Mutton slaughter line veterinary drug residue rapid detection method based on enzyme-linked immunosorbent assay

Abstract

The invention relates to the technical field of mutton veterinary drug residue detection, and discloses a mutton slaughter line veterinary drug residue rapid detection method based on enzyme-linked immunosorbent assay, which comprises the following steps: firstly, acquiring a collection time distribution data set, a tissue density data set and a surface pollutant adhesion amount data set of a slaughter line mutton sample; meanwhile, collecting data such as use frequency, component concentration change, temperature fluctuation and pH value stability of a reagent to be detected, and constructing a reagent base frequency mapping relation; a current reference detection reagent is determined according to the data, whether reagent parameters are replaced or adjusted or not is judged through signal response parameters, parameter optimization is conducted in combination with a particle swarm optimization algorithm, and a future effect can be predicted through a feedforward neural network so that adjustment can be conducted in advance. According to the method, the detection process is dynamically optimized by integrating multi-dimensional data, the accuracy, timeliness and stability of detection are improved, and the method is suitable for rapid and efficient detection of veterinary drug residues in the mutton slaughter line.

Description

Technical Field

[0001] The invention relates to the technical field of mutton veterinary drug residue detection, in particular to an enzyme-linked immunosorbent assay-based rapid detection method for veterinary drug residues in a mutton slaughtering line. Background Art

[0002] With the increasing intensity of food safety regulations, mutton, a key meat consumer product, is facing increasing public concern regarding veterinary drug residues. While the widespread use of veterinary drugs in the livestock industry effectively prevents and treats animal diseases and improves breeding efficiency, excessive residues or illegal use can enter the human body through the food chain, posing potential threats to human health, such as triggering allergic reactions and developing drug resistance. This can also impact the quality and safety image of my country's mutton products and their international trade competitiveness.

[0003] Currently, there are various methods for detecting veterinary drug residues in mutton slaughtering lines. Among them, enzyme-linked immunosorbent assay (ELISA) has been widely used in the field of rapid detection due to its advantages such as high specificity, high sensitivity, and relatively simple operation. However, existing ELISA-based detection methods still have many shortcomings in practical application.

[0004] From a sample collection perspective, traditional methods often rely on fixed sampling points and single time points, failing to fully consider the dynamic changes occurring throughout the slaughtering process. Mutton samples from different slaughtering stages vary in their collection time distribution, and the varying tissue density of different parts of the meat can affect sample representativeness. Furthermore, the amount of surface contaminants can also vary with the slaughtering process. Ignoring these factors can easily lead to samples that fail to accurately reflect the actual veterinary drug residue situation, compromising the reliability of test results.

[0005] When it comes to selecting test reagents, existing methods often rely on experience or fixed standards to select benchmark test reagents, without comprehensively considering factors such as the reagent's historical usage frequency, component concentration stability, and reaction conditions (such as temperature and pH). The performance of different test reagents is affected by numerous factors, including changes in component concentration over time, temperature fluctuations during the reaction phase, and pH stability, all of which directly impact the accuracy and repeatability of the test. Improper selection of benchmark reagents can lead to abnormal test signal responses and increase the risk of misdiagnosis.

[0006] Adjustments to reagent parameters during the testing process lack dynamism and scientific rationality. Traditional methods often rely on manual experience to adjust reagent parameters when deviations occur, making it difficult to quickly and accurately find the optimal parameter combination. Furthermore, they lack a mechanism to predict future test results, making it impossible to proactively avoid potential test errors. This results in low test efficiency and makes it difficult to meet the rapid and efficient testing needs of the mutton slaughtering line. Therefore, a rapid veterinary drug residue detection method for mutton slaughtering lines is needed that can comprehensively consider multiple factors and dynamically optimize the detection process to improve the accuracy, timeliness, and stability of the test. Summary of the Invention

[0007] The purpose of the present invention is to provide a rapid detection method for veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides a method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay, the method comprising: The collection time distribution data, tissue density data of different parts, and surface contaminant attachment data of mutton samples at each link of the slaughter line were obtained to obtain the current collection time distribution data set, the current tissue density data set, and the current contaminant attachment data set; the frequency data of each candidate test reagent being used as a benchmark reagent, the change data of the component concentration of each candidate test reagent over time, the temperature fluctuation data at different reaction stages, and the pH value stability data were collected to construct the final reagent-based frequency mapping relationship; The current benchmark detection reagent is determined based on the base frequency mapping relationship of the final reagent, the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set; by collecting the signal response parameters after using the current benchmark detection reagent, it is judged whether the current benchmark detection reagent needs to be replaced or the parameters adjusted to obtain a judgment result; according to the judgment result, the parameters of the current benchmark detection reagent are adjusted to obtain the final current detection parameter combination.

[0009] Preferably, obtaining the current acquisition time distribution dataset, the current tissue density dataset and the current pollutant attachment amount dataset includes the following steps: determining the specific detection section of the mutton slaughtering line to be detected and the corresponding several sampling points to obtain a set of sampling points to be selected; then determining several types of parameter types that affect the possibility of the sampling point being selected as the benchmark point to obtain a set of parameter types that affect the selection of the benchmark sampling point; setting a first current statistical period; combining the first current statistical period and the set of parameter types that affect the selection of the benchmark sampling point to obtain the sample acquisition time distribution data, tissue density data of different parts and surface pollutant attachment amount data of each sampling point in the set of sampling points to be selected, and obtain the current acquisition time distribution dataset, the current tissue density dataset and the current pollutant attachment amount dataset.

[0010] Preferably, the collection and construction of the final reagent as the base frequency mapping relationship includes the following steps: setting a historical statistical period; combining the set of candidate detection reagents and the set of influencing parameter types for the selection of benchmark reagents, collecting the frequency data of each candidate detection reagent being used as a benchmark reagent during the historical statistical period, the component concentration change data of each candidate detection reagent over time, the temperature fluctuation data at different reaction stages, and the pH value stability data, to obtain a historical candidate detection reagent as the base frequency data set, a historical component concentration change data set, a historical temperature fluctuation data set, and a historical pH value stability data set; using the historical candidate detection reagent as the base frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set, and the historical pH value stability data set to construct the final reagent as the base frequency mapping relationship and the final component concentration weight change data set.

[0011] Preferably, the particle swarm optimization algorithm is used to construct the final reagent-based frequency mapping relationship and the final component concentration weight change data set.

[0012] Preferably, the determination of the current benchmark detection reagent includes the following steps: setting the current original benchmark detection reagent; substituting each data in the current acquisition time distribution data set, the current tissue density data set, the current pollutant attachment amount data set and the final component concentration weight change data set into the final reagent base frequency mapping relationship for mapping, and obtaining the current candidate detection reagent base frequency data set; when the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is the same as the current original benchmark detection reagent, the current benchmark detection reagent is not replaced; otherwise, the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is used as the current benchmark detection reagent, and both are recorded as the current benchmark detection reagent.

[0013] Preferably, the determination of whether the current benchmark detection reagent needs to be replaced or the parameters adjusted includes the following steps: setting a second current statistical period; setting several time nodes within the second current statistical period to obtain a current time node set; setting several types of parameter types that can reflect the detection effect after using the benchmark detection reagent to obtain a benchmark detection reagent effect parameter type set; combining the benchmark detection reagent effect parameter type set and the current time node set, collecting the signal response parameters after using the current benchmark detection reagent to obtain a current use effect parameter matrix; then collecting the average effect parameters of several time nodes before using the current benchmark detection reagent to obtain a historical average effect parameter set; calculating the difference data between the historical average effect parameter set and each row of data in the current use effect parameter matrix to obtain a current effect difference data set; setting a first effect difference critical value and a second effect difference critical value; when the current effect difference data set contains current effect difference data that is less than the second effect difference critical value, replacing the current benchmark detection reagent back to the current original benchmark detection reagent; when the current effect difference data set contains current effect difference data that is greater than or equal to the second effect difference critical value and less than the first effect difference critical value, entering the parameter adjustment step; otherwise, entering the prediction step.

[0014] Preferably, the parameter adjustment of the current benchmark detection reagent based on the judgment result includes the following steps: setting an initial current detection parameter combination; using the initial current detection parameter combination to adjust the parameters of the current benchmark detection reagent and implement detection; after the parameter adjustment of the current benchmark detection reagent and the implementation of the detection are completed, the current effect parameters are collected according to the benchmark detection reagent effect parameter type set to obtain the current adjusted effect parameter set; the difference data between the current adjusted effect parameter set and the historical average effect parameter set is calculated to obtain the current adjusted difference data; when the current adjusted difference data is greater than or equal to the first effect difference critical value, the initial current detection parameter combination is used as the final current detection parameter combination; otherwise, the initial current detection parameter combination is optimized until the current adjusted difference data is greater than or equal to the first effect difference critical value.

[0015] Preferably, the optimization of the initial current detection parameter combination includes the following steps: setting the value range of the initial current detection parameter combination to obtain the current parameter value range; constructing a detection parameter optimization particle swarm; setting the maximum number of iterations and the current number of iterations of the detection parameter optimization particle swarm, which are respectively recorded as the maximum number of iterations of parameter optimization and the current number of iterations of parameter optimization; setting the initial position of each particle in the detection parameter optimization particle swarm according to the current parameter value range to obtain a second initial position set; constructing the fitness function of the detection parameter optimization particle swarm; starting iteration, and setting the current number of iterations of the parameter optimization to 1 before iteration; using the fitness function of the detection parameter optimization particle swarm in each round of iteration to calculate the fitness value of the position of each particle in the detection parameter optimization particle swarm updated in the previous round of iteration and performing the fitness function of the previous round of iteration on the position of each particle in the detection parameter optimization particle swarm. The position of each particle in the detection parameter optimization particle swarm updated during the generation process is updated; when the current number of iterations of parameter optimization reaches the maximum number of iterations of parameter optimization, the iteration is stopped to obtain the second final global optimal fitness and the second final global optimal position; otherwise, the iteration is continued until the current number of iterations of parameter optimization reaches the maximum number of iterations of parameter optimization; the second final global optimal fitness is used as the current adjusted difference data after optimization; when the current adjusted difference data after optimization is greater than or equal to the first effect difference critical value, the second final global optimal position is used to adjust the parameters of the current benchmark detection reagent and implement the detection to obtain the final current detection parameter combination; otherwise, the iteration step is returned to continue iterating until the current adjusted difference data after optimization is greater than or equal to the first effect difference critical value.

[0016] Preferably, the prediction step includes the following steps: predicting the usage effect parameters of future time nodes based on the current usage effect parameter matrix and using a feedforward neural network model to obtain a future usage effect parameter matrix; then calculating the difference data between the historical average effect parameter set and each row of data in the future usage effect parameter matrix to obtain a future effect difference data set; when the future effect difference data set contains future effect difference data that is less than the second effect difference critical value, entering the parameter adjustment step; otherwise, no processing is performed.

[0017] Preferably, obtaining the surface pollutant attachment data of each sampling point in the set of selected sampling points includes the following steps: wiping the surface of the mutton sample at each sampling point in the set of selected sampling points, placing the wipe in a buffer solution for oscillation elution, determining the pollutant concentration in the eluate by spectrophotometry, and calculating the surface pollutant attachment data in combination with the sample surface area.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This method first collects data on the time distribution of lamb samples collected from each stage of the slaughtering line, including tissue density at different locations and the amount of surface contaminants attached. This method comprehensively considers temporal and spatial variations in samples, ensuring representativeness and avoiding detection bias caused by inappropriate sample selection. By determining the sections and sampling points to be tested, and incorporating the parameters that influence sampling point selection and the statistical time period, systematically collecting relevant sample data lays a solid foundation for subsequent testing.

[0019] In the selection of test reagents, this method constructs a frequency mapping relationship between the final reagent and the base, integrates historical usage frequency, component concentration changes, temperature fluctuations, and pH stability data, and uses a particle swarm optimization algorithm for analysis to achieve the scientific determination of the current benchmark test reagent. This process breaks away from the limitations of traditional experience-based reagent selection, making reagent selection more in line with actual testing needs, reducing detection errors caused by reagent mismatch, and improving detection stability.

[0020] This method introduces a dynamic parameter adjustment mechanism. By collecting the signal response parameters after using the current benchmark test reagent, it determines whether the reagent needs to be replaced or the parameters need to be adjusted. It then optimizes the test parameters using a particle swarm optimization algorithm, quickly finding the optimal parameter combination. This dynamic adjustment not only promptly corrects deviations during the test process but also uses a feedforward neural network model to predict future test results, preemptively avoiding potential problems and ensuring the continuity and efficiency of the test process.

[0021] To acquire data on surface contaminant deposition, swab sampling combined with spectrophotometry improves data accuracy. A rational iteration mechanism ensures precise parameter adjustments during parameter optimization. This method optimizes the entire process, from sample collection to reagent selection and parameter adjustment. This significantly improves the efficiency and accuracy of veterinary drug residue testing on the mutton slaughtering line, providing strong technical support for food safety supervision, protecting consumer health, and enhancing the quality and safety of mutton products. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a diagram showing the working principle of the method for rapid detection of veterinary drug residues in a mutton slaughtering line based on enzyme-linked immunosorbent assay according to the present invention; Figure 2 Flowchart for obtaining the data set of collection time distribution, tissue density and contaminant attachment amount; Figure 3 Flowchart for constructing the base frequency mapping relationship of the final reagent; Figure 4 Flowchart for determining current benchmark test reagents; Figure 5 Flowchart for predicting future use effect parameters. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 5 The present invention provides a method for rapid detection of veterinary drug residues in mutton slaughtering lines based on enzyme-linked immunosorbent assay, the method comprising: The collection time distribution data of mutton samples at each link of the slaughtering line, the tissue density data of different parts, and the surface pollutant attachment amount data are obtained to obtain the current collection time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set.

[0025] Collect the frequency data of each candidate detection reagent being used as a benchmark reagent, the change data of the component concentration of each candidate detection reagent over time, the temperature fluctuation data at different reaction stages, and the pH stability data, and construct the final reagent-based frequency mapping relationship.

[0026] The current benchmark detection reagent is determined based on the final reagent base frequency mapping relationship, the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set.

[0027] By collecting the signal response parameters after using the current reference detection reagent, it is determined whether the current reference detection reagent needs to be replaced or the parameters need to be adjusted, and a determination result is obtained.

[0028] The parameters of the current benchmark detection reagent are adjusted according to the judgment result to obtain the final current detection parameter combination.

[0029] Example 1: This example needs to follow the following specific procedures when obtaining the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set. Clarify the specific detection section of the mutton slaughtering line to be tested. This step should be determined based on the actual layout and production process of the slaughtering line. For example, the slaughtering line can be divided into different sections such as the slaughtering area, the depilation area, and the cutting area. After determining the specific detection section, set a number of sampling points for the section. The setting of these sampling points needs to comprehensively consider the production characteristics of each link of the slaughtering line and the risk areas where veterinary drug residues may exist, thereby forming a set of sampling points to be selected.

[0030] Several parameter types were identified that influence the likelihood of a sampling point being selected as a benchmark point. These parameters were determined based on the actual conditions of the mutton slaughter process and testing requirements. For example, factors such as the production flow rate at the sampling point, its distance from potential veterinary drug contact, and the representativeness of the sample were considered. This resulted in a set of parameters influencing the selection of benchmark sampling points.

[0031] A first current statistical period is set. This period should be determined based on factors such as the slaughter line's production cycle and testing frequency. For example, it can be set to a production shift, a day, or a week. After the first current statistical period is set, a set of influencing parameter types is selected based on this period and the baseline sampling point, and data is collected from each sampling point in the selected sampling point set.

[0032] For each sampling point, sample collection time distribution data needs to be collected. This requires recording the specific time of each sample collection at that sampling point within the first current statistical period, thereby forming the sample collection time distribution data for that sampling point. At the same time, tissue density data for different parts of the mutton is collected. Because tissue density varies across different parts of the mutton, it is necessary to measure the tissue density of the different parts of the mutton corresponding to each sampling point (such as the leg, back, and abdomen). The measurement method can use relevant density measurement standards or commonly used measurement methods to obtain tissue density data for different parts.

[0033] When obtaining data on the amount of surface contaminants attached, the surface of the mutton sample at each sampling point is wiped and sampled. The specific operation is to use a suitable wiping tool (such as a cotton swab or a wiping cloth) to wipe evenly within the specified area of ​​the sample surface, ensuring that the wiping range and force are consistent to ensure the accuracy of the sampling. The wipe is placed in a buffer solution. The buffer solution here needs to be selected according to the nature of the contaminant and the subsequent detection method. The buffer solution is then shaken and eluted. The shaking conditions (such as shaking frequency, time, etc.) also need to be reasonably set to ensure that the contaminants can be fully eluted from the wipe into the buffer solution.

[0034] After elution is complete, the pollutant concentration in the eluate is determined by spectrophotometry. Spectrophotometry is an analytical method based on the selective absorption of light by a substance. During measurement, a series of standard solutions of pollutants of known concentrations must be prepared to establish a concentration-absorbance standard curve. The eluate is then measured under the same conditions, and the corresponding pollutant concentration is obtained from the absorbance on the standard curve.

[0035] After determining the contaminant concentration in the eluate, the surface contaminant load is calculated based on the sample surface area. This calculation requires accurate measurement using appropriate calculation methods or measurement tools based on the specific shape and size of the mutton sample. By multiplying the contaminant concentration by the sample surface area, the surface contaminant load for that sampling point is determined.

[0036] Through the above series of operations, the sample collection time distribution data, tissue density data of different parts, and surface pollutant attachment data are collected for each sampling point, and finally the current collection time distribution data set, current tissue density data set, and current pollutant attachment data set are obtained. These data sets contain various key data of each sampling point in the first current statistical period, providing important basic data support for subsequent detection work. Throughout the data collection process, it is necessary to strictly control the operating conditions of each link to ensure the accuracy and reliability of the data. For example, when wiping samples, the standardization of sampling must be ensured, and when measuring pollutant concentrations, the operating procedures of the spectrophotometry method must be strictly followed to avoid data errors due to improper operation. At the same time, the collected data must be recorded and organized in detail to ensure data traceability.

[0037] Example 2: This example follows the detailed process for collecting and constructing the final reagent-based frequency mapping relationship. A historical statistical period is set, taking into account factors such as the reagent's lifespan and the accumulated historical production data of the slaughtering line. For example, the past month, three months, or six months can be selected as the historical statistical period. The specific duration is determined based on the feasibility of actual data collection and testing requirements.

[0038] After setting the historical statistical period, data collection is conducted using a set of candidate test reagents and a set of parameters influencing the selection of benchmark reagents. The candidate test reagent set is a pre-determined combination of multiple reagents used for veterinary drug residue testing in the mutton slaughtering line, while the set of parameters influencing the selection of benchmark reagents includes various parameters that influence the selection of a test reagent as a benchmark reagent, such as the reagent's sensitivity, specificity, stability, changes in component concentration over time, adaptability to temperature fluctuations during different reaction stages, and pH stability.

[0039] During the historical statistical period, for each candidate test reagent, data on its frequency of use as a benchmark reagent is collected. This requires detailed records of the specific time and scenario in which each reagent was determined as a benchmark reagent during the historical period, thereby forming the frequency of use data for each candidate test reagent. At the same time, data on the changes in the component concentration of each candidate test reagent over time is collected. This requires testing and recording the component concentration of each reagent at certain time intervals during the historical statistical period, such as once a day or at regular intervals, to obtain a complete data series on the changes in component concentration over time.

[0040] It is also necessary to collect data on temperature fluctuations and pH stability during different reaction stages. During the enzyme-linked immunosorbent assay (ELISA) process, different reaction stages (such as the antigen-antibody binding stage and the enzyme-catalyzed reaction stage) may have different requirements for temperature and pH. Therefore, it is necessary to record the actual temperature fluctuations during each reaction stage within the historical statistical period, including the highest, lowest, average, and fluctuation range of the temperature. At the same time, record the stability of the pH value during each reaction stage, such as the specific pH value and the range of variation.

[0041] Through the above collection work, we obtained a historical dataset of candidate test reagent base frequencies, a historical dataset of component concentration changes, a historical dataset of temperature fluctuations, and a historical dataset of pH stability. These datasets cover various key data related to candidate test reagents during the historical statistical period, providing a foundation for the subsequent construction of the final reagent base frequency mapping relationship and the final dataset of component concentration weight changes.

[0042] When constructing the final reagent-based frequency mapping relationship and the final component concentration weight change dataset, the particle swarm optimization algorithm was used. The particle swarm optimization algorithm is a swarm intelligence optimization algorithm that simulates the foraging behavior of bird flocks. Its basic idea is to find the optimal solution through collaboration and information sharing among individuals in the swarm.

[0043] During the specific implementation process, it is first necessary to determine the relevant parameters of the particle swarm optimization algorithm, such as the number of particles, the maximum number of iterations, the inertia weight, the learning factor, etc. The setting of these parameters will affect the convergence speed and optimization effect of the algorithm, and they need to be reasonably adjusted according to the actual data conditions and problem characteristics.

[0044] The particle swarm optimization algorithm uses a historical dataset of candidate test reagent frequencies, a historical dataset of component concentration changes, a historical dataset of temperature fluctuations, and a historical dataset of pH stability as input data. Each particle represents a possible mapping relationship or weight distribution scheme, and the particle's position indicates the corresponding parameter value.

[0045] During the algorithm iteration process, each particle adjusts its position based on its own historical optimal position and the swarm's global optimal position to find a better solution. Through continuous iteration, the particle swarm gradually converges to the optimal solution, resulting in the final reagent-based frequency mapping relationship and the final component concentration weight change data set.

[0046] The final reagent-based frequency mapping relationship describes the frequency mapping relationship of various candidate detection reagents being used as benchmark reagents under different data input conditions, while the final component concentration weight change data set determines the weight changes of different component concentrations during the detection process.

[0047] Throughout the data collection and algorithm construction process, attention should be paid to the accuracy and completeness of the data. For example, when collecting usage frequency data, it is necessary to ensure the accuracy of the records to avoid omissions or erroneous records; when collecting component concentration change data, it is necessary to ensure the scientific nature of the detection method and the accuracy of the detection equipment; when using the particle swarm optimization algorithm, the algorithm parameters should be reasonably set to ensure that the algorithm can effectively converge to the optimal solution. At the same time, the collected data should be preprocessed, such as data cleaning and normalization, to eliminate noise and outliers in the data and improve the processing effect of the algorithm. In addition, the constructed base frequency mapping relationship of the final reagent and the final component concentration weight change data set need to be verified to ensure their rationality and effectiveness. This can be done by comparing with historical data or conducting actual detection verification.

[0048] Example 3: In this example, the following specific steps are required to determine the current benchmark detection reagent: Set the current original benchmark detection reagent, which can be a candidate detection reagent determined based on previous detection experience, reagent commonness, or preliminary screening results.

[0049] Each data point in the current collection time distribution dataset, current tissue density dataset, current contaminant attachment dataset, and final component concentration weight change dataset is substituted into the final reagent base frequency mapping relationship for mapping. The current collection time distribution dataset contains the distribution of sample collection times at each sampling point on the slaughter line within the set first current statistical period; the current tissue density dataset records the tissue density values ​​of different parts of the mutton at each sampling point; the current contaminant attachment dataset reflects the amount of contaminants attached to the surface of the mutton samples at each sampling point; and the final component concentration weight change dataset is obtained by processing historical data using the particle swarm optimization algorithm, which determines the weight changes of different component concentrations during detection.

[0050] The final reagent base frequency mapping relationship is constructed through historical data analysis and particle swarm optimization. It establishes a correspondence between the input data and the base frequencies of the candidate test reagents. During the mapping process, for each input data point, the corresponding base frequency data for each candidate test reagent is calculated based on the mapping relationship, resulting in the current candidate test reagent base frequency dataset. This dataset contains the base frequency values ​​of all candidate test reagents under the current data input.

[0051] Compare the base frequency data in the base frequency data set of the current candidate test reagent to find the largest base frequency data, and determine the candidate test reagent corresponding to the largest base frequency data. Next, compare and judge the corresponding candidate test reagent with the current original reference test reagent.

[0052] If the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is the same as the current original benchmark detection reagent, it means that under the current data input situation, the original benchmark detection reagent is still the most suitable as the benchmark detection reagent. Therefore, the current benchmark detection reagent is not replaced and the original current original benchmark detection reagent is maintained.

[0053] If the candidate detection reagent corresponding to the largest base frequency data is different from the current original benchmark detection reagent, it indicates that under the current detection conditions, the corresponding candidate detection reagent is more suitable as a benchmark detection reagent than the original benchmark detection reagent. At this time, the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is used as the current benchmark detection reagent and is recorded as the current benchmark detection reagent.

[0054] During the entire process of determining the current benchmark test reagent, the following points require attention. First, the accuracy of the current acquisition time distribution dataset, the current tissue density dataset, and the current contaminant attachment dataset is crucial. The accuracy of this data directly impacts the reliability of the base frequency data obtained after substituting it into the mapping relationship. Therefore, during the data collection phase, it is imperative to strictly follow the prescribed methods and procedures to ensure the authenticity and validity of the data.

[0055] The rationality of the final mapping relationship between reagent base frequencies will also have a significant impact on the results. This mapping relationship is based on historical data and the particle swarm optimization algorithm. During the construction process, it is necessary to ensure the integrity and representativeness of the historical data, and to reasonably set the parameters of the particle swarm optimization algorithm to ensure that the mapping relationship can accurately reflect the relationship between the input data and the base frequencies.

[0056] When performing data substitution and mapping calculations, the accuracy of the calculation process must be ensured to avoid biased results due to calculation errors. The obtained dataset of the base frequency of the current candidate test reagents needs to be carefully analyzed and compared to ensure that the maximum base frequency data found and the corresponding candidate test reagents are accurate.

[0057] When determining whether to replace the benchmark test reagent, the established conditions must be strictly followed and the judgment criteria cannot be changed arbitrarily. If the benchmark test reagent needs to be replaced, after the new current benchmark test reagent is determined, the necessary records and records must be kept for traceability and management of subsequent testing work.

[0058] Example 4: This example follows the following specific process when determining whether the current benchmark test reagent needs to be replaced or its parameters adjusted. A second current statistical period is set. This period is determined based on factors such as the slaughter line's production cycle, testing frequency, and the current usage of the benchmark test reagent. For example, if the current benchmark test reagent has been used for a period of time, the second current statistical period can be set to the most recent production shift, or to one or two days, depending on actual needs.

[0059] After setting the second current statistical period, set several time nodes within that period to form the current time node set. The setting of time nodes should take into account the timeliness of the test and the representativeness of the data. For example, in an 8-hour production shift, you can set a time node for each hour, i.e., the time nodes are the 1st hour, the 2nd hour, and so on to the 8th hour. Time nodes can also be set based on key links in the production process or periods where test results may fluctuate.

[0060] Several parameter types that can reflect the test results after using the benchmark test reagent are set to obtain a set of benchmark test reagent effect parameter types. The selection of these parameter types should be related to the characteristics of the enzyme-linked immunosorbent assay method and the goals of veterinary drug residue testing, such as absorbance value, test signal intensity, test result accuracy index, test repeatability index, etc.

[0061] Combined with the benchmark detection reagent effect parameter type set and the current time node set, the signal response parameters after using the current benchmark detection reagent are collected. In specific operations, at each time node, the mutton sample is tested using the current benchmark detection reagent according to the standard process of enzyme-linked immunosorbent assay, and the signal response parameter value corresponding to each parameter type is recorded. For example, at the time node of the first hour, after the sample is tested, the absorbance value is recorded as A1, the detection signal intensity is S1, the accuracy index is P1, the repeatability index is R1, etc.; at the time node of the second hour, the same test is performed and the corresponding parameter values ​​A2, S2, P2, R2, etc. are recorded, and so on, and finally the current use effect parameter matrix is ​​obtained. Each row of data in the matrix corresponds to a time node, and each column of data corresponds to a parameter type.

[0062] Collect the average effect parameters of several time nodes before the current benchmark detection reagent is used to obtain a historical average effect parameter set. The "several time nodes before the current benchmark detection reagent is used" here can be determined according to the actual situation. For example, select the effect parameters of the same time node in the three production shifts before the current benchmark detection reagent is used, and calculate the average effect parameters of each time node. Assuming that the effect parameters in the first hour of the previous three shifts are A1-1, A1-2, and A1-3, calculate their average value as the average absorbance value corresponding to the first hour in the historical average effect parameter set. Similarly, calculate the average effect parameters of other parameter types and time nodes to form a historical average effect parameter set.

[0063] Calculate the difference between the historical average effect parameter set and each row of data in the current effect parameter matrix to obtain the current effect difference data set. For each parameter type at each time node, subtract the corresponding average effect parameter value in the historical average effect parameter set from the data in the current effect parameter matrix to obtain the difference data. For example, the absorbance difference in the first hour is A1 minus the average A1, and the signal intensity difference is S1 minus the average S1. These difference data are then sorted to form the current effect difference data set.

[0064] Set the first and second effect difference thresholds. These thresholds should be determined based on factors such as the accuracy requirements of the test method, the performance characteristics of the reagents, and the acceptable error range in actual production. For example, the first effect difference threshold could be set to 0.2, and the second effect difference threshold could be set to 0.1. The specific values ​​should be adjusted based on actual conditions.

[0065] After completing the above settings and calculations, the current effect difference data set is analyzed and judged. If the current effect difference data in the current effect difference data set is less than the second effect difference threshold, it means that the detection effect of the current benchmark detection reagent has significantly decreased compared to the historical average effect and is beyond the acceptable range. In this case, the current benchmark detection reagent needs to be replaced with the current original benchmark detection reagent.

[0066] When the current effect difference data set contains current effect difference data that is greater than or equal to the second effect difference critical value and less than the first effect difference critical value, it indicates that although the detection effect of the current benchmark detection reagent is somewhat different from the historical average effect, it has not yet reached the level where the reagent needs to be replaced. At this time, the parameter adjustment step is entered to adjust the relevant parameters of the current benchmark detection reagent to optimize the detection effect.

[0067] If all current effect difference data in the current effect difference data set are greater than or equal to the first effect difference critical value, it means that the detection effect of the current benchmark detection reagent is within an acceptable range compared with the historical average effect, or may even be better. At this time, the prediction step is entered to perform predictive analysis on future detection effects.

[0068] Throughout the judgment process, attention must be paid to the accuracy and consistency of data collection. For example, when collecting the current effect parameters and the historical average effect parameters, the same testing equipment, testing methods, and operating procedures must be used to avoid data deviations due to different testing conditions. At the same time, for the setting of time nodes and the selection of parameter types, it is necessary to ensure that they can accurately reflect the changes in the detection effect. When calculating difference data, the accuracy of the calculation must be guaranteed to avoid human errors. In addition, the setting of the first effect difference critical value and the second effect difference critical value needs to be fully investigated and demonstrated to ensure their rationality and scientificity to avoid misjudgment. Through the above rigorous operations and judgments, it is possible to accurately determine whether the current benchmark detection reagents need to be replaced or the parameters need to be adjusted, providing correct guidance for subsequent testing work.

[0069] Example 5: In this embodiment, when adjusting the parameters of the current benchmark detection reagent according to the judgment result, the following specific process needs to be followed. Assuming that after passing the judgment step of Example 4, it is determined that the parameter adjustment step needs to be entered, the initial current detection parameter combination is set first. The setting of the initial current detection parameter combination needs to be combined with the characteristics of the current benchmark detection reagent and the conventional parameter range of the enzyme-linked immunosorbent assay. For example, the combination may include initial setting values ​​for parameters such as reaction temperature, reaction time, reagent addition amount, and buffer pH value, such as the reaction temperature is set to 37°C, the reaction time is set to 30 minutes, the reagent addition amount is set to 100μL, and the buffer pH value is set to 7.4, etc.

[0070] The initial current test parameter combination was used to adjust the parameters of the current benchmark test reagent and conduct the test. During the test, the ELISA test process was strictly followed according to the adjusted parameters, and the lamb samples were processed and tested to ensure the standardization and consistency of the test process.

[0071] After the test is completed, the current effect parameters are collected according to the benchmark test reagent effect parameter type set to obtain the current adjusted effect parameter set. For example, if the benchmark test reagent effect parameter type set includes parameter types such as absorbance value, detection signal intensity, and repeatability index, after the test is completed, the current absorbance value is recorded as A after adjustment, the detection signal intensity is recorded as S after adjustment, the repeatability index is recorded as R after adjustment, etc., to form the current adjusted effect parameter set.

[0072] Calculate the difference between the current adjusted effect parameter set and the historical average effect parameter set to obtain the current adjusted difference data. For each parameter type, subtract the corresponding average effect parameter value in the historical average effect parameter set from the data in the current adjusted effect parameter set to obtain the difference data. For example, the absorbance difference is A adjusted minus average A, the signal intensity difference is S adjusted minus average S, and so on. These difference data are combined to obtain the current adjusted difference data.

[0073] When the current adjusted difference data is greater than or equal to the first effect difference critical value, it means that after the parameters of the current benchmark detection reagent are adjusted through the initial current detection parameter combination, the detection effect has reached the expected requirements. At this time, the initial current detection parameter combination is used as the final current detection parameter combination.

[0074] If the current adjusted difference data is less than the first effect difference critical value, the initial current detection parameter combination needs to be optimized. During optimization, first set the value range of the initial current detection parameter combination to obtain the current parameter value range. For example, the reaction temperature value range can be set to 35°C-40°C, the reaction time value range can be set to 20 minutes-40 minutes, the reagent addition amount value range can be set to 80μL-120μL, the buffer pH value value range can be set to 7.0-7.8, etc.

[0075] Construct a particle swarm for detection parameter optimization. This swarm consists of multiple particles, each representing a possible combination of detection parameters. Set the maximum number of iterations and the current number of iterations for the particle swarm, which are recorded as the maximum number of parameter optimization iterations and the current number of parameter optimization iterations, respectively. For example, the maximum number of parameter optimization iterations can be set to 100, and the current number of parameter optimization iterations can be initially set to 1.

[0076] The initial position of each particle in the particle swarm is optimized by setting the detection parameters according to the current parameter value range to obtain a second set of initial positions. Each particle's initial position corresponds to a set of detection parameter combinations, and each parameter value in this set of parameter combinations is randomly generated within the corresponding value range.

[0077] Construct a fitness function for the detection parameter optimization particle swarm. This function is used to evaluate the quality of each particle position, that is, to evaluate the effectiveness of the corresponding detection parameter combination. The construction of the fitness function needs to be based on the current adjusted difference data. For example, the fitness function can be set to a function that is positively correlated with the current adjusted difference data. The larger the difference data, the higher the fitness value.

[0078] The iteration begins, and before the iteration, the number of parameter optimization iterations is set to 1. During each iteration, the fitness function of the detection parameter optimization particle swarm is used to calculate the fitness value of the position of each particle in the detection parameter optimization particle swarm updated during the previous iteration. The position of each particle in the detection parameter optimization particle swarm updated during the previous iteration is also updated. The update of particle positions follows the rules of the particle swarm optimization algorithm. Each particle adjusts its position based on its own historical optimal position and the global optimal position of the swarm to find a more optimal detection parameter combination.

[0079] When the current number of iterations of parameter optimization reaches the maximum number of iterations of parameter optimization, the iteration is stopped to obtain the second final global optimal fitness and the second final global optimal position. The detection parameter combination corresponding to the second final global optimal position is the optimized detection parameter combination.

[0080] Otherwise, continue to iterate until the current iteration number of parameter optimization reaches the maximum iteration number of parameter optimization.

[0081] The second final global optimal fitness is used as the optimized current adjusted difference data. When the optimized current adjusted difference data is greater than or equal to the first effect difference critical value, the second final global optimal position is used to adjust the parameters of the current benchmark detection reagent and implement the detection to obtain the final current detection parameter combination.

[0082] If the current adjusted difference data after optimization is still smaller than the first effect difference critical value, the process returns to the iteration step and continues iterating until the current adjusted difference data after optimization is larger than or equal to the first effect difference critical value.

[0083] Furthermore, in the prediction step, the current usage effect parameter matrix is ​​used to predict the usage effect parameters for future time nodes using a feedforward neural network model, resulting in a future usage effect parameter matrix. The feedforward neural network model is a commonly used machine learning model with a multi-layered structure that can predict the future using historical data. For example, the data in the current usage effect parameter matrix is ​​input, and after calculation by the feedforward neural network model, the predicted values ​​for each parameter type at each future time node are output, forming a future usage effect parameter matrix.

[0084] The difference between the historical average effect parameter set and each row of data in the future use effect parameter matrix is ​​then calculated to obtain the future effect difference dataset. For each parameter type at each future time node, the corresponding average effect parameter value in the historical average effect parameter set is subtracted from the predicted data in the future use effect parameter matrix to obtain the difference data, forming the future effect difference dataset.

[0085] When the future effect difference data in the future effect difference data set is less than the second effect difference critical value, it means that the predicted future detection effect may decline significantly. At this time, the parameter adjustment step is entered to adjust the parameters of the current benchmark detection reagent.

[0086] Otherwise, it means that the predicted future detection effect is within an acceptable range and no processing is performed.

[0087] Throughout the parameter adjustment and prediction process, it is important to ensure that the initial setting of the current detection parameter combination is reasonable, avoiding excessive deviation from the practical range. Failure to do so may lead to inefficient optimization. The current parameter value range must also be determined based on the characteristics of the test reagent and the capabilities of the testing equipment to ensure that the parameters vary within a reasonable range. When constructing the fitness function, key indicators of the test effect must be fully considered so that the function accurately reflects the strengths and weaknesses of the parameter combination. During the iterative process of the particle swarm optimization algorithm, it is important to ensure algorithm convergence and appropriately set parameters such as the inertia weight and learning factor to prevent the algorithm from becoming trapped in a local optimal solution. For feedforward neural network models, sufficient historical data is required for training to improve the model's predictive accuracy. Furthermore, throughout the entire process, each step of the operation and data must be strictly recorded to ensure process traceability and the reliability of the results.

[0088] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0089] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A rapid detection method for veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay, characterized in that: The following steps are involved: The collection time distribution data, tissue density data of different parts, and surface contaminant attachment data of mutton samples at each link of the slaughter line were obtained to obtain the current collection time distribution data set, the current tissue density data set, and the current contaminant attachment data set; the frequency data of each candidate test reagent being used as a benchmark reagent, the change data of the component concentration of each candidate test reagent over time, the temperature fluctuation data at different reaction stages, and the pH value stability data were collected to construct the final reagent-based frequency mapping relationship; The current benchmark detection reagent is determined based on the base frequency mapping relationship of the final reagent, the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set; by collecting the signal response parameters after using the current benchmark detection reagent, it is judged whether the current benchmark detection reagent needs to be replaced or the parameters adjusted to obtain a judgment result; according to the judgment result, the parameters of the current benchmark detection reagent are adjusted to obtain the final current detection parameter combination.

2. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 1, wherein Obtaining the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set includes the following steps: determining the specific detection section of the mutton slaughtering line to be inspected and the corresponding several sampling points to obtain a set of sampling points to be selected; then determining several types of parameter types that affect the possibility of the sampling point being selected as the benchmark point to obtain a set of parameter types that affect the selection of the benchmark sampling point; setting a first current statistical period; combining the first current statistical period and the set of parameter types that affect the selection of the benchmark sampling point to obtain the sample acquisition time distribution data, tissue density data of different parts, and surface pollutant attachment amount data of each sampling point in the set of sampling points to be selected, to obtain the current acquisition time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set.

3. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 2, wherein The collecting and constructing of the final reagent as base frequency mapping relationship includes the following steps: setting a historical statistical period; combining the set of candidate detection reagents and the set of parameter types affecting the selection of benchmark reagents, collecting the frequency data of each candidate detection reagent being used as a benchmark reagent during the historical statistical period, the component concentration change data of each candidate detection reagent over time, the temperature fluctuation data at different reaction stages, and the pH value stability data, to obtain a historical candidate detection reagent as base frequency data set, a historical component concentration change data set, a historical temperature fluctuation data set, and a historical pH value stability data set; using the historical candidate detection reagent as base frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set, and the historical pH value stability data set to construct the final reagent as base frequency mapping relationship and the final component concentration weight change data set.

4. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 3, wherein: The particle swarm optimization algorithm is used to construct the final reagent-based frequency mapping relationship and the final component concentration weight change data set.

5. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 4, wherein The determining of the current benchmark detection reagent includes the following steps: setting the current original benchmark detection reagent; substituting each data in the current acquisition time distribution data set, the current tissue density data set, the current pollutant attachment amount data set and the final component concentration weight change data set into the final reagent base frequency mapping relationship for mapping, to obtain the current candidate detection reagent base frequency data set; when the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is the same as the current original benchmark detection reagent, the current benchmark detection reagent is not replaced; otherwise, the candidate detection reagent corresponding to the largest base frequency data in the current candidate detection reagent base frequency data set is used as the current benchmark detection reagent, and both are recorded as the current benchmark detection reagent.

6. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 5, wherein: The determination of whether the current benchmark detection reagent needs to be replaced or the parameters adjusted includes the following steps: setting a second current statistical period; setting several time nodes within the second current statistical period to obtain a current time node set; setting several types of parameter types that can reflect the detection effect after using the benchmark detection reagent to obtain a benchmark detection reagent effect parameter type set; combining the benchmark detection reagent effect parameter type set and the current time node set, collecting the signal response parameters after using the current benchmark detection reagent to obtain a current use effect parameter matrix; then collecting the average effect parameters of several time nodes before using the current benchmark detection reagent to obtain a historical average effect parameter set; calculating the difference data between the historical average effect parameter set and each row of data in the current use effect parameter matrix to obtain a current effect difference data set; setting a first effect difference critical value and a second effect difference critical value; when the current effect difference data set contains current effect difference data that is less than the second effect difference critical value, replacing the current benchmark detection reagent back to the current original benchmark detection reagent; when the current effect difference data set contains current effect difference data that is greater than or equal to the second effect difference critical value and less than the first effect difference critical value, entering the parameter adjustment step; otherwise, entering the prediction step.

7. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 6, wherein: The parameter adjustment of the current benchmark detection reagent based on the judgment result includes the following steps: setting an initial current detection parameter combination; adjusting the parameters of the current benchmark detection reagent and implementing the detection using the initial current detection parameter combination; after the parameter adjustment and detection of the current benchmark detection reagent are completed, collecting the current effect parameters according to the benchmark detection reagent effect parameter type set to obtain the current adjusted effect parameter set; calculating the difference data between the current adjusted effect parameter set and the historical average effect parameter set to obtain the current adjusted difference data; when the current adjusted difference data is greater than or equal to the first effect difference critical value, using the initial current detection parameter combination as the final current detection parameter combination; otherwise, optimizing the initial current detection parameter combination until the current adjusted difference data is greater than or equal to the first effect difference critical value.

8. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 7, wherein: Optimizing the initial current detection parameter combination includes the following steps: setting the value range of the initial current detection parameter combination to obtain the current parameter value range; constructing a detection parameter optimization particle swarm; setting the maximum number of iterations and the current number of iterations of the detection parameter optimization particle swarm, which are respectively recorded as the maximum number of iterations of parameter optimization and the current number of iterations of parameter optimization; setting the initial position of each particle in the detection parameter optimization particle swarm according to the current parameter value range to obtain a second initial position set; constructing the fitness function of the detection parameter optimization particle swarm; starting iteration, and setting the current number of iterations of the parameter optimization to 1 before iteration; in each round of iteration, using the fitness function of the detection parameter optimization particle swarm to calculate the fitness value of the position of each particle in the detection parameter optimization particle swarm updated in the previous round of iteration and performing the fitness function of the previous round of iteration on the position of each particle in the detection parameter optimization particle swarm. The position of each particle in the detection parameter optimization particle swarm obtained by the update process is updated; when the current number of iterations of parameter optimization reaches the maximum number of iterations of parameter optimization, the iteration is stopped to obtain the second final global optimal fitness and the second final global optimal position; otherwise, the iteration is continued until the current number of iterations of parameter optimization reaches the maximum number of iterations of parameter optimization; the second final global optimal fitness is used as the current adjusted difference data after optimization; when the current adjusted difference data after optimization is greater than or equal to the first effect difference critical value, the second final global optimal position is used to adjust the parameters of the current benchmark detection reagent and implement the detection to obtain the final current detection parameter combination; otherwise, the iteration step is returned to continue iterating until the current adjusted difference data after optimization is greater than or equal to the first effect difference critical value.

9. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 6, wherein: The prediction step includes the following steps: predicting the usage effect parameters of future time nodes based on the current usage effect parameter matrix and using a feedforward neural network model to obtain a future usage effect parameter matrix; then calculating the difference data between the historical average effect parameter set and each row of data in the future usage effect parameter matrix to obtain a future effect difference data set; when the future effect difference data in the future effect difference data set contains future effect difference data that is less than a second effect difference critical value, entering a parameter adjustment step; otherwise, no processing is performed.

10. The method for rapid detection of veterinary drug residues in mutton slaughtering line based on enzyme-linked immunosorbent assay according to claim 2, characterized in that: Obtaining surface pollutant attachment data for each sampling point in the set of candidate sampling points includes the following steps: wiping the surface of the mutton sample at each sampling point in the set of candidate sampling points, placing the wipe in a buffer solution for oscillation elution, determining the pollutant concentration in the eluate by spectrophotometry, and calculating the surface pollutant attachment data based on the sample surface area.

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