Method and system for detecting pesticide residues in agricultural products based on mass spectrometry
Patent Information
- Application Number
- CN202611453253.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-18
- Publication Date
- 2026-10-23
AI Technical Summary
而实际检测过程中易受复杂基质的干扰，例如样品中的色素、脂类这些共提取物可能影响MS色谱图的准确提取，例如存在严重基线漂移和峰面积偏差，进而降低残留物质的检测精度；此外，前处理环节常涉及高温或极端pH条件，可能导致农药发生降解，进而存在农药残留检测准确性不高的缺陷
[0033]本申请通过采集被检测样本的MS色谱图，通过深入分析色谱中相邻谱峰的分布距离特征以及基线的形态变化特征，对存在的基线偏移程度进行准确评估，基于评估结果进行基线校正算法中参数优化，进而对MS色谱图进行基线校正处理，其优势在于能够减小实际检测过程中的复杂基质干扰；然后，基于MS色谱图中氟酰脲组分对应时间范围内曲线中数据信噪比，结合该曲线对称程度及曲线中数据变化的混乱程度，构建第一显著系数，深入分析了谱峰数据在噪声干扰下存在的锯齿状波动及非对称特征，准确获取了被检测样本对应谱峰存在的扰动影响特征，基于此调整滤波处理技术中的滤波窗口，进而对被检测样本的基线校正后的MS色谱图进行去噪处理，得到更为精确的MS色谱图，有助于提高农药残留检测的准确性。
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Figure CN122962147A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of pesticide residue detection technology, specifically to a method and system for detecting pesticide residues in agricultural products based on mass spectrometry technology. Background Technology
[0002] Today, with increasing public concern for food safety and health, pesticide residue testing in agricultural products has become a crucial link in ensuring the safety of the food supply chain. Fluorourazone (fluorourea), a novel fluorinated insect growth regulator, is widely used for pest control in fruits, vegetables, and grains. However, improper use can easily lead to excessive residues, endangering human health; therefore, establishing an efficient and accurate testing process is of paramount importance.
[0003] Liquid chromatography-mass spectrometry (LC-MS) has become one of the mainstream techniques for pesticide residue detection due to its high sensitivity and selectivity. However, in actual detection processes, it is susceptible to interference from complex matrices. For example, co-extractants such as pigments and lipids in the sample may affect the accurate extraction of MS chromatograms, resulting in severe baseline drift and peak area deviation, thereby reducing the detection accuracy of residues. In addition, the pretreatment process often involves high temperatures or extreme pH conditions, which may lead to pesticide degradation, resulting in low accuracy in pesticide residue detection. Summary of the Invention
[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for detecting pesticide residues in agricultural products based on mass spectrometry technology. The specific technical solution adopted is as follows:
[0005] In a first aspect, embodiments of this application provide a method for detecting pesticide residues in agricultural products based on mass spectrometry technology, the method comprising the following steps:
[0006] Construct a mapping relationship between the concentration of fluorourea in a standard fluorourea solution and the peak area in its MS chromatogram; obtain the MS chromatogram of the sample being tested;
[0007] The degree of separation of chromatographic peaks and the degree of baseline curvature in MS chromatograms are analyzed to construct a baseline shift coefficient for MS chromatograms, which is used to adjust the number of iterations in the baseline correction algorithm. The MS chromatograms are then baseline corrected in combination with the baseline correction algorithm.
[0008] The curves of the fluorourea components within the corresponding time range are obtained from the baseline-corrected MS chromatograms; based on the signal-to-noise ratio of the data in the curves, combined with the symmetry of the curves after smoothing and the degree of disorder of the data changes in the curves, the first significance coefficient of the curves is constructed.
[0009] The filter window size is adjusted based on the first significance coefficient, and the MS chromatogram after baseline correction is filtered using a filtering algorithm. Pesticide residues are then detected using the filtered MS chromatogram and the mapping relationship.
[0010] In one embodiment, the process of obtaining the baseline offset coefficient is as follows:
[0011] Obtain the peak values of the chromatographic peaks in the MS chromatogram, calculate the time interval between two adjacent peak values, and use the ratio of the mean of all the time intervals in the MS chromatogram to the total time length of the MS chromatogram as the chromatographic peak separation coefficient.
[0012] The baseline curve of the MS chromatogram is obtained by spline fitting, the curvature at each time point on the baseline curve is calculated, and the mean of the absolute values of curvature at all times is denoted as the baseline curvature.
[0013] The baseline offset coefficient of the MS chromatogram is constructed based on the separation degree coefficient and the baseline curvature.
[0014] In one embodiment, the baseline offset coefficient is specifically:
[0015] The baseline offset coefficient is obtained by weighting and summing the inverse proportional mapping value of the separation degree coefficient with the baseline curvature.
[0016] In one embodiment, the process of adjusting the number of iterations in the baseline correction algorithm is as follows:
[0017] The product of the normalized value of the baseline offset coefficient and the preset maximum number of iterations is used as the number of iterations in the adjusted baseline correction algorithm.
[0018] In one embodiment, the process of obtaining the first significance coefficient is as follows:
[0019] The curves corresponding to the time range of the fluorourea component in the MS chromatogram are denoted as peak curves.
[0020] The degree of data variation disorder on both sides of the maximum value in the spectral peak curve is analyzed to determine the peak distortion coefficient of the spectral peak curve.
[0021] Analyze the symmetry of the spectral peak curves to construct the asymmetry coefficients of the spectral peak curves;
[0022] The first significance coefficient is determined based on the signal-to-noise ratio, the peak distortion coefficient, and the asymmetry coefficient.
[0023] In one embodiment, the peak distortion coefficient is the mean of the absolute values of the coefficients of variation of the first-order difference sequences of the curves on the left and right sides of the maximum value in the spectral peak curve.
[0024] In one embodiment, the process of obtaining the asymmetric coefficients is as follows:
[0025] Two data points at the same height on the spectral peak curve are obtained. The time distance between the data point to the left of the peak and the corresponding peak is recorded as the left distance, and the time distance between the data point to the right of the peak and the corresponding peak is recorded as the right distance. The difference between the left distance and the right distance is calculated and recorded as the first difference.
[0026] The sum of the first differences at multiple preset heights on the spectral peak curve is used as the asymmetry coefficient of the spectral peak curve.
[0027] In one embodiment, the first significance coefficient is directly proportional to the peak distortion coefficient and the asymmetry coefficient, and inversely proportional to the signal-to-noise ratio.
[0028] In one embodiment, the process of adjusting the filter window size based on the first significance coefficient is as follows:
[0029] Calculate the window approximation of the MS chromatogram of the tested sample. , The expression is: ,in, The normalized value of the first significance coefficient of the MS chromatogram of the tested sample. , These are the minimum and maximum values of the preset filter window size, respectively;
[0030] The odd number that is closest to the approximate value of the window is used as the adjusted filter window size.
[0031] Secondly, embodiments of this application also provide a pesticide residue detection system for agricultural products based on mass spectrometry technology, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.
[0032] The embodiments of this application have at least the following beneficial effects:
[0033] This application acquires MS chromatograms of the tested samples, and through in-depth analysis of the distribution distance characteristics of adjacent peaks and the morphological changes of the baseline, accurately assesses the degree of baseline shift. Based on the assessment results, the parameters in the baseline correction algorithm are optimized, and the MS chromatograms are then baseline-corrected. Its advantage lies in reducing interference from complex matrices in the actual detection process. Then, based on the signal-to-noise ratio of the data in the curve corresponding to the fluorourea component within the time range of the MS chromatogram, combined with the symmetry of the curve and the degree of disorder in the data changes within the curve, a first significance coefficient is constructed. The sawtooth fluctuations and asymmetric characteristics of the peak data under noise interference are analyzed in depth, accurately obtaining the perturbation influence characteristics of the corresponding peaks of the tested samples. Based on this, the filter window in the filtering technology is adjusted, and the baseline-corrected MS chromatograms of the tested samples are then denoised to obtain more accurate MS chromatograms, which helps improve the accuracy of pesticide residue detection. Attached Figure Description
[0034] Figure 1 A flowchart illustrating the steps of a method for detecting pesticide residues in agricultural products based on mass spectrometry, as provided in one embodiment of this application;
[0035] Figure 2 This is a schematic diagram illustrating the process of obtaining the baseline offset coefficient. Detailed Implementation
[0036] The following, in conjunction with the accompanying drawings, details the specific scheme of the method and system for detecting pesticide residues in agricultural products based on mass spectrometry technology provided in this application.
[0037] Please see Figure 1 The document illustrates a flowchart of a method for detecting pesticide residues in agricultural products based on mass spectrometry, according to an embodiment of this application. The method includes the following steps:
[0038] Step S1: Construct a mapping relationship between the concentration of fluorourea in the standard fluorourea solution and the peak area in its MS chromatogram; obtain the MS chromatogram of the sample to be tested.
[0039] This application targets the monitoring of flufenacetate residues in agricultural products, employing liquid chromatography-mass spectrometry (LC-MS) to acquire MS chromatographic data of tested samples of similar agricultural products. Each chromatographic peak in the MS chromatogram represents a component, and its peak height or peak area can be used as a quantitative basis. First, by measuring the peak area (or peak height) response of a series of flufenacetate standard solutions of known concentrations in the instrument, a mathematical relationship between flufenacetate concentration and response value is established. Specifically, using acetonitrile as a solvent, a series of flufenacetate standard solutions with concentrations of 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, and 1 mg / L are prepared. Each concentration of flufenacetate standard solution is analyzed three times using MS chromatograms, and the average area of the flufenacetate chromatographic peak at each concentration is calculated as the peak area at that concentration. Finally, the least squares fitting technique is used to obtain a fitted straight line between flufenacetate concentration and chromatographic peak area. The equation of this fitted straight line is used as the mapping relationship between flufenacetate concentration and chromatographic peak area. Subsequent detection is based on this mapping relationship. The least squares fitting technique is a well-known technique, and its specific process will not be described in detail here.
[0040] It should be noted that, regarding the selection of the concentration of the fluorourea standard solution, implementers may also use fluorourea standard solutions of other concentrations, and this application does not impose specific restrictions.
[0041] In the testing of each batch of samples, this embodiment follows the steps below for the extraction and detection of fluorourea in agricultural products. First, 10g of the sample to be tested is weighed into a 50mL centrifuge tube, 10mL of acetonitrile is added, and the mixture is vortexed at 2500rpm for 10min and then ultrasonically extracted for 15min. Then, 5g of NaCl is added to the centrifuge tube and immediately shaken well, followed by centrifugation at 4000rpm for 5min to obtain the extract. Next, the extract is further purified, and 2mL of the extract is transferred to a centrifuge tube containing 50mg of PSA and 50mg of C18 adsorbent. The tube is then vortexed at 2500rpm for 5min and centrifuged at 4000rpm for 5min. The supernatant is collected and analyzed using liquid chromatography-mass spectrometry (LC-MS) to obtain the corresponding MS chromatogram data of the sample. The implementer may also use other methods to obtain the MS chromatogram data of the sample; this application does not impose specific limitations.
[0042] Step S2: Analyze the separation degree of chromatographic peaks and the curvature of the baseline in the MS chromatogram, construct the baseline offset coefficient of the MS chromatogram, adjust the number of iterations in the baseline correction algorithm, and perform baseline correction on the MS chromatogram in combination with the baseline correction algorithm.
[0043] In actual testing, pigments and lipids in the matrix are difficult to completely remove. These residual substances can inhibit or enhance the ionization efficiency of the target analytes, leading to deviations in the MS chromatograms and varying degrees of noise and baseline interference. To obtain more accurate chromatographic data, baseline correction and noise reduction are performed.
[0044] First, background interference from the instrument and incomplete purification of the sample matrix can cause slow changes or overall shifts in chromatographic data. This drift interferes with the identification and integration of target chromatographic peaks, leading to inaccurate measurements of peak area or peak height, thus affecting the accuracy and reliability of target analyte quantification. Baseline correction can obtain a smoother baseline, making chromatographic peaks clearer and laying the foundation for accurate subsequent quantitative analysis.
[0045] Ideally, MS chromatograms exhibit narrow peak widths, large spacing between adjacent peaks, and a flat, relatively smooth baseline. Greater interference leads to denser peak distribution, more complex baseline drift, and greater baseline curvature. Therefore, the severity of baseline drift is analyzed based on this characteristic.
[0046] For the MS chromatogram of the sample being tested, the peak points are obtained. In this embodiment, the peak point acquisition process is as follows: A least-squares fitting technique is used to obtain a fitting curve for all data in the MS chromatogram, and the maximum value of the obtained fitting curve is calculated as the peak point. Then, the time interval between any two adjacent peak points is calculated; this time interval reflects the degree of separation between adjacent chromatographic peaks. Finally, the ratio of the mean of the time intervals between all adjacent peak points in the MS chromatogram to the total time length of the MS chromatogram is used as the chromatographic peak separation coefficient, denoted as [missing value]. The result The smaller the value, the denser the distribution of peaks in the MS chromatogram. Further, the baseline curve of the MS chromatogram is obtained using spline fitting, and then the curvature of the baseline curve at all time points is calculated. It should be noted that the accuracy of the baseline obtained at this time is relatively low and is only used to analyze the degree to which the baseline deviates from a flat state. The curvature at each time point is normalized to eliminate the influence of units. In this embodiment, the maximum curvature of all historically detected MS chromatograms is used as the preset maximum curvature, and the ratio of the curvature at each time point to the preset maximum curvature is used as the normalized result of the curvature at each time point. Many existing normalization methods exist, and implementers can also use other normalization methods to normalize the curvature; this application does not impose specific limitations.
[0047] The mean of all the absolute values of curvature after normalization is calculated and denoted as the baseline curvature. The result This reflects the complex characteristics of the baseline morphological changes in the MS chromatogram. The spline fitting method and curvature calculation are well-known techniques, and their specific processes will not be elaborated further.
[0048] Then, the baseline offset coefficient is calculated. Preferably, in this embodiment, the baseline offset coefficient is expressed as follows:
[0049]
[0050] In the formula, The baseline shift coefficient of the MS chromatogram; The peak separation coefficient of the MS chromatogram; Baseline curvature of the MS chromatogram; , In order to adjust the parameters, in this embodiment, in order to... and Adding them with equal weights , The values are all set to 0.5. Among them, In other embodiments of this application, the implementer may set the parameters according to the actual situation. , The value of .
[0051] income The larger the value, the greater the severity of baseline drift in the MS chromatogram. The distance distribution characteristics between peaks are reflected by analyzing the ratio of the mean time difference between adjacent peaks to the total duration, while the degree of baseline deviation from a flat state is reflected by calculating the baseline curvature. In cases of severe baseline drift, the spacing between peaks will decrease significantly, and the baseline morphology will exhibit more complex fluctuations.
[0052] This application utilizes the asymmetric least squares method for baseline correction. The number of iterations in this algorithm directly affects the baseline processing results. When the baseline drift in the MS chromatogram is more severe, a larger number of iterations is often required for gradual adjustment to accurately capture the complex shape of the baseline; conversely, if the baseline of the MS chromatogram is relatively flat, a smaller number of iterations can be used for rapid convergence.
[0053] Therefore, this application normalizes the baseline shift coefficient of the MS chromatogram. Preferably, in this embodiment, the maximum value of the baseline shift coefficient of all samples in all historical batches is obtained, and the ratio of the baseline shift coefficient of the sample being tested in the current batch to the maximum value is used as the normalized value of the baseline shift coefficient of the sample being tested in the current batch. In other embodiments of this application, the implementer may also use other normalization methods to normalize the baseline shift coefficient. In the prior art, the iteration number of the asymmetric least squares method is usually in the range of [10, 30]. Therefore, in this embodiment, the iteration number of the algorithm is set to the range of [10, 30]. Furthermore, a more suitable iteration number is selected according to the baseline state of the MS chromatogram during the actual detection process. Specifically, the normalized value of the baseline shift coefficient of the MS chromatogram is multiplied by the maximum iteration number to obtain the adaptively adjusted iteration number, which is then input into the asymmetric least squares method to perform baseline correction on the MS chromatogram. Its advantage lies in its ability to perform targeted correction based on the degree of influence from the matrix, ensuring baseline correction accuracy while improving the accuracy of subsequent denoising analysis. The maximum number of iterations is 30, and a lower limit of 10 iterations is set to ensure the basic correction capabilities of the asymmetric least squares algorithm.
[0054] Step S3: Obtain the curves of the fluorourea components within the corresponding time range from the baseline-corrected MS chromatogram; based on the signal-to-noise ratio of the data in the curves, combined with the symmetry of the curves and the degree of disorder of the data changes in the curves, construct the first significance coefficient of the curves.
[0055] Different processing conditions (such as temperature or pH) can significantly affect the stability of pesticide residues, which is reflected in the morphology of the MS chromatogram. Taking fluorouracil as an example, it is relatively stable in high-acid, high-oil, or high-moisture crops, but easily degrades in high-starch crops, leading to increased noise and a decreased signal-to-noise ratio in the MS chromatogram. The peaks in such chromatograms often exhibit sawtooth-like fluctuations and irregular peak shape distortions; in contrast, ideally, the chromatographic peaks should be smoother, exhibiting a symmetrical or near-symmetrical Gaussian peak shape.
[0056] To obtain the above characteristics, this application uses the least squares method to perform curve fitting on the baseline-corrected MS chromatogram. Because different components have different adsorption, partition, or ion exchange capacities, the elution times of each component from the chromatographic column are inconsistent. Therefore, the time range of the chromatographic peak corresponding to the fluorourea component in the known MS chromatogram is obtained. The curve within this time range is extracted from the baseline-corrected and fitted MS chromatogram of the tested sample and denoted as the peak curve. First, the signal-to-noise ratio of the data in this peak curve is calculated and denoted as... The result This reflects the noise level present in this segment of the MS chromatogram. Then, the portion to the left of the maximum value in the peak curve is taken as the rising edge of the peak curve, and the portion to the right of the maximum value is taken as the falling edge. When the peak distortion is more pronounced, the corresponding difference value will frequently show large changes, while the difference value of a smooth peak is smaller and changes more gradually. Therefore, the first-order difference sequence of the chromatographic data corresponding to the rising and falling edges is calculated separately, and then the coefficient of variation of the obtained first-order difference sequence is calculated. The mean of the absolute values of the two coefficients of variation is taken as the peak distortion coefficient of the chromatogram, denoted as [missing value]. The result The larger the value, the higher the dispersion of local fluctuations relative to the average change in the spectral peak data, and the more obvious the corresponding sawtooth fluctuation characteristics. Furthermore, to obtain the symmetry characteristics of the spectral peak curve, two data points at the same height on the spectral peak curve are obtained. The time distance between the left data point and the maximum value point of the spectral peak curve is recorded as the left distance, and the time distance between the right data point and the corresponding peak point is recorded as the right distance. The difference between the left distance and the right distance is calculated and recorded as the first difference. The half-width at half-maximum (WHM) of the spectral peak curve is obtained. It should be noted that the calculation of the peak WHM is a well-known technique, and the specific process will not be elaborated further. 10%, 20%, and 30% of the peak height of the spectral peak curve are then obtained respectively. The first difference at the 90% position is calculated, and the sum is accumulated. The ratio of this sum to the half-width at half-maximum (WHM) of the spectral peak curve is then used as the asymmetry coefficient, denoted as [missing value]. The result The larger the value, the worse the symmetry of the spectral peak curve. The difference can be the absolute value of the difference, the square of the difference, etc. In this embodiment, the first difference is the absolute value of the difference between the left distance and the right distance. It should be noted that in other embodiments of this application, the implementer can also obtain the asymmetry coefficient by calculating skewness or comparing the similarity between the rising and falling edges.
[0057] Then, the first significance coefficient of the sawtooth fluctuations and asymmetric characteristics of the spectral peak data under noise interference is calculated. Preferably, in this embodiment, the expression for the first significance coefficient is:
[0058]
[0059] In the formula, The first significance coefficient of the spectral peak curve; is the peak distortion coefficient of the spectral peak curve; The asymmetry coefficient of the spectral peak curve; The signal-to-noise ratio is mentioned. As a preset minimum positive number, in this embodiment, The value is set to 0.01; This is the normalization function. In other embodiments of this application, the implementer can set it according to the actual situation. The value of .
[0060] In this embodiment, the signal-to-noise ratio normalization is achieved by analyzing the MS chromatograms of all samples. This is achieved by normalizing the values to their maximum and minimum values. The purpose of this is to avoid the denominator being 0.
[0061] income The larger the value, the more pronounced the sawtooth fluctuations and asymmetric characteristics of the corresponding spectral peak data under noise interference. The larger the value, the more drastic the local changes in the spectral peak data; The larger the value, the greater the difference in the horizontal distance from the rising and falling edges to the peak apex at the same peak height on both sides of the spectral peak curve, and the lower the symmetry of the left and right sides of the spectral peak curve. It directly reflects the degree to which noise affects peak shape quality.
[0062] Step S4: Adjust the filter window size based on the first significance coefficient, filter the baseline-corrected MS chromatogram using the filtering algorithm, and perform pesticide residue detection using the filtered MS chromatogram and the mapping relationship.
[0063] This application utilizes SG filtering technology to denoise MS chromatograms. Since the abnormal states of data in MS chromatograms are influenced by multiple factors, the degree of disturbance is irregular. The processing effect of SG filtering technology is highly sensitive to the size of the filtering window. Therefore, applying different levels of filtering based on the obtained first significance coefficient helps to obtain more accurate MS chromatograms. Specifically, in the prior art, the value range of the filtering window size is usually [5, 13]. Therefore, in this embodiment, the value range of the filtering window size is set to an odd number within the range of [5, 13]. The first significance coefficient of the MS chromatograms of all samples in the same batch is normalized using the minimax method to obtain the normalized value of the first significance coefficient of the MS chromatogram of the tested sample. The approximate window value of the filtering window for the MS chromatogram of the tested sample is then calculated, expressed as: ,in, This is an approximation of the window value. The normalized value of the first significance coefficient of the MS chromatogram of the tested sample. , In this embodiment, the minimum and maximum values of the preset filter window size are respectively given. , The values are 5 and 13, respectively. The odd number closest to this window size is used as the adaptively adjusted filter window size, which is then used as the filter window size in the SG filtering algorithm. The baseline-corrected MS chromatogram is used as the input to the algorithm for denoising. In other embodiments of this application, the implementer can set the minimum and maximum values of the filter window size according to the actual situation.
[0064] Thus, a more accurate MS chromatogram was obtained through baseline correction and noise reduction. Then, the peak area of the corresponding peak for fluorourea was calculated based on the obtained MS chromatogram. This calculation step is a well-known technique and will not be elaborated further. The obtained peak area was input into the mapping relationship between fluorourea concentration and chromatographic peak area obtained above to obtain the concentration of fluorourea in the sample being tested. This enables the detection of pesticide residues in the sample, helping to improve the accuracy of pesticide residue detection.
[0065] A schematic diagram illustrating the process of obtaining the baseline offset coefficient is shown below. Figure 2 As shown.
[0066] Based on the same inventive concept as the above methods, this application also provides a pesticide residue detection system for agricultural products based on mass spectrometry technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for pesticide residue detection of agricultural products based on mass spectrometry technology.
[0067] In summary, this application provides a method for detecting pesticide residues in agricultural products based on mass spectrometry. By acquiring the MS chromatogram of the sample to be tested, and through in-depth analysis of the distribution distance characteristics of adjacent peaks and the morphological changes of the baseline, the degree of baseline shift is accurately assessed. Based on the assessment results, the parameters in the baseline correction algorithm are optimized, and the MS chromatogram is then processed for baseline correction. Its advantage lies in reducing the interference of complex matrices in the actual detection process. Then, based on the signal-to-noise ratio of the data in the curve corresponding to the fluorourea component in the MS chromatogram within the time range, combined with the symmetry of the curve and the degree of disorder of the data changes in the curve, a first significance coefficient is constructed. The sawtooth fluctuations and asymmetric characteristics of the peak data under noise interference are analyzed in depth, and the perturbation influence characteristics of the corresponding peaks of the sample to be tested are accurately obtained. Based on this, the filter window in the filtering technology is adjusted, and the MS chromatogram of the sample after baseline correction is then denoised to obtain a more accurate MS chromatogram, which helps to improve the accuracy of pesticide residue detection.
Claims
1. A method for detecting pesticide residues in agricultural products based on mass spectrometry, characterized in that, The method includes the following steps: Construct a mapping relationship between the concentration of fluorourea in a standard fluorourea solution and the peak area in its MS chromatogram; obtain the MS chromatogram of the sample being tested; The degree of separation of chromatographic peaks and the degree of baseline curvature in MS chromatograms are analyzed to construct a baseline shift coefficient for MS chromatograms, which is used to adjust the number of iterations in the baseline correction algorithm. The MS chromatograms are then baseline corrected in combination with the baseline correction algorithm. The curves of the fluorourea components within the corresponding time range are obtained from the baseline-corrected MS chromatograms; based on the signal-to-noise ratio of the data in the curves, combined with the symmetry of the curves and the degree of disorder of the data changes in the curves, the first significance coefficient of the curves is constructed. The filter window size is adjusted based on the first significance coefficient, and the MS chromatogram after baseline correction is filtered using a filtering algorithm. Pesticide residues are then detected using the filtered MS chromatogram and the mapping relationship.
2. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 1, characterized in that, The process for obtaining the baseline offset coefficient is as follows: Obtain the peak values of the chromatographic peaks in the MS chromatogram, calculate the time interval between two adjacent peak values, and use the ratio of the mean of all the time intervals in the MS chromatogram to the total time length of the MS chromatogram as the chromatographic peak separation coefficient. The baseline curve of the MS chromatogram is obtained by spline fitting, the curvature at each time point on the baseline curve is calculated, and the mean of the absolute values of curvature at all times is denoted as the baseline curvature. The baseline offset coefficient of the MS chromatogram is constructed based on the separation degree coefficient and the baseline curvature.
3. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 2, characterized in that, The baseline offset coefficient is specifically: The baseline offset coefficient is obtained by weighting and summing the inverse proportional mapping value of the separation degree coefficient with the baseline curvature.
4. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 1, characterized in that, The process of adjusting the number of iterations in the baseline correction algorithm is as follows: The product of the normalized value of the baseline offset coefficient and the preset maximum number of iterations is used as the number of iterations in the adjusted baseline correction algorithm.
5. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 1, characterized in that, The process of obtaining the first significance coefficient is as follows: The curves corresponding to the time range of the fluorourea component in the MS chromatogram are denoted as peak curves. The degree of data variation disorder on both sides of the maximum value in the spectral peak curve is analyzed to determine the peak distortion coefficient of the spectral peak curve. Analyze the symmetry of the spectral peak curves to construct the asymmetry coefficients of the spectral peak curves; The first significance coefficient is determined based on the signal-to-noise ratio, the peak distortion coefficient, and the asymmetry coefficient.
6. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 5, characterized in that, The peak distortion coefficient is the mean absolute value of the coefficients of variation of the first-order difference sequences of the curves on the left and right sides of the maximum value in the spectral peak curve.
7. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 5, characterized in that, The process of obtaining the asymmetric coefficients is as follows: Two data points at the same height on the spectral peak curve are obtained. The time distance between the data point to the left of the peak and the corresponding peak is recorded as the left distance, and the time distance between the data point to the right of the peak and the corresponding peak is recorded as the right distance. The difference between the left distance and the right distance is calculated and recorded as the first difference. The sum of the first differences at multiple preset heights on the spectral peak curve is used as the asymmetry coefficient of the spectral peak curve.
8. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 5, characterized in that, The first significance coefficient is directly proportional to the peak distortion coefficient and the asymmetry coefficient, and inversely proportional to the signal-to-noise ratio.
9. The method for detecting pesticide residues in agricultural products based on mass spectrometry as described in claim 1, characterized in that, The process of adjusting the filter window size based on the first significance coefficient is as follows: Calculate the window approximation of the MS chromatogram of the tested sample. , The expression is: ,in, The normalized value of the first significance coefficient of the MS chromatogram of the tested sample. , These are the minimum and maximum values of the preset filter window size, respectively; The odd number that is closest to the approximate value of the window is used as the adjusted filter window size.
10. A pesticide residue detection system for agricultural products based on mass spectrometry technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.