Method and system for detecting residual amount of plant growth regulator in hydroponic vegetables

Abstract

The embodiment of the present invention relates to the technical field of growth regulator residue detection, and specifically discloses a method and system for detecting the residue of plant growth regulators in hydroponic vegetables. The embodiment of the present invention obtains hydroponic vegetable samples by obtaining regulation monitoring data, selecting a residue detection area, sampling the residue detection area; performing sample pretreatment on the hydroponic vegetable samples to obtain powdered vegetable samples; selecting an extraction solvent, injecting the vegetable powder sample, extracting and purifying, and obtaining purified vegetable samples; performing plant growth regulator detection on the purified vegetable samples to obtain residue detection data; based on the sample label information, performing identification and quantitative analysis of the residue detection data, obtaining and displaying the residue detection results. The rapid detection of the residue of plant growth regulators in hydroponic vegetables can be achieved with high accuracy, efficiency, and sensitivity, which has great practical significance for ensuring food safety, promoting human health, and social and economic development.

Description

Technical Field

[0001] The invention belongs to the technical field of growth regulator residue detection, and in particular relates to a method and a system for detecting plant growth regulator residues in hydroponic vegetables. Background Art

[0002] Hydroponics is a widely popular vegetable planting model in recent years. Compared with traditional planting models, it has the advantages of high yield, quick effect, good taste, good quality, and high economic benefits. Plant growth regulators may be used in the hydroponics process to improve crop growth, increase crop yield and quality, but plant growth regulators such as 6-benzyladenine, sodium 4-chlorophenoxyacetate, and gibberellins are low-toxic pesticides and may cause precocious puberty, fertility disorders, premature aging, cancer, and other diseases.

[0003] Plant growth regulators are a type of artificially synthesized chemical compounds that have similar physiological effects and chemical structures to plant endogenous hormones. They can regulate various physiological development processes and environmental responses of plants, affecting plant germination, growth, flowering, and fruiting. They are currently used in the production and cultivation of hydroponic vegetables in China, and have shown great potential for increasing production and considerable economic benefits.

[0004] However, most plant growth regulators, like other pesticides, have certain toxicity. For example, excessive intake of 6-benzyladenine can irritate the skin and mucous membranes, cause damage to the esophagus and gastric mucosa, and cause nausea and vomiting. Some plant growth regulators or degradation products have certain developmental toxicity, teratogenicity and mutagenicity, posing a potential threat to human health.

[0005] In recent years, due to the blind and irrational use of plant growth regulators, the problem of plant growth regulator residues in hydroponic vegetables has become increasingly serious, posing a hidden danger to the quality and safety of hydroponic vegetables. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a method and system for detecting the residual amount of plant growth regulators in hydroponic vegetables, aiming to solve the problems raised in the background technology.

[0007] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for detecting the residual amount of plant growth regulators in hydroponic vegetables, the method specifically comprising the following steps: Acquire adjustment monitoring data, select a residue detection area, sample the residue detection area, obtain hydroponic vegetable samples, and record sample label information; Performing sample pretreatment on the hydroponic vegetable sample to obtain a powdered vegetable sample; Selecting an extraction solvent, injecting it into the vegetable powder sample, performing extraction and purification treatment, and obtaining a purified vegetable sample; Based on liquid chromatography-tandem mass spectrometry technology, the purified vegetable samples are tested for plant growth regulators to obtain residue detection data; Based on the sample label information, the plant growth regulators are identified and quantitatively analyzed on the residue detection data, and the residue detection results are obtained and displayed.

[0008] A detection system for the residual amount of plant growth regulators in hydroponic vegetables, the system comprising a detection area sampling unit, a sample pretreatment unit, a sample extraction and purification unit, a growth regulator detection unit and an identification and quantitative analysis unit, wherein: A detection area sampling unit, used to obtain adjustment monitoring data, select a residue detection area, sample the residue detection area, obtain hydroponic vegetable samples, and record sample label information; A sample pretreatment unit, used for performing sample pretreatment on the hydroponic vegetable sample to obtain a powdered vegetable sample; A sample extraction and purification unit, used for selecting an extraction solvent, injecting the vegetable powder sample, performing extraction and purification treatment, and obtaining a purified vegetable sample; A growth regulator detection unit, used to perform plant growth regulator detection on the purified vegetable sample based on liquid chromatography-tandem mass spectrometry technology to obtain residue detection data; The identification and quantitative analysis unit is used to identify and quantitatively analyze the plant growth regulators on the residue detection data, and obtain and display the residue detection results.

[0009] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention obtains the regulation monitoring data, selects the residue detection area, samples the residue detection area, and obtains the hydroponic vegetable sample; performs sample pretreatment on the hydroponic vegetable sample to obtain the powder vegetable sample; selects the extraction solvent, injects the vegetable powder sample, extracts and purifies it, and obtains the purified vegetable sample; performs plant growth regulator detection on the purified vegetable sample to obtain the residue detection data; based on the sample label information, the residue detection data is identified and quantitatively analyzed to obtain and display the residue detection result. The rapid detection of the residue of plant growth regulators in hydroponic vegetables can be realized accurately, efficiently, and with high sensitivity, which has great practical significance for ensuring food safety, promoting human health, and social and economic development. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0011] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0012] Figure 2 A flow chart of residual detection area sampling in the method provided by an embodiment of the present invention is shown.

[0013] Figure 3 A flow chart of obtaining powdered vegetable samples in the method provided in an embodiment of the present invention is shown.

[0014] Figure 4 A flow chart of extraction and purification processing in the method provided by an embodiment of the present invention is shown.

[0015] Figure 5 A flow chart of obtaining residual detection data in the method provided in an embodiment of the present invention is shown.

[0016] Figure 6 A flow chart of the identification and quantitative analysis of regulators in the method provided in an embodiment of the present invention is shown.

[0017] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0018] Figure 8 The structure block diagram of the detection area sampling unit in the system provided by the embodiment of the present invention is shown.

[0019] Fig. 9 The structure block diagram of the sample extraction and purification unit in the system provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understandable that most plant growth regulators, like other pesticides, have certain toxicity. For example, excessive intake of 6-benzyladenine can irritate the skin and mucous membranes, cause damage to the esophagus and gastric mucosa, and cause nausea and vomiting. Some plant growth regulators or degradation products have certain developmental toxicity, teratogenicity and mutagenicity, posing a potential threat to human health. In recent years, due to the blind and unreasonable use of plant growth regulators, the problem of plant growth regulator residues in hydroponic vegetables has become increasingly serious, posing a hidden danger to the quality and safety of hydroponic vegetables.

[0022] To solve the above problems, the embodiment of the present invention obtains the regulation monitoring data, selects the residue detection area, samples the residue detection area, obtains the hydroponic vegetable sample, and records the sample label information; performs sample pretreatment on the hydroponic vegetable sample to obtain the powder vegetable sample; selects the extraction solvent, injects the vegetable powder sample, performs extraction and purification treatment, and obtains the purified vegetable sample; based on the liquid chromatography-tandem mass spectrometry technology, performs plant growth regulator detection on the purified vegetable sample to obtain the residue detection data; based on the sample label information, performs identification and quantitative analysis of the plant growth regulator on the residue detection data, obtains and displays the residue detection results. It can realize the rapid detection of the residue of plant growth regulators in hydroponic vegetables with high accuracy, efficiency and sensitivity, which has great practical significance for ensuring food safety, promoting human health and social and economic development.

[0023] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0024] Specifically, the method for detecting the residual amount of plant growth regulators in hydroponic vegetables comprises the following steps: Step S101, obtain adjustment monitoring data, select a residue detection area, sample the residue detection area, obtain hydroponic vegetable samples, and record sample label information.

[0025] In an embodiment of the present invention, a vegetable hydroponic space is determined, and the vegetable hydroponic space is divided into a plurality of hydroponic detection areas according to a preset division size, and regulation monitoring data on the types and usage of plant growth regulators in the vegetable hydroponic space is obtained, and the total amount of plant growth regulators used in different areas is quantitatively analyzed according to the plurality of hydroponic detection areas, and a residual detection area with the largest amount of plant growth regulators used is selected from the plurality of hydroponic detection areas, and then the residual detection area is sampled to obtain hydroponic vegetable samples, and sample label information such as the hydroponic vegetable variety, residual detection area, sampling time and sampling location of the hydroponic vegetable samples is recorded.

[0026] Specifically, Figure 2 A flow chart of residual detection area sampling in the method provided by an embodiment of the present invention is shown.

[0027] Among them, in the preferred embodiment provided by the present invention, the obtaining of the adjustment monitoring data, selecting the residue detection area, sampling the residue detection area, obtaining the hydroponic vegetable sample, and recording the sample label information specifically include the following steps: Step S1011, determining the vegetable hydroponic space and dividing it into multiple hydroponic detection areas; Step S1012, obtaining adjustment monitoring data; Step S1013, performing adjustment quantification analysis on the adjustment monitoring data, and selecting a residual detection area with the largest adjustment quantification from the multiple hydroponic detection areas; Step S1014, sampling the residue detection area to obtain hydroponic vegetable samples; Step S1015, recording the sample label information of the hydroponic vegetable sample, wherein the sample label information includes the hydroponic vegetable variety, residue detection area, sampling time and sampling location.

[0028] Among them, in the preferred embodiment provided by the present invention, the adjustment quantitative analysis of the adjustment monitoring data and the selection of the residual detection area with the largest adjustment quantification from the multiple hydroponic detection areas specifically include the following steps: Obtain the global minimum and maximum values ​​of each monitoring parameter in the adjustment monitoring data of each hydroponic detection area; The data value of each monitoring parameter is standardized according to the global minimum value and maximum value of each monitoring parameter to obtain the standardized monitoring parameter; The corresponding weight is given according to the fluctuation degree (i.e. variance) of the distribution of each monitoring parameter; Define a risk range of low risk, medium risk and high risk for each monitoring parameter based on expert knowledge or historical data; According to the interval of the monitoring parameter value in the risk range, a triangular membership function model is used to calculate the membership degree of each monitoring parameter; For example: If the parameter value is lower than the set low risk value, the membership is 0.

[0029] If the parameter value is between the low risk value and the medium risk value, the membership increases linearly as the parameter value increases; for example, the closer the value is to the medium risk value, the higher the membership.

[0030] If the parameter value is between the medium risk value and the high risk value, the membership decreases linearly; the closer the value is to the high risk value, the lower the membership.

[0031] If the parameter value is higher than the set high risk value, the membership is 0.

[0032] The membership degree of each monitoring parameter is weighted and summed with the corresponding weight to obtain the fuzzy score of each hydroponic detection area; Calculate the spatial distances between different hydroponic detection areas to obtain the location distribution of the hydroponic detection areas; The global optimization is performed with maximizing the fuzzy score as the optimization goal and the distance between the hydroponic detection areas as the penalty item. After the optimization is completed, the residual detection area is obtained.

[0033] In an embodiment of the present invention, a dynamic weight mechanism is introduced, and the weight is dynamically adjusted according to the fluctuation (variance) of the data distribution. Dimensions with high fluctuations (which may represent more significant influencing factors) are given higher weights, while dimensions with low fluctuations contribute less. This design improves the adaptability of the model. Fuzzy logic is also introduced, which is a theoretical framework suitable for dealing with non-deterministic problems. In actual residue detection, different adjustment monitoring data may not be completely independent and there is a certain degree of uncertainty.

[0034] By introducing fuzzy membership functions (such as triangular membership functions), data can be soft-classified to make the model more robust. Then, through the global optimization objective function, not only the maximum adjustment quantification value of a single area is selected, but also the spatial distribution between regions is considered to avoid excessive concentration of high-risk areas and ignore potential problems in other areas. This global optimization method can improve monitoring coverage. Through the combination of fuzzy scoring functions and global optimization objectives, not only the quality of a single area is considered, but also the total quantification value and spatial distribution are collaboratively optimized. The final hydroponic detection area has a higher risk score, and its distribution location can also ensure a wider coverage, which can better capture the complex relationships and trends of the data and solve the limitations of traditional linear methods.

[0035] Furthermore, the method for detecting the residual amount of plant growth regulators in hydroponic vegetables further comprises the following steps: Step S102, performing sample pretreatment on the hydroponic vegetable sample to obtain a powdered vegetable sample.

[0036] In an embodiment of the present invention, a sample grinding medium is selected from a plurality of preselected grinding media, and then the hydroponic vegetable sample is pre-treated by sample grinding through the sample grinding medium, and the hydroponic vegetable sample is ground into powder to obtain a powdered vegetable sample.

[0037] It is understandable that the multiple pre-selected grinding media may be ceramic balls, stainless steel balls, etc.

[0038] Specifically, Figure 3 A flow chart of obtaining powdered vegetable samples in the method provided in an embodiment of the present invention is shown.

[0039] Among them, in the preferred embodiment provided by the present invention, the sample pretreatment of the hydroponic vegetable sample to obtain the powdered vegetable sample specifically includes the following steps: Step S1021, selecting a sample grinding medium; Step S1022, pre-processing the hydroponic vegetable sample by sample grinding using the sample grinding medium to obtain a powdered vegetable sample.

[0040] Furthermore, the method for detecting the residual amount of plant growth regulators in hydroponic vegetables further comprises the following steps: Step S103, selecting an extraction solvent, injecting the vegetable powder sample, performing extraction and purification treatment, and obtaining a purified vegetable sample.

[0041] In an embodiment of the present invention, a residue detection request uploaded by a staff member is received, the residue detection request is analyzed, an extraction solvent is selected, and the extraction solvent is injected into a vegetable powder sample, and then a mixing, shaking and centrifugal extraction process is performed, and temperature monitoring and control are performed in real time to prevent degradation or solvent volatilization. The sample extract is obtained, and then an SPE column is selected according to the residue detection request. The sample extract is purified according to the SPE column to remove impurities and obtain a purified vegetable sample.

[0042] It is understood that the extraction solvent may be acetonitrile, acetone or methanol.

[0043] Specifically, Figure 4 A flow chart of extraction and purification processing in the method provided by an embodiment of the present invention is shown.

[0044] Among them, in the preferred embodiment provided by the present invention, the selection of extraction solvent, injection of the vegetable powder sample, extraction and purification, and obtaining the purified vegetable sample specifically include the following steps: Step S1031, receiving a residue detection request; Step S1032, selecting an extraction solvent according to the residue detection request; Step S1033, injecting the extraction solvent into the vegetable powder sample; Step S1034, performing extraction processing of mixing, shaking and centrifugation, and performing real-time temperature monitoring and control to obtain a sample extract; Step S1035, selecting an SPE column according to the residue detection request; Step S1036, purifying the sample extract according to the SPE column to obtain a purified vegetable sample.

[0045] Among them, in the preferred embodiment provided by the present invention, the extraction process of mixing, shaking and centrifuging, and real-time temperature monitoring and control, and obtaining the sample extract specifically include the following steps: A concentration variation model was constructed based on the diffusion of vegetable powder samples in the extraction solvent, the influence of centrifugal force driving the outward migration of compounds, and the adsorption and desorption behavior of compounds at the liquid-solid interface. The temperature distribution model is constructed based on the diffusion effect of heat conduction in space, the influence of mechanical energy of oscillation and centrifugation being converted into thermal energy, and the behavior of heat exchange caused by the temperature difference between the device and the environment; According to the dynamic relationship between oscillation frequency and centrifugal force, a coupling model is constructed; Using the concentration variation model, the concentration distribution of the target compound and the non-target compound that changes dynamically with time and space is obtained to obtain the concentration of the target compound and the concentration of the non-target compound; The temperature distribution model is used to obtain the change of temperature distribution in the device over time and space to obtain the temperature deviation; The coupling model is used to obtain the optimal dynamic change curve of centrifugal force and oscillation frequency during the extraction process, so as to select the centrifugal force and oscillation frequency parameter values ​​with the optimal energy consumption; A multi-objective optimization function is constructed with maximizing the concentration of target compounds, minimizing the concentration of non-target compounds, minimizing energy consumption, and minimizing temperature deviation as optimization objectives; The multi-objective optimization function is optimized using the gradient descent method. After the optimization is completed, the best combination of oscillation frequency, centrifugal force and temperature is obtained; The optimal combination of oscillation frequency, centrifugal force and temperature is experimentally verified, and after the verification is completed, a sample extract is obtained.

[0046] In the embodiment of the present invention, the traditional extraction method is usually based on static conditions, while the present invention takes into account the dynamic changes of centrifugal force, oscillation frequency and temperature and their coupling effects, and combines the dynamic distribution of the concentration of target compounds and non-target compounds to design a better solution. This dynamic optimization can adjust the extraction parameters in real time to maximize efficiency and minimize resources. And through the introduction of dynamic variables and the application of global optimization algorithms, the model can find the optimal parameter combination in real time, which is suitable for different stages of the extraction process, avoiding the inefficiency of "trial and error" in traditional empirical methods, and improving the accuracy of extraction process optimization.

[0047] Furthermore, the method for detecting the residual amount of plant growth regulators in hydroponic vegetables further comprises the following steps: Step S104, based on liquid chromatography-tandem mass spectrometry technology, the purified vegetable sample is tested for plant growth regulators to obtain residue detection data.

[0048] In an embodiment of the present invention, liquid chromatography-tandem mass spectrometry technology is used to automatically inject purified vegetable samples, select a chromatographic column, perform chromatographic column separation, and then perform tandem mass spectrometry detection to obtain residue detection data.

[0049] It is understandable that liquid chromatography-tandem mass spectrometry is suitable for analyzing thermally unstable and highly polar compounds, and does not require derivatization treatment. In addition, liquid chromatography-tandem mass spectrometry has the advantages of high sensitivity and accuracy, and a wide range of applications. In a preferred embodiment of the present invention, the residual amounts of 8 drugs (4-chlorophenoxyacetic acid, indoleacetic acid, indolebutyric acid, 1-naphthylacetic acid, 6-benzyladenine, 2,4-D, gibberellin and carbendazim) in bean sprouts are simultaneously determined by liquid chromatography-tandem mass spectrometry: the sample is extracted and concentrated with 0.1% glacial acetic acid-acetonitrile solution, and purified by a dispersed solid phase extractant, and 1mmol / L ammonium acetate and methanol are used as mobile phases, and the detection is performed using ESI+ and ESI- modes. The results show that the average recovery rate of the method is 71.6% to 87.9%, the relative standard deviation is not more than 14.6%, and the lower limit of quantification is 5μg / kg, which can be used to determine the residual amount of plant growth regulators in bean sprouts.

[0050] Specifically, Figure 5 A flow chart of obtaining residual detection data in the method provided in an embodiment of the present invention is shown.

[0051] Among them, in the preferred embodiment provided by the present invention, the plant growth regulator detection of the purified vegetable sample based on liquid chromatography-tandem mass spectrometry technology and obtaining the residue detection data specifically include the following steps: Step S1041, automatically injecting the purified vegetable sample; Step S1042, selecting a chromatographic column and performing chromatographic column separation; Step S1043, perform tandem mass spectrometry detection to obtain residue detection data.

[0052] Among them, in the preferred embodiment provided by the present invention, the selecting of the chromatographic column and the chromatographic column separation specifically include the following steps: Obtain the chromatographic column parameters, mobile phase parameters, flow rate, column temperature, gradient time, and properties of target compounds in vegetable samples; Based on the interaction between the pH value of the mobile phase and the acidity and alkalinity of the functional groups of the target compounds, the joint effect of flow rate and column length on separation performance, and the relationship between gradient time and the molecular polarity of the target compounds, feature combination and nonlinear transformation are used to generate additional high-order interaction features; Extract the molecular properties of the target compound, perform molecular simulation based on the molecular properties of the target compound to calculate the interaction energy characteristics, generate the characteristic value of the filler-compound interaction based on the structural characteristics of the chromatographic filler surface and the interaction energy characteristics, and use the characteristic value of the filler-compound interaction as an auxiliary feature; The mobile phase gradient change curve is segmented, and the slope and change rate of each segment are extracted to generate time series data. The time series data is input into the time series convolutional network to extract time series features and obtain dynamic features. The mobile phase parameters and filler type are embedded and converted into numerical vectors, and then concatenated with the chromatographic column parameters, flow rate and column temperature to obtain the first high-dimensional vector; The auxiliary features and the high-order interactive features are concatenated to form a second high-dimensional vector, and the first high-dimensional vector and the second high-dimensional vector are concatenated to form a static feature; The static features are input into the static branch, and the static features are processed through the attention mechanism to obtain weighted features. The weighted features are sent into the fully connected neural network to capture the static interaction relationship between the static features and obtain the first high-order feature representation; The dynamic features are input into the dynamic branch, and the influence of dynamic changes in the dynamic features on the separation performance is captured by the time series convolutional network to obtain the second high-order feature representation; The first high-order feature representation and the second high-order feature representation are spliced ​​to form a fusion feature, and the fusion feature is respectively sent to four independent prediction heads to obtain the resolution of the predicted target compound, the column efficiency of the predicted target compound, the selectivity factor of the predicted target compound, and the predicted total separation time; Determine whether the predicted resolution of the target compound, the predicted column efficiency of the target compound, the predicted selectivity factor of the target compound and the predicted total separation time meet the actual needs. If so, select the corresponding parameters for chromatographic column separation.

[0053] In order to ensure the prediction effect of the model, its four prediction heads can construct corresponding loss functions and train the overall model through weighted joint optimization.

[0054] In an embodiment of the present invention, the fusion of experimental features, high-order interactive features, dynamic time series features and theoretical calculation features can fully describe the complexity of the chromatographic separation process, overcoming the problem of information loss that may be caused by using only experimental parameters in traditional models. By introducing the interactive features of compound molecular simulation and experimental conditions, the model can more accurately capture the physicochemical behavior between the target compound and the filler. In addition, a multi-task learning architecture is adopted. Multi-task learning solves the problems of overfitting or insufficient feature utilization that may be caused by traditional single-task prediction models by sharing underlying features, and is suitable for experimental scenarios that require comprehensive evaluation of separation performance. By fusing experimental data and theoretical calculation data, the model can not only reflect the laws of real experimental data, but also use theoretical calculation results to make up for the problem of insufficient experimental data. Experimental personnel do not need to rely entirely on traditional experience and manual analysis. Through the prediction results and optimization suggestions of the model, the whole process from experimental design to result analysis can be intelligentized.

[0055] Furthermore, the method for detecting the residual amount of plant growth regulators in hydroponic vegetables further comprises the following steps: Step S105, based on the sample label information, identifying and quantitatively analyzing the plant growth regulators on the residue detection data, and obtaining and displaying the residue detection results.

[0056] In an embodiment of the present invention, peak identification and peak area integration are performed on the residue detection data, and a calibration curve is drawn according to the preset standard detection data. According to the peak identification, the residual multiple plant growth regulators are determined, and then based on the calibration curve, the peak area integration is analyzed to determine the detection residues corresponding to the multiple plant growth regulators. Then, based on the sample label information, multiple plant growth regulators and the corresponding detection residues, the residue detection results are generated, and the residue detection results are visualized and displayed to the staff.

[0057] Specifically, Figure 6 A flow chart of the identification and quantitative analysis of regulators in the method provided in an embodiment of the present invention is shown.

[0058] Among them, in the preferred embodiment provided by the present invention, the identification and quantitative analysis of the residue detection data of the plant growth regulator based on the sample label information, and the acquisition and display of the residue detection results specifically include the following steps: Step S1051, performing peak identification and peak area integration on the residue detection data; Step S1052, drawing a calibration curve according to preset standard test data; Step S1053, determining a plurality of plant growth regulators according to peak identification; Step S1054, analyzing the peak area integral based on the calibration curve to determine the detected residual amounts of the plurality of plant growth regulators; Step S1055, generating and displaying residue detection results according to the sample label information, the plurality of plant growth regulators and the corresponding detection residue amounts.

[0059] Among them, in the preferred embodiment provided by the present invention, the peak area integral is analyzed based on the calibration curve to determine the detection residues of the plurality of plant growth regulators, which specifically includes the following steps: Obtain the peak area data displayed by the chromatograph at each time point; Perform nonlinear transformation on the peak area at each time point to obtain the nonlinear contribution value of the peak area; Calculate the temperature difference between the temperature at each time point and the standard temperature, and generate a temperature correction factor based on the temperature difference; The nonlinear contribution value of the peak area is adjusted using the temperature correction factor to obtain the temperature-corrected peak area contribution value at each time point; Calculating the mean and standard deviation of the distribution volume of the plant growth regulator during the purification process at each time point, and adjusting the temperature-corrected peak area contribution value at each time point according to the mean and standard deviation to obtain an adjusted peak area contribution value; Obtaining a nonlinear correction factor of the calibration curve according to the nonlinear relationship between concentration and peak area, and correcting the adjusted peak area contribution value using the nonlinear correction factor to obtain a corrected peak area contribution value; Integrate the corrected peak area contribution value to obtain the total contribution value; A random error term that obeys the normal distribution is introduced and added to the total contribution value to obtain the final monitored residue.

[0060] In an embodiment of the present invention, by dynamically monitoring and integrating the data such as the chromatographic peak area and temperature at each time point during the experiment, the instantaneous change of the target compound in the entire detection process can be captured, rather than relying solely on an average value or data at a single moment. This time resolution improves the accuracy of the calculation results, especially in a complex experimental environment, and dynamic integration can better describe the real-time changes of the detection conditions. Among them, temperature and purification efficiency are important factors that can dynamically change during the chromatographic detection process. By dynamically correcting the impact of these conditions on the final detection results, it is possible to more realistically reflect the actual situation in the experiment and avoid the deviation that may be caused by the traditional static model. And the concentration correction part is also introduced. In the range of high concentration or low concentration, the relationship between the chromatographic peak area and the concentration is often no longer a simple linear relationship, but may have a saturation effect or nonlinear response. By introducing nonlinear fitting parameters, the instrument response behavior in different concentration intervals can be better described, thereby improving the versatility of the detection method.

[0061] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0062] Among them, in another preferred embodiment provided by the present invention, the detection system of the residual amount of plant growth regulator in hydroponic vegetables comprises: The detection area sampling unit 101 is used to obtain the adjustment monitoring data, select the residue detection area, sample the residue detection area, obtain the hydroponic vegetable sample, and record the sample label information.

[0063] In an embodiment of the present invention, the detection area sampling unit 101 determines a vegetable hydroponic space, and divides the vegetable hydroponic space into a plurality of hydroponic detection areas according to a preset division size, and obtains regulation monitoring data on the type and usage of plant growth regulators in the vegetable hydroponic space, and quantitatively analyzes the total amount of plant growth regulators used in different areas according to the plurality of hydroponic detection areas, and selects a residual detection area with the largest amount of plant growth regulators used from the plurality of hydroponic detection areas, and then samples the residual detection area to obtain hydroponic vegetable samples, and records sample label information such as the hydroponic vegetable variety, residual detection area, sampling time and sampling location of the hydroponic vegetable samples.

[0064] Specifically, Figure 8 It shows a structural block diagram of the detection area sampling unit 101 in the system provided by an embodiment of the present invention.

[0065] Among them, in the preferred embodiment provided by the present invention, the detection area sampling unit 101 specifically includes: The area division module 1011 is used to determine the vegetable hydroponic space and divide it into multiple hydroponic detection areas; A data acquisition module 1012 is used to acquire adjustment monitoring data; The area selection module 1013 is used to perform adjustment quantitative analysis on the adjustment monitoring data, and select a residual detection area with the largest adjustment quantification from the multiple hydroponic detection areas; The area sampling module 1014 is used to sample the residue detection area to obtain hydroponic vegetable samples; The information recording module 1015 is used to record the sample label information of the hydroponic vegetable sample, and the sample label information includes the hydroponic vegetable variety, residue detection area, sampling time and sampling location.

[0066] Furthermore, the detection system for the residual amount of plant growth regulators in hydroponic vegetables also includes: The sample preprocessing unit 102 is used to perform sample preprocessing on the hydroponic vegetable sample to obtain a powdered vegetable sample.

[0067] In an embodiment of the present invention, the sample preprocessing unit 102 selects a sample grinding medium from a plurality of preselected grinding media, and then performs sample grinding preprocessing on the hydroponic vegetable sample through the sample grinding medium, grinding the hydroponic vegetable sample into powder to obtain a powdered vegetable sample.

[0068] The sample extraction and purification unit 103 is used to select an extraction solvent, inject the vegetable powder sample, perform extraction and purification, and obtain a purified vegetable sample.

[0069] In an embodiment of the present invention, the sample extraction and purification unit 103 receives a residue detection request uploaded by a staff member, analyzes the residue detection request, selects an extraction solvent, and then injects the extraction solvent into the vegetable powder sample, and then performs mixing, shaking and centrifugal extraction processing, and performs real-time temperature monitoring and control to prevent degradation or solvent volatilization, obtains the sample extract, and then selects an SPE column according to the residue detection request, and purifies the sample extract according to the SPE column to remove impurities and obtain a purified vegetable sample.

[0070] Specifically, Fig. 9 It shows a structural block diagram of the sample extraction and purification unit 103 in the system provided by an embodiment of the present invention.

[0071] Among them, in the preferred embodiment provided by the present invention, the sample extraction and purification unit 103 specifically includes: A request receiving module 1031 is used to receive a residue detection request; A solvent selection module 1032, for selecting an extraction solvent according to the residue detection request; A sample injection module 1033, used to inject the extraction solvent into the vegetable powder sample; The extraction processing module 1034 is used to perform extraction processing such as mixing, shaking and centrifugation, and to monitor and control the temperature in real time to obtain the sample extract; An SPE column selection module 1035 is used to select an SPE column according to the residue detection request; The purification module 1036 is used to purify the sample extract according to the SPE column to obtain a purified vegetable sample.

[0072] Furthermore, the detection system for the residual amount of plant growth regulators in hydroponic vegetables also includes: The growth regulator detection unit 104 is used to perform plant growth regulator detection on the purified vegetable sample based on liquid chromatography-tandem mass spectrometry technology to obtain residue detection data.

[0073] In the embodiment of the present invention, the growth regulator detection unit 104 uses liquid chromatography-tandem mass spectrometry technology to automatically inject purified vegetable samples, select a chromatographic column, perform chromatographic column separation, and then perform tandem mass spectrometry detection to obtain residue detection data.

[0074] The identification and quantitative analysis unit 105 is used to identify and quantitatively analyze the plant growth regulators on the residue detection data, and obtain and display the residue detection results.

[0075] In an embodiment of the present invention, the identification and quantitative analysis unit 105 performs peak identification and peak area integration on the residual detection data, and draws a calibration curve based on the preset standard detection data. According to the peak identification, multiple residual plant growth regulators are determined, and then the peak area integral is analyzed based on the calibration curve to determine the detection residues corresponding to the multiple plant growth regulators. Then, based on the sample label information, multiple plant growth regulators and the corresponding detection residues, the residue detection results are generated, and the residue detection results are visualized and displayed to the staff.

[0076] Among them, in the preferred embodiment provided by the present invention, the identification and quantitative analysis unit specifically includes: A data identification module, used for performing peak identification and peak area integration on the residue detection data; A calibration curve drawing module is used to draw a calibration curve according to preset standard test data; A regulator identification module, for determining a plurality of plant growth regulators according to peak identification; A residue analysis module, used to analyze the peak area integral based on the calibration curve to determine the detected residues of the plurality of plant growth regulators; The result display module is used to generate and display the residue detection result according to the sample label information, the plurality of plant growth regulators and the corresponding detection residue amounts.

[0077] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0079] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the residual amount of plant growth regulators in hydroponic vegetables, characterized in that: The method specifically comprises the following steps: Acquire adjustment monitoring data, select a residue detection area, sample the residue detection area, obtain hydroponic vegetable samples, and record sample label information; Performing sample pretreatment on the hydroponic vegetable sample to obtain a powdered vegetable sample; Selecting an extraction solvent, injecting it into the vegetable powder sample, performing extraction and purification treatment, and obtaining a purified vegetable sample; Based on liquid chromatography-tandem mass spectrometry technology, the purified vegetable samples are tested for plant growth regulators to obtain residue detection data; Based on the sample label information, identifying and quantitatively analyzing the plant growth regulators on the residue detection data, and obtaining and displaying the residue detection results; The steps of obtaining the adjustment monitoring data, selecting the residue detection area, sampling the residue detection area, obtaining the hydroponic vegetable samples, and recording the sample label information specifically include the following steps: Determine the vegetable hydroponic space and divide it into multiple hydroponic testing areas; Obtaining regulation monitoring data; Performing regulation quantitative analysis on the regulation monitoring data, and selecting a residual detection area with the largest regulation quantification from the multiple hydroponic detection areas; Sampling the residue detection area to obtain hydroponic vegetable samples; The sample label information of the hydroponic vegetable sample is recorded, wherein the sample label information includes the hydroponic vegetable variety, residue detection area, sampling time and sampling location.

2. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 1, characterized in that: The step of performing adjustment quantitative analysis on the adjustment monitoring data and selecting the residual detection area with the largest adjustment quantification from the multiple hydroponic detection areas specifically comprises the following steps: Obtain the global minimum and maximum values ​​of each monitoring parameter in the adjustment monitoring data of each hydroponic detection area; According to the global minimum and maximum values ​​of each monitoring parameter, the data value of each monitoring parameter is standardized to obtain the standardized monitoring parameter; The corresponding weight is given according to the fluctuation degree of each monitoring parameter distribution; Define a risk range of low risk, medium risk and high risk for each monitoring parameter based on expert knowledge or historical data; According to the interval of the monitoring parameter value in the risk range, a triangular membership function model is used to calculate the membership degree of each monitoring parameter; The membership degree of each monitoring parameter is weighted and summed with the corresponding weight to obtain the fuzzy score of each hydroponic detection area; Calculate the spatial distances between different hydroponic detection areas to obtain the location distribution of the hydroponic detection areas; The global optimization is performed with maximizing the fuzzy score as the optimization goal and the distance between the hydroponic detection areas as the penalty item. After the optimization is completed, the residual detection area is obtained.

3. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 2, characterized in that: The sample pretreatment of the hydroponic vegetable sample to obtain the powdered vegetable sample specifically comprises the following steps: Select sample grinding media; The hydroponic vegetable sample is subjected to sample grinding pretreatment by means of the sample grinding medium to obtain a powdered vegetable sample.

4. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 3, characterized in that: The step of selecting an extraction solvent, injecting the vegetable powder sample, performing extraction and purification, and obtaining a purified vegetable sample specifically comprises the following steps: receiving residue testing requests; selecting an extraction solvent according to the residue detection request; injecting the extraction solvent into the vegetable powder sample; Performing extraction processing such as mixing, shaking and centrifugation, and performing real-time temperature monitoring and control to obtain sample extract; Selecting an SPE column according to the residue detection request; The sample extract is purified according to the SPE column to obtain a purified vegetable sample.

5. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 4, characterized in that: The mixing, shaking and centrifuging extraction process and real-time temperature monitoring and control to obtain the sample extract specifically include the following steps: A concentration variation model was constructed based on the diffusion of vegetable powder samples in the extraction solvent, the influence of centrifugal force driving the outward migration of compounds, and the adsorption and desorption behavior of compounds at the liquid-solid interface. The temperature distribution model is constructed based on the diffusion effect of heat conduction in space, the influence of mechanical energy of oscillation and centrifugation being converted into thermal energy, and the behavior of heat exchange caused by the temperature difference between the device and the environment; According to the dynamic relationship between oscillation frequency and centrifugal force, a coupling model is constructed; Using the concentration variation model, the concentration distribution of the target compound and the non-target compound that changes dynamically with time and space is obtained to obtain the concentration of the target compound and the concentration of the non-target compound; Using the temperature distribution model, the temperature distribution in the device is obtained as a function of time and space to obtain the temperature deviation; The coupling model is used to obtain the optimal dynamic change curve of centrifugal force and oscillation frequency during the extraction process, so as to select the centrifugal force and oscillation frequency parameter values ​​with the optimal energy consumption; A multi-objective optimization function is constructed with maximizing the concentration of target compounds, minimizing the concentration of non-target compounds, minimizing energy consumption, and minimizing temperature deviation as optimization objectives; The multi-objective optimization function is optimized using the gradient descent method. After the optimization is completed, the best combination of oscillation frequency, centrifugal force and temperature is obtained; The optimal combination of oscillation frequency, centrifugal force and temperature is experimentally verified, and after the verification is completed, a sample extract is obtained.

6. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 5, characterized in that: The method of performing plant growth regulator detection on the purified vegetable sample based on liquid chromatography-tandem mass spectrometry technology and obtaining residue detection data specifically comprises the following steps: Carrying out automated sampling on the purified vegetable sample; Select a chromatographic column and perform chromatographic column separation; Tandem mass spectrometry was performed to obtain residue detection data.

7. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 6, characterized in that: The step of selecting a chromatographic column and performing chromatographic column separation specifically comprises the following steps: Obtain the chromatographic column parameters, mobile phase parameters, flow rate, column temperature, gradient time, and properties of target compounds in vegetable samples; Based on the interaction between the pH value of the mobile phase and the acidity and alkalinity of the functional groups of the target compounds, the joint effect of flow rate and column length on separation performance, and the relationship between gradient time and the molecular polarity of the target compounds, feature combination and nonlinear transformation are used to generate additional high-order interaction features; Extract the molecular properties of the target compound, perform molecular simulation based on the molecular properties of the target compound to calculate the interaction energy characteristics, generate the characteristic value of the filler-compound interaction based on the structural characteristics of the chromatographic filler surface and the interaction energy characteristics, and use the characteristic value of the filler-compound interaction as an auxiliary feature; The mobile phase gradient change curve is segmented, and the slope and change rate of each segment are extracted to generate time series data. The time series data is input into the time series convolutional network to extract time series features and obtain dynamic features. The mobile phase parameters and filler type are embedded and converted into numerical vectors, and then concatenated with the chromatographic column parameters, flow rate and column temperature to obtain the first high-dimensional vector; The auxiliary features and the high-order interactive features are concatenated to form a second high-dimensional vector, and the first high-dimensional vector and the second high-dimensional vector are concatenated to form a static feature; The static features are input into the static branch, and the static features are processed through the attention mechanism to obtain weighted features. The weighted features are sent into the fully connected neural network to capture the static interaction relationship between the static features and obtain the first high-order feature representation; The dynamic features are input into the dynamic branch, and the influence of dynamic changes in the dynamic features on the separation performance is captured by the time series convolutional network to obtain the second high-order feature representation; The first high-order feature representation and the second high-order feature representation are spliced ​​to form a fusion feature, and the fusion feature is respectively sent to four independent prediction heads to obtain the resolution of the predicted target compound, the column efficiency of the predicted target compound, the selectivity factor of the predicted target compound, and the predicted total separation time; Determine whether the predicted resolution of the target compound, the predicted column efficiency of the target compound, the predicted selectivity factor of the target compound and the predicted total separation time meet the actual needs. If so, select the corresponding parameters for chromatographic column separation.

8. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 7, characterized in that: The identification and quantitative analysis of the residue detection data of the plant growth regulator based on the sample label information, and obtaining and displaying the residue detection results specifically include the following steps: Performing peak identification and peak area integration on the residue detection data; Draw a calibration curve based on the preset standard test data; Based on the peak identification, multiple plant growth regulators were determined; Based on the calibration curve, the peak area integral is analyzed to determine the detected residual amounts of the plurality of plant growth regulators; Residue detection results are generated and displayed based on the sample label information, the plurality of plant growth regulators and the corresponding detection residue amounts.

9. The method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to claim 8, characterized in that: The step of analyzing the peak area integral based on the calibration curve to determine the detected residual amounts of the plurality of plant growth regulators specifically comprises the following steps: Obtain the peak area data displayed by the chromatograph at each time point; Perform nonlinear transformation on the peak area at each time point to obtain the nonlinear contribution value of the peak area; Calculate the temperature difference between the temperature at each time point and the standard temperature, and generate a temperature correction factor based on the temperature difference; The nonlinear contribution value of the peak area is adjusted using the temperature correction factor to obtain the temperature-corrected peak area contribution value at each time point; Calculating the mean and standard deviation of the distribution volume of the plant growth regulator during the purification process at each time point, and adjusting the temperature-corrected peak area contribution value at each time point according to the mean and standard deviation to obtain an adjusted peak area contribution value; Obtaining a nonlinear correction factor of the calibration curve according to the nonlinear relationship between concentration and peak area, and correcting the adjusted peak area contribution value using the nonlinear correction factor to obtain a corrected peak area contribution value; Integrate the corrected peak area contribution value to obtain the total contribution value; A random error term that obeys the normal distribution is introduced and added to the total contribution value to obtain the final monitored residue.

10. A system for detecting the residual amount of plant growth regulators in hydroponic vegetables, the system being applied to the method for detecting the residual amount of plant growth regulators in hydroponic vegetables according to any one of claims 1 to 9, characterized in that: The system comprises a detection area sampling unit, a sample pretreatment unit, a sample extraction and purification unit, a growth regulator detection unit and an identification and quantitative analysis unit, wherein: A detection area sampling unit, used to obtain adjustment monitoring data, select a residue detection area, sample the residue detection area, obtain hydroponic vegetable samples, and record sample label information; A sample pretreatment unit, used for performing sample pretreatment on the hydroponic vegetable sample to obtain a powdered vegetable sample; A sample extraction and purification unit, used for selecting an extraction solvent, injecting the vegetable powder sample, performing extraction and purification treatment, and obtaining a purified vegetable sample; A growth regulator detection unit, used to perform plant growth regulator detection on the purified vegetable sample based on liquid chromatography-tandem mass spectrometry technology to obtain residue detection data; The identification and quantitative analysis unit is used to identify and quantitatively analyze the plant growth regulators on the residue detection data, and obtain and display the residue detection results.

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