessed serum samples; S2. Antibody biomarker extraction and characteristic parameter extraction: Porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent was added to the pretreated serum sample, and the antigen-antibody complex was formed by incubation. The complex was separated by high performance liquid chromatography, and three types of characteristic parameters, namely peak area, retention time and absorbance peak value, were extracted to construct a characteristic matrix. S3. Feature processing and antibody signal enhancement based on four-algorithm fusion: Input the feature matrix into the feature screening algorithm based on mutual information to remove redundant features that are weakly correlated with antibody concentration, and obtain the screened feature matrix; The filtered feature matrix is ​​input into a weight allocation algorithm based on hierarchical analysis, and the feature weights are dynamically allocated by combining error feedback to obtain the final weight vector. The filtered feature matrix and the final weight vector are input into a feature fusion algorithm based on weighted fusion to obtain the initial fusion feature values. The initial fused feature values ​​are input into an error correction algorithm based on adaptive iteration. The error is calculated and fed back to the weight allocation algorithm. After iterative correction, the final fused feature values ​​are obtained. S4. Quantitative Calibration and Antibody Concentration Output: Based on the final fusion characteristic value obtained by processing a standard sample with known antibody concentration, a standard curve is constructed. The final fusion characteristic value of the sample to be tested is substituted into the standard curve to calculate the antibody concentration. Parallel tests are performed on the same sample to verify the detection accuracy and output the antibody concentration that meets the accuracy requirements.

2. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S3, the mutual information threshold of the feature screening algorithm based on mutual information is obtained by training with standard samples. The standard samples are porcine reproductive and respiratory syndrome (PRRS) serum standards with known antibody concentrations. The mutual information threshold is determined by calculating the average value of the mutual information values ​​between each feature parameter and the corresponding antibody concentration of all standard samples.

3. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S3, when constructing the judgment matrix based on the weight allocation algorithm of hierarchical analysis, the quantitative detection accuracy is taken as the target layer, the selected feature parameters are taken as the criterion layer, the importance of the feature parameters is compared pairwise to determine the elements of the judgment matrix, the maximum eigenvalue and eigenvector of the judgment matrix are calculated, the standard weight vector is obtained after normalization, and the rationality of the weight allocation is verified by consistency test.

4. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S3, the fusion coefficient of the feature fusion algorithm based on weighted fusion is determined by standard sample verification. The fusion coefficient is the value that maximizes the correlation between the fusion feature value and the standard antibody concentration. The fusion process combines the average value of the screened feature parameters for mean compensation.

5. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S3, the error correction algorithm based on adaptive iteration sets the number of iterations and the convergence threshold. The number of iterations is determined by training with standard samples, and the convergence threshold is the relative deviation threshold of the fused feature values. When the difference between the fused feature values ​​of two adjacent iterations is less than the convergence threshold or the number of iterations is reached, the iteration stops.

6. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S4, the standard curve is fitted by linear regression, and the slope and intercept of the standard curve are calculated by least squares method. The standard samples are five groups of serum standards for porcine reproductive and respiratory syndrome (PRRS) with different antibody concentrations.

7. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S4, the parallel tests are performed three times. The relative standard deviation of the three test results is calculated. When the relative standard deviation is less than 5%, the antibody concentration is output as the final test result; otherwise, the test is repeated.

8. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S2, the components of the porcine reproductive and respiratory syndrome (PRRS) antibody-specific binding reagent are N protein antigen and GP5 protein antigen.

9. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S2, the detection parameters of the high performance liquid chromatograph are as follows: column temperature 30℃, mobile phase is a mixture of methanol and water, mobile phase flow rate 1mL / min, and detection wavelength 254nm.

10. The method for quantitative detection of porcine reproductive and respiratory syndrome (PRRS) serum antibodies according to claim 1, characterized in that, In step S1, the pretreatment includes: the blood sample from the anterior vena cava of pigs is allowed to stand, centrifuged to separate the serum, diluted with phosphate buffer and filtered, the initial absorbance of the filtered serum is detected, and the initial volume and the volume after dilution of the serum are recorded to obtain the pretreated serum sample.

Images