Agricultural product quality safety detection system
By designing the agricultural product quality and safety detection system, using high-definition cameras and decision trees, ultrasonic extraction technology, SVM classification algorithms, mass spectrometry separation modules and detection and identification modules, the problem of inaccurate detection results in the existing technology and inability to cope with the characteristics of diversified agricultural products is solved, and efficient and accurate agricultural product quality and safety detection is achieved.
Patent Information
- Application Number
- CN202411854892.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-03-25
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing agricultural product quality and safety testing methods rely on manual operations, resulting in inaccurate testing results, which cannot fully reflect the true quality of agricultural products, and cannot effectively deal with the characteristics of diversified agricultural products, resulting in a decrease in classification accuracy.
Design a quality and safety detection system for agricultural products, including a collection and processing module, a classification and extraction module, a mass spectrometry separation module and a detection and identification module. Agriculture product image data is collected through high-definition cameras and a decision tree is constructed for classification; ultrasonic extraction technology and SVM classification algorithm are used to extract and classify target compounds; optimal separation of target compounds is achieved through mass spectrometry separation module; detection and identification module monitors target compounds in real time and conducts quantitative analysis.
It improves the accuracy and efficiency of agricultural product quality and safety testing, can effectively process and analyze large number of agricultural product samples, ensure the accuracy and consistency of classification, reduce human operation errors, improve processing efficiency, and significantly improve the scientificity and accuracy of agricultural product safety and quality testing.
Smart Images
Figure CN119688871A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of agricultural product quality safety, and in particular to an agricultural product quality safety detection system. Background Art
[0002] The quality and safety of agricultural products have attracted widespread attention worldwide, especially in the context of increasing agricultural production and consumers' increasing demands for food safety. The possible presence of pollutants in agricultural products, such as pesticide residues, heavy metals and other harmful substances, not only poses a threat to consumers' health, but also affects the sustainable development of the ecological environment.
[0003] The existing technology has the following deficiencies: the existing agricultural product quality and safety testing methods rely on manual operations and are easily affected by human factors, resulting in inaccurate test results and failure to fully reflect the true quality of agricultural products. When processing target compounds of agricultural products, they are unable to effectively cope with the diverse characteristics of agricultural products, resulting in decreased classification accuracy. There is a lack of ability to select the optimal extraction conditions based on the specific characteristics of the target compounds, resulting in waste of resources and low extraction efficiency.
[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention
[0005] The purpose of the present invention is to provide a system for detecting the quality and safety of agricultural products to solve the problems in the above-mentioned background technology.
[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an agricultural product quality and safety detection system, comprising a collection and processing module, a classification and extraction module, a mass spectrometry separation module and a detection and identification module; Collection and processing module: By randomly sampling agricultural products in different farmlands and using high-definition cameras to collect sampled image data of agricultural products, a decision tree based on material characteristics and image surface characteristics is constructed to achieve independent classification of sampled agricultural products, and automated equipment is used for cleaning, cutting and extraction processing; 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.
[0007] Preferably, agricultural products corresponding to different farmlands 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, a tree structure of agricultural product types is constructed, branch nodes within the tree structure represent material characteristics and image surface characteristics of agricultural product types, leaf nodes within the tree structure represent agricultural product type results, multiple decision trees are constructed by randomly selecting material characteristics and image surface characteristics, material characteristics and image surface characteristics of branch nodes are randomly selected according to leaf nodes for optimal splitting, the sampled agricultural products are independently classified, and automated equipment is selected to clean, cut and extract the sampled agricultural products and monitor the status of the processing steps.
[0008] Preferably, ultrasonic extraction is used to extract the target compounds in the sampled agricultural products, a target compound property database is established, different target compound properties 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, and the molecular property characteristics and solubility characteristics are extracted. The target compounds with different category labels are separated by the SVM classification algorithm, and the molecular property characteristics and solubility characteristics are mapped to the high-dimensional space using the kernel function to construct an optimal hyperplane. The optimal extraction solvents with different category labels are separated by maximizing the separation through the high-dimensional space. At the projection point of the optimal hyperplane, the specific formula is:
[0009] in represents the classification result of the optimal extraction solvent, represents the category label of the i-th target compound, represents the Lagrange multiplier, represents the kernel function that maps the molecular characteristics or solubility characteristics of the target compound of the i-th different category label to the optimal extraction solvent of the different category label, The bias term represents the distance between the hyperplane and the origin. The category label of the optimal extraction solvent is determined by the positive and negative category 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 determined to be a positive category. When the classification result of the optimal extraction solvent is greater than 0, the optimal extraction solvent is determined to be a negative category. 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.
[0010] Preferably, a chromatographic column is selected according to the molecular characteristics of the target compounds 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, the flow rate is set to 1.5 mL / min and the column temperature is set to 25°C according to the chromatographic column rules, a low concentration of the optimal extraction solvent is used as the starting point, a gradient mechanism is set to gradually increase the proportion of the optimal extraction solvent to separate the target compounds in the sampled agricultural products, a quadrupole mass spectrometer is selected according to the target compounds 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, the outflow port is connected to the ion source inlet of the mass spectrometer using a polytetrafluoroethylene pipe, and the positive ion mode and the negative ion mode are set to match the target compounds in the sampled agricultural products.
[0011] Preferably, start LC-MS and ensure that the high performance liquid chromatography is connected to the mass spectrometer normally and 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 the signal peak, set the signal background noise twice as the signal intensity threshold, eliminate the influence of baseline drift through baseline correction, set the peak width parameter to 0.1min to adapt to the retention time and chromatographic separation of the target compound, generate a peak list containing the mass-to-charge ratio, retention time and signal intensity of each peak, establish a retention time library and mass-to-charge ratio library of the expected target compound, and compare the identified peak list with the retention time library and mass-to-charge ratio library of the expected target compound one by one, select a stable isotope labeled target compound as an internal standard, calculate the peak integrated area ratio of the target compound and the internal standard, and perform tandem mass spectrometry analysis on the target compound, and the specific formula is:
[0012] in, represents the concentration of the target compound, represents the peak integrated area of the target compound, represents the known concentration of the internal standard, Represent the peak integrated 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, reveal the specific bonding status and functional group characteristics of the molecules of the target compound in the sampled agricultural products for structural confirmation, output the mass-to-charge ratio of the target compound in the sampled agricultural products and compare the mass-to-charge ratio of the sampled agricultural product pollutants to identify the pollutants, make a table listing all the sampled agricultural product pollutants and the corresponding concentrations, draw chromatograms and mass spectra to display and annotate the quantitative results of the sampled agricultural product pollutants.
[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The image data of agricultural products is collected by high-definition cameras, and the tree structure of agricultural product types is constructed by combining the extraction of material characteristics and image surface features. Different types of agricultural products and their characteristics can be organized and managed in a hierarchical manner, and a large number of agricultural product samples can be effectively processed and analyzed to ensure the accuracy and consistency of classification. In the process of constructing multiple decision trees, material characteristics and image features are randomly selected for optimal splitting, which further improves the flexibility and adaptability of classification. Cleaning, cutting and extraction processing are performed by automated equipment, which can reduce errors caused by human operations and improve processing efficiency.
[0014] 2. Through ultrasonic extraction technology, combined with the established target compound characteristic database, the target compounds in agricultural products can be efficiently extracted, and the characteristics of different target compounds can be associated with the corresponding optimal extraction solvents and extraction conditions to ensure the optimization of the extraction process. The extracted molecular characteristic features and solubility characteristic features are mapped to high-dimensional space using the support vector machine classification algorithm, and the optimal hyperplane is constructed to achieve 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 accurate signal detection. By comparing with the retention time library and mass-to-charge ratio library of the expected target compounds, the pollutants in agricultural products can be accurately identified and quantitatively analyzed. Stable isotope labels are selected as internal standards, and the peak integrated area ratio of the target compound and the internal standard is calculated, which improves the reliability of quantitative analysis. The quantitative results of pollutants are displayed by drawing chromatograms and mass spectra, which significantly improves the scientificity and accuracy of agricultural product safety and quality testing. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.
[0016] Figure 1 The present invention is a method flow chart of a system for detecting agricultural product quality and safety.
[0017] Figure 2 The present invention is a module schematic diagram of a system for detecting agricultural product quality and safety. DETAILED DESCRIPTION
[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.
[0019] The present invention provides Figure 1 The agricultural product quality and safety detection system shown includes a collection and processing module, a classification and extraction module, a mass spectrometry separation module, and a detection and identification module; Collection and processing module: By randomly sampling agricultural products in different farmlands and using high-definition cameras to collect sampled image data of agricultural products, a decision tree based on material characteristics and image surface characteristics is constructed to achieve independent classification of sampled agricultural products, and automated equipment is used for cleaning, cutting and extraction processing; Select agricultural products corresponding to different farmlands as sampling points, randomly sample agricultural products and use high-definition cameras to collect sampling image data of agricultural products, mark random sampling numbers, extract material characteristics and image surface characteristics of agricultural product types, construct a tree structure of agricultural product types, and use branch nodes inside the tree structure to represent material characteristics and image surface characteristics of agricultural product types. Leaf nodes inside the tree structure represent agricultural product type results. Construct multiple decision trees by randomly selecting material characteristics and image surface characteristics. Randomly select material characteristics and image surface characteristics of branch nodes according to leaf nodes for optimal splitting, independently classify sampled agricultural products, select automated equipment to clean, cut and extract sampled agricultural products, and monitor the status of the processing steps.
[0020] Several representative farmlands were selected as sampling points, and different types of agricultural products were randomly sampled. High-definition cameras were used to record the image data of each agricultural product, and a unique random sampling number was assigned to each sample to ensure the diversity and representativeness of the samples. Different material characteristics and image surface features were randomly selected to construct multiple decision trees, and the best split was performed for each tree to ensure that each branch node could effectively distinguish different types of agricultural products. Once the classification was completed, automated equipment was selected to clean, cut and extract the classified sampled agricultural products. During this process, the status of each processing step was monitored in real time to ensure the efficiency and consistency of the entire process, and to ensure that high-quality target compounds were ultimately extracted.
[0021] 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; The target compounds in the sampled agricultural products are extracted by ultrasonic wave, 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 characteristic features are extracted, and the target compounds with different category labels are separated by the SVM classification algorithm, and the molecular characteristic features and solubility characteristic features are mapped to the high-dimensional space by 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:
[0022] in represents the classification result of the optimal extraction solvent, represents the category label of the i-th target compound, represents the Lagrange multiplier, represents the kernel function that maps the molecular characteristics or solubility characteristics of the target compound of the i-th different category label to the optimal extraction solvent of the different category label, The bias term represents the distance between the hyperplane and the origin. The category label of the optimal extraction solvent is determined by the positive and negative category 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 determined to be a positive category. When the classification result of the optimal extraction solvent is greater than 0, the optimal extraction solvent is determined to be a negative category. 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.
[0023] 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; A chromatographic column was selected according to the molecular characteristics of the target compounds in the sampled agricultural products, the mobile phase was configured according to the optimal extraction solvent, acid was added to adjust the pH value of the mobile phase to achieve the best separation effect, the mobile phase was filtered through a 0.45 μm filter, and an ultrasonic degasser was used to remove bubbles in the optimal extraction solvent. The flow rate was set to 1.5 mL / min and the column temperature was set to 25 °C according to the chromatographic column rules. The low concentration of the optimal extraction solvent was used as the starting point, and a gradient mechanism was set to gradually increase the proportion of the optimal extraction solvent to separate the target compounds in the sampled agricultural products. A quadrupole mass spectrometer was selected according to the target compounds in the sampled agricultural products, the optimal extraction solvent was matched, the spray voltage was adjusted to 3 kV, the auxiliary gas was set to nitrogen, the ion source temperature was 100 °C, the outflow port was connected to the ion source inlet of the mass spectrometer using a polytetrafluoroethylene pipe, and the positive ion mode and the negative ion mode were set to match the target compounds in the sampled agricultural products.
[0024] Select a chromatographic column based on the molecular characteristics of the target compounds in the sampled agricultural products. For example, for compounds with greater polarity, select a C18 reverse phase column, and for compounds with less polarity, select a hydrophilic interaction column. Configure the mobile phase based on the extraction solvent characteristics of the target compound 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 degree of ionization 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 the column temperature to 25°C. Use a low concentration of the optimal extraction solvent as the starting point and implement a gradient elution mechanism. Starting from the starting solvent ratio, gradually increase the ratio of the optimal extraction solvent, which helps to gradually improve the separation efficiency of the target compound at different separation stages, thereby obtaining the best separation result.
[0025] 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, identify pollutants and draw quantitative result tables and diagrams; Start LC-MS and ensure that the HPLC and mass spectrometer are connected normally and 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 the signal peak. Set the signal intensity threshold to twice the signal background noise. Eliminate the influence of baseline drift through baseline correction. Set the peak width parameter to 0.1min to adapt to the retention time and chromatographic separation of the target compound. Generate a peak list containing the mass-to-charge ratio, retention time and signal intensity of each peak. Establish the retention time library and mass-to-charge ratio library of the expected target compound, and compare the identified peak list with the retention time library and 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 integrated area ratio of the target compound and the internal standard, and perform tandem mass spectrometry analysis on the target compound. The specific formula is:
[0026] in, represents the concentration of the target compound, represents the peak integrated area of the target compound, represents the known concentration of the internal standard, Represent the peak integrated 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, reveal the specific bonding status and functional group characteristics of the molecules of the target compound in the sampled agricultural products for structural confirmation, output the mass-to-charge ratio of the target compound in the sampled agricultural products and compare the mass-to-charge ratio of the sampled agricultural product pollutants to identify the pollutants, make a table listing all the sampled agricultural product pollutants and the corresponding concentrations, draw chromatograms and mass spectra to display and annotate the quantitative results of the sampled agricultural product pollutants.
[0027] Example 1 In the classification and extraction module of the present invention, it is assumed that the molecular characteristics of the target compound A are a molecular weight of 150 g / mol and a polarity of 0.5 logP, the molecular characteristics of the target compound B are a molecular weight of 200 g / mol and a polarity of 1.2 logP, the solubility characteristics of the solvent X are a solubility of 10 g / L, and the solubility characteristics of the solvent Y are a solubility of 5 g / L. Through the SVM classification algorithm, the classification result of the target compound A is -0.3, marked as a positive class, and the optimal extraction solvent is X. The classification result of the target compound B is 0.5, marked as a negative class, and the optimal extraction solvent is Y.
[0028] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection 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: Collection and processing module: By randomly sampling agricultural products in different farmlands and using high-definition cameras to collect sampled image data of agricultural products, a decision tree based on material characteristics and image surface characteristics is constructed to achieve independent classification of sampled agricultural products, and automated equipment is used for cleaning, cutting and extraction processing; 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 serial numbers are marked, material characteristics and image surface characteristics of agricultural product types are extracted, a tree structure of agricultural product types is constructed, branch nodes inside the tree structure represent material characteristics and image surface characteristics of agricultural product types, leaf nodes inside the tree structure represent agricultural product type results, multiple decision trees are constructed by randomly selecting material characteristics and image surface characteristics, material characteristics and image surface characteristics of branch nodes are randomly selected according to leaf nodes for optimal splitting, sampled agricultural products are independently classified, and automated equipment is selected to clean, cut and extract the sampled agricultural products and monitor the status of the processing steps.
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: ； in represents the classification result of the optimal extraction solvent, represents the category label of the i-th target compound, represents the Lagrange multiplier, represents the kernel function that maps the molecular characteristics or solubility characteristics of the target compound of the i-th different category label to the optimal extraction solvent of the different category label, 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, and the signal intensity threshold is set to 2 times the signal background noise. The influence of the baseline drift is eliminated by baseline correction. The peak width parameter is set to 0.1 min to adapt to the retention time and chromatographic separation of the target compound, and a peak list containing the mass-to-charge ratio, retention time and signal intensity of each peak is generated. The retention time library and mass-to-charge ratio library of the expected target compound are established, and the identified peak list is compared with the retention time library of the expected target compound. Compare them one by one with the mass-to-charge ratio library, select the stable isotope labeled target compound as the internal standard, calculate the peak integrated area ratio of the target compound and the internal standard and perform tandem mass spectrometry analysis on the target compound, select ions from the parent ion for fragmentation to generate daughter ions, record the mass-to-charge ratio and fragment ion spectrum of the parent ion, observe the fragment ion spectrum and compare the mass-to-charge ratio and relative abundance of each daughter ion, output the mass-to-charge ratio of the target compound in the sampled agricultural products, compare the mass-to-charge ratio of the pollutants in the sampled agricultural products for pollutant identification, make a table listing all the pollutants in the sampled agricultural products and the corresponding concentrations, draw chromatograms and mass spectra 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 internal standard is: ； in, represents the concentration of the target compound, represents the peak integrated area of the target compound, represents the known concentration of the internal standard, represents the integrated peak area of the internal standard.