High-precision food pesticide residue detection system
The high-precision food pesticide residue detection system utilizes gas chromatography-mass spectrometry and a hash algorithm for a spectral database to solve the problem of limited detection range of existing detection instruments, achieving high-precision detection and intuitive result display for multiple types of pesticides.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-03
- Publication Date
- 2026-04-03
AI Technical Summary
Existing rapid pesticide residue detection instruments have limited detection range and cannot cover other categories of pesticides except organophosphates and carbamates, resulting in low detection efficiency.
A high-precision food pesticide residue detection system was designed, including a central computer, a sample acquisition and pretreatment module, a sample detection module, a data processing module, and a data visualization module. The system analyzes the samples using gas chromatography-mass spectrometry and combines a spectral database and hash algorithm for qualitative and quantitative analysis.
It enables high-precision detection of pesticide residues in food samples in various states, reduces the influence of impurities, improves the detection range and accuracy, and intuitively displays the analysis results through various charts.
Smart Images
Figure CN121784199A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of pesticide residue detection technology, specifically a high-precision food pesticide residue detection system. Background Technology
[0002] In modern agriculture, to ensure crop yield and quality, the cultivation of crops increasingly relies on various types of pesticides. However, after crops mature, excessive use of pesticides leads to pesticide residues in the crops. Pesticide residues refer to trace amounts of pesticide precursors, toxic metabolites, degradation products, and impurities that remain in organisms, agricultural products, and the environment after pesticide use. These residues affect consumer safety and, in severe cases, can cause illness, abnormal development, or even direct poisoning and death. To ensure food safety, the types and concentrations of pesticide residues need to be strictly controlled to prevent negative impacts on human health.
[0003] Currently, the common method for detecting pesticide residues in crops is to use rapid detection instruments, such as the common enzyme inhibition-based rapid pesticide residue detector. However, such instruments can only detect organophosphates and carbamates, and cannot detect organochlorines, pyrethroids, and other types of pesticides, resulting in a detection blind spot and affecting the efficiency of pesticide residue detection in food. Summary of the Invention
[0004] The purpose of this invention is to provide a high-precision food pesticide residue detection system to solve the problem that the rapid pesticide residue detection instruments mentioned in the background art have limited detection range and cannot cover the types of pesticide residues.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a high-precision food pesticide residue detection system, comprising a central computer, a sample acquisition and preprocessing module, a sample detection module, a data processing module, a data visualization module, and a chart output module. The central computer is the control center of the system. The central computer is connected and interconnected with the sample acquisition and preprocessing module, the sample detection module, the data processing module, and the data visualization module. The sample acquisition and preprocessing module is connected to the sample detection module. The sample detection module is connected to the data processing module. The data processing module is connected to the data visualization module. The data visualization module is connected to the chart output module. Preferably, the sample collection and pretreatment module collects and preprocesses the analytes for pesticide residue detection in food. The pretreatment of the food is categorized by its form: solid foods, fruit and vegetable foods, liquid foods, and semi-solid or paste foods. The pretreatment of solid foods includes grinding; the ground solid food powder is extracted using acetonitrile, acetone, or a mixture thereof; the extracted solid food sample solution is then subjected to solid-phase extraction to remove macromolecular impurities, yielding the analytical sample of the solid food. The pretreatment of fruit and vegetable foods includes homogenization; the homogenized sample… The food sample solution is extracted with acetonitrile, and then subjected to solid-phase extraction to remove impurities such as organic acids and sugars, yielding an analytical sample of fruit and vegetable products. The pretreatment of liquid foods includes dilution, followed by solvent extraction. The extracted liquid food sample solution is then subjected to solid-phase extraction to remove impurities such as fats and proteins, yielding an analytical sample of liquid foods. The pretreatment of semi-solid or paste-like foods includes dissolution, followed by solid-phase extraction with an extractant to remove impurities, yielding an analytical sample of semi-solid or paste-like foods.
[0006] By employing the above technical solution, different pretreatments are performed on samples in different states, which facilitates the extraction of effective components from the samples as much as possible and reduces the impact of impurities on the results.
[0007] Preferably, the sample detection module receives the analytical sample processed by the sample acquisition and preprocessing module, and analyzes the analytical sample. The analytical sample analysis process uses a gas chromatography-mass spectrometry (GC-MS) instrument to obtain the sample mass spectrum of the analytical sample.
[0008] Using the above technical solution, the mass spectrum of the sample can be obtained by using the gas chromatography-mass spectrometry instrument set in the sample detection module.
[0009] Preferably, the data processing module receives the sample mass spectrum obtained from the sample detection module, processes and analyzes the sample mass spectrum, and performs qualitative and quantitative analysis of the pesticide components in the sample. The data processing module includes a mass spectrum preprocessing section, a mass spectrum analysis section, a sample qualitative analysis section, and a sample quantitative analysis section.
[0010] Using the above technical solution, the data processing module can process the analyzed sample mass spectrum to determine the composition of pesticide compounds in the sample.
[0011] Preferably, the mass spectrum preprocessing section includes noise removal and baseline calibration. The noise removal uses a moving average method to smooth the signal curve of the sample mass spectrum, making the peak shape clearer. The baseline calibration eliminates or reduces background signal interference in the sample mass spectrum. The mass spectrum analysis section includes ion peak identification, which identifies and analyzes the peak position and intensity of the mass spectrum. The sample qualitative analysis section compares the identified sample mass spectrum with the mass spectrum of known compounds to qualitatively analyze the composition of the sample. The sample quantitative analysis uses a standard curve and external standard method for calculation. The calculation process is as follows: Standard curve plotting: Prepare a series of standard compound solutions of known concentrations, perform mass spectrometry analysis under the same mass spectrometry conditions, obtain the relationship curve between the mass spectrum peak intensity and concentration of the standard compounds, and fit the curve equation; calculate the mass spectrum peak area in the sample mass spectrum, and substitute the result into the standard curve equation to calculate the concentration of the substance in the sample.
[0012] By using the above technical solution, the mass spectrum of the sample can be processed to qualitatively analyze the sample components and quantitatively determine the concentration of each component in the sample based on the standard curve equation of the substance.
[0013] Preferably, in the qualitative analysis of the sample, the mass spectrum of the sample is compared with a spectral database of known compounds to qualitatively analyze the sample composition. The comparison process is as follows: a spectral database is constructed and searched, and the spectral database is divided into different spectral library subsets according to the mass-to-charge ratio range of substances; the possible matching spectral library subset is quickly located according to the main mass-to-charge ratio range of the sample mass spectrum, and further comparison is performed in the spectral library subset; a hash algorithm is used to convert the key features of the mass spectra in the spectral database into hash values, and the hash values of the key features corresponding to the sample mass spectrum are compared to quickly eliminate mass spectra with large differences; the remaining spectra after the initial screening by hash value are precisely compared with the sample mass spectrum to determine the composition of the sample. The hash value calculation of the mass spectrum features includes the ion peak mass-to-charge ratio, peak position intensity, and relative abundance. The hash value calculation steps are as follows: extract the ion peak mass-to-charge ratio, peak position intensity, and relative abundance in the mass spectrum as key features; use the MD5 hash algorithm, input the mass spectrum features, and generate a hash value of fixed length.
[0014] By adopting the above technical solution, the scope of the spectrum comparison process can be narrowed and the comparison efficiency improved by constructing a subset of the spectrum database.
[0015] Preferably, the data visualization module visualizes the data results after quantitative analysis of the samples, including line graphs, pie charts, scatter plots, and geographical distribution maps. The line graphs can show the changing trend of pesticide residues in the same sample over time and analyze the dynamic changes of pesticide residues. The pie charts can show the proportion of different types of pesticide residues within the same sample. The scatter plots can show the relative relationship between residue levels and food indicators, and preliminarily determine whether there is a linear or non-linear relationship between the two variables. The geographical distribution map combines sample analysis data with geographical information to present the regional distribution characteristics of pesticide residues.
[0016] Using the above technical solution, the data visualization module can be used to visualize the analysis results of the samples in charts and graphs.
[0017] Compared with the prior art, the beneficial effects of the present invention are: This high-precision food pesticide residue detection system: 1. In this invention, food samples in various states undergo different pretreatments. By using different forms of pretreatment, the influence of impurities in the samples on the analytical results is minimized. During the sample analysis, gas chromatography-mass spectrometry is used to analyze the samples. This method has strong separation and identification capabilities, ensuring high precision of the sample analysis results. The results obtained after sample analysis and processing are displayed in charts using a visualization module. Multiple chart formats can display the sample analysis results from different perspectives, thus providing a clear and intuitive observation of pesticide residue data in food from multiple aspects, facilitating subsequent analysis. 2. In the process of comparing the sample mass spectrum with the spectral database in this invention, the spectral database is divided into multiple spectral library subsets according to the mass-to-charge ratio range. Based on the main mass-to-charge ratio range of the sample mass spectrum, a specific spectral library subset can be quickly located, reducing the comparison range. At the same time, hash values are calculated for both the spectral database and the sample mass spectrum using a hash algorithm. The comparison of hash values is used for initial screening, further reducing the comparison range of the sample mass spectrum. The hash values of the mass spectrum obtained by the hash algorithm have strong anti-collision properties, making it difficult to find any two different data blocks with the same hash value. Therefore, the accuracy rate is high in the mass spectrum comparison and screening process. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the sample acquisition and pretreatment module of the present invention; Figure 3 This is a schematic diagram of the sample detection module structure of the present invention; Figure 4 This is a schematic diagram of the data processing module structure of the present invention; Figure 5This is a schematic diagram of the data visualization module structure of the present invention; Figure 6 This is a schematic diagram of the spectral database comparison process structure of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Please see Figures 1-6 The present invention provides a technical solution: a high-precision food pesticide residue detection system.
[0021] The central computer serves as the system's control center. It is connected and communicates with the sample acquisition and preprocessing module, the sample detection module, the data processing module, and the data visualization module. The sample acquisition and preprocessing module is connected to the sample detection module, the sample detection module is connected to the data processing module, the data processing module is connected to the data visualization module, and the data visualization module is connected to the chart output module. like Figure 1 As shown, the central computer can control the entire system. The sample collection and pretreatment module collects the food samples to be tested and pre-processes them according to their state to facilitate subsequent analysis. The pre-processed food samples are input into the sample detection module, which analyzes and obtains the mass spectrum of the samples. The obtained mass spectra are compared and analyzed to determine the components and contents of pesticide residues in the food samples. The data is then input into the data visualization module for chart conversion, and the chart output module displays the charts.
[0022] The sample collection and pretreatment module collects and preprocesses the analytes for pesticide residue detection in food. Pretreatment of the tested food is categorized by its form: solid foods, fruits and vegetables, liquid foods, and semi-solid or paste-like foods. For solid foods, pretreatment includes grinding. The ground solid food powder is extracted using acetonitrile, acetone, or a mixture thereof. The extracted solid food sample solution is then subjected to solid-phase extraction to remove macromolecular impurities, yielding the analytical sample. For fruits and vegetables, pretreatment includes homogenization. The homogenized sample is extracted with acetonitrile. The extracted food sample solution is then subjected to solid-phase extraction to remove impurities such as organic acids and sugars, yielding the analytical sample. The analysis samples are prepared for vegetable-based foods. For liquid foods, the pretreatment includes dilution, followed by solvent extraction. The extracted liquid food sample solution is then subjected to solid-phase extraction to remove impurities such as fat and protein, yielding the liquid food analysis sample. For semi-solid or paste-like foods, the pretreatment includes dissolution. The dissolved semi-solid or paste-like food sample solution is then subjected to solid-phase extraction after adding an extractant to remove impurities, yielding the semi-solid or paste-like food analysis sample. The sample detection module receives the analysis sample processed by the sample acquisition and pretreatment module and analyzes it. The analysis process uses gas chromatography-mass spectrometry (GC-MS) to obtain the sample mass spectrum. like Figure 2 and Figure 3 As shown, in the pretreatment process of food samples, the state of the food sample is first determined. For solid foods, grinding is performed, and the ground powder is extracted with a solvent. Large molecular impurities in the sample liquid are removed by solid-phase extraction. Similarly, for fruits and vegetables, the samples are crushed and homogenized, and then extracted and impurity removed. For liquid foods, dilution, extraction, and impurity removal are performed. For semi-solid or paste-like foods, dissolution, extraction, and impurity removal are performed. The processed analytical samples are then obtained. After inputting the analytical samples into a gas chromatography-mass spectrometry (GC-MS) instrument, the mass spectrum of the food sample is obtained.
[0023] The data processing module receives the sample mass spectrum from the sample detection module and processes and analyzes it. It performs qualitative and quantitative analysis of pesticide components within the sample. The data processing module includes a mass spectrum preprocessing section, a mass spectrum analysis section, a sample qualitative analysis section, and a sample quantitative analysis section. The mass spectrum preprocessing section includes noise removal and baseline calibration. Noise removal uses a moving average method to smooth the signal curve of the sample mass spectrum, making the peaks clearer. The baseline calibration eliminates or reduces background signal interference in the sample mass spectrum. The mass spectrum analysis section includes ion peak identification, which is crucial for the analysis of the mass spectrum. The peak positions and intensities of the mass spectra are identified and analyzed. The qualitative analysis of the samples compares the identified sample mass spectra with those of known compounds to qualitatively determine the sample composition. Quantitative analysis of the samples uses standard curves and the external standard method for calculation. The calculation process is as follows: Standard curve plotting: A series of standard compound solutions of known concentrations are prepared and analyzed by mass spectrometry under the same conditions to obtain the relationship between the mass peak intensity and concentration of the standard compounds. The curve equation is then fitted. The peak areas in the sample mass spectra are calculated, and the results are substituted into the standard curve equation to calculate the concentration of the substances in the sample. like Figure 4 As shown, the mass spectrum of the sample obtained from the analysis is input into the data processing module. First, the mass spectrum is preprocessed, including noise removal and baseline calibration to ensure the accuracy of subsequent analysis. The preprocessed mass spectrum is then analyzed for peak position and intensity. The mass spectrum is compared with the mass spectra of known compounds to qualitatively analyze the composition of the sample. Based on the compared compound composition, a series of standard compound solutions of known concentrations are prepared to obtain the relationship curve between the mass spectrum peak intensity and concentration of the standard compounds. The curve equation is then fitted, and the concentration of substances in the sample is calculated using the external standard method, thereby enabling quantitative analysis of pesticide residues in the sample.
[0024] In the qualitative analysis of samples, the mass spectrum of the sample is compared with a database of known compounds to qualitatively analyze the sample composition. This comparison process is as follows: A spectral database is constructed and searched, and the database is divided into different subsets based on the mass-to-charge ratio range of the substances; the sample mass spectrum's main mass-to-charge ratio range is used to quickly locate potentially matching subsets, and further comparison is performed within these subsets; a hash algorithm is used to convert key features of the mass spectra in the spectral database into hash values, and the hash values of the key features corresponding to the sample mass spectrum are compared to quickly eliminate mass spectra with significant differences; the remaining spectra after initial hash value screening are precisely compared with the sample mass spectrum to determine the sample composition; the hash value calculation of mass spectrum features includes ion peak mass-to-charge ratio, peak position intensity, and relative abundance. The hash value calculation steps are as follows: Ion peak mass-to-charge ratio, peak position intensity, and relative abundance are extracted from the mass spectrum as key features; the MD5 hash algorithm is used, inputting the mass spectrum features to generate a fixed-length hash value; like Figure 6 As shown, during the mass spectrum comparison process, the spectral database is first constructed and searched based on the mass-to-charge ratio range of the substances. The spectral database is divided into different spectral library subsets. Simultaneously, a hash algorithm is used to convert the key features of the mass spectra in the spectral database into hash values, resulting in the corresponding hash value for each substance's mass spectrum. Using the main mass-to-charge ratio range of the sample mass spectrum, the sample mass spectrum is quickly located to a specific spectral library subset, thereby reducing the computational load of the comparison process. At the same time, the hash values of the key features of the sample mass spectrum are calculated, and by comparing them with the hash values in the spectral database, mass spectra with large differences are quickly eliminated, further narrowing the comparison range. The remaining spectra after the initial hash value screening are precisely compared with the sample mass spectrum to determine the composition of the sample.
[0025] The data visualization module visualizes the data results after quantitative analysis of samples, including line graphs, pie charts, scatter plots, and geographic distribution maps. Line graphs can show the trend of pesticide residue changes in the same sample over time and analyze the dynamic changes of pesticide residues. Pie charts can show the proportion of different types of pesticide residues within the same sample. Scatter plots can show the relative relationship between residue levels and food indicators, and preliminarily determine whether there is a linear or non-linear relationship between the two variables. Geographic distribution maps combine sample analysis data with geographic information to present the regional distribution characteristics of pesticide residues. like Figure 5As shown, after quantitative and qualitative analysis, the composition and content of pesticide residues in food samples can be obtained. The data visualization module can be used to visualize the analysis results and input them into the chart output module for display. For example, a line chart can be used to show the trend of pesticide residue changes in the same sample over time, which can analyze the dynamic changes of pesticide residues. A pie chart can show the proportion of different types of pesticide residues within the same sample. A scatter plot can be used to determine the relationship between pesticide residue levels and food index data, and to determine whether there is a linear or non-linear relationship. Combining the analysis results with geographical information can generate the regional distribution characteristics of pesticide residue levels in food, providing data support for further research on pesticide residues in food.
[0026] Working principle: The central computer can control the entire system. After pretreatment of the food sample to reduce the interference of impurities in the sample on the analysis results, the pretreated sample is input into the sample detection module. The mass spectrum of the sample is obtained by gas chromatography-mass spectrometry. The obtained sample mass spectrum is compared with the spectral database to determine the components of pesticide residues in the food sample. The content of pesticide residues is calculated by using a standard curve. The obtained data is input into the data visualization module for chart conversion. In the chart output module, the sample analysis data is displayed using various forms of charts.
[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.
Claims
1. A high-precision food pesticide residue detection system, comprising a central computer, a sample acquisition and preprocessing module, a sample detection module, a data processing module, a data visualization module, and a chart output module, characterized in that: The central computer serves as the system's control center. It is interconnected with the sample acquisition and preprocessing module, the sample detection module, the data processing module, and the data visualization module. The sample acquisition and preprocessing module is connected to the sample detection module, which is connected to the data processing module. The data processing module is connected to the data visualization module, which is connected to the chart output module.
2. The high-precision food pesticide residue detection system according to claim 1, characterized in that: The sample collection and pretreatment module collects and preprocesses the analytes for pesticide residue detection in food. The pretreatment of the food is categorized by its form: solid foods, fruits and vegetables, liquid foods, and semi-solid or paste-like foods. The pretreatment of solid foods includes grinding; the ground solid food powder is extracted using acetonitrile, acetone, or a mixture thereof; the extracted solid food sample solution is then subjected to solid-phase extraction to remove macromolecular impurities, yielding the analytical sample of the solid food. The pretreatment of fruits and vegetables includes homogenization; the homogenized sample is then... The food sample solution is extracted with acetonitrile, and then subjected to solid-phase extraction to remove impurities such as organic acids and sugars, resulting in an analytical sample of fruit and vegetable products. The pretreatment of liquid foods includes dilution, followed by solvent extraction. The extracted liquid food sample solution is then subjected to solid-phase extraction to remove impurities such as fats and proteins, resulting in an analytical sample of liquid foods. The pretreatment of semi-solid or paste-like foods includes dissolution, followed by solid-phase extraction with an extractant to remove impurities, resulting in an analytical sample of semi-solid or paste-like foods.
3. The high-precision food pesticide residue detection system according to claim 1, characterized in that: The sample detection module receives the analytical sample processed by the sample acquisition and preprocessing module, and analyzes the analytical sample. The analytical sample analysis process uses gas chromatography-mass spectrometry to obtain the sample mass spectrum.
4. The high-precision food pesticide residue detection system according to claim 1, characterized in that: The data processing module receives the sample mass spectrum obtained from the sample detection module, processes and analyzes the sample mass spectrum, and performs qualitative and quantitative analysis of the pesticide components in the sample. The data processing module includes a mass spectrum preprocessing section, a mass spectrum analysis section, a sample qualitative analysis section, and a sample quantitative analysis section.
5. The high-precision food pesticide residue detection system according to claim 4, characterized in that: The mass spectrum preprocessing section includes noise removal and baseline calibration. The noise removal uses a moving average method to smooth the signal curve of the sample mass spectrum, making the peak shape clearer. The baseline calibration eliminates or reduces background signal interference in the sample mass spectrum. The mass spectrum analysis section includes ion peak identification, which identifies and analyzes the peak position and intensity of the mass spectrum. The sample qualitative analysis section compares the identified sample mass spectrum with the mass spectrum of known compounds to qualitatively analyze the composition of the sample. The sample quantitative analysis uses a standard curve and external standard method for calculation. The calculation process is as follows: Standard curve plotting: A series of standard compound solutions of known concentrations are prepared and mass spectrometry is performed under the same mass spectrometry conditions to obtain the relationship curve between the mass spectrum peak intensity and concentration of the standard compounds, and the curve equation is fitted. The peak areas in the mass spectrum of the sample are calculated, and the results are substituted into the standard curve equation to calculate the concentration of the substance in the sample.
6. The high-precision food pesticide residue detection system according to claim 5, characterized in that: The qualitative analysis of the sample involves comparing the sample mass spectrum with a database of known compounds to qualitatively analyze the sample composition. This comparison process is as follows: A spectral database is constructed and searched, and the database is divided into different subsets based on the mass-to-charge ratio range of the substances. The sample mass spectrum's main mass-to-charge ratio range is used to quickly locate potentially matching subsets, and further comparisons are performed within these subsets. A hash algorithm is used to convert key features of the mass spectra in the spectral database into hash values, and the hash values of the key features corresponding to the sample mass spectrum are compared to quickly eliminate mass spectra with significant differences. The remaining spectra after initial hash value screening are precisely compared with the sample mass spectrum to determine the sample composition. The hash value calculation of the mass spectrum features includes the ion peak mass-to-charge ratio, peak position intensity, and relative abundance. The hash value calculation steps are as follows: The ion peak mass-to-charge ratio, peak position intensity, and relative abundance in the mass spectrum are extracted as key features; the MD5 hash algorithm is used to input the mass spectrum features and generate a fixed-length hash value.
7. The high-precision food pesticide residue detection system according to claim 1, characterized in that: The data visualization module visualizes the data results after quantitative analysis of the samples, including line graphs, pie charts, scatter plots, and geographical distribution maps. The line graphs can show the changing trend of pesticide residues in the same sample over time and analyze the dynamic changes of pesticide residues. The pie charts can show the proportion of different types of pesticide residues within the same sample. The scatter plot can show the relative relationship between residue levels and food indicators, and make a preliminary judgment on whether there is a linear or non-linear relationship between the two variables; The geographical distribution map combines sample analysis data with geographical information to present the regional distribution characteristics of pesticide residues.