Food pesticide residue detection system and method

Abstract

The invention discloses a food pesticide residue detection system and method, and relates to the technical field of pesticide detection.The method comprises the following steps that fluorescent probes are added into a simulated first mixed solution and a simulated second mixed solution respectively, and a first indicating solution and a second indicating solution are obtained; screening the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value according to the ideal second variable, matching the pesticide residue according to the third fluorescence intensity to obtain a second excitation wavelength, and establishing a first mapping relation, a second mapping relation and a third mapping relation. According to the method, the to-be-detected sample is pretreated, the detection sensitivity and accuracy are improved, proper simulation temperature and reaction duration are set according to product information to optimize reaction conditions, obtain fluorescence intensity and analyze the reaction effect, the detection sensitivity of pesticide residues is improved through the use of the fluorescent probe, and the detection time is shortened. The detection result is more accurate through multi-step parameter optimization and mapping relation establishment.

Description

Technical Field

[0001] The present invention relates to the technical field of pesticide detection, and in particular to a food pesticide residue detection system and method. Background Art

[0002] In recent years, food pesticide residue detection technology is developing in the direction of high sensitivity, high throughput and intelligence, using big data and artificial intelligence technology for data analysis, improving the accuracy and reliability of detection results, promoting the establishment of intelligent monitoring systems, and using spectral detection technology, such as near-infrared spectroscopy and Raman spectroscopy, to achieve non-destructive detection and quickly obtain results.

[0003] At present, a Chinese invention patent with publication number CN117688452B discloses a method and system for detecting and warning of pesticide residues in food based on a neural network. The method selects median food historical data as benchmark data to train the model, detects the food to be tested through the food pesticide residue detection model, evaluates the status of food pesticide residues through food pesticide residue detection data, and issues warnings for food pesticide residues. However, the related technology does not match reaction parameters under multiple conditions, lacks adaptability and flexibility in detection, does not hierarchically set experimental conditions according to the method of controlling variables, lacks comprehensiveness and accuracy of the experiment, and has certain limitations. Summary of the invention

[0004] The technical problem solved by the present invention is that the related technology does not match the reaction parameters under various conditions, lacks the adaptability and flexibility of detection, does not set the experimental conditions in a hierarchical manner according to the method of controlling variables, lacks the comprehensiveness and accuracy of the test, and has certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, a method for detecting pesticide residues in food comprises the following steps: Step S100, obtaining a packaging code, identifying the packaging code, obtaining product information, screening samples to be tested according to the product information, pre-processing the samples to be tested, dividing the pre-processed samples to be tested, and obtaining sub-samples; Step S200, setting the first simulation parameter to the first quantitative and the first variable to obtain a first mixed solution, setting the second quantitative and the second variable to obtain a second mixed solution, matching the simulation temperature and the reaction time according to the product information, simulating the first mixed solution and the second mixed solution, and adding the fluorescent probe to the simulated first mixed solution and the simulated second mixed solution, respectively, to obtain a first indicator solution and a second indicator solution; Step S300, matching the corresponding fluorescence spectrum according to the product information, selecting the first excitation wavelength according to the fluorescence spectrum, sending the first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtaining the first fluorescence intensity and the second fluorescence intensity, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable; Step S400, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, setting the first excitation wavelength as the irradiation wavelength, continuously changing the irradiation wavelength, and continuously acquiring the corresponding fluorescence intensity, acquiring the fluorescence intensity with the maximum value, which is recorded as the third fluorescence intensity, matching the pesticide residue according to the third fluorescence intensity, and sending the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; Step S500, establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

[0006] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, step S100 includes the following sub-steps: Step S101, obtaining a packaging surface image, configuring a packaging code on the packaging surface image, connecting to a network, identifying the packaging code, and obtaining product information, the product information including product batch, product name, and product weight, the product batch being represented by the product storage year and product storage month; Step S102, screening samples to be tested according to product information; Step S103, pre-processing the test sample, the pre-processing includes crushing and screening, the crushing is physical crushing, and the screening is physical screening of impurities with diameters greater than the first value; Step S104, dividing the pre-processed sample to be detected to obtain sub-samples, wherein the method for dividing the pre-processed sample to be detected includes: obtaining the weight of the sample to be detected, setting the first value as a weight gradient, dividing the weight of the sample to be detected by taking the weight gradient and its multiple as the weight standard to obtain sub-samples, wherein the weight gradient is less than the weight of the sample to be detected; Step S105, obtaining the weight of each sub-sample, there are N sub-samples of the same weight.

[0007] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, the screening method of the sample to be detected comprises: Obtain product batch and product weight, classify products with the same product batch and weight to obtain product categories, count the number of products in each product category, traverse the number of products, select the product number with the largest value, set the product category corresponding to the product number with the largest value as the sample to be tested, and set the corresponding product name and product batch as the label of the sample to be tested.

[0008] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, step S200 includes the following sub-steps: Step S201, setting first simulation parameters, wherein the first simulation parameters include solvent weight, solvent polarity, solvent viscosity, solvent pH value and subsample weight; Step S202, setting the solvent weight, solvent polarity, solvent viscosity and solvent pH value as a first quantitative value, setting the sub-sample weight as a first variable, and obtaining a first mixed solution; Step S203, setting the sub-sample weight and the solvent weight as the second quantitative, setting the solvent polarity, solvent viscosity and solvent pH value as the second variable, to obtain a second mixed solution, wherein the second mixed solution includes a solvent polarity variable second mixed solution, a solvent viscosity variable second mixed solution and a solvent pH value variable second mixed solution; Step S204, obtaining a product name, calling a test database, inputting the product name into the test database, matching a simulation temperature and a reaction time corresponding to the product name, and simulating the first mixed solution and the second mixed solution; Step S205 , adding fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain a first indicator solution and a second indicator solution.

[0009] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, the method for mixing the second mixed solution with a solvent polarity variable, the second mixed solution with a solvent viscosity variable and the second mixed solution with a solvent pH value variable comprises: The sub-sample weight, solvent weight, solvent viscosity and solvent pH value are set as the second quantitative value, and the solvent polarity is set as the second variable to obtain a solvent polarity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent pH value are set as the second quantitative value, and the solvent viscosity is set as the second variable to obtain a solvent viscosity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent viscosity are set as the second quantitative value, and the solvent pH value is set as the second variable to obtain a second mixed solution with solvent pH value variable.

[0010] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, step S300 includes the following sub-steps: Step S301, obtaining a product name, calling a test database, inputting the product name into the test database, matching a corresponding fluorescence spectrum, identifying an absorption characteristic peak of the fluorescence spectrum, and setting an excitation wavelength corresponding to a maximum value of the absorption characteristic peak as a first excitation wavelength. The absorption characteristic peak identification method includes: Acquire an absorption spectrum, perform filtering and graying on the absorption spectrum, extract a shape feature of the absorption spectrum after filtering and graying, record it as a first feature, acquire a standard absorption peak image, extract a shape feature of the standard absorption peak image, record it as a second feature, calculate the similarity between the first feature and the second feature according to a cosine similarity formula, traverse the similarities, select the similarity with the largest value, obtain a portion of the absorption spectrum corresponding to the similarity with the largest value, and set it as an absorption characteristic peak; Step S302, sending a first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, and acquiring a first fluorescence intensity corresponding to the first indicator liquid and a second fluorescence intensity corresponding to the second indicator liquid; Step S303, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable.

[0011] As a preferred solution of the method for detecting pesticide residues in food of the present invention, the screening method of the ideal first variable and the ideal second variable comprises: obtaining the first fluorescence intensity and the second fluorescence intensity; Traversing the first fluorescence intensities, sorting the first fluorescence intensities in descending order, obtaining the first fluorescence intensity with the largest value, and selecting the first variable corresponding to the first fluorescence intensity with the largest value as the ideal first variable; Traversing the second fluorescence intensities, sorting the second fluorescence intensities in descending order, obtaining the second fluorescence intensity with the largest value, and selecting the second variable corresponding to the second fluorescence intensity with the largest value as the ideal second variable; The calculation method of the ideal sample concentration includes: Obtaining a subsample weight and a solvent weight corresponding to the ideal first variable, calculating a ratio of the subsample weight to the solvent weight, and setting the ratio of the subsample weight to the solvent weight as an ideal sample concentration; The screening method of the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value comprises: The solvent polarity, solvent viscosity and solvent pH value corresponding to the ideal second variable are obtained and set as the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value respectively.

[0012] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, step S400 includes the following sub-steps: Step S401, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameters, and setting the first excitation wavelength as the irradiation wavelength; Step S402, setting the second value as the change amount, continuously changing the illumination wavelength, wherein the continuous change is represented by a continuous increase or a continuous decrease, and continuously acquiring the corresponding fluorescence intensity; Step S403, traversing the continuously acquired fluorescence intensities, sorting the continuously acquired fluorescence intensities in descending order, obtaining the fluorescence intensity with the largest value, and recording it as the third fluorescence intensity; Step S404, calling a pesticide residue database, inputting the third fluorescence intensity into the pesticide residue database, matching the pesticide residue corresponding to the third fluorescence intensity, and sending a second laser control instruction according to the third fluorescence intensity to obtain a second excitation wavelength, wherein the setting method of the second laser control instruction includes: Obtain the change amount corresponding to the third fluorescence intensity, recorded as the first change amount, calculate the sum of the first change amount and the illumination wavelength, recorded as the first sum value, set the first sum value as the second excitation wavelength, and adjust the illumination wavelength according to the second excitation wavelength until the illumination wavelength is equal to the second excitation wavelength, then stop adjusting the illumination wavelength.

[0013] As a preferred embodiment of the method for detecting pesticide residues in food according to the present invention, step S500 includes the following sub-steps: Step S501, establishing a first mapping relationship between product name and ideal sample concentration, ideal solvent polarity, ideal solvent viscosity, and ideal solvent pH value; Step S502, establishing a second mapping relationship among an ideal sample concentration, an ideal solvent polarity, an ideal solvent viscosity, an ideal solvent pH value, and a second excitation wavelength; Step S503, establishing a third mapping relationship between the second excitation wavelength and the third fluorescence intensity; Step S504, obtaining the current product name, obtaining the corresponding current ideal sample concentration, current ideal solvent polarity, current ideal solvent viscosity and current ideal solvent pH value according to the first mapping relationship, obtaining the current second excitation wavelength according to the second mapping relationship, and obtaining the current third fluorescence intensity according to the third mapping relationship; Step S505, calling a pesticide residue database, inputting the current third fluorescence intensity into the pesticide residue database, and matching the pesticide residue corresponding to the current third fluorescence intensity.

[0014] In a second aspect, a food pesticide residue detection system includes a collection module, an analysis module and a construction module; The acquisition module is used to obtain the packaging code, identify the packaging code, obtain product information, screen the samples to be detected according to the product information, pre-treat the samples to be detected, divide the pre-treated samples to be detected to obtain sub-samples, set the first simulation parameter, set it to the first quantitative and the first variable, obtain the first mixed solution, set the second quantitative and the second variable, obtain the second mixed solution, match the simulation temperature and the reaction time according to the product information, simulate the first mixed solution and the second mixed solution, and add fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain the first indicator solution and the second indicator solution; The analysis module is used to match the corresponding fluorescence spectrum according to the product information, select the first excitation wavelength according to the fluorescence spectrum, send the first laser control instruction, irradiate the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtain the first fluorescence intensity and the second fluorescence intensity, screen the ideal first variable according to the first fluorescence intensity, screen the ideal second variable according to the second fluorescence intensity, calculate the ideal sample concentration according to the ideal first variable, screen the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value according to the ideal second variable, set the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, set the first excitation wavelength as the irradiation wavelength, continuously change the irradiation wavelength, and continuously obtain the corresponding fluorescence intensity, obtain the fluorescence intensity with the maximum value, record it as the third fluorescence intensity, match the pesticide residue according to the third fluorescence intensity, and send the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; The construction module is used to establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, to establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and to establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

[0015] The beneficial effects of the present invention are as follows: by pretreating the sample to be detected to improve the sensitivity and accuracy of the detection, the pretreated sample is divided into multiple sub-samples to facilitate multiple experiments or detection under different conditions, the appropriate simulation temperature and reaction time are set according to the product information to optimize the reaction conditions, the indicator liquid is irradiated with a laser to obtain the fluorescence intensity, the reaction effect is analyzed, the experimental variables are adjusted according to the fluorescence intensity feedback, and the ideal sample concentration is calculated. The use of fluorescent probes improves the detection sensitivity of pesticide residues, and multi-step parameter optimization and mapping relationship establishment make the detection results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the basic flow of a method for detecting pesticide residues in food provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0018] Example, see Figure 1 , as an embodiment of the present invention, provides a method for detecting pesticide residues in food, comprising the following steps: Step S100, obtaining a packaging code, identifying the packaging code, obtaining product information, screening samples to be tested according to the product information, pre-processing the samples to be tested, dividing the pre-processed samples to be tested, and obtaining sub-samples; Step S200, setting the first simulation parameter to the first quantitative and the first variable to obtain a first mixed solution, setting the second quantitative and the second variable to obtain a second mixed solution, matching the simulation temperature and the reaction time according to the product information, simulating the first mixed solution and the second mixed solution, and adding the fluorescent probe to the simulated first mixed solution and the simulated second mixed solution, respectively, to obtain a first indicator solution and a second indicator solution; Step S300, matching the corresponding fluorescence spectrum according to the product information, selecting the first excitation wavelength according to the fluorescence spectrum, sending the first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtaining the first fluorescence intensity and the second fluorescence intensity, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable; Step S400, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, setting the first excitation wavelength as the irradiation wavelength, continuously changing the irradiation wavelength, and continuously acquiring the corresponding fluorescence intensity, acquiring the fluorescence intensity with the maximum value, which is recorded as the third fluorescence intensity, matching the pesticide residue according to the third fluorescence intensity, and sending the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; Step S500, establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

[0019] The present invention pre-treats the sample to be detected to improve the sensitivity and accuracy of the detection, divides the pre-treated sample into multiple sub-samples to facilitate multiple experiments or detection under different conditions, sets appropriate simulation temperature and reaction time according to product information to optimize the reaction conditions, utilizes laser to irradiate indicator liquid, obtains fluorescence intensity, analyzes the reaction effect, adjusts experimental variables according to fluorescence intensity feedback, and calculates the ideal sample concentration. The use of fluorescent probes improves the detection sensitivity of pesticide residues, and multi-step parameter optimization and mapping relationship establishment make the detection results more accurate.

[0020] The step S100 includes the following sub-steps: Step S101, obtaining a packaging surface image, configuring a packaging code on the packaging surface image, connecting to a network, identifying the packaging code, and obtaining product information, the product information including product batch, product name, and product weight, the product batch being represented by the product storage year and product storage month; Step S102, screening samples to be tested according to product information; Step S103, pre-processing the test sample, the pre-processing includes crushing and screening, the crushing is physical crushing, and the screening is physical screening of impurities with diameters greater than the first value; Step S104, dividing the pre-processed sample to be detected to obtain sub-samples, wherein the method for dividing the pre-processed sample to be detected includes: obtaining the weight of the sample to be detected, setting the first value as a weight gradient, dividing the weight of the sample to be detected by taking the weight gradient and its multiple as the weight standard to obtain sub-samples, wherein the weight gradient is less than the weight of the sample to be detected; Step S105, obtaining the weight of each sub-sample, there are N sub-samples of the same weight.

[0021] The screening method of the sample to be detected comprises: Obtain product batch and product weight, classify products with the same product batch and weight to obtain product categories, count the number of products in each product category, traverse the number of products, select the product number with the largest value, set the product category corresponding to the product number with the largest value as the sample to be tested, and set the corresponding product name and product batch as the label of the sample to be tested.

[0022] In specific implementation, through accurate identification of product information and meticulous preprocessing, interference in pesticide residue detection was reduced and detection accuracy was improved. The acquisition of product batch information provided a basis for quality control and product traceability, and helped to take timely measures when problems arose. By dividing sub-samples, multiple tests of different samples helped to gain a more comprehensive understanding of the pesticide residues in the samples and increased the representativeness of the data. The automated image recognition and weight measurement processes provided the possibility for future automated testing and improved work efficiency.

[0023] The step S200 includes the following sub-steps: Step S201, setting first simulation parameters, wherein the first simulation parameters include solvent weight, solvent polarity, solvent viscosity, solvent pH value and subsample weight; Step S202, setting the solvent weight, solvent polarity, solvent viscosity and solvent pH value as a first quantitative value, setting the sub-sample weight as a first variable, and obtaining a first mixed solution; Step S203, setting the sub-sample weight and the solvent weight as the second quantitative, setting the solvent polarity, solvent viscosity and solvent pH value as the second variable, to obtain a second mixed solution, wherein the second mixed solution includes a solvent polarity variable second mixed solution, a solvent viscosity variable second mixed solution and a solvent pH value variable second mixed solution; Step S204, obtaining a product name, calling a test database, inputting the product name into the test database, matching a simulation temperature and a reaction time corresponding to the product name, and simulating the first mixed solution and the second mixed solution; Step S205 , adding fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain a first indicator solution and a second indicator solution.

[0024] In specific implementation, by setting precise simulation parameters, the sensitivity of detection for pesticide residues can be significantly improved, ensuring that even trace amounts of residues can be detected. By setting different solvent parameters, various combinations of experiments can be carried out to evaluate their impact on the test results, providing data support for determining the optimal extraction conditions. By combining the test database with product information, the optimal experimental conditions can be intelligently selected to reduce human intervention and possible errors. By effectively adding fluorescent probes and optimizing detection methods, the efficiency of the detection process can be improved, thereby accelerating the product detection cycle.

[0025] The method for mixing the solvent polarity variable second mixed liquid, the solvent viscosity variable second mixed liquid and the solvent pH value variable second mixed liquid comprises: The sub-sample weight, solvent weight, solvent viscosity and solvent pH value are set as the second quantitative value, and the solvent polarity is set as the second variable to obtain a solvent polarity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent pH value are set as the second quantitative value, and the solvent viscosity is set as the second variable to obtain a solvent viscosity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent viscosity are set as the second quantitative value, and the solvent pH value is set as the second variable to obtain a second mixed solution with solvent pH value variable.

[0026] In specific implementation, by changing the polarity, viscosity and pH value of the solvent, their effects on the pesticide residue detection results are systematically evaluated, and the optimal solvent conditions are determined to provide a basis for subsequent analysis and ensure the efficiency and accuracy of pesticide extraction.

[0027] The step S300 includes the following sub-steps: Step S301, obtaining a product name, calling a test database, inputting the product name into the test database, matching a corresponding fluorescence spectrum, identifying an absorption characteristic peak of the fluorescence spectrum, and setting an excitation wavelength corresponding to a maximum value of the absorption characteristic peak as a first excitation wavelength. The absorption characteristic peak identification method includes: Acquire an absorption spectrum, perform filtering and graying on the absorption spectrum, extract a shape feature of the absorption spectrum after filtering and graying, record it as a first feature, acquire a standard absorption peak image, extract a shape feature of the standard absorption peak image, record it as a second feature, calculate the similarity between the first feature and the second feature according to a cosine similarity formula, traverse the similarities, select the similarity with the largest value, obtain a portion of the absorption spectrum corresponding to the similarity with the largest value, and set it as an absorption characteristic peak; Step S302, sending a first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, and acquiring a first fluorescence intensity corresponding to the first indicator liquid and a second fluorescence intensity corresponding to the second indicator liquid; Step S303, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable.

[0028] In specific implementation, by accurately matching the fluorescence spectrum and the characteristic absorption peak, it is possible to ensure that the wavelength of the excitation light accurately corresponds to the absorption characteristics of the target substance, thereby greatly improving the sensitivity and accuracy of the detection. Through laser regulation and fluorescence intensity measurement, the optimal experimental conditions are automatically screened out without manual adjustment, saving time and experimental costs. The precise measurement of fluorescence intensity combined with variable screening helps to ensure the consistency of experimental results and ensure that experiments in different batches or under different conditions can obtain reliable results.

[0029] The screening method of the ideal first variable and the ideal second variable comprises: obtaining a first fluorescence intensity and a second fluorescence intensity; Traversing the first fluorescence intensities, sorting the first fluorescence intensities in descending order, obtaining the first fluorescence intensity with the largest value, and selecting the first variable corresponding to the first fluorescence intensity with the largest value as the ideal first variable; Traversing the second fluorescence intensities, sorting the second fluorescence intensities in descending order, obtaining the second fluorescence intensity with the largest value, and selecting the second variable corresponding to the second fluorescence intensity with the largest value as the ideal second variable; The calculation method of the ideal sample concentration includes: Obtaining a subsample weight and a solvent weight corresponding to the ideal first variable, calculating a ratio of the subsample weight to the solvent weight, and setting the ratio of the subsample weight to the solvent weight as an ideal sample concentration; The screening method of the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value comprises: The solvent polarity, solvent viscosity and solvent pH value corresponding to the ideal second variable are obtained and set as the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value respectively.

[0030] In specific implementation, by selecting the variable corresponding to the maximum fluorescence intensity, the strongest output of the fluorescence signal in the experiment is ensured, the sensitivity of the detection is improved, and the accurately calculated sample concentration helps to improve the reliability and accuracy of the experimental results and avoid interference caused by excessively high or low concentrations. The ideal solvent polarity, viscosity and pH value help to improve the efficiency and selectivity of the reaction, making the analysis results more credible.

[0031] The step S400 includes the following sub-steps: Step S401, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameters, and setting the first excitation wavelength as the irradiation wavelength; Step S402, setting the second value as the change amount, continuously changing the illumination wavelength, wherein the continuous change is represented by a continuous increase or a continuous decrease, and continuously acquiring the corresponding fluorescence intensity; Step S403, traversing the continuously acquired fluorescence intensities, sorting the continuously acquired fluorescence intensities in descending order, obtaining the fluorescence intensity with the largest value, and recording it as the third fluorescence intensity; Step S404, calling a pesticide residue database, inputting the third fluorescence intensity into the pesticide residue database, matching the pesticide residue corresponding to the third fluorescence intensity, and sending a second laser control instruction according to the third fluorescence intensity to obtain a second excitation wavelength, wherein the setting method of the second laser control instruction includes: Obtain the change amount corresponding to the third fluorescence intensity, recorded as the first change amount, calculate the sum of the first change amount and the illumination wavelength, recorded as the first sum value, set the first sum value as the second excitation wavelength, and adjust the illumination wavelength according to the second excitation wavelength until the illumination wavelength is equal to the second excitation wavelength, then stop adjusting the illumination wavelength.

[0032] In specific implementation, the sensitivity of detection is significantly improved by continuously adjusting the irradiation wavelength and maximizing the fluorescence intensity. By matching with the database, the pesticide residue can be quickly and accurately obtained, providing reliable data support for analysis. By dynamically adjusting the irradiation wavelength, the optimal state of laser irradiation is ensured, thereby improving the accuracy of the test results. The automated parameter setting and control process reduces manual intervention and improves the efficiency and reliability of the experiment.

[0033] The step S500 includes the following sub-steps: Step S501, establishing a first mapping relationship between product name and ideal sample concentration, ideal solvent polarity, ideal solvent viscosity, and ideal solvent pH value; Step S502, establishing a second mapping relationship among an ideal sample concentration, an ideal solvent polarity, an ideal solvent viscosity, an ideal solvent pH value, and a second excitation wavelength; Step S503, establishing a third mapping relationship between the second excitation wavelength and the third fluorescence intensity; Step S504, obtaining the current product name, obtaining the corresponding current ideal sample concentration, current ideal solvent polarity, current ideal solvent viscosity and current ideal solvent pH value according to the first mapping relationship, obtaining the current second excitation wavelength according to the second mapping relationship, and obtaining the current third fluorescence intensity according to the third mapping relationship; Step S505, calling a pesticide residue database, inputting the current third fluorescence intensity into the pesticide residue database, and matching the pesticide residue corresponding to the current third fluorescence intensity.

[0034] In specific implementation, by establishing a mapping relationship, various parameters required for the experiment can be quickly obtained, reducing experimental preparation time and improving work efficiency. The establishment of a mapping relationship ensures the accurate response of different products under specific conditions and improves the accuracy of fluorescence detection. It is applicable to a variety of products and experimental conditions, has good versatility, and is suitable for research and application in different fields.

[0035] A food pesticide residue detection system, the system is used to implement a food pesticide residue detection method, including a collection module, an analysis module and a construction module; The acquisition module is used to obtain the packaging code, identify the packaging code, obtain product information, screen the samples to be detected according to the product information, pre-treat the samples to be detected, divide the pre-treated samples to be detected to obtain sub-samples, set the first simulation parameter, set it to the first quantitative and the first variable, obtain the first mixed solution, set the second quantitative and the second variable, obtain the second mixed solution, match the simulation temperature and the reaction time according to the product information, simulate the first mixed solution and the second mixed solution, and add fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain the first indicator solution and the second indicator solution; The analysis module is used to match the corresponding fluorescence spectrum according to the product information, select the first excitation wavelength according to the fluorescence spectrum, send the first laser control instruction, irradiate the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtain the first fluorescence intensity and the second fluorescence intensity, screen the ideal first variable according to the first fluorescence intensity, screen the ideal second variable according to the second fluorescence intensity, calculate the ideal sample concentration according to the ideal first variable, screen the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value according to the ideal second variable, set the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, set the first excitation wavelength as the irradiation wavelength, continuously change the irradiation wavelength, and continuously obtain the corresponding fluorescence intensity, obtain the fluorescence intensity with the maximum value, record it as the third fluorescence intensity, match the pesticide residue according to the third fluorescence intensity, and send the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; The construction module is used to establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, to establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and to establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

[0036] The present invention pre-treats the sample to be detected to improve the sensitivity and accuracy of the detection, divides the pre-treated sample into multiple sub-samples to facilitate multiple experiments or detection under different conditions, sets appropriate simulation temperature and reaction time according to product information to optimize the reaction conditions, utilizes laser to irradiate indicator liquid, obtains fluorescence intensity, analyzes the reaction effect, adjusts experimental variables according to fluorescence intensity feedback, and calculates the ideal sample concentration. The use of fluorescent probes improves the detection sensitivity of pesticide residues, and multi-step parameter optimization and mapping relationship establishment make the detection results more accurate.

[0037] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0038] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting pesticide residues in food, characterized in that: The following steps are involved: Step S100, obtaining a packaging code, identifying the packaging code, obtaining product information, screening samples to be tested according to the product information, pre-processing the samples to be tested, dividing the pre-processed samples to be tested, and obtaining sub-samples; Step S200, setting the first simulation parameter to the first quantitative and the first variable to obtain a first mixed solution, setting the second quantitative and the second variable to obtain a second mixed solution, matching the simulation temperature and the reaction time according to the product information, simulating the first mixed solution and the second mixed solution, and adding the fluorescent probe to the simulated first mixed solution and the simulated second mixed solution, respectively, to obtain a first indicator solution and a second indicator solution; Step S300, matching the corresponding fluorescence spectrum according to the product information, selecting the first excitation wavelength according to the fluorescence spectrum, sending the first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtaining the first fluorescence intensity and the second fluorescence intensity, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable; Step S400, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, setting the first excitation wavelength as the irradiation wavelength, continuously changing the irradiation wavelength, and continuously acquiring the corresponding fluorescence intensity, acquiring the fluorescence intensity with the maximum value, which is recorded as the third fluorescence intensity, matching the pesticide residue according to the third fluorescence intensity, and sending the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; Step S500, establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

2. A method for detecting pesticide residues in food according to claim 1, characterized in that: The step S100 includes the following sub-steps: Step S101, obtaining a packaging surface image, configuring a packaging code on the packaging surface image, connecting to a network, identifying the packaging code, and obtaining product information, the product information including product batch, product name, and product weight, the product batch being represented by the product storage year and product storage month; Step S102, screening samples to be tested according to product information; Step S103, pre-processing the test sample, the pre-processing includes crushing and screening, the crushing is physical crushing, and the screening is physical screening of impurities with diameters greater than the first value; Step S104, dividing the pre-processed sample to be detected to obtain sub-samples, wherein the method for dividing the pre-processed sample to be detected includes: obtaining the weight of the sample to be detected, setting the first value as a weight gradient, dividing the weight of the sample to be detected by taking the weight gradient and its multiple as the weight standard to obtain sub-samples, wherein the weight gradient is less than the weight of the sample to be detected; Step S105, obtaining the weight of each sub-sample, there are N sub-samples of the same weight.

3. A method for detecting pesticide residues in food according to claim 2, characterized in that: The screening method of the sample to be detected comprises: Obtain product batch and product weight, classify products with the same product batch and weight to obtain product categories, count the number of products in each product category, traverse the number of products, select the product number with the largest value, set the product category corresponding to the product number with the largest value as the sample to be tested, and set the corresponding product name and product batch as the label of the sample to be tested.

4. A method for detecting pesticide residues in food according to claim 1, characterized in that: The step S200 includes the following sub-steps: Step S201, setting first simulation parameters, wherein the first simulation parameters include solvent weight, solvent polarity, solvent viscosity, solvent pH value and subsample weight; Step S202, setting the solvent weight, solvent polarity, solvent viscosity and solvent pH value as a first quantitative value, setting the sub-sample weight as a first variable, and obtaining a first mixed solution; Step S203, setting the sub-sample weight and the solvent weight as the second quantitative, setting the solvent polarity, solvent viscosity and solvent pH value as the second variable, to obtain a second mixed solution, wherein the second mixed solution includes a solvent polarity variable second mixed solution, a solvent viscosity variable second mixed solution and a solvent pH value variable second mixed solution; Step S204, obtaining a product name, calling a test database, inputting the product name into the test database, matching a simulation temperature and a reaction time corresponding to the product name, and simulating the first mixed solution and the second mixed solution; Step S205 , adding fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain a first indicator solution and a second indicator solution.

5. A method for detecting pesticide residues in food according to claim 4, characterized in that: The method for mixing the solvent polarity variable second mixed liquid, the solvent viscosity variable second mixed liquid and the solvent pH value variable second mixed liquid comprises: The sub-sample weight, solvent weight, solvent viscosity and solvent pH value are set as the second quantitative value, and the solvent polarity is set as the second variable to obtain a solvent polarity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent pH value are set as the second quantitative value, and the solvent viscosity is set as the second variable to obtain a solvent viscosity variable second mixed solution; The sub-sample weight, solvent weight, solvent polarity and solvent viscosity are set as the second quantitative value, and the solvent pH value is set as the second variable to obtain a second mixed solution with solvent pH value variable.

6. A method for detecting pesticide residues in food according to claim 1, characterized in that: The step S300 includes the following sub-steps: Step S301, obtaining a product name, calling a test database, inputting the product name into the test database, matching a corresponding fluorescence spectrum, identifying an absorption characteristic peak of the fluorescence spectrum, and setting an excitation wavelength corresponding to a maximum value of the absorption characteristic peak as a first excitation wavelength. The absorption characteristic peak identification method includes: Acquire an absorption spectrum, perform filtering and graying on the absorption spectrum, extract a shape feature of the absorption spectrum after filtering and graying, record it as a first feature, acquire a standard absorption peak image, extract a shape feature of the standard absorption peak image, record it as a second feature, calculate the similarity between the first feature and the second feature according to a cosine similarity formula, traverse the similarities, select the similarity with the largest value, obtain a portion of the absorption spectrum corresponding to the similarity with the largest value, and set it as an absorption characteristic peak; Step S302, sending a first laser control instruction, irradiating the first indicator liquid and the second indicator liquid according to the first laser control instruction, and acquiring a first fluorescence intensity corresponding to the first indicator liquid and a second fluorescence intensity corresponding to the second indicator liquid; Step S303, screening the ideal first variable according to the first fluorescence intensity, screening the ideal second variable according to the second fluorescence intensity, calculating the ideal sample concentration according to the ideal first variable, and screening the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value according to the ideal second variable.

7. A method for detecting pesticide residues in food according to claim 6, characterized in that: The screening method of the ideal first variable and the ideal second variable comprises: obtaining a first fluorescence intensity and a second fluorescence intensity; Traversing the first fluorescence intensities, sorting the first fluorescence intensities in descending order, obtaining the first fluorescence intensity with the largest value, and selecting the first variable corresponding to the first fluorescence intensity with the largest value as the ideal first variable; Traversing the second fluorescence intensities, sorting the second fluorescence intensities in descending order, obtaining the second fluorescence intensity with the largest value, and selecting the second variable corresponding to the second fluorescence intensity with the largest value as the ideal second variable; The calculation method of the ideal sample concentration includes: Obtaining a subsample weight and a solvent weight corresponding to the ideal first variable, calculating a ratio of the subsample weight to the solvent weight, and setting the ratio of the subsample weight to the solvent weight as an ideal sample concentration; The screening method of the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value comprises: The solvent polarity, solvent viscosity and solvent pH value corresponding to the ideal second variable are obtained and set as the ideal solvent polarity, ideal solvent viscosity and ideal solvent pH value respectively.

8. A method for detecting pesticide residues in food according to claim 1, characterized in that: The step S400 includes the following sub-steps: Step S401, setting the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameters, and setting the first excitation wavelength as the irradiation wavelength; Step S402, setting the second value as the change amount, continuously changing the illumination wavelength, wherein the continuous change is represented by a continuous increase or a continuous decrease, and continuously acquiring the corresponding fluorescence intensity; Step S403, traversing the continuously acquired fluorescence intensities, sorting the continuously acquired fluorescence intensities in descending order, obtaining the fluorescence intensity with the largest value, and recording it as the third fluorescence intensity; Step S404, calling a pesticide residue database, inputting the third fluorescence intensity into the pesticide residue database, matching the pesticide residue corresponding to the third fluorescence intensity, and sending a second laser control instruction according to the third fluorescence intensity to obtain a second excitation wavelength, wherein the setting method of the second laser control instruction includes: Obtain the change amount corresponding to the third fluorescence intensity, recorded as the first change amount, calculate the sum of the first change amount and the illumination wavelength, recorded as the first sum value, set the first sum value as the second excitation wavelength, and adjust the illumination wavelength according to the second excitation wavelength until the illumination wavelength is equal to the second excitation wavelength, then stop adjusting the illumination wavelength.

9. A method for detecting pesticide residues in food according to claim 1, characterized in that: The step S500 includes the following sub-steps: Step S501, establishing a first mapping relationship between product name and ideal sample concentration, ideal solvent polarity, ideal solvent viscosity, and ideal solvent pH value; Step S502, establishing a second mapping relationship among an ideal sample concentration, an ideal solvent polarity, an ideal solvent viscosity, an ideal solvent pH value, and a second excitation wavelength; Step S503, establishing a third mapping relationship between the second excitation wavelength and the third fluorescence intensity; Step S504, obtaining the current product name, obtaining the corresponding current ideal sample concentration, current ideal solvent polarity, current ideal solvent viscosity and current ideal solvent pH value according to the first mapping relationship, obtaining the current second excitation wavelength according to the second mapping relationship, and obtaining the current third fluorescence intensity according to the third mapping relationship; Step S505, calling a pesticide residue database, inputting the current third fluorescence intensity into the pesticide residue database, and matching the pesticide residue corresponding to the current third fluorescence intensity.

10. A food pesticide residue detection system, characterized in that: It includes acquisition module, analysis module and construction module; The acquisition module is used to obtain the packaging code, identify the packaging code, obtain product information, screen the samples to be detected according to the product information, pre-treat the samples to be detected, divide the pre-treated samples to be detected to obtain sub-samples, set the first simulation parameter, set it to the first quantitative and the first variable, obtain the first mixed solution, set the second quantitative and the second variable, obtain the second mixed solution, match the simulation temperature and the reaction time according to the product information, simulate the first mixed solution and the second mixed solution, and add fluorescent probes to the simulated first mixed solution and the simulated second mixed solution respectively to obtain the first indicator solution and the second indicator solution; The analysis module is used to match the corresponding fluorescence spectrum according to the product information, select the first excitation wavelength according to the fluorescence spectrum, send the first laser control instruction, irradiate the first indicator liquid and the second indicator liquid according to the first laser control instruction, obtain the first fluorescence intensity and the second fluorescence intensity, screen the ideal first variable according to the first fluorescence intensity, screen the ideal second variable according to the second fluorescence intensity, calculate the ideal sample concentration according to the ideal first variable, screen the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value according to the ideal second variable, set the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity and the ideal solvent pH value as the second simulation parameter, set the first excitation wavelength as the irradiation wavelength, continuously change the irradiation wavelength, and continuously obtain the corresponding fluorescence intensity, obtain the fluorescence intensity with the maximum value, record it as the third fluorescence intensity, match the pesticide residue according to the third fluorescence intensity, and send the second laser control instruction according to the third fluorescence intensity to obtain the second excitation wavelength; The construction module is used to establish a first mapping relationship between the product name and the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, and the ideal solvent pH value, to establish a second mapping relationship between the ideal sample concentration, the ideal solvent polarity, the ideal solvent viscosity, the ideal solvent pH value and the second excitation wavelength, and to establish a third mapping relationship between the second excitation wavelength and the third fluorescence intensity.

Images