Wine pesticide residue detection method and system

Abstract

The invention provides a wine pesticide residue detection method and system, and belongs to the technical field of pesticide detection. The method comprises the following steps: acquiring each time of historical pesticide spraying information of grapes in a whole growth cycle process based on weather change information after each time of historical pesticide spraying and growth information of corresponding target grapes during the corresponding time of historical pesticide spraying; based on the acquired information, constructing a combination matrix after the corresponding time of historical pesticide spraying, and analyzing the combination matrix to determine pesticide residues of the target grapes after the corresponding time of spraying so as to construct a residue influence set; screening a target detection mode from a pesticide detection database based on the residue influence set, and performing chromatographic analysis on the grape wine made from the target grape to obtain an initial residue set; and performing residue optimization on the initial residue set according to the residue influence set, and determining and outputting the output quality grade of the wine. An analysis mode is comprehensively screened from three aspects of pesticide spraying condition, weather change condition and grape growth condition, and the detection efficiency is improved.

Description

Technical Field

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

[0002] With the improvement of living standards, people are paying more and more attention to the health of diet. The drinking preference has gradually changed from strong liquor to low-alcohol liquor, and from grain wine to fruit wine. Since wine contains natural antioxidants, drinking in moderation is beneficial to human health. Therefore, wine has become one of the most popular alcoholic beverages in the world.

[0003] In order to improve the yield and quality of grapes during their growth period, more or less pesticides are used. Some pesticides are not easy to decompose after application and will remain on the fruit to varying degrees and enter the wine through fermentation. The common method of pesticide detection is to use chromatographic analysis to directly detect the wine to determine the presence of pesticide residues. However, the chromatographic analysis method used is generally fixed, which may result in insensitive measurement of certain residues, resulting in low detection efficiency.

[0004] Therefore, the present invention provides a method and system for detecting pesticide residues in wine. Summary of the invention

[0005] The present invention provides a method and system for detecting pesticide residues in wine, which are used to determine the possible residue conditions before grape brewing by comprehensively analyzing the pesticide spraying conditions, weather changes, and grape growth conditions involved in the entire life cycle of grapes, and then reasonably screen the chromatographic analysis method to effectively ensure the sensitivity and accuracy of the measurement of pesticide residue parameters, thereby improving the detection efficiency.

[0006] The present invention provides a method for detecting pesticide residues in wine, comprising:

[0007] Step 1: Obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the target grapes at the time of the corresponding historical pesticide spraying;

[0008] Step 2: construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set;

[0009] Step 3: Based on the residue impact set, a target detection method is selected from the pesticide detection database to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set;

[0010] Step 4: Optimize the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

[0011] Preferably, a combination matrix corresponding to the historical pesticide spraying is constructed based on the historical pesticide spraying information, weather change information and growth information, including:

[0012] Construct a row vector {P i ,Q i ,C i}, where Pi is the i-th historical pesticide spraying information; Qi is the weather change information after the i-th historical pesticide spraying; Ci is the growth information of the target grapes corresponding to the i-th historical pesticide spraying;

[0013] Construct a combination matrix based on all row vectors Among them, the value of i is 1, 2, 3, ..., n.

[0014] Preferably, analyzing the combination matrix to determine the pesticide residues in the target grapes after the corresponding spraying includes:

[0015] Extracting a first pesticide dosage and a first pesticide concentration from historical pesticide spraying information of each row vector in the combination matrix, and determining a first volatility coefficient of each pesticide component in the first pesticide dosage from a component-volatility database based on the pesticide components of the first pesticide dosage;

[0016] Determining a second volatility coefficient of the first pesticide from a name-volatility database according to the name of the first pesticide;

[0017] Determine the concentration difference between the first pesticide concentration and the standard registration concentration of the first pesticide application, and at the same time, input the weather change information after the corresponding historical pesticide spraying into the volatilization analysis model matched with the corresponding first pesticide application to obtain the concentration volatilization factor set of the first pesticide application;

[0018] Determining the initial residue of the target grapes after the corresponding spraying of the target grapes based on the first volatility coefficient, the second volatility coefficient, the concentration difference and the concentration volatility factor set;

[0019] right as well as Conduct mutual analysis to determine the pesticide penetration factor;

[0020] The corresponding initial residue is analyzed based on the pesticide penetration factor to obtain the pesticide residue of the target grape after the corresponding spraying.

[0021] Preferably, as well as Inter-analysis is performed to determine pesticide penetration factors, including:

[0022] Initialize the combination matrix Z and obtain The first mean Pave, based on The second mean Cave, based on The third mean Qave;

[0023] The combination of Pi and Ci is respectively input into the first association analysis model to obtain the first permeability factor based on the combination of Pi and Ci, and the first auxiliary factor Df1 is calculated;

[0024]

[0025] Among them, (s1i) ave is the average value based on all s1i; s1i is the i-th first permeability factor; is the variance based on all s1i;

[0026] Based on the first auxiliary factor Df1, the first mean Pave, and the second mean Cave, a first linear function Df1=α1×G1(Pave, Cave)+a1 is constructed, wherein a1 is a constant, G1(Pave, Cave) is a variable function based on Pave, Cave; α1 is a variable coefficient;

[0027] The combination of Qi and Ci is respectively input into the second association analysis model to obtain the second permeability factor based on the combination of Qi and Ci, and a second linear function Df2=α2×G2(Qave, Cave)+a2 based on the third mean Qave and the second mean Cave is constructed, wherein Df2 is the second auxiliary factor based on all second permeability factors; a2 is a constant, G2(Qave, Cave) is a variable function based on Qave, Cave; α2 is a variable coefficient;

[0028] Based on the first linear function and the second linear function, we can obtain The first linear function and the second linear function are fused to obtain the pesticide penetration factor.

[0029] Preferably, obtaining the pesticide penetration factor comprises:

[0030]

[0031]

[0032] Wherein, Ct is the corresponding pesticide penetration factor; G1(Pave1, Cave1) is the variable function of the corresponding spraying and the mean Pave1 based on the historical pesticide spraying information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; G2(Qave1, Cave1) is the variable function of the corresponding spraying and the mean Qave1 based on the weather change information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; Δ1 is the constant under the corresponding spraying; m is the number of corresponding sprayings.

[0033] Preferably, based on the residue impact set, a target detection method is screened from a pesticide detection database to perform chromatographic analysis on the wine produced from the target grapes to obtain an initial residue set, including:

[0034] Determine the pesticide composition of each pesticide spraying in the residue impact concentration based on the residual concentration of the corresponding pesticide residue, and extract the residue sequence of the same residue parameter;

[0035] Based on the residue sequence of each residue parameter, the required parameters are screened, and based on the parameter accuracy combination of the required parameters, at least two target detection methods are screened from the pesticide detection data;

[0036] Based on each target detection method, chromatographic analysis is performed on the wine produced from the target grapes to obtain a chromatogram of each target detection method;

[0037] Perform data analysis of the same residual parameter on all chromatograms to obtain the current value of the corresponding residual parameter;

[0038] The current values ​​of all residual parameters are counted to obtain the initial residual set.

[0039] Preferably, performing residue optimization on the initial residue set comprises:

[0040] Obtain the residual variance based on each residual series separately;

[0041] The residual optimization is performed on the current value of the corresponding residual parameter according to the residual variance.

[0042] Preferably, determining the output quality grade of the wine comprises:

[0043] Determine the total number of spraying times N in the whole growth cycle and determine the spraying ratio n / N in combination with the historical number of spraying times n, and determine the reference planning value in combination with the final planning value of each residual parameter in the whole growth cycle;

[0044] Obtain the average value of the residual optimization value and the reference planning value of each residual parameter, and obtain the quality impact factor matching the parameter from the value-parameter-impact comparison table;

[0045] Extract the maximum impact factor from all quality impact factors and combine the factor variances of the remaining impact factors to obtain the final impact factor;

[0046] Match the final impact factor with the factor-grade comparison table to obtain the output quality grade.

[0047] The present invention provides a wine pesticide residue detection system, comprising:

[0048] An information acquisition module is used to obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the corresponding target grapes at the time of the corresponding historical pesticide spraying;

[0049] An impact analysis module is used to construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set;

[0050] A chromatographic analysis module, used for selecting a target detection method from a pesticide detection database based on the residue impact set to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set;

[0051] The grade determination module is used to perform residue optimization on the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

[0052] Compared with the prior art, the beneficial effects of this application are as follows:

[0053] By conducting a comprehensive analysis of the pesticide spraying, weather changes, and grape growth conditions involved in the entire life cycle of grapes, we can determine the possible residues that may exist before grape brewing, and then rationally screen the chromatographic analysis method to effectively ensure the sensitivity and accuracy of the measurement of pesticide residue parameters, thereby improving detection efficiency.

[0054] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 This is a flow chart of a method for detecting pesticide residues in wine according to an embodiment of the present invention;

[0058] Figure 2 4 is a structural diagram of a wine pesticide residue detection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] The present invention provides a method for detecting pesticide residues in wine. Figure 1 As shown, including:

[0061] Step 1: Obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the target grapes at the time of the corresponding historical pesticide spraying;

[0062] Step 2: construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set;

[0063] Step 3: Based on the residue impact set, a target detection method is selected from the pesticide detection database to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set;

[0064] Step 4: Optimize the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

[0065] In this embodiment, the target garden refers to a garden where grapes are grown, and the target grapes refer to grapes used for brewing.

[0066] In this embodiment, the full growth cycle refers to the entire process of grapes from flowering to fruiting and to ripening for picking. In order to ensure the good growth of grapes during the full growth cycle, pesticides will be sprayed, and the number of times the pesticides are sprayed and the type of pesticides to be sprayed each time are pre-planned, and it is only necessary to spray on time. However, in the actual spraying process, since the drug concentration is manually configured according to the instructions, there will always be certain human errors, such as too much water, too little medicine, etc., which will cause a certain deviation between the concentration and the plan.

[0067] In this embodiment, the pesticide spraying information includes: the pesticide used and the actual concentration and the standard registration concentration of the pesticide used.

[0068] Weather change information refers to the daily weather temperature, humidity, windiness, and weather conditions from the time of spraying to the time of next spraying.

[0069] Growth information refers to the growth status of the grapes at the time of the corresponding spraying, such as the size of the fruit, the hardness of the fruit, etc.

[0070] In this embodiment, the combined matrix is ​​constructed from three aspects: pesticide spraying information, weather change information, and growth information.

[0071] In this embodiment, the combination matrix is ​​analyzed to determine the pesticide residue after the corresponding spraying, and the pesticide residue includes the estimated residue conditions of different residue parameters.

[0072] In this embodiment, the residual impact set = {pesticide residues after each spraying}.

[0073] In this embodiment, the pesticide detection database contains detection methods that match different estimated residue combinations, and the detection methods include: liquid-liquid extraction, solid phase extraction, solid phase microextraction, matrix solid phase dispersion extraction, dispersed solid phase extraction, etc. The sensitivity of different methods to the measured pesticide residue parameters is different.

[0074] In this embodiment, the initial residue set is obtained by numerically analyzing the same residue parameters after chromatographic analysis in a target detection manner.

[0075] In this embodiment, the purpose of residual optimization is to further ensure that the residual parameters are closer to the actual ones.

[0076] In this embodiment, the output quality level is determined based on the residue optimization result.

[0077] The beneficial effect of the above technical solution is: through a comprehensive analysis of the pesticide spraying conditions, weather changes, and grape growth conditions involved in the entire life cycle of grapes, the possible residues before grape brewing can be determined, and then the chromatographic analysis method can be reasonably screened to effectively ensure the sensitivity and accuracy of the measurement of pesticide residue parameters, thereby improving the detection efficiency.

[0078] The present invention provides a method for detecting pesticide residues in wine, which constructs a combination matrix corresponding to the historical pesticide spraying based on historical pesticide spraying information, weather change information and growth information, including:

[0079] Construct the row vector {Pi, Qi, Ci} of each historical pesticide spraying, where Pi is the information of the i-th historical pesticide spraying; Qi is the weather change information after the i-th historical pesticide spraying; Ci is the growth information of the target grapes corresponding to the i-th historical pesticide spraying;

[0080] Construct a combination matrix based on all row vectors Among them, the value of i is 1, 2, 3, ..., n.

[0081] In this embodiment, n is the actual total number of historical pesticide sprayings of the target grapes in the whole growth cycle. P1 is the first historical pesticide spraying information; Q1 is the weather change information after the first historical pesticide spraying; C1 is the growth information of the target grapes corresponding to the first historical pesticide spraying; Pn is the nth historical pesticide spraying information; Qn is the weather change information after the nth historical pesticide spraying; Cn is the growth information of the target grapes corresponding to the nth historical pesticide spraying.

[0082] The beneficial effect of the above technical solution is that by obtaining the row vector of each pesticide spraying, it is easy to construct a combination matrix, which provides a basis for subsequent analysis of pesticide residues.

[0083] The present invention provides a method for detecting pesticide residues in wine, which analyzes the combination matrix to determine the pesticide residues in the target grapes after the corresponding spraying, comprising:

[0084] Extracting a first pesticide dosage and a first pesticide concentration from historical pesticide spraying information of each row vector in the combination matrix, and determining a first volatility coefficient of each pesticide component in the first pesticide dosage from a component-volatility database based on the pesticide components of the first pesticide dosage;

[0085] Determining a second volatility coefficient of the first pesticide from a name-volatility database according to the name of the first pesticide;

[0086] Determine the concentration difference between the first pesticide concentration and the standard registration concentration of the first pesticide application, and at the same time, input the weather change information after the corresponding historical pesticide spraying into the volatilization analysis model matched with the corresponding first pesticide application to obtain the concentration volatilization factor set of the first pesticide application;

[0087] Determining the initial residue of the target grapes after the corresponding spraying of the target grapes based on the first volatility coefficient, the second volatility coefficient, the concentration difference and the concentration volatility factor set;

[0088] right as well as Conduct mutual analysis to determine the pesticide penetration factor;

[0089] The corresponding initial residue is analyzed based on the pesticide penetration factor to obtain the pesticide residue of the target grape after the corresponding spraying.

[0090] In this embodiment, the pesticides to be sprayed and the pesticide components are pre-planned, and the component-volatility database includes the pesticide components involved in different pesticides and the volatility coefficients of different components. The volatility coefficients of the components in the corresponding pesticides can be directly obtained based on the database.

[0091] For example, pesticide ingredients include organochlorine, organophosphorus, organic nitrogen, organic sulfur, carbamate, pyrethroid, amide compounds, urea compounds, ether compounds, phenolic compounds, phenoxycarboxylic acids, amidines, triazoles, heterocyclic compounds, benzoic acids, organometallic compounds, etc. It is known that different ingredients have different volatility coefficients due to differences in their chemical and physical properties.

[0092] In this embodiment, the name of the drug refers to the name of the pesticide. For example, mancozeb is used to treat downy mildew, and "mancozeb" is the name of the pesticide.

[0093] In this embodiment, the name-volatile database contains different medication names and volatility coefficients matching the names, and the database is pre-planned, that is, the ingredient-volatile database and the name-volatile database both exist as reference standards for volatility.

[0094] In this embodiment, the standard concentration of each pesticide is pre-planned, and only at this concentration can the best control effect be achieved.

[0095] In this embodiment, the concentration difference = the first pesticide concentration - the standard registration concentration.

[0096] In this embodiment, the volatilization analysis model is obtained by training a neural network model based on the volatilization conditions of pesticides under different weather conditions as samples. Therefore, a concentration volatilization factor set can be obtained, wherein the concentration volatilization factor set = {volatilization factor of pesticide under the weather conditions of the first day, volatilization factor of pesticide under the weather conditions of the first to second days, volatilization factor of pesticide under the weather conditions of the first to third days...}.

[0097] In this embodiment, the initial residue of the target grapes after the corresponding spraying refers to the residue one day before the time corresponding to the next spraying of pesticides.

[0098] In this embodiment, the initial residue refers to the initial residue of each residual parameter (pesticide component involved in the spraying of pesticides) involved one day before the time corresponding to the next spraying of pesticides after the pesticides are sprayed, and Among them, Yave is the factor mean of the first ceiling (R / 2) volatility factors in the volatility factor set, is the factor variance of the remaining volatility factor; Y Nc is the volatilization correction coefficient of the corresponding spraying pesticide based on the concentration difference Nc. When Nc is greater than or equal to 0, Y Nc The value is greater than 0, otherwise, Y Nc The value is less than 0; Hd2 is the corresponding second volatility coefficient; (Hdj) cc To extract the volatility coefficient of the corresponding residual parameter from all the first volatility coefficients.

[0099] In this embodiment, the value range of the pesticide penetration factor is 0 to 1.

[0100] In this embodiment, the final residual amount of the corresponding residual parameter=the initial residual amount of the corresponding residual parameter (1+corresponding pesticide penetration factor), and the final residual amount is estimated.

[0101] The beneficial effects of the above technical solution are: by obtaining the volatility coefficient from both the pesticide composition and the name of the drug, and combining the weather change information to obtain the concentration volatility factor set, the initial residue can be effectively obtained, and due to the interaction between weather, growth, concentration, etc., the initial residue is further adjusted by determining the pesticide penetration factor, thereby ensuring the accuracy of pesticide residue acquisition and providing a reliable basis for subsequent matching detection methods.

[0102] The present invention provides a method for detecting pesticide residues in wine. as well as Inter-analysis is performed to determine pesticide penetration factors, including:

[0103] Initialize the combination matrix Z and obtain The first mean Pave, based on The second mean Cave, based on The third mean Qave;

[0104] The combination of Pi and Ci is respectively input into the first association analysis model to obtain the first permeability factor based on the combination of Pi and Ci, and the first auxiliary factor Df1 is calculated;

[0105]

[0106] Among them, (s1i) ave is the average value based on all s1i; s1i is the i-th first permeability factor; is the variance based on all s1i;

[0107] Based on the first auxiliary factor Df1, the first mean Pave, and the second mean Cave, a first linear function Df1=α1×G1(Pave, Cave)+a1 is constructed, wherein a1 is a constant, G1(Pave, Cave) is a variable function based on Pave, Cave; α1 is a variable coefficient;

[0108] The combination of Qi and Ci is respectively input into the second association analysis model to obtain the second permeability factor based on the combination of Qi and Ci, and a second linear function Df2=α2×G2(Qave, Cave)+a2 based on the third mean Qave and the second mean Cave is constructed, wherein Df2 is the second auxiliary factor based on all second permeability factors; a2 is a constant, G2(Qave, Cave) is a variable function based on Qave, Cave; α2 is a variable coefficient;

[0109] Based on the first linear function and the second linear function, we can obtain The first linear function and the second linear function are fused to obtain the pesticide penetration factor.

[0110] Preferably, obtaining the pesticide penetration factor comprises:

[0111]

[0112]

[0113] Wherein, Ct is the corresponding pesticide penetration factor; G1(Pave1, Cave1) is the variable function of the corresponding spraying and the mean Pave1 based on the historical pesticide spraying information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; G2(Qave1, Cave1) is the variable function of the corresponding spraying and the mean Qave1 based on the weather change information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; Δ1 is the constant under the corresponding spraying; m is the number of corresponding sprayings.

[0114] In this embodiment, the initialization process is to make the matrix Convert to Among them, Pb1, Qb1, Cb1, Pbn, Qbn, Cbn are the standard values ​​after the corresponding initialization processing, and the matching of the standard values ​​is based on the information-value database matching, which contains different information and standard values ​​matching the information. The standard values ​​are pre-set and the value range is from 0 to 1.

[0115] In this embodiment, based on The first mean value Pave is obtained by averaging the sum of Pb1...Pbn. Similarly, The second mean Cave is obtained by averaging the cumulative sum of Cb1...Bbn. The third mean value Qave is obtained by averaging the accumulated sum of Qb1...Qbn.

[0116] In this embodiment, the first association analysis model is obtained by training the neural network model based on samples of different sprayed pesticides, growth information and the corresponding pesticide penetration during the growth of grapes. Therefore, the penetration factor based on the combination of Pi and Ci can be directly obtained, and the value range is 0 to 1.

[0117] In this embodiment, the value of a1 is 0.04, and the value of a2 is 0.02.

[0118] In this embodiment, the second association analysis model is obtained by training the neural network model based on the sample of the impact of weather change information on the penetration of pesticides after spraying pesticides on grapes under corresponding growth information. Therefore, the penetration factor based on the combination of Qi and Ci can be directly obtained, and the value range is 0 to 1.

[0119] In this embodiment, Among them, (s2i) ave is the average value based on all s2i; s2i is the i-th second permeability factor; is the variance based on all s2i.

[0120] In this embodiment, if the corresponding spraying is the fifth spraying, then the value of m is 5.

[0121] The beneficial effect of the above technical solution is: by analyzing the combination of Pi and Ci, and the combination of Qi and Ci, the variable coefficients of the first linear function and the second linear function are calculated, thereby facilitating function fusion and obtaining the pesticide penetration factor, providing an accurate basis for further matching the detection method.

[0122] The present invention provides a method for detecting pesticide residues in wine, which includes screening a target detection method from a pesticide detection database based on a residue impact set, performing chromatographic analysis on the wine made from the target grapes, and obtaining an initial residue set, including:

[0123] Determine the final residual amount of the pesticide components used in each pesticide spraying in the residue impact concentration based on the corresponding pesticide residues, and extract the residue sequence of the same residue parameter;

[0124] The final residues in the residue sequence of each residue parameter are accumulated and summed to obtain the accumulated and residual amounts of the corresponding residue parameters, and all accumulated and residual amounts are sorted by size to select at least two target detection methods from the pesticide detection data;

[0125] Based on each target detection method, chromatographic analysis is performed on the wine produced from the target grapes to obtain a chromatogram of each target detection method;

[0126] Perform data analysis of the same residual parameter on all chromatograms to obtain the current value of the corresponding residual parameter;

[0127] The current values ​​of all residual parameters are counted to obtain the initial residual set.

[0128] In this embodiment, the residual impact set includes the pesticide residues after each spraying, and each pesticide residue includes the final residual amount of different pesticide components.

[0129] In this embodiment, the residue sequence = {the final residue amount of the corresponding residue parameter after different pesticide sprayings}, and the cumulative sum is calculated by accumulating the final residue amounts involved in the corresponding residue sequence.

[0130] In this embodiment, after size sorting, the detection methods of residual parameters with large accumulation and retention can be matched first to determine the sensitivity of detection of corresponding residual parameters and ensure the accuracy of detection. It should be noted that different detection methods may have different detection sensitivities for different residual parameters, so priority is given to reasonable sensitivity detection of residual parameters that meet the requirements of size sorting and are ranked high.

[0131] In this example, the chromatogram is used to determine the residual profile of the residual parameters present in the wine.

[0132] In this embodiment, the current value of the corresponding residual parameter is: the detection weight of the corresponding target detection method multiplied by the detection value of the corresponding residual parameter, and the final addition is obtained, wherein the sum of the detection weights of all target detection methods is 1, and the detection weight of the target detection method matched first is greater than the detection weights of the target detection methods matched subsequently.

[0133] In this embodiment, the chromatogram measures the concentration, and the concentration is mass-converted to obtain the detection value of the corresponding residual parameter.

[0134] In this embodiment, the initial residual set = {the current value of each residual parameter}.

[0135] The beneficial effect of the above technical solution is: by analyzing the estimated residual influence set to obtain the residual sequence and then accumulating and processing it, the residual parameters before the high school entrance examination that are prioritized in size are matched with the detection method, effectively ensuring the sensitivity of the detection of the required parameters, and then conducting wine testing through the detection method, so as to ensure the accuracy of the test results.

[0136] The present invention provides a method for detecting pesticide residues in wine, which optimizes the initial residue set, comprising:

[0137] Obtain the residual variance based on each residual series separately;

[0138] The residual optimization is performed on the current value of the corresponding residual parameter according to the residual variance.

[0139] The result of residual optimization = corresponding current value + corresponding residual variance.

[0140] The beneficial effect of the above technical solution is: the current value is optimized through the residue sequence, which further ensures that the result is consistent with the actual situation and further ensures the safety of wine drinking.

[0141] The present invention provides a method for detecting pesticide residues in wine, which is used to determine the output quality grade of wine, comprising:

[0142] Determine the total number of spraying times N in the whole growth cycle and determine the spraying ratio n / N in combination with the historical number of spraying times n, and determine the reference planning value in combination with the final planning value of each residual parameter in the whole growth cycle;

[0143] Obtain the average value of the residual optimization value and the reference planning value of each residual parameter, and obtain the quality impact factor matching the parameter from the value-parameter-impact comparison table;

[0144] Extract the maximum impact factor from all quality impact factors and combine the factor variances of the remaining impact factors to obtain the final impact factor;

[0145] Match the final impact factor with the factor-grade comparison table to obtain the output quality grade.

[0146] In this embodiment, the final planned value of each residual parameter in the whole growth cycle is pre-planned and serves as a reference standard.

[0147] Reference planning value = final planning value of corresponding residual parameter × (n / N).

[0148] In this embodiment, the average value=(residual optimization value of the corresponding residual parameter+reference planning value) / 2.

[0149] In this embodiment, the value-parameter-impact comparison table includes average values ​​under different residual parameters and quality impact factors matching the average values, which are pre-planned, and the value range of the quality impact factor is 0 to 1.

[0150] In this embodiment, the final impact factor=maximum impact factor+factor variance.

[0151] In this embodiment, the factor-level comparison table includes different final impact factors and levels matching the factors. For example, the final impact factor is 0.4, and the corresponding output quality level is level three. The higher the level, the less qualified the wine quality.

[0152] The beneficial effect of the above technical solution is: based on the final planning value, and combined with the spraying ratio to obtain the reference planning value, and then combined with the residual optimization value, the quality influencing factor is effectively matched to provide a basis for grade matching.

[0153] The present invention provides a wine pesticide residue detection system. Figure 2 As shown, including:

[0154] An information acquisition module is used to obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the corresponding target grapes at the time of the corresponding historical pesticide spraying;

[0155] An impact analysis module is used to construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set;

[0156] A chromatographic analysis module, used for selecting a target detection method from a pesticide detection database based on the residue impact set to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set;

[0157] The grade determination module is used to perform residue optimization on the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

[0158] The beneficial effect of the above technical solution is: through a comprehensive analysis of the pesticide spraying conditions, weather changes, and grape growth conditions involved in the entire life cycle of grapes, the possible residues before grape brewing can be determined, and then the chromatographic analysis method can be reasonably screened to effectively ensure the sensitivity and accuracy of the measurement of pesticide residue parameters, thereby improving the detection efficiency.

[0159] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting pesticide residues in wine, characterized in that: include: Step 1: Obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the target grapes at the time of the corresponding historical pesticide spraying; Step 2: construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set; Step 3: Based on the residue impact set, a target detection method is selected from the pesticide detection database to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set; Step 4: Optimize the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

2. The method for detecting pesticide residues in wine according to claim 1, characterized in that: Based on the historical pesticide spraying information, weather change information and growth information, a combination matrix corresponding to the historical pesticide spraying is constructed, including: Construct the row vector {Pi, Qi, Ci} of each historical pesticide spraying, where Pi is the information of the i-th historical pesticide spraying; Qi is the weather change information after the i-th historical pesticide spraying; Ci is the growth information of the target grapes corresponding to the i-th historical pesticide spraying; Construct a combination matrix based on all row vectors Among them, the value of i is 1, 2, 3, ..., n.

3. The method for detecting pesticide residues in wine according to claim 2, characterized in that: The combination matrix is ​​analyzed to determine the pesticide residues in the target grapes after the corresponding spraying, including: Extracting a first pesticide dosage and a first pesticide concentration from historical pesticide spraying information of each row vector in the combination matrix, and determining a first volatility coefficient of each pesticide component in the first pesticide dosage from a component-volatility database based on the pesticide components of the first pesticide dosage; Determining a second volatility coefficient of the first pesticide from a name-volatility database according to the name of the first pesticide; Determine the concentration difference between the first pesticide concentration and the standard registration concentration of the first pesticide application, and at the same time, input the weather change information after the corresponding historical pesticide spraying into the volatilization analysis model matched with the corresponding first pesticide application to obtain the concentration volatilization factor set of the first pesticide application; Determining the initial residue of the target grapes after the corresponding spraying of the target grapes based on the first volatility coefficient, the second volatility coefficient, the concentration difference and the concentration volatility factor set; right as well as Conduct mutual analysis to determine the pesticide penetration factor; The corresponding initial residue is analyzed based on the pesticide penetration factor to obtain the pesticide residue of the target grape after the corresponding spraying.

4. The method for detecting pesticide residues in wine according to claim 3, characterized in that: right as well as Inter-analysis is performed to determine pesticide penetration factors, including: Initialize the combination matrix Z and obtain The first mean Pave, based on The second mean Cave, based on The third mean Qave; The combination of Pi and Ci is respectively input into the first association analysis model to obtain the first permeability factor based on the combination of Pi and Ci, and the first auxiliary factor Df1 is calculated; Among them, (s1i) ave is the average value based on all s1i; s1i is the i-th first permeability factor; is the variance based on all s1i; Based on the first auxiliary factor Df1, the first mean Pave, and the second mean Cave, a first linear function Df1=α1×G1(Pave, Cave)+a1 is constructed, wherein a1 is a constant, G1(Pave, Cave) is a variable function based on Pave, Cave; α1 is a variable coefficient; The combination of Qi and Ci is respectively input into the second association analysis model to obtain the second permeability factor based on the combination of Qi and Ci, and a second linear function Df2=α2×G2(Qave, Cave)+a2 based on the third mean Qave and the second mean Cave is constructed, wherein Df2 is the second auxiliary factor based on all second permeability factors; a2 is a constant, G2(Qave, Cave) is a variable function based on Qave, Cave; α2 is a variable coefficient; Based on the first linear function and the second linear function, we can obtain The first linear function and the second linear function are fused to obtain the pesticide penetration factor.

5. The method for detecting pesticide residues in wine according to claim 4, characterized in that: Obtain pesticide penetration factors, including: Wherein, Ct is the corresponding pesticide penetration factor; G1(Pave1, Cave1) is the variable function of the corresponding spraying and the mean Pave1 based on the historical pesticide spraying information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; G2(Qave1, Cave1) is the variable function of the corresponding spraying and the mean Qave1 based on the weather change information involved before the corresponding spraying, and the mean Cave1 based on the growth information of the target grape; Δ1 is the constant under the corresponding spraying; m is the number of corresponding sprayings.

6. The method for detecting pesticide residues in wine according to claim 1, characterized in that: Based on the residue impact set, a target detection method is selected from the pesticide detection database to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set, including: Determine the pesticide composition of each pesticide spraying in the residue impact concentration based on the residual concentration of the corresponding pesticide residue, and extract the residue sequence of the same residue parameter; Based on the residue sequence of each residue parameter, the required parameters are screened, and based on the parameter accuracy combination of the required parameters, at least two target detection methods are screened from the pesticide detection data; Based on each target detection method, chromatographic analysis is performed on the wine produced from the target grapes to obtain a chromatogram of each target detection method; Perform data analysis of the same residual parameter on all chromatograms to obtain the current value of the corresponding residual parameter; The current values ​​of all residual parameters are counted to obtain the initial residual set.

7. The method for detecting pesticide residues in wine according to claim 6, characterized in that: Performing residual optimization on the initial residual set includes: Obtain the residual variance based on each residual series separately; The residual optimization is performed on the current value of the corresponding residual parameter according to the residual variance.

8. The method for detecting pesticide residues in wine according to claim 7, characterized in that: Determine the output quality level of the wine, including: Determine the total number of spraying times N in the whole growth cycle and determine the spraying ratio n / N in combination with the historical number of spraying times n, and determine the reference planning value in combination with the final planning value of each residual parameter in the whole growth cycle; Obtain the average value of the residual optimization value and the reference planning value of each residual parameter, and obtain the quality impact factor matching the parameter from the value-parameter-impact comparison table; Extract the maximum impact factor from all quality impact factors and combine the factor variances of the remaining impact factors to obtain the final impact factor; Match the final impact factor with the factor-grade comparison table to obtain the output quality grade.

9. A wine pesticide residue detection system, characterized in that: include: An information acquisition module is used to obtain each historical pesticide spraying information of the target grapes in the target garden during the entire growth cycle, weather change information after each historical pesticide spraying, and growth information of the corresponding target grapes at the time of the corresponding historical pesticide spraying; An impact analysis module is used to construct a combination matrix after the corresponding historical pesticide spraying based on the historical pesticide spraying information, weather change information and growth information, and analyze the combination matrix to determine the pesticide residue of the target grape after the corresponding spraying to construct a residue impact set; A chromatographic analysis module, used for selecting a target detection method from a pesticide detection database based on the residue impact set to perform chromatographic analysis on the wine made from the target grapes to obtain an initial residue set; The grade determination module is used to perform residue optimization on the initial residue set according to the residue influence set, determine the output quality grade of the wine and output it.

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