Agricultural product quality safety detection system

Abstract

The invention discloses an agricultural product quality safety detection system, and relates to the field of agricultural product quality safety, and the system comprises an acquisition processing module which carries out the random sampling of agricultural products in different farmlands, employs a high-definition camera to collect the sampling image data of the agricultural products, and builds a decision tree based on the material features and surface image features, independent classification of the sampled agricultural products is realized; the method comprises the following steps: randomly sampling agricultural products of different farmlands, acquiring image data by using a high-definition camera, constructing a decision tree to realize independent classification, extracting a target compound by using an ultrasonic extraction technology, establishing a characteristic database, selecting an optimal extraction solvent by using an SVM algorithm, and selecting a chromatographic column and a mobile phase according to the characteristics of the target compound. The chromatographic conditions are optimized to realize optimal separation, liquid chromatography-mass spectrometry is started to monitor a target compound in real time, the structure and concentration are confirmed to identify pollutants, and a table and a diagram of quantitative results are drawn.

Description

Technical Field

[0001] The present invention relates to the field of agricultural product quality and safety, and particularly to a detection system for agricultural product quality and safety. Background Art

[0002] Globally, the quality and safety issues of agricultural products have attracted extensive attention, especially in the context of the continuous increase in agricultural production and the growing demands of consumers for food safety. Pollutants that may exist in agricultural products, such as pesticide residues, heavy metals, and other harmful substances, not only pose a threat to consumers' health but also affect the sustainable development of the ecological environment.

[0003] The existing technologies have the following deficiencies: The existing methods for detecting the quality and safety of agricultural products rely on manual operations, are easily affected by human factors, resulting in inaccurate detection results and being unable to comprehensively reflect the true quality of agricultural products. When dealing with the target compounds of agricultural products, they are unable to effectively cope with the diverse characteristics of agricultural products, leading to a decline in classification accuracy, lacking the ability to select the optimal extraction conditions according to the specific characteristics of the target compounds, resulting in waste of resources and low extraction efficiency.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a detection system for agricultural product quality and safety to solve the problems in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A detection system for agricultural product quality and safety, including a collection and processing module, a classification and extraction module, a mass spectrometry separation module, and a detection and identification module;

[0007] Collection and Processing Module: Randomly sample agricultural products in different farmlands and use a high-definition camera to collect the sampling image data of the agricultural products, construct a decision tree based on material characteristics and surface image characteristics, and achieve independent classification of the sampled agricultural products;

[0008] Classification and Extraction Module: Extract the target compounds from the sampled agricultural products by ultrasonic extraction, establish a characteristic database, and use the SVM classification algorithm to maximize the separation of different category labels of the optimal extraction solvent in the high-dimensional space, and extract the target compounds through a microporous filter and air pressure application;

[0009] Mass Spectrometry Separation Module: According to the characteristics of the target compounds in the sampled agricultural products, select a suitable chromatographic column and configure the mobile phase, set the optimized flow rate, column temperature, and gradient mechanism, select a quadrupole mass spectrometry to match the optimal extraction solvent, and adjust the spray voltage and ion source to achieve the best separation of the target compounds;

[0010] Detection and identification module: Start LC-MS and monitor target compounds in real time. Set signal intensity threshold and peak width parameters, compare with the retention time library and mass-to-charge ratio library of expected target compounds one by one, calculate the peak integration area ratio using stable isotope-labeled internal standards, confirm the structure and concentration of target compounds through tandem mass spectrometry analysis, identify pollutants, and draw quantitative result tables and diagrams.

[0011] Preferably, select different farmlands corresponding to agricultural products as sampling points, randomly sample agricultural products, collect sampling image data of agricultural products using a high-definition camera, label the random sampling serial numbers, extract the material characteristics and image surface characteristics of agricultural product types, construct a tree structure of agricultural product types, represent the material characteristics and image surface characteristics of agricultural product types by the branch nodes inside the tree structure, and represent the agricultural product type results by the leaf nodes inside the tree structure. Construct multiple decision trees by randomly selecting material characteristics and image surface characteristics, perform optimal classification according to the material characteristics and image surface characteristics of the branch nodes randomly selected by the leaf nodes, and classify the sampled agricultural products independently.

[0012] Preferably, extract the target compounds in the sampled agricultural products by ultrasonic extraction, establish a target compound property database, associate different target compound properties with corresponding optimal extraction solvents and extraction conditions, and label the category labels of target compounds and corresponding optimal extraction solvents. Extract molecular property characteristics and solubility property characteristics, use the SVM classification algorithm to separate target compounds with different category labels, and use the kernel function to map the molecular property characteristics and solubility property characteristics to a high-dimensional space to construct an optimal hyperplane. Maximize the separation of optimal extraction solvents with different category labels in the high-dimensional space, and the projection point on this optimal hyperplane. The specific formula is:

[0013]

[0014] where f(x) represents the classification result of the optimal extraction solvent, y i represents the category label of the i-th target compound, α i represents the Lagrange multiplier, K(x i, x) represents the kernel function that maps the molecular property characteristics or solubility property characteristics of the target compound with the i-th different category label to the optimal extraction solvent for different category labels. b represents the bias term that adjusts the distance between the hyperplane and the origin. The category label of the optimal extraction solvent is determined by the positive and negative class relationship of the classification result of the optimal extraction solvent. When the classification result of the optimal extraction solvent is less than 0, it is determined that the optimal extraction solvent is the positive class. When the classification result of the optimal extraction solvent is greater than 0, it is determined that the optimal extraction solvent is the negative class. The positive and negative class signals are fed back to the target compound property database and the category label of the optimal extraction solvent corresponding to the current target compound is automatically generated. Connect the microporous filter to load the target compound in the sampled agricultural product and slowly apply air pressure. Define the dilution factor as 5 times and mix well using a vortex mixer.

[0015] Preferably, select the chromatographic column according to the molecular property characteristics of the target compound in the sampled agricultural product, configure the mobile phase according to the optimal extraction solvent, add acid to adjust the pH value of the mobile phase to achieve the best separation effect, filter the mobile phase through a 0.45 μm filter, and use an ultrasonic degasser to remove the bubbles in the optimal extraction solvent. Set the flow rate to 1.5 mL / min and the column temperature to 25 °C according to the chromatographic column rules. Use the low-concentration optimal extraction solvent as the starting point and set the gradient mechanism to gradually increase the proportion of the optimal extraction solvent to separate the target compound in the sampled agricultural product. Select the quadrupole mass spectrometer according to the target compound in the sampled agricultural product, match the optimal extraction solvent, adjust the spray voltage to 3 kV, set the auxiliary gas to nitrogen, and the ion source temperature to 100 °C. Connect the outlet to the ion source inlet of the mass spectrometer using a polytetrafluoroethylene tube, and set the positive ion mode and negative ion mode to match the target compound in the sampled agricultural product.

[0016] Preferably, start the LC-MS and ensure that the high-performance liquid chromatography is properly connected to the mass spectrometer and has been preheated. Monitor the target compound in the sampled agricultural product in real time through the mass spectrometer interface, select the peak detection function to identify the signal peaks, set 2 times the signal background noise as the signal intensity threshold, eliminate the influence of baseline drift through baseline correction, set the peak width parameter to 0.1 min to adapt to the retention time of the target compound and the chromatographic separation situation, generate a peak list containing the mass-to-charge ratio, retention time, and signal intensity of each peak, establish a retention time library and a mass-to-charge ratio library for the expected target compound, and compare the identified peak list with the retention time library and the mass-to-charge ratio library of the expected target compound one by one. Select the stable isotope-labeled target compound as the internal standard, calculate the peak integral area ratio of the target compound and the internal standard, and perform tandem mass spectrometry analysis on the target compound. The specific formula is:

[0017]

[0018] Among them, C g represents the concentration of the target compound, Ag The peak integration area of the target compound, C s The known concentration of the internal standard, A s The peak integration area of the internal standard. Select ions from the parent ion to fragment and generate daughter ions. Record the mass-to-charge ratio of the parent ion and the fragment ion spectrum. Observe the fragment ion spectrum and compare the mass-to-charge ratio and relative abundance of each daughter ion to reveal the specific bonding conditions and functional group characteristics of the molecules of the target compound in the sampled agricultural products for structure confirmation. Output the mass-to-charge ratio of the target compound in the sampled agricultural products. Then, identify the pollutants based on the mass-to-charge ratio of the pollutants in the sampled agricultural products. Make a table listing all the pollutants in the sampled agricultural products and their corresponding concentrations. Draw a chromatogram and a mass spectrum to display and annotate the quantitative results of the pollutants in the sampled agricultural products.

[0019] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0020] 1. By collecting the image data of agricultural products through a high-definition camera and combining the extraction of material characteristics and image surface characteristics, a tree structure of agricultural product types is constructed, enabling different types of agricultural products and their characteristics to be organized and managed in a hierarchical manner. It can effectively process and analyze a large number of agricultural product samples, ensuring the accuracy and consistency of classification. During the construction of multiple decision trees, material characteristics and image characteristics are randomly selected for optimal classification, further improving the flexibility and adaptability of classification.

[0021] 2. Through the ultrasonic extraction technology and in combination with the established database of the characteristics of target compounds, the target compounds in agricultural products can be efficiently extracted. The characteristics of different target compounds are associated with the corresponding optimal extraction solvents and extraction conditions to ensure the optimization of the extraction process. The classification algorithm of support vector machines is used to map the extracted molecular characteristic features and dissolution characteristic features into a high-dimensional space to construct an optimal hyperplane, realizing the accurate classification of target compounds with different category labels. In the mass spectrometry analysis stage, the target compounds are monitored in real time and appropriate thresholds and parameters are set to ensure the accurate detection of signals. By comparing with the retention time library and mass-to-charge ratio library of the expected target compounds, the pollutants in agricultural products are accurately identified and quantitatively analyzed. Stable isotope labeling is selected as the internal standard, and the ratio of the peak integration area of the target compound to that of the internal standard is calculated, improving the reliability of quantitative analysis. By drawing a chromatogram and a mass spectrum to display the quantitative results of the pollutants, the scientificity and accuracy of the safety quality detection of agricultural products are significantly improved. Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a method flowchart of a system for detecting the quality and safety of agricultural products according to the present invention.

[0024] Figure 2 It is a schematic diagram of the modules of a system for detecting the quality and safety of agricultural products according to the present invention. Detailed implementation manners

[0025] Now, the exemplary embodiments will be described more comprehensively with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0026] The present invention provides a system for detecting the quality and safety of agricultural products as shown in Figure 1 which includes a collection and processing module, a classification and extraction module, a mass spectrometry separation module, and a detection and identification module;

[0027] Collection and processing module: Randomly sample agricultural products in different farmlands and use a high-definition camera to collect the sampled image data of the agricultural products, construct a decision tree based on material characteristics and surface image characteristics, and realize the independent classification of the sampled agricultural products;

[0028] Select different farmlands corresponding to agricultural products as sampling points, randomly sample agricultural products and use a high-definition camera to collect the sampled image data of the agricultural products, label the random sampling serial numbers, extract the material characteristics and image surface characteristics of the agricultural product types, construct a tree-like structure of the agricultural product types, represent the material characteristics and image surface characteristics of the agricultural product types by the branch nodes inside the tree-like structure, and represent the agricultural product type results by the leaf nodes inside the tree-like structure. Construct multiple decision trees by randomly selecting material characteristics and image surface characteristics, and perform the best classification according to the material characteristics and image surface characteristics of the branch nodes randomly selected by the leaf nodes to independently classify the sampled agricultural products.

[0029] Select several representative farmlands as sampling points, randomly sample different types of agricultural products, simultaneously use a high-definition camera to record the image data of each agricultural product, and assign a unique random sampling serial number to each sample to ensure the diversity and representativeness of the samples. Randomly select different material characteristics and image surface characteristics to construct multiple decision trees, and perform the best split for each tree to ensure that each branch node can effectively distinguish different types of agricultural products.

[0030] Classification extraction module: Ultrasonic extraction is used to extract the target compounds in the sampled agricultural products, a characteristic database is established, and the SVM classification algorithm is used to maximize the separation of different category labels of the optimal extraction solvent through high-dimensional space, and the target compounds are extracted through microporous filters and air pressure;

[0031] Before ultrasonic extraction of target compounds in sampled agricultural products, it is necessary to select automated equipment for surface cleaning. After surface cleaning, automated equipment is used to cut the cleaned agricultural products. During the cleaning and cutting process, the status of each processing step is monitored in real time to ensure the efficiency and consistency of the entire process and improve the consistency of compounds in subsequent extractions.

[0032] Ultrasonic extraction is used to extract the target compounds in the sampled agricultural products, and a target compound characteristic database is established. The characteristics of different target compounds are associated with the corresponding optimal extraction solvents and extraction conditions, and the category labels of the target compounds and the corresponding optimal extraction solvents are marked. The molecular characteristic features and solubility characteristics are extracted, and the target compounds with different category labels are separated by the SVM classification algorithm. The molecular characteristic features and solubility characteristics are mapped to the high-dimensional space using the kernel function to construct the optimal hyperplane. The optimal extraction solvents with different category labels are separated to the maximum extent through the high-dimensional space. At the projection point of the optimal hyperplane, the specific formula is:

[0033]

[0034] Where f(x) represents the classification result of the optimal extraction solvent, y i represents the category label of the i-th target compound, α i represents the Lagrange multiplier, K(x i ,x) represents the kernel function that maps the molecular characteristic features or solubility characteristic features of the target compound with different category labels to the optimal extraction solvent with different category labels, b represents the bias term that adjusts the distance between the hyperplane and the origin, and the category label of the optimal extraction solvent is judged by the positive and negative class relationship of the classification result of the optimal extraction solvent. When the classification result of the optimal extraction solvent is less than 0, the optimal extraction solvent is judged to be a positive class, and when the classification result of the optimal extraction solvent is greater than 0, the optimal extraction solvent is judged to be a negative class. The positive and negative signals are fed back to the target compound characteristic database and the category label of the optimal extraction solvent corresponding to the current target compound is automatically generated. The microporous filter is connected to load the target compound in the sampled agricultural products and air pressure is slowly applied. The dilution multiple is defined as 5 times, and the mixture is fully mixed using a vortex mixer.

[0035] Mass spectrometry separation module: According to the characteristics of target compounds in the sampled agricultural products, select an appropriate chromatographic column and configure the mobile phase, set and optimize the flow rate, column temperature and gradient mechanism, select a quadrupole mass spectrometry to match the optimal extraction solvent, and adjust the spray voltage and ion source to achieve the best separation of target compounds;

[0036] Select a chromatographic column according to the molecular property characteristics of the target compounds in the sampled agricultural products, configure the mobile phase according to the optimal extraction solvent, add acid to adjust the pH value of the mobile phase to achieve the best separation effect, filter the mobile phase through a 0.45 μm filter, and use an ultrasonic degasser to remove the bubbles in the optimal extraction solvent. Set the flow rate to 1.5 mL / min and the column temperature to 25 °C according to the chromatographic column rules. Take the low-concentration optimal extraction solvent as the starting point, and set the gradient mechanism to gradually increase the proportion of the optimal extraction solvent to separate the target compounds in the sampled agricultural products. Select a quadrupole mass spectrometry according to the target compounds in the sampled agricultural products, match the optimal extraction solvent, adjust the spray voltage to 3 kV, set the auxiliary gas to nitrogen, and the ion source temperature to 100 °C. Connect the outlet with the ion source inlet of the mass spectrometer using a polytetrafluoroethylene pipe, and set the positive ion mode and negative ion mode to match the target compounds in the sampled agricultural products.

[0037] Select a chromatographic column according to the molecular property characteristics of the target compounds in the sampled agricultural products. For example, for compounds with relatively high polarity, select a C18 reversed-phase chromatographic column; for compounds with low polarity, select a hydrophilic interaction chromatographic column. Configure the mobile phase according to the extraction solvent characteristics of the target compounds to ensure the best separation effect. In the configuration of the mobile phase, add an appropriate amount of acid to adjust the pH value of the mobile phase to optimize the separation of the compounds. The appropriate pH value can affect the ionization degree of the compounds, thereby improving the separation efficiency. According to the rules of the chromatographic column, set the flow rate to 1.5 mL / min and set the column temperature to 25 °C. Take the low-concentration optimal extraction solvent as the starting point and implement the gradient elution mechanism. Starting from the initial solvent ratio, gradually increase the proportion of the optimal extraction solvent, which helps to gradually improve the separation efficiency of the target compounds at different separation stages, thereby obtaining the best separation result.

[0038] Detection and identification module: Start the LC-MS and monitor the target compounds in real time. Set the signal intensity threshold and peak width parameters, compare them one by one with the retention time library and mass-to-charge ratio library of the expected target compounds, use stable isotope-labeled internal standards to calculate the peak integration area ratio, confirm the structure and its concentration of the target compounds through tandem mass spectrometry analysis, identify pollutants and draw a quantitative result table and diagram;

[0039] Start the LC-MS and ensure that the high-performance liquid chromatography is properly connected to the mass spectrometer and has been preheated. Monitor the target compounds in the sampled agricultural products in real time through the mass spectrometer interface. Select the peak detection function to identify signal peaks, set 2 times the signal background noise as the signal intensity threshold, eliminate the influence of baseline drift through baseline correction, set the peak width parameter to 0.1 min to adapt to the retention time of the target compounds and the chromatographic separation situation, generate a peak list containing the mass-to-charge ratio, retention time, and signal intensity of each peak, establish a retention time library and a mass-to-charge ratio library for the expected target compounds, and compare the identified peak list with the retention time library and the mass-to-charge ratio library of the expected target compounds one by one. Select the stable isotope-labeled target compound as the internal standard, calculate the ratio of the peak integration areas of the target compound and the internal standard, and perform tandem mass spectrometry analysis on the target compound. The specific formula is as follows:

[0040]

[0041] Among them, C g represents the concentration of the target compound, A g represents the peak integration area of the target compound, C s represents the known concentration of the internal standard, A s represents the peak integration area of the internal standard. Select ions from the parent ion for fragmentation to generate daughter ions, record the mass-to-charge ratio of the parent ion and the fragment ion spectrum, observe the fragment ion spectrum and compare the mass-to-charge ratio and relative abundance of each daughter ion to reveal the specific bonding conditions and functional group characteristics of the molecules of the target compounds in the sampled agricultural products for structure confirmation, output the mass-to-charge ratio of the target compounds in the sampled agricultural products. Then, identify the pollutants based on the mass-to-charge ratio of the pollutants in the sampled agricultural products, make a table listing all the pollutants in the sampled agricultural products and their corresponding concentrations, and draw chromatograms and mass spectra to display and annotate the quantitative results of the pollutants in the sampled agricultural products.

[0042] Example 1

[0043] In the classification extraction module of the present invention, assume that the molecular characteristics of target compound A are a molecular weight of 150 g / mol and a polarity of 0.5 logP, the molecular characteristics of target compound B are a molecular weight of 200 g / mol and a polarity of 1.2 logP, the dissolution characteristics of solvent X are a solubility of 10 g / L, and the dissolution characteristics of solvent Y are a solubility of 5 g / L. Through the SVM classification algorithm, the classification result of target compound A is -0.3, marked as the positive class, and the optimal extraction solvent is X; the classification result of target compound B is 0.5, marked as the negative class, and the optimal extraction solvent is Y.

[0044] Only certain exemplary embodiments of the present invention have been described by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.

Claims

1. A system for detecting the quality and safety of agricultural products, characterized in that: It includes acquisition and processing module, classification and extraction module, mass spectrometry separation module and detection and identification module: Acquisition and processing module: By randomly sampling agricultural products in different farmlands and using high-definition cameras to collect sampling image data of agricultural products, a decision tree based on material characteristics and surface image characteristics is constructed to achieve independent classification of sampled agricultural products; Classification extraction module: Ultrasonic extraction is used to extract the target compounds in the sampled agricultural products, a characteristic database is established, and the SVM classification algorithm is used to maximize the separation of different category labels of the optimal extraction solvent through high-dimensional space, and the target compounds are extracted through microporous filters and air pressure; Mass spectrometry separation module: According to the characteristics of the target compounds in the sampled agricultural products, select the appropriate chromatographic column and configure the mobile phase, set the optimized flow rate, column temperature and gradient mechanism, select the quadrupole mass spectrometer to match the optimal extraction solvent, and adjust the spray voltage and ion source to achieve the best separation of the target compounds; Detection and identification module: Start LC-MS and monitor the target compound in real time, set the signal intensity threshold and peak width parameters, compare the retention time library and mass-to-charge ratio library of the expected target compound one by one, use stable isotope labeled internal standards to calculate the peak integrated area ratio, confirm the structure and concentration of the target compound through tandem mass spectrometry analysis, identify pollutants and draw quantitative result tables and diagrams.

2. The agricultural product quality and safety detection system according to claim 1, characterized in that: In the acquisition and processing module, different farmlands corresponding to agricultural products are selected as sampling points, agricultural products are randomly sampled and sampling image data of agricultural products are collected using a high-definition camera, random sampling numbers are marked, material characteristics and image surface characteristics of agricultural product types are extracted, and a tree structure of agricultural product types is constructed. The branch nodes inside the tree structure represent the material characteristics and image surface characteristics of the agricultural product types, and the leaf nodes inside the tree structure represent the agricultural product type results. Multiple decision trees are constructed by randomly selecting material characteristics and image surface characteristics. The material characteristics and image surface characteristics of branch nodes are randomly selected according to the leaf nodes for optimal classification, and the sampled agricultural products are independently classified.

3. The agricultural product quality and safety detection system according to claim 1, characterized in that: In the classification and extraction module, ultrasonic extraction is used to extract target compounds in sampled agricultural products, a target compound property database is established, different target compound properties are associated with corresponding optimal extraction solvents and extraction conditions, and the category labels of the target compounds and the corresponding optimal extraction solvents are marked, molecular property features and solubility characteristics are extracted, the SVM classification algorithm is used to separate target compounds with different category labels, and the molecular property features and solubility characteristics are mapped to high-dimensional space using a kernel function to construct an optimal hyperplane, the optimal extraction solvents with different category labels are separated maximally through high-dimensional space, and the category label of the optimal extraction solvent is judged at the projection point of the optimal hyperplane through the positive and negative class relationship of the classification result of the optimal extraction solvent, the microporous filter is connected to load the target compounds in the sampled agricultural products and air pressure is slowly applied, the dilution multiple is defined as 5 times, and a vortex mixer is used to fully mix.

4. The agricultural product quality and safety detection system according to claim 3, characterized in that: The specific formula of the SVM classification algorithm is: Where f(x) represents the classification result of the optimal extraction solvent, y i represents the category label of the i-th target compound, α i represents the Lagrange multiplier, K(x i ,x) represents the kernel function that maps the molecular characteristic features or solubility characteristic features of the target compound with different category labels to the optimal extraction solvent with different category labels, and b represents the bias term that adjusts the distance between the hyperplane and the origin.

5. The agricultural product quality and safety detection system according to claim 3, characterized in that: The positive and negative class relationship of the classification result of the optimal extraction solvent determines the category label of the optimal extraction solvent specifically includes: when the classification result of the optimal extraction solvent is less than 0, the optimal extraction solvent is judged to be a positive class; when the classification result of the optimal extraction solvent is greater than 0, the optimal extraction solvent is judged to be a negative class; the positive and negative class signals are fed back to the target compound characteristic database and the category label of the optimal extraction solvent corresponding to the current target compound is automatically generated.

6. The agricultural product quality and safety detection system according to claim 1, characterized in that: In the mass spectrometry separation module, a chromatographic column is selected according to the molecular characteristics of the target compound in the sampled agricultural products, a mobile phase is configured according to the optimal extraction solvent, an acid is added to adjust the pH value of the mobile phase to achieve the best separation effect, the mobile phase is filtered through a 0.45 μm filter, and an ultrasonic degasser is used to remove bubbles in the optimal extraction solvent. According to the chromatographic column rule, the flow rate is set to 1.5 mL / min and the column temperature is set to 25° C., a low-concentration optimal extraction solvent is used as a starting point, a gradient mechanism is set to gradually increase the proportion of the optimal extraction solvent to separate the target compound in the sampled agricultural products, a quadrupole mass spectrometer is selected according to the target compound in the sampled agricultural products, the optimal extraction solvent is matched, the spray voltage is adjusted to 3 kV, the auxiliary gas is set to nitrogen, the ion source temperature is 100° C., a polytetrafluoroethylene pipe is used to connect the outflow port to the ion source inlet of the mass spectrometer, and a positive ion mode and a negative ion mode are set to match the target compound in the sampled agricultural products.

7. The agricultural product quality and safety detection system according to claim 1, characterized in that: In the detection and identification module, the LC-MS is started and it is ensured that the high performance liquid chromatography is connected to the mass spectrometer normally and has been preheated, the target compounds in the sampled agricultural products are monitored in real time through the mass spectrometer interface, the peak detection function is selected to identify the signal peak, 2 times of the signal background noise is set as the signal intensity threshold, the influence of the baseline drift is eliminated by baseline correction, the peak width parameter is set to 0.1min to adapt to the retention time and chromatographic separation of the target compound, a peak list containing the mass-to-charge ratio, retention time and signal intensity of each peak is generated, a retention time library and a mass-to-charge ratio library of the expected target compound are established, and the identified peak list is compared with the retention time library and the mass-to-charge ratio library of the expected target compound. The mass-to-charge ratio library is compared one by one, and the stable isotope-labeled target compound is selected as the internal standard. The peak integrated area ratio of the target compound and the internal standard is calculated and the target compound is subjected to tandem mass spectrometry analysis. Ions are selected from the parent ion for fragmentation to generate daughter ions, and the mass-to-charge ratio and fragment ion spectrum of the parent ion are recorded. The fragment ion spectrum is observed to compare the mass-to-charge ratio and relative abundance of each ion, and the mass-to-charge ratio of the target compound in the sampled agricultural products is output. Then, the mass-to-charge ratio of the pollutants in the sampled agricultural products is used to identify the pollutants, and a table is made to list all the pollutants in the sampled agricultural products and the corresponding concentrations. Chromatograms and mass spectra are drawn to display and annotate the quantitative results of the pollutants in the sampled agricultural products.

8. The agricultural product quality and safety detection system according to claim 7, characterized in that: The specific formula for calculating the peak integrated area ratio of the target compound and the target is: Among them, C g represents the concentration of the target compound, A g represents the peak integrated area of ​​the target compound, C s represents the known concentration of the internal standard, A s represents the integrated peak area of ​​the internal standard.

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