Rapid detection method for veterinary drug residues in sheep slaughter line based on enzyme-linked immunoassay

Abstract

The present application relates to the technical field of mutton veterinary drug residue detection, and discloses a rapid detection method for mutton veterinary drug residues on a slaughter line based on enzyme-linked immunoassay, which first acquires a data set of the collection time distribution, tissue density and surface pollutant adhesion amount of mutton samples on the slaughter line, simultaneously collects data such as the usage frequency, ingredient concentration change, temperature fluctuation and pH stability of the candidate detection reagent, and constructs a frequency mapping relationship of the reagent as the reference; the current reference detection reagent is determined according to the above data, whether to replace or adjust the reagent parameters is judged through signal response parameters, parameter optimization is carried out in combination with a particle swarm optimization algorithm, and the future effect can also be predicted through a feedforward neural network to make an advance adjustment. The method dynamically optimizes the detection process by comprehensively using multidimensional data, improves the accuracy, timeliness and stability of the detection, and is suitable for rapid and efficient detection of mutton veterinary drug residues on a slaughter line.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detection of veterinary drug residues in mutton, in particular to a rapid detection method for veterinary drug residues on a mutton slaughter line based on enzyme-linked immunoassay. BACKGROUND

[0002] With the increasing intensity of food safety supervision, mutton as an important meat consumer product, its veterinary drug residue problem has been increasingly concerned by the society. The wide application of veterinary drugs in animal husbandry can effectively prevent and treat animal diseases and improve breeding efficiency, but excessive residues or illegal use will enter the human body through the food chain, posing a potential threat to human health, such as causing allergic reactions, producing drug resistance, etc., and also affecting the quality and safety image of China's mutton products and international trade competitiveness.

[0003] At present, there are various methods for detecting veterinary drug residues on a mutton slaughter line, among which enzyme-linked immunoassay has certain application in the field of rapid detection due to its strong specificity, high sensitivity, and relatively simple operation. However, the existing detection methods based on enzyme-linked immunoassay still have many shortcomings in practical application.

[0004] From the perspective of sample collection, traditional methods often rely on fixed sampling points and single time point sampling, without fully considering the dynamic changes of each link on the slaughter line. There are differences in the distribution of sampling time for mutton samples at different slaughter links, and the tissue density of different parts of mutton is different, which will affect the representativeness of the samples; at the same time, the amount of surface contaminants will also change with the slaughter process, if these factors are ignored, it is easy to lead to the samples collected cannot accurately reflect the actual veterinary drug residues, and then affect the reliability of the detection results.

[0005] In terms of selection of detection reagents, the existing methods often choose benchmark detection reagents based on experience or fixed standards, without considering factors such as historical usage frequency, component concentration stability, reaction conditions (such as temperature, pH value) of the reagents. The performance of different detection reagents is affected by many factors, and the change of component concentration with time, temperature fluctuation and pH value stability of the reaction stage will directly affect the accuracy and repeatability of the detection. If the benchmark reagent is not properly selected, it will lead to abnormal detection signal response and increase the risk of misjudgment.

[0006] The adjustment of reagent parameters in the detection process lacks dynamicity and scientificity. When the reagent parameters deviate in traditional methods, manual experience adjustment is often used, it is difficult to quickly and accurately find the optimal parameter combination, and there is no prediction mechanism for future detection effect, which cannot avoid potential detection errors in advance, leading to low detection efficiency, and it is difficult to meet the rapid and efficient detection demand of mutton slaughter line. Therefore, a rapid detection method for veterinary drug residues on a mutton slaughter line is needed, which can comprehensively consider multiple factors and dynamically optimize the detection process, in order to improve the accuracy, timeliness and stability of the detection. SUMMARY

[0007] The present application aims to provide a rapid detection method of veterinary drug residues in mutton slaughter line based on enzyme-linked immunoassay to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides a rapid detection method of veterinary drug residues in mutton slaughter line based on enzyme-linked immunoassay, which comprises:

[0009] Obtain the sample collection time distribution data, the tissue density data of different parts and the surface contaminant attachment amount data of mutton samples at each link of the slaughter line to obtain the current sample collection time distribution data set, the current tissue density data set and the current contaminant attachment amount data set; collect the usage frequency data of each candidate detection reagent as a reference reagent, the component concentration data of each candidate detection reagent changing with time, the temperature fluctuation data of different reaction stages and the pH value stability data and construct the final reagent as base frequency mapping relationship;

[0010] Determine the current reference detection reagent according to the final reagent as base frequency mapping relationship, the current sample collection time distribution data set, the current tissue density data set and the current contaminant attachment amount data set; judge whether the current reference detection reagent needs to be replaced or parameter adjusted by collecting the signal response parameters after using the current reference detection reagent to obtain the judgment result; adjust the parameters of the current reference detection reagent according to the judgment result to obtain the final current detection parameter combination.

[0011] Preferably, obtaining the current sample collection time distribution data set, the current tissue density data set and the current contaminant attachment amount data set comprises the following steps: determining the specific detection section of the mutton slaughter line to be detected and the corresponding several sampling points to obtain the candidate sampling point set; then determining several types of parameters that affect the possibility of sampling points being selected as reference points to obtain the reference sampling point selection influence parameter type set; setting a first current statistical period; combining the first current statistical period and the reference sampling point selection influence parameter type set to obtain the sample collection time distribution data of each sampling point in the candidate sampling point set, the tissue density data of different parts and the surface contaminant attachment amount data to obtain the current sample collection time distribution data set, the current tissue density data set and the current contaminant attachment amount data set.

[0012] Preferably, the collecting and constructing the final reagent-to-benchmark frequency mapping relationship comprises the following steps: setting a historical statistics period; combining the candidate detection reagent set and the benchmark reagent selection influence parameter type set, collecting the use frequency data of each candidate detection reagent as a benchmark reagent, the component concentration change data of each candidate detection reagent over time, the temperature fluctuation data of different reaction stages, and the pH value stability data in the historical statistics period, to obtain the historical candidate detection reagent-to-benchmark frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set, and the historical pH value stability data set; and constructing the final reagent-to-benchmark frequency mapping relationship and the final component concentration weight change data set using the historical candidate detection reagent-to-benchmark frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set, and the historical pH value stability data set.

[0013] Preferably, the constructing the final reagent-to-benchmark frequency mapping relationship and the final component concentration weight change data set uses a particle swarm optimization algorithm.

[0014] Preferably, the determining the current benchmark detection reagent comprises the following steps: setting a 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-to-benchmark frequency mapping relationship to obtain a current candidate detection reagent-to-benchmark frequency data set; when the candidate detection reagent corresponding to the maximum reagent-to-benchmark frequency data in the current candidate detection reagent-to-benchmark 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 maximum reagent-to-benchmark frequency data in the current candidate detection reagent-to-benchmark frequency data set is used as the current benchmark detection reagent, and all are recorded as the current benchmark detection reagent.

[0015] Preferably, the judging whether the current reference detection reagent needs to be replaced or parameter adjusted comprises the following steps: setting a second current statistical period; setting a plurality of time nodes in the second current statistical period to obtain a current time node set; setting a plurality of types of parameters capable of reflecting the detection effect after using the reference detection reagent to obtain a reference detection reagent effect parameter type set; combining the reference detection reagent effect parameter type set and the current time node set, collecting signal response parameters after using the current reference detection reagent to obtain a current use effect parameter matrix; collecting average effect parameters of a plurality of time nodes before using the current reference 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 threshold and a second effect difference threshold; when there is current effect difference data smaller than the second effect difference threshold in the current effect difference data set, replacing the current reference detection reagent back to the current original reference detection reagent; when there is current effect difference data greater than or equal to the second effect difference threshold and smaller than the first effect difference threshold in the current effect difference data set, entering a parameter adjustment step; otherwise, entering a prediction step.

[0016] Preferably, the parameter adjusting the current reference detection reagent according to the judging result comprises the following steps: setting an initial current detection parameter combination; parameter adjusting the current reference detection reagent using the initial current detection parameter combination and implementing detection; after the parameter adjusting the current reference detection reagent and implementing detection, collecting current effect parameters according to the reference detection reagent effect parameter type set to obtain a 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 current adjusted difference data; when the current adjusted difference data is greater than or equal to the first effect difference threshold, taking the initial current detection parameter combination as a 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 threshold.

[0017] Preferably, the optimization of the initial current detection parameter combination comprises the following steps: setting a value range of the initial current detection parameter combination to obtain a current parameter value range; constructing a detection parameter optimization particle swarm; setting a maximum iteration number of the detection parameter optimization particle swarm and a current iteration number, denoted as a parameter optimization maximum iteration number and a parameter optimization current iteration number, respectively; setting an 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 a fitness function of the detection parameter optimization particle swarm; starting iteration, and setting the parameter optimization current iteration number to 1 before iteration; in each iteration process, the fitness value of the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is calculated by using the fitness function of the detection parameter optimization particle swarm, and the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is updated; when the parameter optimization current iteration number reaches the parameter optimization maximum iteration number, the iteration is stopped, and a second final global best fitness and a second final global best position are obtained; otherwise, the iteration is continued until the parameter optimization current iteration number reaches the parameter optimization maximum iteration number; the second final global best fitness is taken as the optimized current adjusted difference data; when the optimized current adjusted difference data is greater than or equal to the first effect difference threshold, the current reference detection reagent is adjusted in parameters by using the second final global best position, and detection is implemented to obtain a final current detection parameter combination; otherwise, the iteration step is returned to continue iteration until the optimized current adjusted difference data is greater than or equal to the first effect difference threshold.

[0018] Preferably, the prediction step comprises the following steps: predicting the use effect parameters of a future time node according to the current use effect parameter matrix and by using a feedforward neural network model to obtain a future use effect parameter matrix; and calculating the difference data between the historical average effect parameter set and each row of data in the future use effect parameter matrix to obtain a future effect difference data set; when there is future effect difference data less than the second effect difference threshold in the future effect difference data set, the parameter adjustment step is entered; otherwise, no processing is performed.

[0019] Preferably, the surface pollutant attachment amount data of each sampling point in the selected sampling point set comprises the following steps: wiping and sampling the surface of the mutton sample of each sampling point in the selected sampling point set, placing the wiping material in a buffer solution for oscillation elution, measuring the pollutant concentration in the eluent by using a spectrophotometric method, and calculating the surface pollutant attachment amount data in combination with the sample surface area.

[0020] Compared with the prior art, the present application has the following beneficial effects:

[0021] The method first acquires the data of the collection time distribution of the sheep meat samples at each link of the slaughter line, the tissue density of different parts, and the attachment amount of surface pollutants, comprehensively considers the differences of the samples in time and space, ensures the representativeness of the collected samples, and avoids the detection deviation caused by improper sample selection. By determining the to-be-detected section and the sampling point, and combining the parameter type and the statistical period affecting the selection of the sampling point, the system collects the sample related data, and lays a solid sample foundation for subsequent detection.

[0022] In the selection of detection reagents, the method constructs the mapping relationship of the final reagent as the base frequency, comprehensively considers the historical use frequency, the concentration change of components, the temperature fluctuation and the pH value stability and the like data, analyzes by using the particle swarm optimization algorithm, and realizes the scientific determination of the current benchmark detection reagent. This process breaks away from the limitation of the traditional experience-based selection of reagents, makes the reagent selection more in line with the actual detection demand, reduces the detection error caused by the mismatch of reagents, and improves the stability of detection.

[0023] The method introduces a dynamic parameter adjustment mechanism, acquires the signal response parameters after using the current benchmark detection reagent, judges whether the reagent needs to be replaced or the parameters need to be adjusted, and optimizes the detection parameters by using the particle swarm optimization algorithm, so that the optimal parameter combination can be quickly found. This dynamic adjustment not only can correct the deviation in the detection process in time, but also can predict the future detection effect through the feedforward neural network model, avoid potential problems in advance, and ensure the continuity and efficiency of the detection process.

[0024] In the data acquisition link of the attachment amount of surface pollutants, wiping sampling combined with spectrophotometry is adopted, so as to improve the accuracy of the data; and a reasonable iteration mechanism is set in the parameter optimization process, so as to ensure the accuracy of the parameter adjustment. The method realizes the whole process optimization from sample collection to reagent selection and parameter adjustment, greatly improves the efficiency and precision of the veterinary drug residue detection of the sheep slaughter line, provides strong technical support for food safety supervision, helps to protect the health of consumers, and improves the quality and safety level of sheep products. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A working principle diagram of the rapid detection method of the veterinary drug residue of the sheep slaughter line based on enzyme-linked immunoassay is described.

[0026] Figure 2 A flowchart for acquiring the data set of the collection time distribution, the tissue density and the attachment amount of pollutants;

[0027] Figure 3 A flowchart for constructing the mapping relationship of the final reagent as the base frequency;

[0028] Figure 4 A flowchart for determining the current benchmark detection reagent;

[0029] Figure 5 Flowchart for predicting future use effect parameters. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0031] Please refer to Figures 1-5 The present application provides a rapid detection method for veterinary drug residues in mutton based on enzyme-linked immunoassay, which comprises the following steps:

[0032] Obtain the collection time distribution data of mutton samples at each link of the slaughter line, the tissue density data of different parts, and the data of the amount of surface pollutants attached, to obtain the current collection time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set.

[0033] Collect the use frequency data of each candidate detection reagent as a reference reagent, the component concentration data of each candidate detection reagent changing with time, the temperature fluctuation data of different reaction stages, and the pH value stability data, and construct the final reagent as a base frequency mapping relationship.

[0034] Determine the current reference detection reagent according to the final reagent as a base frequency mapping relationship, the current collection time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set.

[0035] Collect the signal response parameters after using the current reference detection reagent, determine whether the current reference detection reagent needs to be replaced or the parameters need to be adjusted, and obtain a judgment result.

[0036] According to the judgment result, the current reference detection reagent is adjusted in parameters to obtain a final current detection parameter combination.

[0037] Embodiment 1: In this embodiment, when obtaining the current collection time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set, the following specific process operation is required. The specific detection section of the mutton slaughter line to be detected is determined, which is determined according to the actual layout and production process of the slaughter line, for example, the slaughter line can be divided into different sections such as slaughtering area, dehairing area, and cutting area. After determining the specific detection section, a plurality of sampling points are set for the section, and the setting of these sampling points needs to consider the production characteristics of each link of the slaughter line and the risk area where veterinary drug residues may exist, thereby forming a set of candidate sampling points.

[0038] A plurality of parameter types affecting the possibility of the sampling point being selected as the reference point are determined, and the determination of the parameter types needs to be combined with the actual situation in the sheep slaughtering process and the detection requirements. For example, the production flow size of the position where the sampling point is located, the distance from the possible contact with the veterinary drug link, the representativeness of the sample, and the like parameter types, thereby obtaining the parameter type set affecting the selection of the reference sampling point.

[0039] A first current statistical period is set, and the setting of the period needs to be determined according to the production cycle of the slaughtering line, the detection frequency and the like factors, for example, it can be set as one production shift, one day or one week, etc. After the first current statistical period is set, the data of each sampling point in the set of sampling points to be selected is collected in combination with the period and the parameter type set affecting the selection of the reference sampling point.

[0040] For each sampling point, sample collection time distribution data needs to be collected, which requires recording the specific time of collecting the sample of the sampling point in the first current statistical period, thereby forming the sample collection time distribution data of the sampling point. At the same time, the tissue density data of different parts is collected. Since there are differences in the tissue density of different parts of sheep meat, the tissue density of different parts (such as legs, back, abdomen, etc.) corresponding to each sampling point needs to be measured. The measurement method can use related density measurement standards or commonly used measurement methods, and then the tissue density data of different parts is obtained.

[0041] When obtaining the surface contaminant attachment amount data, the surface of the sheep sample of each sampling point is swabbed. The specific operation is to use a suitable swabbing tool (such as a swabbing cotton swab or a swabbing cloth) to uniformly swab in the specified area of the sample surface, and ensure the uniformity of the swabbing range and intensity to ensure the accuracy of the sampling. The swabbing object is placed in a buffer solution. The buffer solution needs to be selected according to the nature of the contaminant and the subsequent detection method. Then the buffer solution is shaken and eluted. The conditions of the shaking (such as shaking frequency, time, etc.) also need to be reasonably set to ensure that the contaminant can be fully eluted from the swabbing object to the buffer solution.

[0042] After elution, the concentration of the contaminant in the eluent is determined by spectrophotometry. Spectrophotometry is an analysis method based on the selective absorption characteristics of substances to light. In the determination, a series of standard solutions of the contaminant with known concentrations need to be prepared to establish a standard curve of concentration and absorbance. Then the eluent is determined under the same conditions, and the corresponding contaminant concentration is obtained from the standard curve according to the absorbance.

[0043] After obtaining the concentration of pollutants in the eluent, the surface pollutant attachment amount data is calculated in combination with the sample surface area. The calculation of the sample surface area needs to be accurately measured according to the specific shape and size of the mutton sample, using appropriate calculation methods or measurement tools. By multiplying the pollutant concentration and the sample surface area, the surface pollutant attachment amount data of the sampling point can be obtained.

[0044] Through the above series of operations, the sample collection time distribution data, the tissue density data of different parts, and the surface pollutant attachment amount data of each sampling point are collected, and finally the current collection time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set are obtained. These data sets contain the key data of each sampling point in the first current statistical period, providing important basic data support for subsequent detection work. During the entire data collection process, the operation conditions of each link need to be strictly controlled to ensure the accuracy and reliability of the data, for example, the sampling specification needs to be ensured during wiping sampling, and the spectrophotometric method operation process needs to be strictly followed during the determination of pollutant concentration, so as to avoid data errors caused by improper operation. At the same time, the collected data needs to be recorded and sorted in detail to ensure the traceability of the data.

[0045] Example 2: In this embodiment, the following detailed process needs to be followed when collecting and constructing the final reagent baseline frequency mapping relationship. Set the historical statistical period, which needs to be determined by considering factors such as the use cycle of the detection reagent, the production history data accumulation of the slaughter line, etc. For example, the past one month, three months, or half a year can be selected as the historical statistical period, and the specific length is determined according to the feasibility of actual data collection and detection requirements.

[0046] After setting the historical statistical period, data collection is performed in combination with the set of candidate detection reagents and the set of baseline reagent selection influence parameter types. The set of candidate detection reagents is a combination of multiple reagents pre-determined for mutton slaughter line veterinary drug residue detection, and the set of baseline reagent selection influence parameter types contains various parameter types that affect the selection of detection reagents as baseline reagents, such as the detection sensitivity, specificity, stability, composition concentration change over time, adaptability to temperature fluctuations at different reaction stages, and stability to pH value, etc.

[0047] During the historical statistical period, the frequency data of each candidate detection reagent used as a reference reagent is collected. This requires detailed recording of the specific time and scene of each reagent being determined as a reference reagent in the historical period, thereby forming the frequency data of each candidate detection reagent. At the same time, the concentration change data of each component of each candidate detection reagent over time is collected, which requires detection and recording of the concentration of each component at certain time intervals during the historical statistical period, such as once a day or once every certain time, to obtain a complete data sequence of the concentration change over time.

[0048] Temperature fluctuation data and pH value stability data of different reaction stages are also collected. In enzyme-linked immunoassay, different reaction stages (such as antigen-antibody binding stage, enzyme catalytic reaction stage, etc.) may have different requirements for temperature and pH value, so it is necessary to record the temperature fluctuation during the actual occurrence of each reaction stage, including the maximum value, minimum value, average value and fluctuation range, etc., and record the pH value stability in each reaction stage, such as the specific value, change range, etc.

[0049] Through the above collection work, the historical candidate detection reagent frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set and the historical pH value stability data set are obtained. These data sets cover various key data related to the candidate detection reagent in the historical statistical period, providing a basis for subsequent construction of the final reagent frequency mapping relationship and the final component concentration weight change data set.

[0050] In constructing the final reagent frequency mapping relationship and the final component concentration weight change data set, a particle swarm optimization algorithm is used. Particle swarm optimization algorithm is a swarm intelligence optimization algorithm that simulates the foraging behavior of bird flocks. The basic idea is to find the optimal solution through the cooperation and information sharing between individuals in the group.

[0051] In the specific implementation process, first, the related parameters of the particle swarm optimization algorithm need to be determined, 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 needs to be adjusted reasonably according to the actual data situation and problem characteristics.

[0052] The historical candidate detection reagent frequency data set, the historical component concentration change data set, the historical temperature fluctuation data set and the historical pH value stability data set are used as input data and substituted into the particle swarm optimization algorithm. Each particle represents a possible mapping relationship or weight distribution scheme, and the position of the particle represents the corresponding parameter value.

[0053] During the iteration process of the algorithm, each particle adjusts its position according to its own historical optimal position and the global optimal position of the group to find a better solution. Through continuous iteration, the particle swarm gradually converges to the optimal solution, thereby obtaining the final reagent reference frequency mapping relationship and the final component concentration weight change dataset.

[0054] The final reagent reference frequency mapping relationship describes the frequency mapping relationship of various candidate detection reagents being used as reference reagents under different data input conditions, while the final component concentration weight change dataset determines the weight change of different component concentrations in the detection process.

[0055] During the entire 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, ensure the accuracy of the records and avoid omissions or errors; when collecting component concentration change data, ensure the scientificity of the detection method and the accuracy of the detection equipment; when using the particle swarm optimization algorithm, set the algorithm parameters reasonably 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, normalization, etc., to eliminate noise and outliers in the data and improve the processing effect of the algorithm. In addition, the final reagent reference frequency mapping relationship and the final component concentration weight change dataset constructed should be verified to ensure their rationality and effectiveness, which can be verified by comparing with historical data or actual detection, etc.

[0056] In this embodiment, when determining the current reference detection reagent, the following specific steps are required. Set the current original reference detection reagent, which can be a certain candidate detection reagent determined according to past detection experience, commonality of reagents, or preliminary screening results.

[0057] Substitute each data in the current acquisition time distribution dataset, the current tissue density dataset, the current pollutant attachment amount dataset, and the final component concentration weight change dataset into the final reagent reference frequency mapping relationship for mapping respectively. The current acquisition time distribution dataset contains the distribution of sample collection time 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 sheep meat at each sampling point; the current pollutant attachment amount dataset reflects the attachment amount of pollutants on the surface of sheep meat 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 change of different component concentrations in the detection.

[0058] The final reagent frequency mapping relationship is constructed by analyzing historical data and particle swarm optimization algorithm, which establishes the corresponding relationship between input data and candidate reagent frequency. In the mapping process, for each input data, the corresponding reagent frequency data of each candidate reagent is calculated according to the mapping relationship, so as to obtain the current candidate reagent frequency data set. The data set contains the frequency values of all candidate reagents under the current data input condition.

[0059] The reagent frequency data in the current candidate reagent frequency data set is compared, the maximum reagent frequency data is found, and the corresponding candidate reagent is determined. Next, the corresponding candidate reagent is compared with the current original reference reagent to determine whether the corresponding candidate reagent is the most suitable reference reagent under the current data input condition.

[0060] If the maximum reagent frequency data in the current candidate reagent frequency data set corresponds to the same candidate reagent as the current original reference reagent, it means that under the current data input condition, the original reference reagent is still the most suitable reference reagent, so the current reference reagent is not replaced, and the current original reference reagent is maintained.

[0061] If the maximum reagent frequency data corresponds to a different candidate reagent from the current original reference reagent, it means that under the current detection condition, the corresponding candidate reagent is more suitable as the reference reagent than the original reference reagent. At this time, the candidate reagent corresponding to the maximum reagent frequency data in the current candidate reagent frequency data set is taken as the current reference reagent, and it is recorded as the current reference reagent.

[0062] During the entire process of determining the current reference reagent, the following points need to be noted. First, the accuracy of the current time distribution data set, the current tissue density data set, and the current pollutant attachment amount data set is crucial, as the accuracy of these data directly affects the reliability of the reagent frequency data obtained after being substituted into the mapping relationship. Therefore, during the data collection stage, strict operation according to the specified method and process must be ensured to ensure the authenticity and effectiveness of the data.

[0063] The construction of the final reagent frequency mapping relationship also has an important impact on the result. The mapping relationship is obtained based on historical data and particle swarm optimization algorithm, and in the construction process, the integrity and representativeness of the historical data need to be ensured, and the parameters of the particle swarm optimization algorithm need to be reasonably set to ensure that the mapping relationship can accurately reflect the relationship between input data and reagent frequency.

[0064] In the process of data input and mapping calculation, the accuracy of the calculation process should be ensured to avoid deviation caused by calculation errors. For the obtained current candidate detection reagent frequency data set, careful analysis and comparison are needed to ensure the accuracy of the maximum frequency data and its corresponding candidate detection reagent.

[0065] When determining whether to replace the reference detection reagent, the set conditions should be strictly followed and the judgment standard should not be changed arbitrarily. If the reference detection reagent needs to be replaced, after determining the new current reference detection reagent, it also needs to be recorded and archived as necessary for the subsequent detection work traceability and management.

[0066] In the process of determining whether to replace the current reference detection reagent or adjust the parameters, the following specific process should be followed. Set the second current statistical period, which should be set in combination with the production cycle of the slaughter line, the detection frequency, and the use of the current reference detection reagent. For example, if the current reference detection reagent has been used for a period of time, the second current statistical period can be set as the latest production shift, or one day, two days, etc. according to actual needs.

[0067] After setting the second current statistical period, set several time nodes in this period to form a current time node set. The setting of time nodes should consider the timeliness of detection and the representativeness of data. For example, in an 8-hour production shift, each hour can be set as a time node, i.e. the first hour, the second hour, …, the eighth hour. Alternatively, the time nodes can be set according to the key links in the production process or the periods when the detection effect may fluctuate.

[0068] Set several types of parameters that can reflect the detection effect after using the reference detection reagent to obtain a set of reference detection reagent effect parameter types. The selection of these parameter types should be related to the characteristics of enzyme-linked immunoassay method and the target of veterinary drug residue detection, such as absorbance value, detection signal strength, detection result accuracy index, detection repeatability index, etc.

[0069] The signal response parameters after using the current reference detection reagent are collected in combination with the reference detection reagent effect parameter type set and the current time node set. In the specific operation, at each time node, the current reference detection reagent is used to detect the mutton sample according to the standard process of enzyme-linked immunoassay, and the signal response parameter value corresponding to each parameter type is recorded. For example, at the first hour time node, after detecting the sample, the absorbance value A1, the detection signal intensity S1, the accuracy index P1, the repeatability index R1, etc. are recorded; at the second hour time node, the corresponding parameter values A2, S2, P2, R2, etc. are recorded after detection, and so on. 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.

[0070] The average effect parameters of several time nodes before using the current reference detection reagent are collected to obtain the historical average effect parameter set. The “several time nodes before using the current reference detection reagent” can be determined according to the actual situation, for example, the effect parameters of the same time node in the previous 3 production shifts are selected, and the average effect parameters of each time node are calculated. Assuming that the effect parameters of the first hour in the previous 3 shifts are A1-1, A1-2, A1-3, the average value is calculated as the average absorbance value of the first hour in the historical average effect parameter set. Similarly, the average effect parameters of other parameter types and time nodes are calculated to form the historical average effect parameter set.

[0071] The difference data between the historical average effect parameter set and each row of data in the current use effect parameter matrix is calculated to obtain the current effect difference data set. For each parameter type of each time node, the difference data is obtained by subtracting the corresponding average effect parameter value in the historical average effect parameter set from the data in the current use effect parameter matrix. For example, the absorbance difference of the first hour is A1-average A1, the signal intensity difference is S1-average S1, etc. After sorting these difference data, the current effect difference data set is formed.

[0072] The first effect difference threshold and the second effect difference threshold are set. The setting of the two thresholds needs to be determined according to the accuracy requirements of the detection method, the performance characteristics of the reagent, and the acceptable error range in actual production, etc. For example, the first effect difference threshold can be set to 0.2, and the second effect difference threshold can be set to 0.1. The specific value needs to be adjusted according to the actual situation.

[0073] After the above settings and calculations are completed, the current effect difference data set is analyzed and judged. When there is current effect difference data in the current effect difference data set that is less than the second effect difference threshold, it indicates that the detection effect of the current reference reagent has decreased significantly compared with the historical average effect, which is beyond the acceptable range, at this time the current reference reagent needs to be replaced back to the current original reference reagent.

[0074] When there is current effect difference data in the current effect difference data set that is greater than or equal to the second effect difference threshold and less than the first effect difference threshold, it indicates that the detection effect of the current reference reagent has certain differences with the historical average effect, but has not reached the degree of reagent replacement, at this time the parameter adjustment step is entered, and the related parameters of the current reference reagent are adjusted to optimize the detection effect.

[0075] If all the current effect difference data in the current effect difference data set is greater than or equal to the first effect difference threshold, it indicates that the detection effect of the current reference reagent is within the acceptable range compared with the historical average effect, or even better, at this time the prediction step is entered, and the future detection effect is predicted and analyzed.

[0076] During the entire judgment process, attention needs to be paid to the accuracy and consistency of data collection. For example, when collecting the current use effect parameters and the historical average effect parameters, the same detection equipment, detection method and operation process should be used to avoid data deviation caused by different detection conditions. At the same time, the setting of time nodes and the selection of parameter types need to ensure that they can accurately reflect the changes of detection effect. When calculating the difference data, the accuracy of the calculation needs to be ensured to avoid human errors. In addition, the setting of the first effect difference threshold and the second effect difference threshold needs to be fully researched and demonstrated to ensure its rationality and scientificity to avoid misjudgment. Through the above rigorous operation and judgment, it can be accurately determined whether the current reference reagent needs to be replaced or the parameters adjusted, and correct guidance is provided for subsequent detection work.

[0077] In the parameter adjustment of the current reference reagent according to the judgment result, the following specific process needs to be operated. Assuming that after the judgment step of embodiment 4, it is determined that the parameter adjustment step needs to be entered, at this time the initial current detection parameter combination is set. The setting of the initial current detection parameter combination needs to be combined with the characteristics of the current reference reagent and the conventional parameter range of enzyme-linked immunoassay, for example, the combination can include the initial setting values of reaction temperature, reaction time, reagent addition amount, buffer pH value and other parameters, such as reaction temperature set to 37℃, reaction time set to 30 minutes, reagent addition amount set to 100μL, buffer pH value set to 7.4, etc.

[0078] The initial current detection parameter combination is used to adjust the parameters of the current reference detection reagent and implement detection. During the detection, the operation process of enzyme-linked immunoassay is strictly performed according to the adjusted parameters, the sheep meat sample is processed and detected, and the standardization and consistency of the detection process are ensured.

[0079] After the detection is completed, the current effect parameters are collected according to the reference detection reagent effect parameter type set, and the current adjusted effect parameter set is obtained. For example, if the reference detection reagent effect parameter type set contains absorbance value, detection signal intensity, repeatability index and the like, after the detection is completed, the current absorbance value is recorded as A adjusted, the detection signal intensity is recorded as S adjusted, and the repeatability index is recorded as R adjusted, and the current adjusted effect parameter set is formed.

[0080] The difference data between the current adjusted effect parameter set and the historical average effect parameter set is calculated, and the current adjusted difference data is obtained. For each parameter type, the data in the current adjusted effect parameter set is subtracted from the corresponding average effect parameter value in the historical average effect parameter set to obtain the difference data. For example, the absorbance difference is A adjusted- average A, the signal intensity difference is S adjusted- average S, and the like. After the difference data is integrated, the current adjusted difference data is obtained.

[0081] When the current adjusted difference data is greater than or equal to the first effect difference threshold, it is indicated that the detection effect has reached the expected requirement after the initial current detection parameter combination is used to adjust the parameters of the current reference detection reagent, and at this time the initial current detection parameter combination is used as the final current detection parameter combination.

[0082] If the current adjusted difference data is less than the first effect difference threshold, the initial current detection parameter combination needs to be optimized. When optimizing, the value range of the initial current detection parameter combination is set to obtain the current parameter value range. For example, the value range of the reaction temperature can be set to 35-40°C, the value range of the reaction time can be set to 20-40 minutes, the value range of the reagent addition amount can be set to 80-120 μL, and the value range of the buffer pH value can be set to 7.0-7.8.

[0083] A detection parameter optimization particle swarm is constructed, which is composed of a plurality of particles, and each particle represents a possible detection parameter combination. The maximum iteration number of the detection parameter optimization particle swarm and the current iteration number are set, which are respectively denoted as parameter optimization maximum iteration number and parameter optimization current iteration number. For example, the parameter optimization maximum iteration number can be set to 100 times, and the parameter optimization current iteration number is initially set to 1.

[0084] The initial position of each particle in the detection parameter optimization particle swarm is set according to the current parameter value range, and a second initial position set is obtained. The initial position of each particle corresponds to a set of detection parameter combinations, and each parameter value in the set of parameter combinations is randomly generated within the corresponding value range.

[0085] A fitness function of the detection parameter optimization particle swarm is constructed, which is used to evaluate the pros and cons of each particle position, that is, to evaluate the effect 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 as a function positively correlated with the current adjusted difference data. The larger the difference data is, the higher the fitness value is.

[0086] Start iteration, and set the parameter optimization current iteration number to 1 before iteration. In each iteration process, the fitness value of the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is calculated by using the fitness function of the detection parameter optimization particle swarm in the last iteration process, and the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is updated. The update of the particle position follows the rules of the particle swarm optimization algorithm. Each particle adjusts its position according to its historical optimal position and the global optimal position of the group to find a better detection parameter combination.

[0087] When the parameter optimization current iteration number reaches the parameter optimization maximum iteration number, stop iteration, and 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.

[0088] Otherwise, continue iteration until the parameter optimization current iteration number reaches the parameter optimization maximum iteration number.

[0089] 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 threshold, the current reference detection reagent is parameter adjusted and detected by using the second final global optimal position, and a final current detection parameter combination is obtained.

[0090] If the optimized current adjusted difference data is still less than the first effect difference threshold, return to the iteration step to continue iteration until the optimized current adjusted difference data is greater than or equal to the first effect difference threshold.

[0091] In addition, in the prediction step, the use effect parameter matrix at the current time node is used to predict the use effect parameters at the future time node by using a feedforward neural network model, to obtain a future use effect parameter matrix. The feedforward neural network model is a commonly used machine learning model, which has a multi-layer structure and can predict the future through historical data. For example, the data in the current use effect parameter matrix is input into the feedforward neural network model, and the predicted values of each parameter type at each future time node are output after calculation, forming the future use effect parameter matrix.

[0092] The difference data between the historical average effect parameter set and each row of data in the future use effect parameter matrix is calculated again to obtain a future effect difference data set. For each parameter type at each future time node, the predicted data in the future use effect parameter matrix is subtracted from the corresponding average effect parameter value in the historical average effect parameter set to obtain the difference data, forming the future effect difference data set.

[0093] When there is future effect difference data less than the second effect difference threshold in the future effect difference data set, it indicates that the predicted future detection effect may decrease significantly, and at this time, the parameter adjustment step is entered to adjust the parameters of the current reference detection reagent.

[0094] Otherwise, it indicates that the predicted future detection effect is within an acceptable range and no processing is performed.

[0095] In the entire parameter adjustment and prediction process, attention should be paid to the reasonable setting of the initial current detection parameter combination to avoid deviating too much from the actual feasible range, otherwise it may lead to low efficiency of the optimization process. The setting of the current parameter value range also needs to be combined with the characteristics of the detection reagent and the ability of the detection equipment to ensure that the parameters change within a reasonable interval. When constructing the fitness function, the key indicators of the detection effect should be fully considered to make the function accurately reflect the advantages and disadvantages of the parameter combination. In the iteration process of the particle swarm optimization algorithm, the convergence of the algorithm should be ensured, and parameters such as inertia weight and learning factor should be reasonably set to avoid the algorithm falling into a local optimal solution. For the feedforward neural network model, a sufficient amount of historical data needs to be used for training to improve the prediction accuracy of the model. At the same time, in the entire process, the operation and data of each step should be strictly recorded to ensure the traceability of the process and the reliability of the results.

[0096] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and implementations, it is to be understood that the terminology used is for the purpose of descriptive clarity and that it should be taken in its broadest possible sense. For example, the terms "a", "an", and "the" include both singular and plural referents unless the context clearly dictates otherwise. The terms "comprises", "comprising", "includes", "including" and the like can be used in conjunction with the term "consisting of to include the elements or steps listed after such conjunctive language, but not to the exclusion of other elements or steps. The singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

[0097] While the embodiments of the application have been shown and described herein, it is to be understood that the application is not limited to these embodiments. Rather, many modifications, changes and substitutions are intended to fall within the scope of the present application, which is limited only by the scope of the claims hereinafter appended.

Claims

1. An enzyme-linked immunosorbent-based rapid detection method for veterinary drug residues in sheep slaughter line, characterized in that, The method comprises the following steps: Collecting time distribution data of sheep meat samples at each link of the slaughtering line, tissue density data of different parts, and attachment amount data of surface contaminants, to obtain a current collection time distribution dataset, a current tissue density dataset, and a current contaminant attachment amount dataset; collecting usage frequency data of each candidate detection reagent as a reference reagent, component concentration change data of each candidate detection reagent over time, temperature fluctuation data of different reaction stages, and pH value stability data, and constructing a final reagent-to-reference frequency mapping relationship; Determining a current reference detection reagent according to the final reagent-to-reference frequency mapping relationship, the current collection time distribution dataset, the current tissue density dataset, and the current contaminant attachment amount dataset; collecting signal response parameters after the current reference detection reagent is used, to determine whether the current reference detection reagent needs to be replaced or parameter adjusted, and obtaining a determination result; and adjusting parameters of the current reference detection reagent according to the determination result, to obtain a final current detection parameter combination; The collecting and constructing of the final reagent-to-reference frequency mapping relationship comprises the following steps: setting a historical statistical period; collecting usage frequency data of each candidate detection reagent as a reference reagent, component concentration change data of each candidate detection reagent over time, temperature fluctuation data of different reaction stages, and pH value stability data in the historical statistical period, to obtain a historical candidate detection reagent-to-reference frequency dataset, a historical component concentration change dataset, a historical temperature fluctuation dataset, and a historical pH value stability dataset; and constructing a final reagent-to-reference frequency mapping relationship and a final component concentration weight change dataset by using the historical candidate detection reagent-to-reference frequency dataset, the historical component concentration change dataset, the historical temperature fluctuation dataset, and the historical pH value stability dataset; The final reagent-to-reference frequency mapping relationship and the final component concentration weight change dataset are constructed by using a particle swarm optimization algorithm; The final reagent-to-reference frequency mapping relationship describes the frequency mapping relationship of various candidate detection reagents as reference reagents under different data input conditions, and the final component concentration weight change dataset determines the weight change of different component concentrations in the detection process; The determining of the current reference detection reagent comprises the following steps: setting a current original reference detection reagent; mapping each data in the current collection time distribution dataset, the current tissue density dataset, the current contaminant attachment amount dataset, and the final component concentration weight change dataset into the final reagent-to-reference frequency mapping relationship, to obtain a current candidate detection reagent-to-reference frequency dataset; when the candidate detection reagent corresponding to the maximum reagent-to-reference frequency data in the current candidate detection reagent-to-reference frequency dataset is the same as the current original reference detection reagent, the current reference detection reagent is not replaced; otherwise, the candidate detection reagent corresponding to the maximum reagent-to-reference frequency data in the current candidate detection reagent-to-reference frequency dataset is taken as the current reference detection reagent, and all are recorded as the current reference detection reagent. The signal response parameters include absorbance values and repeatability indexes. The parameter adjustment includes reaction temperature, reaction time, reagent addition amount and buffer pH value.

2. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 1, characterized in that, The acquisition of 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 a specific detection section of the sheep slaughter line to be detected and corresponding sampling points to obtain a set of selected sampling points; determining a plurality of parameter types that affect the possibility of the sampling points being selected as reference points to obtain a set of reference sampling point selection influence parameter types; setting a first current statistical period; combining the first current statistical period and the set of reference sampling point selection influence parameter types to obtain 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 selected sampling points, thereby obtaining the current acquisition time distribution data set, the current tissue density data set and the current pollutant attachment amount data set.

3. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 1, characterized in that, The judgment of whether the current reference detection reagent needs to be replaced or parameter adjusted includes the following steps: setting a second current statistical period; setting a plurality of time nodes in the second current statistical period to obtain a set of current time nodes; setting a plurality of parameter types that can reflect the detection effect after using the reference detection reagent to obtain a set of reference detection reagent effect parameter types; combining the set of reference detection reagent effect parameter types and the set of current time nodes to acquire signal response parameters after using the current reference detection reagent to obtain a current use effect parameter matrix; acquiring average effect parameters of a plurality of time nodes before using the current reference detection reagent to obtain a set of historical average effect parameters; calculating the difference data between the set of historical average effect parameters 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 threshold and a second effect difference threshold; when there is current effect difference data smaller than the second effect difference threshold in the current effect difference data set, the current reference detection reagent is replaced back to the current original reference detection reagent; when there is current effect difference data greater than or equal to the second effect difference threshold and smaller than the first effect difference threshold in the current effect difference data set, the parameter adjustment step is entered; otherwise, the prediction step is entered.

4. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 3, characterized in that, The parameter adjustment of the current reference detection reagent according to the judgment result comprises the following steps: setting an initial current detection parameter combination; adjusting the parameters of the current reference detection reagent using the initial current detection parameter combination and performing detection; after the parameter adjustment and detection of the current reference detection reagent are completed, collecting current effect parameters according to the set of effect parameter types of the reference detection reagent to obtain a set of current adjusted effect parameters; calculating the difference data between the set of current adjusted effect parameters and the set of historical average effect parameters to obtain current adjusted difference data; when the current adjusted difference data is greater than or equal to a first effect difference threshold, the initial current detection parameter combination is taken as a 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 threshold.

5. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 4, characterized in that, The optimization of the initial current detection parameter combination comprises the following steps: setting a value range of the initial current detection parameter combination to obtain a current parameter value range; constructing a detection parameter optimization particle swarm; setting a maximum iteration number of the detection parameter optimization particle swarm and a current iteration number, which are denoted as a parameter optimization maximum iteration number and a parameter optimization current iteration number, respectively; setting an 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 a fitness function of the detection parameter optimization particle swarm; starting iteration, and setting the parameter optimization current iteration number to 1 before iteration; in each iteration process, the fitness value of the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is calculated using the fitness function of the detection parameter optimization particle swarm, and the position of each particle in the detection parameter optimization particle swarm updated in the last iteration process is updated; when the parameter optimization current iteration number reaches the parameter optimization maximum iteration number, the iteration is stopped, and a second final global best fitness and a second final global best position are obtained; otherwise, the iteration is continued until the parameter optimization current iteration number reaches the parameter optimization maximum iteration number; the second final global best fitness is taken as an optimized current adjusted difference data; when the optimized current adjusted difference data is greater than or equal to the first effect difference threshold, the parameters of the current reference detection reagent are adjusted using the second final global best position and detection is performed to obtain a final current detection parameter combination; otherwise, the iteration in the iteration step is returned to continue until the optimized current adjusted difference data is greater than or equal to the first effect difference threshold.

6. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 3, characterized in that, The predicting step comprises the following steps: predicting the use effect parameters of a future time node according to the current use effect parameter matrix and using a feedforward neural network model to obtain a future use effect parameter matrix; then calculating the difference data between the historical average effect parameter set and each row of data in the future use effect parameter matrix to obtain a future effect difference data set; when there is future effect difference data smaller than a second effect difference threshold in the future effect difference data set, entering the parameter adjusting step; otherwise, no processing is performed.

7. The rapid detection method of veterinary drug residues in sheep carcass based on enzyme-linked immunoassay according to claim 2, characterized in that, The surface pollutant adhesion amount data of each sampling point in the selected sampling point set comprises the following steps: wiping and sampling the surface of the mutton sample of each sampling point in the selected sampling point set, placing the wiping material in a buffer solution for oscillation elution, determining the pollutant concentration in the eluent by spectrophotometry, and calculating the surface pollutant adhesion amount data combined with the sample surface area.

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