Pesticide residue inspection and detection method, device and system for agricultural products

Abstract

The invention relates to the technical field of chromatographic analysis, in particular to a pesticide residue inspection and detection method, device and system for agricultural products, and solves the problems that in the prior art, a chromatographic column is lost due to adsorption of a liner tube at a sample injection port during continuous sample injection, and a component specificity compensation mechanism is lacked. The accuracy of a quantitative result is insufficient in a complex matrix long sequence detection process. The method comprises the following steps: acquiring a reference parameter in a clean instrument state and a response attenuation factor of a target pesticide component; extracting a real-time working condition characteristic value from the obtained chromatographic data; recursively updating a first state index according to the first characteristic value, and recursively updating a second state index according to the second characteristic value; and according to the response attenuation factor, the first state index and the second state index, determining an empirical compensation coefficient of the target pesticide component, and correcting the actually measured chromatographic response value of the target pesticide component by using the empirical compensation coefficient.

Description

Technical Field

[0001] This invention relates to the field of chromatographic analysis technology, specifically to a method, apparatus, and system for detecting pesticide residues in agricultural products. Background Technology

[0002] In the field of agricultural product quality and safety monitoring, gas chromatography-mass spectrometry (GC-MS) has become the mainstream method for pesticide residue detection. With the increasing demand for testing, laboratories need to handle the continuous injection and analysis of a large number of samples. Efficient and accurate test results are crucial to ensuring the safety of agricultural products.

[0003] Agricultural products such as rice and citrus contain a large amount of wax, pigments and high-boiling-point matrices. These substances are difficult to completely remove during the sample pretreatment stage. As the continuous injection sequence progresses, the residual matrices will gradually accumulate in the instrument's injection port liner and the front end of the chromatographic column, causing dynamic changes in the instrument's operating conditions.

[0004] Existing technologies typically employ internal standard methods or matrix-matched standard curve methods for quantification. These methods often assume that the instrument's response is linear or static, making them unable to handle the complex dynamic drift during continuous injection. Furthermore, different types of pesticides exhibit significant differences in their sensitivity to two fouling mechanisms: liner adsorption and column runoff. A single internal standard cannot simultaneously and accurately correct the deviations of all components, leading to a significant decrease in the accuracy of quantitative results over time in long-sequence analyses. Operators also find it difficult to accurately determine when to maintain the instrument, which can easily result in blind cleaning or overuse. Summary of the Invention

[0005] To address the technical problems in existing technologies where adsorption by the injection port liner and column loss occur during continuous injection, and where there is a lack of component-specific compensation mechanisms, leading to insufficient accuracy of quantitative results in the detection of long sequences in complex matrices, the present invention aims to provide a method, apparatus, and system for detecting pesticide residues in agricultural products. The specific technical solution adopted is as follows: In a first aspect, a method for detecting pesticide residues in agricultural products is provided, comprising: acquiring baseline parameters and response decay factors of target pesticide components under clean instrument conditions; extracting real-time operating condition characteristic values ​​from the acquired chromatographic data for each injection sample during continuous injection detection; the real-time operating condition characteristic values ​​include a first characteristic value for characterizing the adsorption activity of the injection port and a second characteristic value for characterizing the degree of column bleed; recursively updating a first state index for quantifying the cumulative adsorption risk of the injection port based on the first characteristic value, and recursively updating a second state index for quantifying the cumulative residue risk of the chromatographic column based on the second characteristic value; determining an empirical compensation coefficient for the target pesticide component based on the response decay factor, the first state index, and the second state index, and correcting the measured chromatographic response value of the target pesticide component using the empirical compensation coefficient.

[0006] Based on the above technical solution, in the pesticide residue testing method for agricultural products provided by this invention, by establishing the baseline parameters of the clean instrument and the response attenuation factor of the target pesticide component, the dual-condition characteristic values ​​that characterize the adsorption activity of the injection port and the degree of column bleed are extracted in real time, and the corresponding cumulative risk state index is dynamically updated. Then, the empirical compensation coefficient of each pesticide component is determined in a targeted manner and the measured chromatographic response value is corrected. This effectively addresses the dynamic drift problem of the instrument during continuous injection, solves the quantitative deviation caused by the difference in sensitivity of different pesticide components to the instrument's dirt-related mechanisms, and significantly improves the accuracy, consistency, and reliability of pesticide residue detection in agricultural products.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the reference parameters under clean instrument conditions specifically includes: running a pure solvent blank sample with the same solvent as the sample, and collecting the reference solvent peak width and reference baseline signal intensity from the chromatogram of the pure solvent blank sample as reference parameters.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for extracting real-time operating condition feature values ​​from the obtained chromatographic data specifically includes: analyzing the increment of the solvent peak tailing degree of the sample relative to the reference solvent peak width as a first feature value; and analyzing the increment of the baseline signal intensity of the sample within a preset high-temperature time window relative to the reference baseline signal intensity as a second feature value.

[0009] In conjunction with the first aspect above, in one possible implementation, the method for recursively updating the first state index and the second state index specifically includes: updating the first state index using a recursive calculation model based on a first feature value, a historical first state index, and a preset cumulative parameter; updating the second state index using a recursive calculation model based on a second feature value, a historical second state index, and a preset growth parameter; and performing boundary truncation processing on the updated first and second state indices to restrict their values ​​to a preset range.

[0010] In conjunction with the first aspect above, in one possible implementation, the method further includes: periodically inserting quality control samples of known concentrations for analysis during continuous injection; and calibrating the first state index and / or the second state index based on the measured chromatographic response values ​​and known concentrations of preset components in the quality control samples.

[0011] In conjunction with the first aspect above, in one possible implementation, the response decay factor includes: a first type of factor characterizing the sensitivity of the component to adsorption at the injection port, and a second type of factor characterizing the sensitivity of the component to background interference from column bleed; the method for obtaining the response decay factor of the target pesticide component specifically includes: inserting a mixed standard solution containing the target pesticide component at fixed intervals under clean instrument conditions, collecting solvent peak data, baseline data, and chromatographic response value of the mixed standard solution; establishing correlation models between the response decay rate of the solvent peak data and the chromatographic response value, and between the response decay rate of the baseline data and the chromatographic response value, respectively, through statistical regression analysis, and extracting the first type of factor and the second type of factor from the correlation models.

[0012] In conjunction with the first aspect above, in one possible implementation, the method for determining the empirical compensation coefficient of the target pesticide component specifically includes: assessing the degree of first signal loss of the target pesticide component due to inlet adsorption based on a first type of factor and a first state index; assessing the degree of second signal loss of the target pesticide component due to column bleed based on a second type of factor and a second state index; determining the comprehensive signal loss index of the target pesticide component based on the first signal loss degree and the second signal loss degree; and obtaining the empirical compensation coefficient based on the comprehensive signal loss index through a preset compensation model.

[0013] In conjunction with the first aspect above, in one possible implementation, the method further includes: triggering a cleaning and maintenance prompt or operation for the injection port when the first state index exceeds a first preset threshold; and triggering a shutdown and maintenance prompt for the chromatographic column when the second state index exceeds a second preset threshold.

[0014] Secondly, a pesticide residue testing device for agricultural products is provided, comprising: a parameter acquisition unit, a feature extraction unit, a state update unit, and a compensation calculation unit; the parameter acquisition unit is used to acquire baseline parameters and response decay factors of the target pesticide component under clean instrument conditions; the feature extraction unit is used to extract real-time operating condition feature values ​​from the acquired chromatographic data for each sample during continuous injection testing; the real-time operating condition feature values ​​include a first feature value characterizing the adsorption activity of the injection port and a second feature value characterizing the degree of column bleed; the state update unit is used to recursively update a first state index for quantifying the cumulative adsorption risk of the injection port based on the first feature value, and recursively update a second state index for quantifying the cumulative residue risk of the chromatographic column based on the second feature value; the compensation calculation unit is used to determine the empirical compensation coefficient of the target pesticide component based on the response decay factor, the first state index, and the second state index, and to correct the measured chromatographic response value of the target pesticide component using the empirical compensation coefficient.

[0015] Thirdly, a pesticide residue testing and detection system for agricultural products is provided, including: a pesticide residue testing and detection device for agricultural products as shown in the second aspect, and a chromatography-mass spectrometry analysis device.

[0016] The present invention has the following beneficial effects: By establishing baseline parameters for clean instruments and response attenuation factors for target pesticide components, dual-condition characteristic values ​​representing the adsorption activity of the injection port and the degree of column bleed are extracted in real time. The corresponding cumulative risk state index is dynamically updated, and then the empirical compensation coefficient of each pesticide component is determined and the measured chromatographic response value is corrected. This effectively addresses the dynamic drift problem of the instrument during continuous injection, solves the quantitative deviation caused by the difference in sensitivity of different pesticide components to the instrument's contamination mechanism, and significantly improves the accuracy, consistency and reliability of pesticide residue detection in agricultural products. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A system structure diagram of a pesticide residue testing and detection system for agricultural products provided in one embodiment of the present invention; Figure 2 This is a flowchart of a method for testing and detecting pesticide residues in agricultural products, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a pesticide residue testing method, apparatus, and system for agricultural products according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of a pesticide residue testing method, apparatus, and system for agricultural products provided by the present invention.

[0022] Please see Figure 1 The diagram illustrates a system structure of a pesticide residue testing system for agricultural products according to an embodiment of the present invention. The system includes a pesticide residue testing device 1 and a chromatography-mass spectrometry analysis device 2. The two devices work together in real time via a data transmission link. The chromatography-mass spectrometry analysis device 2 is responsible for completing the physicochemical detection of the sample and outputting raw data. The pesticide residue testing device 1 is responsible for data processing, status assessment, and result correction, and finally outputs accurate pesticide residue detection results. The system achieves dynamic correction and intelligent control in continuous sample injection scenarios throughout the entire process.

[0023] The pesticide residue testing and detection device 1 for agricultural products includes: a parameter acquisition unit 11, a feature extraction unit 12, a status update unit 13, and a compensation calculation unit 14.

[0024] In some implementations, the pesticide residue testing device for agricultural products also includes: a maintenance decision unit 15.

[0025] The parameter acquisition unit 11 is mainly responsible for acquiring the baseline parameters and response decay factors of the target pesticide components under clean instrument conditions. This can be achieved through an embedded data processing module in conjunction with software algorithms, and includes two functional sub-modules: The reference parameter acquisition submodule 111 controls the chromatography-mass spectrometry analysis device 2 to run a pure solvent blank sample that is completely identical to the sample solvent. It then uses the device's built-in chromatography workstation to acquire the chromatogram of the pure solvent blank sample, extracting and storing the reference solvent peak width and baseline signal intensity. These reference parameters are synchronized to the feature extraction unit 12 as a reference standard for subsequent calculations of real-time operating condition feature values, ensuring that the feature values ​​accurately reflect changes in instrument contamination.

[0026] When the chromatographic mass spectrometry analysis equipment 2 is in a clean state, the response decay factor calibration submodule 112 controls the equipment to continuously inject high-concentration matrix extract to construct a differentiated contamination gradient. At fixed intervals, a mixed standard solution containing all target pesticide components is automatically inserted. The data acquisition module collects solvent peak data, baseline data, and chromatographic response values ​​of each target component from the mixed standard solution. Then, through the built-in statistical regression analysis algorithm, correlation models between solvent peak data and response decay rate, and between baseline data and response decay rate are established respectively. From the models, the first type of factor (characterizing the sensitivity of the component to the adsorption at the injection port) and the second type of factor (characterizing the sensitivity of the component to the background interference of column bleed) are extracted. The calibrated response decay factor is stored in the system database for use by the compensation calculation unit 14.

[0027] The feature extraction unit 12 is implemented using a dedicated data processing chip or software algorithm module. Its core function is to extract feature values ​​characterizing the instrument's contamination status from the raw chromatographic data. This unit receives chromatographic data from each sample injection from the chromatographic mass spectrometry analyzer 2, and simultaneously calls the reference solvent peak width and reference baseline signal intensity stored in the parameter acquisition unit 11. The built-in peak identification algorithm locates the solvent main peak, calculates the increment of the current solvent peak tailing degree relative to the reference solvent peak width, and obtains the first feature value (characterizing the injection port adsorption activity). Within a preset high-temperature time window of the chromatographic data, the increment of the current baseline signal intensity relative to the reference baseline signal intensity is calculated, and the second feature value (characterizing the column bleed degree) is obtained. The two extracted real-time operating condition feature values ​​are transmitted in real-time to the status update unit 13 as the core input data for updating the instrument status index.

[0028] The status update unit 13, implemented by an embedded processor and a preset algorithm, is responsible for dynamically tracking the instrument's contamination status and includes a quality control calibration submodule 131. This unit receives the first and second feature values ​​output by the feature extraction unit 12, and, combined with historically stored first and second status indices and preset cumulative and growth parameters, updates the first status index (quantifying the cumulative adsorption risk at the injection port) and the second status index (quantifying the cumulative residual risk on the chromatographic column) respectively through a built-in recursive calculation model. Subsequently, boundary truncation is performed on the updated two status indices to limit their values ​​to a preset reasonable range, avoiding index distortion due to calculation errors. During continuous injection, the quality control calibration submodule 131 triggers the insertion of quality control samples of known concentrations into the chromatographic mass spectrometry analysis device 2 at preset intervals, receives the measured chromatographic response values ​​of the quality control samples, calculates the deviation of preset components based on their known concentrations, and if the deviation exceeds the allowable range, calibrates the corresponding status index. The calibrated status index is synchronized to the compensation calculation unit 14 and the maintenance decision unit 15, and simultaneously stored in the system database.

[0029] The compensation calculation unit 14 is implemented through computer software algorithms or a dedicated data processing module. Its core function is to calculate the compensation coefficient and correct the detection results. This unit first calls the first and second types of factors stored in the parameter acquisition unit 11, and combines them with the first and second state indices output by the state update unit 13 to assess the degree of signal loss caused by inlet adsorption and column bleed, respectively. Based on the two types of loss, a comprehensive signal loss index is calculated, and then substituted into a preset compensation model to obtain the empirical compensation coefficient for the target pesticide component. Finally, the measured chromatographic response value output by the chromatographic mass spectrometry analysis device 2 is called, and corrected using the empirical compensation coefficient to obtain an accurate response value restored to the clean state of the instrument. Then, combined with a preset standard curve equation, the residual concentration of the target pesticide component is calculated, and the final detection result is output through the system interface or stored in the database.

[0030] The maintenance decision unit 15 is implemented using a logic control module or a software early warning module. It receives the first and second state indices output by the state update unit 13 in real time and compares them with preset first and second preset thresholds. When the first state index exceeds the first preset threshold, it issues a port cleaning and maintenance prompt through the system interface or directly sends a control command to the chromatography-mass spectrometry analysis device 2 to trigger automatic cleaning. When the second state index exceeds the second preset threshold, it immediately issues a column shutdown and maintenance prompt, reminding the operator to perform maintenance such as aging or column head cutting. This unit ensures the long-term stable operation of the system by providing timely early warnings and triggering maintenance actions, avoiding deviations in detection results due to excessive instrument contamination.

[0031] The chromatographic mass spectrometry analysis device 2, as the core detection hardware of the system, can be implemented using gas chromatography-mass spectrometry (GC-MS) equipment, and has functions such as automatic sample injection, sample separation, component ionization, and signal detection. This device can continuously inject and analyze agricultural product samples according to a preset detection program. Different components are separated by the chromatographic column, and then the components are converted into detectable electrical signals by the mass spectrometer detector. Finally, for each sample, chromatographic data containing information such as solvent peaks, baselines, and target component response peaks is generated, while simultaneously outputting the measured chromatographic response values ​​of the target pesticide components. This raw data is transmitted in real time to the pesticide residue testing and detection device 1 for agricultural products via a data interface, providing core data support for subsequent feature extraction, status assessment, and result correction.

[0032] Please see Figure 2 The diagram illustrates a flowchart of a method for detecting pesticide residues in agricultural products according to an embodiment of the present invention. The method includes: S1. Obtain the baseline parameters and response decay factor of the target pesticide component under clean instrument conditions.

[0033] In some implementations, methods for obtaining reference parameters under clean instrument conditions include: running a pure solvent blank sample with the same solvent as the sample, and collecting the reference solvent peak width and reference baseline signal intensity from the chromatogram of the pure solvent blank sample as reference parameters.

[0034] Specifically, first perform standard maintenance operations on the instrument, such as replacing the liner and cutting the column head. Then, keep the instrument running continuously for at least 3 blank samples under the chromatographic conditions corresponding to the detection method to stabilize the instrument and eliminate any residual dirt interference from the instrument in the early stage, so that the subsequent benchmark parameters correspond to the true clean state.

[0035] Next, prepare a pure solvent that is exactly the same as the solvent used in the subsequent test samples as a reference solvent. For example, if the test sample uses acetonitrile as a solvent, prepare pure acetonitrile solvent as a blank reference sample to avoid the introduction of additional signal fluctuations due to solvent differences and to ensure that the reference data matches the solvent background of the test sample.

[0036] The blank reference sample is then run under preset chromatographic conditions. The system automatically identifies the solvent peak in the chromatogram, measures and records the time width from the peak apex to 10% of the peak height, and uses this as the reference solvent peak width. Simultaneously, a fixed time window (e.g., 25-28 minutes) at the end of the programmed temperature rise is selected, and the average signal value within this window is calculated and used as the baseline signal intensity. These parameters can be used for incremental comparison to eliminate interference from instrument background and solvent background.

[0037] In some implementations, the response decay factor includes: a first-class factor characterizing the sensitivity of the component to adsorption at the injection port, and a second-class factor characterizing the sensitivity of the component to background interference from column bleed. The method for obtaining the response decay factor of the target pesticide component includes: inserting a mixed standard solution containing the target pesticide component at fixed intervals under clean instrument conditions, collecting solvent peak data, baseline data, and the chromatographic response value of the mixed standard solution; establishing correlation models between the response decay rate of the solvent peak data and the chromatographic response value, and between the response decay rate of the baseline data and the chromatographic response value, respectively, through statistical regression analysis, and extracting the first-class factor and the second-class factor from the correlation models.

[0038] Specifically, the instrument is first reset to the aforementioned clean state, and then high-concentration agricultural product matrix extract (such as rice wax extract) is continuously injected. At each preset fixed number of injections (e.g., every 10 injections), a mixed standard solution containing all target pesticide components is inserted. The effect of continuously injecting matrix extract is to artificially construct an increasingly dirty environment for the instrument, and the effect of intermittently inserting mixed standard solutions is to obtain response data of target components under different levels of dirtiness, providing multi-dimensional samples for establishing the correlation between the degree of contamination and response decay.

[0039] Subsequently, for each injected mixed standard solution, the system automatically collects the solvent peak tailing characteristic value and baseline drift characteristic value in its chromatogram, and records the measured chromatographic response value (peak area) of each target pesticide component, thereby obtaining the correspondence data between the contamination characteristics and the target component response.

[0040] Next, the preset log-linear regression algorithm is invoked, using the collected solvent peak data as the independent variable and the natural logarithm of the response decay rate of the target component (the ratio of the current response value to the clean state response value) as the dependent variable to establish a correlation model between the two. The absolute value of the slope of the model is extracted and used as the first type of factor characterizing the sensitivity of the component to the adsorption at the injection port. At the same time, using the collected baseline data as the independent variable and the natural logarithm of the response decay rate of the same target component as the dependent variable, the log-linear regression algorithm is used again to establish a correlation model, and the slope parameter of the model is extracted and used as the second type of factor characterizing the sensitivity of the component to the background interference of column bleed.

[0041] S2. During continuous injection detection, for each sample, extract real-time operating condition characteristic values ​​from the obtained chromatographic data.

[0042] The real-time operating condition characteristics include a first characteristic value used to characterize the adsorption activity of the injection port, and a second characteristic value used to characterize the degree of column bleed.

[0043] In some implementations, the increment of the solvent peak tailing degree of the analysis sample relative to the reference solvent peak width is used as the first characteristic value; the increment of the baseline signal intensity of the analysis sample within a preset high-temperature time window relative to the reference baseline signal intensity is used as the second characteristic value.

[0044] Specifically, for the chromatographic data of the current needle sample, the first feature value used to characterize the adsorption activity of the injection port is extracted: the system calls the preset chromatographic peak identification algorithm to search for the chromatographic peak with the earliest retention time and the highest signal intensity in the chromatogram, locks it as the solvent main peak, accurately distinguishes the solvent peak from the target pesticide component peak, avoids peak identification deviation that leads to subsequent data distortion, and ensures the accuracy of the feature extraction target.

[0045] The system then locates the peak of the solvent peak and searches backward along the time axis to find the moment when the signal intensity decays to 10% of the peak height. The difference between the two moments is calculated to obtain the current original peak width. The system focuses on the broadening of the solvent peak trailing edge. Since the inlet liner is dirty, it can cause the solvent peak trailing edge to be tailed. Therefore, this action can accurately quantify the peak shape change caused by liner adsorption, providing the original basis for characteristic value calculation.

[0046] Next, the previously stored reference solvent peak width is retrieved, the ratio of the current original peak width to the reference solvent peak width is calculated and subtracted by 1, and then the result is non-negatively processed (if the result is negative, the value is 0), resulting in a dimensionless solvent tailing characteristic value. This value is used as the first characteristic value, transforming the peak width increment into a characteristic index that intuitively reflects the adsorption activity of the injection port. The larger the value, the more severe the liner contamination. For example, when the reference solvent peak width is 0.2 minutes and the current original peak width is 0.3 minutes, the calculated first characteristic value is 0.5.

[0047] After extracting the first feature value, the second feature value, which characterizes the degree of column bleed, is extracted: In the chromatogram of the current sample, a preset high-temperature time window (e.g., 25.0 minutes to 28.0 minutes) at the end of the programmed temperature rise is selected, and the arithmetic mean of the signal intensity of all acquisition points within this window is calculated to obtain the current baseline signal intensity. The temperature at the end of the programmed temperature rise is relatively high, and high-boiling-point impurities accumulated in the column are easily bleed out at this stage. Selecting this interval allows the baseline data to more accurately reflect the residual load of the column and ensures the representativeness of subsequent feature values.

[0048] Then, the previously stored baseline signal strength and the preset detector full-scale constant (e.g., 10) are retrieved. 7 The difference between the current baseline signal intensity and the reference baseline signal intensity is calculated, and then divided by the detector full-scale constant. The result is then non-negatively processed to obtain the baseline drift characteristic value, which is used as the second characteristic value. This normalizes the baseline increment to become a characteristic index representing the degree of column bleed, eliminating the influence of detector range differences and ensuring that the characteristic value stably reflects the column residual state. For example, if the reference baseline signal intensity is 10... 4 The current baseline signal strength is 2×10 4 When the calculated second eigenvalue is 1×10, -3 .

[0049] S3. Update the first state index used to quantify the cumulative adsorption risk at the injection port based on the first characteristic value, and update the second state index used to quantify the cumulative residual risk of the chromatographic column based on the second characteristic value.

[0050] In some implementations, the method for recursively updating the first state index and the second state index includes: updating the first state index using a recursive calculation model based on the first feature value, the historical first state index, and a preset cumulative parameter; updating the second state index using a recursive calculation model based on the second feature value, the historical second state index, and a preset growth parameter; and performing boundary truncation processing on the updated first and second state indices to limit their values ​​to a preset range.

[0051] Specifically, first, perform a recursive update of the first state index: read the first state index corresponding to the previous pair. Simultaneously, retrieve the first feature value extracted from the current needle (nth needle). This corresponds to the historical state and current dirt characteristics required for recursive calculation, providing the input basis for updating the current state index. Then, preset cumulative parameters are called. (i.e., the contamination accumulation coefficient, for example, a value of 0.1, characterizing the growth rate of substrate-induced adsorption on the liner) and the self-cleaning coefficient (For example, a value of 0.05 represents the desorption rate of pollutants adsorbed on the liner by the high-temperature carrier gas), and the calculation is performed using a recursive calculation model: In the formula, the first term The first item is the historical baseline, directly inheriting the liner adsorption state of the previous needle, and serves as the benchmark for the current state; the second item... This is an incremental pollution item, among which It is a weighted average of the current level of contamination (the more severe the solvent tailing, the larger the weighting factor). This is the saturation limiting factor. As the liner adsorption index increases, this term gradually approaches 0, simulating the physical characteristic that pollution growth slows down after the liner adsorption sites are filled. This incremental term is positively correlated with the final calculation result; the third term... The self-cleaning reduction term represents the reduction in adsorption risk caused by the removal of pollutants by the high-temperature carrier gas, and it is negatively correlated with the final calculation result; the three terms work together to obtain the intermediate calculated value. This comprehensively reflects the process from the accumulation of adsorption in the liner to self-cleaning.

[0052] After completing the intermediate calculation of the first state index, it is subjected to boundary truncation: the value is restricted to a preset range of 0.0 to 1.0. If the intermediate calculated value is less than 0.0, it is taken as 0.0, and if it is greater than 1.0, it is taken as 1.0, thus obtaining the final first state index.

[0053] It should be noted that the first state index is essentially a dimensionless indicator that quantifies the cumulative adsorption risk at the injection port. A value of 0 corresponds to a clean instrument with no adsorption risk, while a value of 1 corresponds to complete saturation of the liner adsorption sites and the risk reaching its upper limit. The number of adsorption sites in the liner is finite; negative adsorption risk (i.e., a value less than 0) is impossible, nor is it possible to exceed the physical upper limit to achieve supersaturated adsorption (i.e., a value greater than 1). Intermediate calculated values ​​may exhibit outliers of less than 0 or greater than 1 due to factors such as coefficient fluctuations and data measurement errors during the recursive process. These outliers deviate from the actual physical scenario, causing subsequent compensation calculations based on this index to deviate from the true instrument state. Therefore, it is necessary to forcibly constrain the value to the 0.0-1.0 range through boundary truncation to ensure consistency between the index and actual operating conditions.

[0054] Next, the second state index is recursively updated: the second state index corresponding to the previous pair is read. Simultaneously, retrieve the second feature value obtained from the current needle (nth needle). This provides input for the recursive update, based on historical data of column residual status and current contamination characteristics. Then, preset growth parameters are invoked. (That is, the residual growth factor, for example, a value of 0.2, characterizing the rate at which baseline increment is converted into column residue), calculated through a recursive model: In the formula, the first term The first item is the historical baseline, inheriting the residual state of the chromatographic column from the previous injection; the second item... The residual growth coefficient represents the current residual increment, which transforms the baseline drift characteristic value into the corresponding residual risk increment. This increment is positively correlated with the final calculation result. The two terms are added together to obtain the intermediate calculated value. This aligns with the actual physical characteristics of unidirectional accumulation of chromatographic column residues.

[0055] The intermediate calculated value of the second state index is then subjected to the same boundary truncation process, limiting it to the range of 0.0 to 1.0, to obtain the final second state index, ensuring the rationality of the second state index and its consistency with the actual residual state of the instrument.

[0056] Specifically, before starting continuous injection detection, both the first state index (for quantifying the risk of cumulative adsorption at the injection port) and the second state index (for quantifying the risk of cumulative residue on the column) are initialized to 0.0, assuming a clean, risk-free initial state. Then, for the first sample injection, the initialized first and second state indices are updated. Furthermore, after the system performs injection port maintenance (such as liner replacement), the first state index is forcibly reset to 0.0; after performing column maintenance (such as column head cutting), the second state index is forcibly reset to 0.0, and the baseline parameters (baseline solvent peak width and baseline signal intensity) are reacquired.

[0057] Furthermore, the method also includes: periodically inserting quality control samples of known concentrations for analysis during continuous injection; and calibrating the first state index and / or the second state index based on the measured chromatographic response values ​​and known concentrations of preset components in the quality control samples.

[0058] Specifically, during continuous injection, at preset intervals (e.g., every 20 sample injections), the system automatically triggers the insertion of a quality control sample (usually a matrix-matched standard solution) of known concentration into the chromatographic mass spectrometry analyzer, introducing a reference sample of known concentration to provide an objective basis for calibrating the state index. After the quality control sample analysis is completed, the system selects preset components sensitive to inlet adsorption (e.g., methamidophos) and preset components sensitive to column bleed (e.g., pyrethroids), and obtains the measured chromatographic response values ​​of these components; simultaneously, combined with the known concentration of the quality control sample, the system calls upon the current first state index and second state index to calculate the predicted theoretical response values ​​of these components.

[0059] The relative deviation between the measured response value and the predicted theoretical response value is then calculated (i.e., the absolute value of the ratio of the difference between the two to the predicted theoretical response value). Then, combining industry requirements for pesticide residue detection, instrument performance levels, and preliminary experimental data from the method development phase, a preset threshold is determined. For example, if the preset threshold is 5%, and the absolute value of the relative deviation for a certain component is less than 5%, it indicates that the current recursive model is operating accurately, and the corresponding state index remains unchanged. If the absolute value of the deviation is greater than 5%, the true state index is calculated backward based on the measured decay rate of that component (the ratio of the measured response value to the clean state response value), and the current state index is forcibly updated to this true value. The state index is periodically calibrated to eliminate accumulated errors in the recursive process, ensuring that the state index always accurately reflects the instrument's true operating condition, maintaining the accuracy of state tracking even in long-sequence analyses involving hundreds of needles.

[0060] S4. Based on the response decay factor, the first state index, and the second state index, determine the empirical compensation coefficient of the target pesticide component, and use the empirical compensation coefficient to correct the measured chromatographic response value of the target pesticide component.

[0061] In some implementations, the method for determining the empirical compensation coefficient of the target pesticide component includes: assessing the degree of first signal loss of the target pesticide component due to inlet adsorption based on a first type of factor and a first state index; assessing the degree of second signal loss of the target pesticide component due to column bleed based on a second type of factor and a second state index; determining the comprehensive signal loss index of the target pesticide component based on the degree of first signal loss and the degree of second signal loss; and obtaining the empirical compensation coefficient based on the comprehensive signal loss index through a preset compensation model.

[0062] Specifically, for the target pesticide component to be quantified, the corresponding first-class factor is read. With the second type of factor Among them, the first type of factor It is a dimensionless coefficient characterizing the sensitivity of the component to adsorption at the injection port, a second-order factor. It is a dimensionless coefficient characterizing the sensitivity of this component to column bleed; at the same time, the previously updated first state index is retrieved. With the second state index First state index It is a dimensionless index that quantifies the risk of cumulative adsorption at the injection port, a second-state index. It is a dimensionless index that quantifies the risk of cumulative residue on the chromatographic column. Based on the above parameters, the comprehensive signal loss index of the i-th component in the n-th injection is calculated. : In the formula, The risk of signal loss due to adsorption of the component at the current inlet was quantified, i.e., the first degree of signal loss. The risk of signal loss due to the current column bleed of the component was quantified, namely the second signal loss level. The two independent signal loss levels were combined into a single comprehensive signal loss index by taking the square root of the sum of two squares. This not only preserves the independent contribution of the two losses, but also reasonably reflects the physical logic of the independent action of the two interference sources through the Euclidean distance. The larger the value, the higher the risk of signal loss.

[0063] Call the preset engineering protection lower limit (Dimensionless parameters determined based on detector noise level or minimum quantitation limit, used to avoid excessively small compensation coefficients, e.g., a value of 0.15), combined with the comprehensive signal loss index. The empirical compensation coefficient is obtained through the negative exponential model. : In the formula, when the comprehensive signal loss index When the value is 0 (instrument cleanliness), the result of the natural exponential function term is 1, and the empirical compensation coefficient is... A value of 1 indicates no signal loss; when the comprehensive signal loss index is 1, it represents no signal loss. As the natural exponential function term increases, the empirical compensation coefficient decreases exponentially. It decreases accordingly, consistent with statistical patterns; It is a scaling factor that maps the result of the exponential term to the range of the lower limit of engineering protection to 1. The lower limit of engineering protection ensures that the compensation coefficient will not be too low, and is consistent with the empirical compensation coefficient. They are positively correlated; the empirical compensation coefficient is finally obtained. It is a dimensionless parameter with a value range from the lower limit of engineering protection (e.g., 0.15) to 1. It is the theoretical response retention rate of the target component relative to the cleanliness of the instrument. The smaller the value, the more signal loss there is.

[0064] Furthermore, the measured chromatographic response value is corrected using an empirical compensation coefficient: The measured chromatographic response value (i.e., measured peak area) of the current target pesticide component is read from the integration results of the chromatography workstation. Due to instrument contamination, this measured value is usually lower than the true response value in a clean state. Then, the empirical compensation coefficient is called, and the measured chromatographic response value is divided by this coefficient to obtain the corrected peak area. This calculation, by dividing by the response retention rate, restores the attenuated measured area to the theoretical response level under clean instrument conditions. This is the opposite of the signal attenuation logic caused by contamination, effectively eliminating systematic bias.

[0065] Finally, the final residual concentration of the target pesticide component is calculated: the corrected peak area is substituted into the preset standard curve equation (for example, the equation is peak area y=kx+b, the slope k and intercept b are obtained by pre-calibration of a series of concentration standard solutions), and the residual concentration of the component is calculated. The reduced theoretical peak area is converted into an accurate residual concentration, eliminating the influence of instrument dirt on the quantitative results and ensuring the consistency and accuracy of the detection results under different injection times and different instrument conditions.

[0066] For example, a series of standard solutions with concentrations ranging from 0.05 mg / kg to 1.0 mg / kg were prepared for the calibration standard curve of methamidophos. After the corresponding peak areas were obtained under clean instrument conditions, the equation "peak area = 42500 × concentration + 1750" was fitted by linear regression. For the current agricultural product sample, the measured peak area of ​​methamidophos was 10200. Combining the corresponding empirical compensation coefficient of 0.8, the measured peak area was divided by this coefficient to obtain the corrected peak area of ​​12750. Substituting this into the standard curve equation, the final calculated residual concentration of methamidophos in the sample was approximately 0.259 mg / kg. This result has eliminated the systematic bias caused by instrument contamination and can accurately reflect the actual residual level of the sample.

[0067] In some implementations, the method further includes: triggering a cleaning and maintenance prompt or operation for the injection port when the first state index exceeds a first preset threshold; and triggering a shutdown and maintenance prompt for the chromatographic column when the second state index exceeds a second preset threshold.

[0068] Specifically, the system continuously calls the first state index and the second state index obtained after the state update in real time; at the same time, it calls the preset thresholds that are determined by combining the physical characteristics of the instrument hardware, the verification of pre-experimental data, the effectiveness of maintenance measures and the requirements of detection accuracy. These include the first preset threshold (i.e., the adsorption cleaning threshold, for example, a value of 0.60) and the second preset threshold (i.e., the residual shutdown threshold, for example, a value of 0.85).

[0069] When the system detects that the first state index exceeds the first preset threshold and the second state index is within the normal range (e.g., ≤0.80), it determines that the current contamination is mainly concentrated in the inlet liner and is reversible adsorption contamination, accurately locating the source and type of contamination and avoiding interruption of the normal detection process due to misjudgment. Subsequently, the system does not interrupt the current injection sequence. After the detection of the current sample is completed, it automatically sends a control command to the autosampler, inserts one or two empty syringes containing pure solvent (e.g., acetonitrile) that is the same as the sample solvent, and uses high-temperature carrier gas to carry pure solvent vapor to flush the inner wall of the liner, desorbing and carrying away the contaminants adsorbed on the active sites of the liner. Without affecting the continuity of detection, it reduces the risk of cumulative adsorption in the inlet liner online, quickly bringing the first state index back to a reasonable range and ensuring the signal stability of subsequent sample detection.

[0070] When the system detects that the second state index exceeds the second preset threshold, or that the median value of either the first or second state index (before truncation) continuously exceeds 1.0, it determines that the accumulation of impurities in the current column is approaching its physical limit, or that the system has entered a nonlinear runaway state. This type of contamination cannot be removed by online cleaning. Timely identification of irreversible severe contamination prevents excessive instrument contamination from distorting test results and avoids unnecessary hardware damage. The system then immediately pauses the current injection sequence, automatically adjusts the instrument to standby cooling mode, and displays a clear alarm message on the software interface: "Column contamination is severe; please perform aging or column head cutting." This timely termination of high-risk detection procedures protects the instrument hardware and clearly communicates maintenance requirements to the operator.

[0071] Only after the operator completes the corresponding physical maintenance operations (such as high-temperature aging of the chromatographic column or cutting off the contaminated part at the front end of the column) and confirms the completion of maintenance in the software interface will the system trigger the state initialization reset mode, reset the initial values ​​corresponding to the first state index and the second state index to 0.0, and automatically execute the aforementioned steps of obtaining the reference parameters under clean instrument status, update the reference solvent peak width and the reference baseline signal intensity, and then resume the operation of the injection sequence to ensure that the instrument is restored to a clean and stable state before continuing detection, avoiding incomplete maintenance that could lead to rapid accumulation of contamination again, and ensuring the accuracy and consistency of subsequent detection results.

[0072] Based on the above technical solution, by establishing the baseline parameters of the clean instrument and the response attenuation factor of the target pesticide component, the dual-condition characteristic values ​​that characterize the adsorption activity of the injection port and the degree of column bleed are extracted in real time, and the corresponding cumulative risk state index is dynamically updated. Then, the empirical compensation coefficient of each pesticide component is determined in a targeted manner and the measured chromatographic response value is corrected. This effectively addresses the dynamic drift problem of the instrument during continuous injection, solves the quantitative deviation caused by the difference in sensitivity of different pesticide components to the instrument's dirt-related mechanisms, and significantly improves the accuracy, consistency and reliability of pesticide residue detection in agricultural products.

[0073] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0075] In this embodiment of the invention, the pesticide residue testing device for agricultural products can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or software. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0076] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0077] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for testing and detecting pesticide residues in agricultural products, characterized in that, include: Obtain the baseline parameters and response decay factor of the target pesticide component under clean instrument conditions; During continuous injection testing, real-time operating condition characteristic values ​​are extracted from the obtained chromatographic data for each sample injection. The real-time operating condition characteristic values ​​include a first characteristic value for characterizing the adsorption activity of the injection port and a second characteristic value for characterizing the degree of column bleed. The first state index for quantifying the cumulative adsorption risk at the injection port is updated recursively based on the first feature value, and the second state index for quantifying the cumulative residual risk of the chromatographic column is updated recursively based on the second feature value. Based on the response attenuation factor, the first state index, and the second state index, an empirical compensation coefficient for the target pesticide component is determined, and the measured chromatographic response value of the target pesticide component is corrected using the empirical compensation coefficient.

2. The method for testing and detecting pesticide residues in agricultural products according to claim 1, characterized in that, Obtain baseline parameters under cleanroom instrument conditions, including: Run a blank sample in a pure solvent with the same solvent as the sample, and collect the reference solvent peak width and reference baseline signal intensity from the chromatogram of the pure solvent blank sample as the reference parameters.

3. The method for testing and detecting pesticide residues in agricultural products according to claim 2, characterized in that, Real-time operating condition characteristic values ​​are extracted from the obtained chromatographic data, including: The increment of the solvent peak tailing degree of the analysis sample relative to the peak width of the reference solvent is used as the first characteristic value; The increment of the baseline signal intensity of the sample relative to the reference baseline signal intensity within a preset high-temperature time window is used as the second feature value.

4. The method for testing and detecting pesticide residues in agricultural products according to claim 1, characterized in that, Recursively update the first state index and the second state index, including: The first state index is updated by a recursive calculation model based on the first feature value, the historical first state index, and the preset cumulative parameters. The second state index is updated using a recursive calculation model based on the second feature value, the historical second state index, and the preset growth parameters. The updated first and second state indices are truncated to limit their values ​​to a preset range.

5. The method for testing and detecting pesticide residues in agricultural products according to claim 4, characterized in that, Also includes: During continuous injection, quality control samples of known concentrations are periodically inserted for analysis; The first state index and / or the second state index are calibrated based on the measured chromatographic response values ​​and known concentrations of the preset components in the quality control sample.

6. The method for testing and detecting pesticide residues in agricultural products according to claim 1, characterized in that, The response decay factor includes: a first type of factor characterizing the sensitivity of a component to adsorption at the injection port, and a second type of factor characterizing the sensitivity of a component to background interference from column bleed. Obtain the response decay factor of the target pesticide component, including: With the instrument clean, a mixed standard solution containing the target pesticide component is inserted at fixed intervals, and solvent peak data, baseline data, and chromatographic response values ​​of the target pesticide component are collected. By using statistical regression analysis, correlation models were established between solvent peak data and chromatographic response value attenuation rate, and between baseline data and chromatographic response value attenuation rate. The first type of factor and the second type of factor were then extracted from the correlation models.

7. The method for testing and detecting pesticide residues in agricultural products according to claim 6, characterized in that, Determine the empirical compensation coefficient for the target pesticide component, including: Based on the first type of factor and the first state index, assess the degree of first signal loss of the target pesticide component due to inlet adsorption. Based on the second type of factor and the second state index, assess the degree of second signal loss of the target pesticide component due to column bleed. Based on the first signal loss level and the second signal loss level, the comprehensive signal loss index of the target pesticide component is determined; The empirical compensation coefficient is obtained based on the comprehensive signal loss index and a preset compensation model.

8. The method for detecting pesticide residues in agricultural products according to claim 1, characterized in that, Also includes: When the first state index exceeds the first preset threshold, a cleaning and maintenance prompt or operation is triggered for the injection port; When the second state index exceeds the second preset threshold, a shutdown maintenance prompt is triggered for the chromatographic column.

9. A pesticide residue testing and detection device for agricultural products, characterized in that, include: The system comprises a parameter acquisition unit, a feature extraction unit, a state update unit, and a compensation calculation unit. The parameter acquisition unit is used to acquire the baseline parameters and the response decay factor of the target pesticide component under clean instrument conditions. The feature extraction unit is used to extract real-time operating condition feature values ​​from the obtained chromatographic data for each injection sample during continuous injection detection. The real-time operating condition characteristic values ​​include a first characteristic value for characterizing the adsorption activity of the injection port and a second characteristic value for characterizing the degree of column bleed. The state update unit is used to recursively update the first state index for quantifying the cumulative adsorption risk at the injection port based on the first feature value, and to recursively update the second state index for quantifying the cumulative residual risk of the chromatographic column based on the second feature value. The compensation calculation unit is used to determine the empirical compensation coefficient of the target pesticide component based on the response attenuation factor, the first state index and the second state index, and to correct the measured chromatographic response value of the target pesticide component using the empirical compensation coefficient.

10. A pesticide residue testing system for agricultural products, characterized in that, include: The pesticide residue testing and detection device for agricultural products as described in claim 9, and the chromatography-mass spectrometry analysis equipment.

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