Method and system for detecting pesticide residues in wine

Abstract

The invention discloses a method and a system for detecting pesticide residues in grape wine, and relates to the technical field of grape wine detection. The content of the pesticide residue component in the grape wine is determined according to the component data, the detection parameters of the pesticide residue and the pesticide supplies used in the growth process of the grapes, and the component data, the detection parameters of the pesticide residue and the pesticide supplies used in the growth process of the grapes are integrally considered; the accuracy of the content of pesticide residue components in the wine is ensured; therefore, the grade of the pesticide residue in the wine is determined according to the content of the pesticide residue component in the wine, the service life of the pesticide residue detection equipment and the type of the wine, and interaction of the content of the pesticide residue component in the wine, the service life of the pesticide residue detection equipment and the type of the wine is realized; and the accuracy of the grade of the pesticide residues in the wine is ensured.

Description

Technical Field

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

[0002] With the development of technology, wine is applied in people's lives and is one of people's delicacies. Wine is brewed from grapes over a long time, and control starts from the grape picking. In the prior art, pesticide products are used during the growth of grapes. Based on the detection parameters of pesticide residues in wine, the detection parameters of pesticide residues are collected and a single-dimensional evaluation is carried out along the detection parameters of pesticide residues, resulting in low accuracy of the content of the components of pesticide residues in wine. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides a method and a system for detecting pesticide residues in wine.

[0004] An embodiment of the present invention provides a method for detecting pesticide residues in wine, including: determining a growth data set of grapes based on a database of grapes and the picking batch of grapes;

[0005] Determining quality parameters of wine according to the growth data set of grapes, the surface morphology of grapes, and the brewing data of grapes;

[0006] Determining detection parameters of pesticide residues based on the quality parameters of wine, the detection parameters during the detection of wine, and the component data set of grape skins;

[0007] During the detection of pesticide residues in wine, corresponding component data is output. According to this component data, the detection parameters of pesticide residues, and the pesticide products used during the growth of grapes, the content of the components of pesticide residues in wine is determined;

[0008] Determining the grade of pesticide residues in wine according to the content of the components of pesticide residues in wine, the service life of the pesticide residue detection equipment, and the type of wine.

[0009] An embodiment of the present invention provides a system for detecting pesticide residues in wine. The system for detecting pesticide residues in wine is applied to the above method for detecting pesticide residues in wine. The system for detecting pesticide residues in wine includes:

[0010] A growth data set module, configured to determine a growth data set of grapes based on a database of grapes and the picking batch of grapes;

[0011] A quality parameter module for determining the quality parameters of wine based on the grape growth data set, the surface morphology of the grapes, and the wine-making data of the grapes;

[0012] A detection parameter model for determining the detection parameters of pesticide residues based on the quality parameters of the wine, the detection parameters during the wine detection process, and the grape skin component data set;

[0013] A content model for outputting corresponding component data during the pesticide residue detection of wine, and determining the content of the components of pesticide residues in the wine based on this component data, the detection parameters of pesticide residues, and the pesticide products used during the grape growth process;

[0014] A grade model for determining the grade of pesticide residues in the wine based on the content of the components of pesticide residues in the wine, the service life of the pesticide residue detection equipment, and the type of the wine.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] In the embodiments of the present invention, through the method in the embodiments of the present invention, the grape growth data set is determined based on the grape database and the grape picking batches; the quality parameters of the wine are determined based on the grape growth data set, the surface morphology of the grapes, and the wine-making data of the grapes; the detection parameters of pesticide residues are determined based on the quality parameters of the wine, the detection parameters during the wine detection process, and the grape skin component data set; corresponding component data is output during the pesticide residue detection of the wine, and the content of the components of pesticide residues in the wine is determined based on this component data, the detection parameters of pesticide residues, and the pesticide products used during the grape growth process, which is compatible with the overall consideration of this component data, the detection parameters of pesticide residues, and the pesticide products used during the grape growth process, and ensures the accuracy of the content of the components of pesticide residues in the wine.

[0017] Therefore, the grade of pesticide residues in the wine is determined based on the content of the components of pesticide residues in the wine, the service life of the pesticide residue detection equipment, and the type of the wine, realizing the interaction of the content of the components of pesticide residues in the wine, the service life of the pesticide residue detection equipment, and the type of the wine, and ensuring the accuracy of the grade of pesticide residues in the wine. Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the application scenario of the method for detecting pesticide residues in wine in an embodiment;

[0019] Figure 2 It is a flow chart of the method for detecting pesticide residues in wine in the embodiments of the present invention;

[0020] Figure 3 It is a schematic diagram of the structural composition of the pesticide residue detection system in wine in the embodiments of the present invention. Specific embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0022] Embodiment 1

[0023] The pesticide residue detection method provided in this application is applied to the application environment as Figure 1 shown. Among them, the computer 102 communicates with the server 104 through the network. Among them, the computer 102 is not limited to various personal computers, servers, and wine detection systems, and the server 104 is implemented by an independent server or a server cluster composed of servers.

[0024] Embodiment 2

[0025] Please refer to Figures 1 to 3 , a pesticide residue detection method for wine, which is applied to the detection scenario of pesticide residues in wine; the pesticide residue detection method for wine includes:

[0026] Step S11: Determine the growth data set of grapes based on the grape database and the picking batch of grapes;

[0027] Step S12: Determine the quality parameters of the wine according to the growth data set of the grapes, the surface morphology of the grapes, and the wine-making data of the grapes;

[0028] Step S13: Determine the detection parameters of pesticide residues based on the quality parameters of the wine, the detection parameters during the detection of the wine, and the component data set of the grape skins;

[0029] Step S14: The wine outputs corresponding component data during the pesticide residue detection. According to this component data, the detection parameters of pesticide residues, and the pesticide products used during the growth of the grapes, determine the content of the components of pesticide residues in the wine;

[0030] Step S15: Determine the level of pesticide residues in the wine according to the content of the components of pesticide residues in the wine, the service life of the pesticide residue detection equipment, and the type of the wine;

[0031] In step S11, determine the growth data set of grapes based on the grape database and the picking batch of grapes;

[0032] In the specific implementation process of the present invention, the specific steps are as follows:

[0033] S111: Determine the growth region and variety of the corresponding grapes based on the label information of the wine to be detected;

[0034] S112: Determine the picking batch of the grapes according to the growth region of the grapes, the variety of the grapes, and the brewing time of the wine;

[0035] S113: Associate the picking batch of the grapes with the grape database, and determine the data of the grapes at different growth stages according to the picking batch of the grapes and the grape database;

[0036] S114: Determine the growth data set of the grapes based on the data of the grapes at different growth stages, and the growth data set covers the growth status of the grapes at different times and the corresponding growth environment.

[0037] In the embodiments of the present application, the growth region and variety of the corresponding grapes are determined based on the label information of the wine to be detected; the picking batch of the grapes is determined according to the growth region of the grapes, the variety of the grapes, and the brewing time of the wine, which takes into account the growth region of the grapes, the variety of the grapes, and the brewing time of the wine as a whole, and ensures the accuracy of the picking batch of the grapes.

[0038] At this time, view the label information of the wine to be detected. The label usually contains the basic information of the wine, which is crucial for tracing the growth region and variety of the grapes.

[0039] The label will clearly mark the origin or production area of the wine; the production area is usually adapted to specific climate, soil conditions, and grape varieties, so it is the key information for judging the growth region of the grapes; the label will also indicate the grape variety used in the wine; the grape variety determines the basic biological characteristics and flavor characteristics of the grapes, and is an important factor for evaluating the quality of the wine.

[0040] After determining the growth region and variety of the grapes, the next step is to infer the picking batch of the grapes based on this information and the brewing time of the wine; the growth region and grape variety help narrow down the range of the picking batch; the ripening time of grapes in different growth regions is different, and different grape varieties in the same growth region also have different ripening cycles; the brewing time is usually closely related to the picking time of the grapes; winemakers will choose the best picking time according to factors such as the maturity, flavor characteristics, and weather conditions of the grapes; therefore, by understanding the brewing time, the picking batch of the grapes is further inferred.

[0041] Specifically, assume there is a bottle of wine labeled "Produced in Bordeaux, France, made from Cabernet Sauvignon grapes"; through this step, it is determined that: the grape growing region is Bordeaux, France, which is famous for its unique climate and soil conditions and is suitable for growing various high-quality grape varieties; the grape variety is Cabernet Sauvignon, which is a widely planted high-quality red grape variety known for its rich fruit aroma and abundant tannins.

[0042] Assume the known wine-making time is September 2022; combining the information of the Bordeaux region and Cabernet Sauvignon grapes, it is inferred that: in the Bordeaux region, Cabernet Sauvignon grapes usually ripen between September and October; therefore, the wine-making time in September 2022 corresponds to the picking batch of Cabernet Sauvignon grapes that year.

[0043] Therefore, associate the grape picking batch with the grape database, and determine the data of the grape at different growth stages according to the grape picking batch and the grape database; based on the data of the grape at different growth stages, determine the grape growth data set, which covers the growth status of the grape at different times and the corresponding growth environment, introduce the grape growth data set, and control the growth status of the grape at different times and the corresponding growth environment.

[0044] At this time, associate the previously determined grape picking batch with the grape database; the grape database is a system containing a large amount of information about grape growth, which records the data of different batches of grapes at each growth stage.

[0045] First, it is necessary to ensure that the information of the picking batch (such as production area, variety, picking year, etc.) matches the records in the database; this is usually achieved through indexes or keyword searches in the database; once the picking batch is successfully associated with the database, extract the data of this batch of grapes at different growth stages; these data include the growth conditions of the grape at each stage such as germination period, flowering period, fruit setting period, color change period, maturity period, etc., as well as the corresponding growth environment parameters (such as temperature, humidity, light, rainfall, etc.);

[0046] Integrate the extracted data of the grape at different growth stages to form a complete growth data set; this set not only includes the growth status of the grape at each time point (such as the number of leaves, fruit size, color change, etc.), but also covers the corresponding growth environment information.

[0047] Arrange the data of different growth stages in chronological order to form a continuous record of the growth state; similarly, integrate the growth environment parameters corresponding to each growth stage to form an environmental data set that matches the growth state data; finally, merge the growth state data and the growth environment data to form a complete data set covering the growth state of grapes at different times and the corresponding growth environment.

[0048] Specifically, assume that the grape picking batch of a certain bottle of wine has been determined as "the 3rd batch of Cabernet Sauvignon in Bordeaux, France in 2022"; now, it is necessary to associate this batch with the grape database;

[0049] In the database, records matching "Cabernet Sauvignon in Bordeaux, France in 2022" were found; through further screening, the grape data of "the 3rd batch" was located; from these data, the growth conditions of this batch of grapes in the germination period, flowering period, fruit setting period, color change period, and maturity period were extracted, as well as the growth environment parameters such as temperature, humidity, light, and rainfall corresponding to each stage.

[0050] The data of "the 3rd batch of Cabernet Sauvignon in Bordeaux, France in 2022" grapes in different growth stages were extracted; now, these data are integrated into a complete growth data set; the growth state data set includes: the number of leaves in the germination period, the number of flowers in the flowering period, the fruit size in the fruit setting period, the fruit color change in the color change period, the fruit maturity and sugar content in the maturity period, etc.; the growth environment data set includes: the temperature range in the germination period, the humidity level in the flowering period, the light intensity in the fruit setting period, the rainfall in the color change period, and the temperature difference between day and night in the maturity period, etc. After merging these two data sets, a complete growth data set is obtained, which covers the growth state of this batch of grapes at different times and the corresponding growth environment information.

[0051] In the embodiment of the present application, a growth data set matching table is used to organize the data. The growth data set matching table takes the growth stages of grapes as row headers, and the growth state and growth environment parameters as column headers, and then fills in the specific data values in the corresponding cells.

[0052] Table 1 Example of the growth data set matching table

[0053]

[0054]

[0055] Based on the actual data values (represented here by assumed numerical values) and weights of each parameter, the scores of each parameter are calculated. For example, the score for the number of leaves in the germination stage is 8 (data value) * 0.2 (weight) = 1.6 points. Similarly, the scores of other parameters are calculated; finally, the scores of all parameters in each growth stage are added together to obtain the comprehensive score for that stage. For example, the comprehensive score for the germination stage is 1.6 (number of leaves) + 0.7 (humidity level) = 2.3 points (only the scores of two parameters are shown here, and all parameters should be included in the actual calculation). Similarly, the comprehensive scores of other growth stages are calculated, and finally a comprehensive score reflecting the overall growth condition of the grapes is obtained.

[0056] In step S12, the quality parameters of the wine are determined based on the growth data set of the grapes, the surface morphology of the grapes, and the wine-making data of the grapes.

[0057] In the specific implementation process of the present invention, the specific steps are as follows:

[0058] S121: Collect the growth data set of the grapes;

[0059] S122: Determine multiple images of the grapes at different positions according to the picking batches of the grapes and the screening images of the grapes before wine-making;

[0060] S123: Determine the surface morphology of the grapes according to the multiple images of the grapes at different positions and the storage environment of the grapes;

[0061] S124: Monitor the wine-making process of the grapes in real time, and determine the wine-making data of the grapes based on the dynamic detection of the wine-making of the grapes;

[0062] S125: Determine the quality parameters of the wine based on the growth data set of the grapes, the surface morphology of the grapes, and the wine-making data of the grapes;

[0063] In the embodiment of the present application, the growth data set of the grapes is collected; multiple images of the grapes at different positions are determined according to the picking batches of the grapes and the screening images of the grapes before wine-making; the surface morphology of the grapes is determined according to the multiple images of the grapes at different positions and the storage environment of the grapes, realizing the interaction between the multiple images of the grapes at different positions and the storage environment of the grapes, and ensuring the accuracy of the surface morphology of the grapes.

[0064] At this time, the growth data set of the grapes is collected. At the same time, according to the picking batches of the grapes, the grapes to be used for wine-making are screened, and images of multiple positions are taken; these images help to understand the appearance characteristics of the grapes, including size, shape, color, glossiness, and defects (such as disease spots, insect holes, etc.).

[0065] Determine the picking time and batch of grapes, which helps to trace the origin and growth history of the grapes; at the same time, before wine-making, strictly screen the grapes and remove the grapes that do not meet the requirements; also, take images of the grapes at different positions, such as overall views, close-up views, etc.

[0066] The surface morphology of the grapes will be evaluated by combining the images of the grapes at different positions and the storage environment information; the storage environment includes temperature, humidity, light conditions, and storage time, etc., and these factors all affect the appearance and quality of the grapes.

[0067] Through image analysis software, carefully analyze the images of the grapes at different positions, and extract key information about the surface morphology of the grapes, such as color uniformity, glossiness, the number of defects, etc.; record the storage environment information of the grapes, including temperature, humidity, light conditions, and storage time, etc.; this information helps to understand the impact of the storage environment on the surface morphology of the grapes; through comprehensive analysis of the image and storage environment information, obtain a detailed evaluation report on the surface morphology of the grapes.

[0068] Specifically, assume that the picking batch has been determined as the first batch of Cabernet Sauvignon grapes in September 2023; before wine-making, screen this batch of grapes and take images at multiple positions; take an overall view of the grape clusters to understand the size and shape distribution of the grapes; at the same time, also take a close-up view of a single grape to observe the color, glossiness, and defect conditions of the grape; these images will be used to evaluate the surface morphology of the grapes in the subsequent steps.

[0069] Combined with the images taken in step S122 and the storage environment information, evaluate the surface morphology of the first batch of Cabernet Sauvignon grapes in September 2023; by analyzing the images, it is found that the color of this batch of grapes is uniform, the glossiness is good, and the number of defects is small; at the same time, the storage environment information is also recorded, and it is found that this batch of grapes has been stored for an appropriate time under suitable temperature and humidity conditions; by synthesizing this information, it is concluded that this batch of grapes has good surface morphology and is suitable for making high-quality red wine.

[0070] Therefore, monitor the wine-making process of the grapes in real time, and determine the wine-making data of the grapes based on the dynamic detection of the wine-making of the grapes; determine the quality parameters of the wine based on the growth data set of the grapes, the surface morphology of the grapes, and the wine-making data of the grapes, which takes into account the overall consideration of the growth data set of the grapes, the surface morphology of the grapes, and the wine-making data of the grapes, and ensures the accuracy of the quality parameters of the wine.

[0071] At this time, the wine-making process of grapes is monitored in real time, and key data is recorded to ensure that the wine-making process meets expectations. The wine-making process usually includes multiple stages such as grape crushing, fermentation, maceration (for red wines), pressing, clarification, and aging. Each stage has its specific operating parameters and conditions, and these parameters and conditions have an important impact on the quality of the final wine.

[0072] Monitor key parameters in the wine-making process in real time; these parameters include fermentation temperature, pH value, sugar content, alcohol content, yeast activity, etc.; conduct regular or irregular tests during the wine-making process to obtain data on the chemical composition and physical properties of grape juice or wine; these tests include sugar determination, acidity determination, volatile substance analysis, colorimetry, etc.; record the data obtained from real-time monitoring and dynamic testing in an electronic or paper log for subsequent analysis and use; these data will be used to evaluate the effectiveness of the wine-making process and serve as an important basis for determining the quality parameters of wine.

[0073] Integrate the growth data set, surface morphology of grapes, and wine-making data to evaluate the quality parameters of wine; the quality parameters include alcohol content, acidity, tannin content, aroma complexity, taste balance, etc.; these parameters are important indicators for evaluating the quality of wine, and they are affected not only by the characteristics of the grapes themselves but also by the wine-making process.

[0074] Conduct a comprehensive analysis of the growth data set of grapes (such as climate data, physiological indicators, etc.), surface morphology (such as color uniformity, glossiness, number of defects, etc.), and wine-making data (such as fermentation temperature, sugar content, alcohol content, etc.) to understand their potential impact on the quality parameters of wine. Based on the results of the comprehensive analysis, determine the quality parameters of wine; this requires the use of statistical software or expert systems for data analysis and modeling to obtain accurate estimates of the quality parameters.

[0075] Specifically, assume that a batch of Cabernet Sauvignon red wine from the Bordeaux region in France is being brewed; during the wine-making process, a temperature sensor is installed to monitor the temperature in the fermentation tank, and automated equipment is used to regularly measure the sugar content and alcohol content of the grape juice; at the same time, volatile substance analysis and colorimetry are also carried out to understand the aroma and color characteristics of the wine; after fermentation, the wine is pressed and clarified, and the key data during the whole process is recorded; these data will be used to determine the quality parameters of the wine in the subsequent steps.

[0076] Furthermore, by combining the wine-making data recorded in step S124 with the previously collected grape growth data set and surface morphology information, the quality parameters of this batch of Cabernet Sauvignon red wine from the Bordeaux region of France were evaluated. By analyzing the data, it was found that this batch of wines had moderate alcohol content, balanced acidity, and rich tannin content. At the same time, the wines had a high aroma complexity and good taste balance. These quality parameters indicated that this batch of wines had high quality potential and were suitable for the high-end market. These quality parameters were recorded in the quality assessment report and used as the basis for wine quality certification and marketing in subsequent steps.

[0077] In another embodiment of the present application, an example of the quality parameter allocation is shown in Table 2:

[0078] Table 2 Example of Quality Parameter Allocation

[0079]

[0080] Note: The maturity (sugar content) in the growth data set directly affects the alcohol content of the wine because sugar is converted into alcohol by yeast during fermentation. Although the surface morphology has no direct correlation with the alcohol content and acidity, the skin thickness and color depth indirectly reflect the tannin content because tannins are mainly present in the skin. The alcohol content at the end of fermentation in the wine-making data directly reflects the alcohol content of the wine, while the pH value and acidity adjustment before fermentation affect the final acidity of the wine. The aroma complexity and taste balance are affected by various factors, including the growth environment, grape variety, wine-making process, etc., so comprehensive analysis is required to determine them.

[0081] In step S13, based on the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skin, the detection parameters of pesticide residues are determined;

[0082] In the specific implementation process of the present invention, the specific steps are as follows:

[0083] S131: Obtain the quality parameters of the wine;

[0084] S132: Conduct an on-line detection of the wine, and determine the detection parameters of the wine during the detection process based on the on-line detection of the wine;

[0085] S133: Control the wine-making process of the grapes and conduct spot checks on the grape skins;

[0086] S134: Determine the component data set of the grape skin based on the spot checks of the grape skin;

[0087] S135: Interact with the quality parameters of the wine, the detection parameters during the wine detection process, and the component data set of the grape skins, and determine the detection parameters for pesticide residues based on the interaction of the quality parameters of the wine, the detection parameters during the wine detection process, and the component data set of the grape skins;

[0088] In the embodiments of the present application, the quality parameters of the wine are obtained; the wine is subjected to on-line detection, and the detection parameters during the wine detection process are determined based on the on-line detection of the wine, realizing the on-line detection of the wine and ensuring the accuracy of the detection parameters during the wine detection process.

[0089] At this time, the quality parameters of the wine are obtained. At the same time, on-line detection refers to the real-time or near-real-time detection of the wine during the production or circulation process of the wine; this detection method can timely detect problems that occur during the production or storage process of the wine, thereby ensuring the quality and safety of the wine; on-line detection usually relies on advanced sensors and automated control systems to be able to monitor key indicators such as the temperature, pressure, flow rate, light transmittance, dissolved oxygen, etc. of the wine in real time.

[0090] Optionally, a temperature sensor: used to monitor the temperature of the wine to ensure that it fluctuates within an appropriate range to avoid quality degradation caused by too high or too low temperature; a pressure sensor: used to monitor the pressure change of the wine during transportation to ensure its stable flow and avoid problems such as leakage or blockage; a flow rate sensor: used to monitor the flow rate of the wine to ensure that it meets the production requirements and avoid waste or shortage; a light transmittance sensor: used to monitor the light transmittance of the wine to evaluate its clarity and impurity content; a dissolved oxygen sensor: used to monitor the dissolved oxygen content in the wine to avoid quality degradation caused by oxidation.

[0091] Specifically, on a wine production line, on-line detection was carried out on a batch of Cabernet Sauvignon red wine about to be bottled; through the temperature sensor, it was found that the temperature of the wine was stable at about 18 °C, meeting the production requirements; through the pressure sensor and the flow rate sensor, the pressure and flow rate of the wine during transportation were ensured to be stable; through the light transmittance sensor, it was found that the light transmittance of the wine was good, without obvious impurities or turbidity; finally, through the dissolved oxygen sensor, the dissolved oxygen content in the wine was detected to be within an appropriate range, avoiding quality degradation caused by oxidation; based on the above on-line detection results, the detection parameter set during the detection process of this batch of wine was determined, providing an important basis for subsequent quality control and safety assessment.

[0092] Furthermore, control the wine-making process of the grapes and conduct spot checks on the grape skins; determine the component data set of the grape skins based on the spot checks of the grape skins, introducing the spot checks of the grape skins and ensuring the accuracy of the component data set of the grape skins.

[0093] At this time, controlling the winemaking process of grapes is a crucial step in ensuring the quality of wine; this process involves meticulous management of all aspects from grape picking to fermentation and aging; effective control of the winemaking process includes but is not limited to the following aspects:

[0094] Grape picking management: Ensure that grapes are picked at the optimal ripeness, avoiding over-ripeness or under-ripeness; this usually requires determining the picking time based on grape variety, climate conditions, and soil conditions; Fermentation condition control: During the fermentation process, strictly control conditions such as temperature, humidity, yeast type, and quantity to ensure the smooth progress of the fermentation process and produce the desired flavor characteristics; Maceration and pressing management (for red wines): Maceration time and pressing degree have an important impact on the color, tannin content, and taste of wine; These parameters need to be adjusted according to grape variety and winemaking objectives.

[0095] Aging management: Factors such as aging time, oak barrel type, and usage ratio will all affect the final quality of wine; An aging strategy needs to be formulated based on the style of wine and market demand; Quality monitoring and adjustment: During the winemaking process, it is necessary to regularly detect various indicators of wine, such as alcohol content, acidity, pH value, etc., and make necessary adjustments according to the detection results.

[0096] At the same time, spot-checking and testing grape skins is an important step in ensuring the quality of winemaking raw materials; Grape skins are rich in pigments, tannins, flavor substances, and pesticide residues, etc., which have an important impact on the quality and safety of wine; Spot-checking and testing usually include the following aspects:

[0097] Appearance inspection: Check the color, gloss, and integrity of grape skins to evaluate their quality and pest and disease conditions; Chemical composition analysis: Analyze the content of key components such as pigments, tannins, and polyphenols in grape skins through laboratory analysis to evaluate their contribution to wine quality; Pesticide residue detection: Detect the pesticide residues present in grape skins to ensure the safety of wine and compliance with relevant regulatory requirements.

[0098] Specifically, assume that a batch of Pinot Noir red wine from the Burgundy region of France is being brewed; During the winemaking process, strict control has been exercised over all aspects:

[0099] During grape picking, the optimal ripeness was selected to ensure the sugar-acid balance and flavor substance accumulation of the grapes; During the fermentation process, selected yeast was used, and the fermentation temperature and humidity were strictly controlled to obtain the desired flavor characteristics; During the maceration stage, the maceration time was adjusted according to grape variety and winemaking objectives to ensure that the color and tannin content of the wine met expectations.

[0100] During the pressing stage, a gentle pressing method was adopted to avoid damaging the bitter substances in the grape seeds; during the aging stage, appropriate types and proportions of oak barrels were selected, and the aging time was adjusted according to market demand.

[0101] During the process of brewing the above-mentioned Pinot Noir red wine, spot checks were conducted on the grape skins:

[0102] In terms of appearance inspection, it was found that the grape skins had bright colors, good gloss, and high integrity, indicating good grape quality; in terms of chemical composition analysis, laboratory analysis showed that the pigment and tannin contents in the grape skins were moderate, which made important contributions to the color and taste of the wine;

[0103] In terms of pesticide residue detection, advanced detection instruments and methods were used to ensure that the pesticide residue content in the grape skins was lower than the maximum residue limit stipulated by the state, meeting the safety and regulatory requirements; in summary, by controlling the wine-making process of the grapes and conducting spot checks on the grape skins, the quality and safety of the wine were ensured to meet the expected goals.

[0104] Therefore, the interaction of the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skins, and the determination of the pesticide residue detection parameters based on the interaction of the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skins, realized the interaction of the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skins, ensuring the accuracy of the pesticide residue detection parameters.

[0105] At this time, in-depth chemical composition analysis was carried out on the grape skins to determine their component data set; these component data are crucial for understanding the impact of grape skins on wine quality and also provide a basis for evaluating the pesticide residues present in the wine;

[0106] The component data set of grape skins usually includes but is not limited to the following aspects:

[0107] Pigment content: The pigments in grape skins are the main source of wine color, including anthocyanins, flavonoids, etc.; Tannin content: Tannins are important flavor substances in wine, giving red wine its characteristic astringency and structure; Polyphenol content: Polyphenols are a class of compounds with antioxidant effects and have an important impact on the taste and long-term aging ability of wine;

[0108] Pesticide residues: Although this is not a natural component of grape skins themselves, detecting pesticide residues is an important link in ensuring wine safety; Other components: Such as aroma substances, minerals, etc. Although these components are present in low amounts, they also contribute to the overall flavor of the wine.

[0109] Comprehensively consider the quality parameters of the wine, the detection parameters during the detection process, and the component data set of the grape skins to evaluate the pesticide residues in the wine; this usually involves cross-analysis and comparison of multiple data sets to determine the detection parameters of pesticide residues (such as residue types, residue amounts, etc.);

[0110] Specifically, perform correlation analysis on the quality parameters of the wine (such as alcohol content, acidity, taste, etc.) and the component data set of the grape skins (such as pigment, tannin, polyphenol content, etc.) to understand the specific contributions of these components to the wine quality; at the same time, combine the detection parameters of the wine during the detection process (such as light transmittance, dissolved oxygen content, etc.) to further evaluate the overall quality and safety of the wine; on this basis, more accurately determine the detection parameters of pesticide residues.

[0111] Specifically, during the brewing of a certain batch of Cabernet Sauvignon red wine, spot checks were carried out on the grape skins; through analysis, the following component data set of the grape skins was determined: Pigment content: Anthocyanin content is XX mg / kg, flavonoid content is YY mg / kg; Tannin content: ZZ mg / kg; Polyphenol content: AAA mg / kg; Pesticide residues: Multiple common pesticides were detected, and no residues exceeded the maximum residue limit stipulated by the state.

[0112] During the brewing process of the above-mentioned Cabernet Sauvignon red wine, comprehensively consider the quality parameters, detection parameters of the wine, and the component data set of the grape skins; through cross-analysis and comparison, it is found that there is a significant correlation between the quality parameters of the wine and the component data set of the grape skins; for example, the high pigment content and tannin content in the grape skins endow the wine with bright color and firm taste.

[0113] At the same time, combine parameters such as light transmittance and dissolved oxygen content of the wine during the detection process to evaluate the overall quality and safety of the wine; on this basis, more accurately detect pesticide residues; through advanced detection technologies such as high performance liquid chromatography, determine the types and residue amounts of pesticide residues in the wine, and confirm that all pesticide residues are lower than the maximum residue limit stipulated by the state.

[0114] In another embodiment of the present application, the following is an example of a pesticide residue detection parameter matching table:

[0115] Table 3 Example of Pesticide Residue Detection Parameter Matching Table

[0116]

[0117] The pesticide residue detection parameter matching table correlates the types of pesticide residues that need special attention according to the specific quality parameters, detection parameters of the wine, and grape skin components.

[0118] For example, wines with high alcohol content are more susceptible to organophosphorus pesticides, so it is necessary to specifically detect the residues of such pesticides. Wines with high light transmittance usually indicate good clarity and relatively low risk of pesticide residues, but routine tests are still required to ensure safety. When the grape skin has a high pigment content, special attention should be paid to the pesticide residues introduced during the pigment extraction process.

[0119] In step S14, the wine outputs corresponding component data in the pesticide residue detection. Based on this component data, the detection parameters of pesticide residues, and the pesticides used during the growth of the grapes, the content of the components of pesticide residues in the wine is determined.

[0120] In the specific implementation process of the present invention, the specific steps are as follows:

[0121] S141: Conduct pesticide residue detection on the wine.

[0122] S142: The wine outputs corresponding component data in the pesticide residue detection.

[0123] S143: Determine the corresponding grapes based on the traceability of the wine.

[0124] S144: Determine the pesticides used during the growth of the grapes according to the grapes and the grape database.

[0125] S145: Interact the component data, the detection parameters of pesticide residues, and the pesticides used during the growth of the grapes.

[0126] S146: Determine the first component based on the component data and the detection parameters of pesticide residues, and determine the second component based on the component data and the pesticides used during the growth of the grapes.

[0127] S147: Determine the content of the components of pesticide residues in the wine based on the first component, the second component, and a preset pesticide residue matching table.

[0128] In the embodiment of the present application, pesticide residue detection is performed on the wine; the wine outputs corresponding component data in the pesticide residue detection, and this component data is introduced.

[0129] At this time, for the pesticide residue detection of the wine, a certain amount of wine sample needs to be taken during the detection process, and after appropriate pretreatment (such as dilution, filtration, extraction, etc.), it is then injected into the detection instrument for analysis; the instrument will separate it from the complex sample matrix according to the specific chemical properties of the pesticide, such as molecular weight, polarity, ionization mode, etc., and perform quantitative or qualitative analysis.

[0130] After the detection instrument completes the analysis, it will output a series of composition data; these data are usually presented in the form of chromatograms or mass spectra, and each peak in the figure represents a chemical component in the wine; by analyzing information such as the position, height, and shape of these peaks, the types and concentrations of various components in the wine are determined; optionally, for pesticide residue detection, the detection instrument will pay special attention to those peaks related to pesticides and give the residue amounts of these pesticides in the wine; these data are important bases for subsequent evaluation of the safety of the wine.

[0131] Specifically, a winery produced a batch of wine. To ensure product quality, it decided to conduct pesticide residue detection on this batch of wine; the winery sent the wine samples to a professional third-party testing agency, and this agency used HPLC technology to analyze the samples; after the third-party testing agency used HPLC technology to analyze the wine samples, it output detailed composition data; the data showed that trace amounts of a certain pesticide residue were detected in the wine, and its concentration was far lower than the maximum residue limit stipulated by the state.

[0132] Furthermore, based on the traceability of the wine, the corresponding grapes are determined; according to the grapes and the database of the grapes, the pesticides used during the growth of the grapes are determined, taking into account the overall consideration of the grapes and the database of the grapes, ensuring the accuracy of the pesticides used during the growth of the grapes.

[0133] At this time, to ensure the accuracy and traceability of the detection results, it is necessary to trace the source of the wine; this usually involves checking documents such as production records, batch information, and purchase vouchers to determine information such as the grape variety, origin, and picking time used for brewing this wine; these information are crucial for subsequent analysis of the source and cause of pesticide residues.

[0134] Once the source of the grapes is determined, by consulting relevant databases or production records, find out which pesticides were used during the growth of these grapes; these databases usually contain information such as the pesticide use records, types of pesticides, usage amounts, and usage times of grape growers; by analyzing this information, preliminarily judge the source and cause of pesticide residues in the wine.

[0135] Specifically, assume that a winery produced a batch of Cabernet Sauvignon wine and sent it to a third-party testing agency for pesticide residue testing. The test results showed that trace amounts of carbofuran pesticide residues were detected in the wine. The winery immediately initiated a traceability process. By reviewing production records and batch information, it was determined that the Cabernet Sauvignon grapes used in this batch of wine came from a specific plantation. After further reviewing the pesticide usage records of the plantation, it was found that the plantation did use carbofuran pesticide during the grape growth process, and the usage time and amount were consistent with the pesticide residue situation in the test report. Subsequently, the winery communicated with the plantation, adjusted the pesticide usage plan, and strengthened the quality control measures during the wine production process to ensure the safety of subsequent products.

[0136] Therefore, the component data, the detection parameters of pesticide residues, and the pesticide products used during grape growth are interacted. The first component is determined based on the component data and the detection parameters of pesticide residues, and the second component is determined based on the component data and the pesticide products used during grape growth. Based on the first component, the second component, and a preset pesticide residue matching table, the content of the pesticide residue components in the wine is determined, taking into account the overall consideration of the first component, the second component, and the preset pesticide residue matching table, ensuring the accuracy of the content of the pesticide residue components in the wine.

[0137] At this point, in this step, it is necessary to integrate and interact the component data collected previously (output from step S142), the detection parameters of pesticide residues (such as the sensitivity and accuracy of the detection method), and the information on pesticide products used during grape growth. This usually involves data comparison, information matching, and comprehensive analysis. The purpose is to ensure the accuracy and consistency of all relevant information and provide a reliable basis for subsequent component determination and content evaluation. Optionally, the system will compare this information with a preset database to identify the pesticide residue components present in the wine and assess their potential risks.

[0138] Based on the information collected previously, the specific components in the wine are determined. The first component refers to the chemical components directly detected through the component data, including pesticide residues, other additives, or natural components of the wine itself. The second component refers to the pesticide residue components inferred based on the information on pesticide products used during grape growth. Optionally, chemometric methods (such as principal component analysis, partial least squares method, etc.) will be used to process the component data to identify the main chemical components in the wine. At the same time, combined with the pesticide product information, it is inferred which pesticides remain in the wine and they are used as the second component for further analysis and evaluation.

[0139] Use a preset pesticide residue matching table to evaluate the content of pesticide residues in wine; this matching table usually contains information such as the standard residue limits, detection methods, and toxicological data of various pesticides; by comparing the detected pesticide residues with the matching table, determine the specific content of pesticide residues in the wine and evaluate whether it meets national or international safety standards; specifically, quantitative analysis methods will be used to determine the specific content of pesticide residues; at the same time, combined with the toxicological data in the matching table, evaluate the risk level of pesticide residues to consumer health.

[0140] Specifically, in step S144, it has been determined that the pesticide used during grape growth is carbofuran; in step S145, interact this information with the component data output in step S142 and the detection parameters of pesticide residues; the system will automatically compare the chemical properties of carbofuran with the detection parameters to ensure the accuracy of the detection results; at the same time, the system will also check whether carbofuran is in the preset pesticide residue database and evaluate its potential risk level.

[0141] Through the interactive analysis in step S145, it is determined that the first component present in the wine is a variety of natural phenols (such as flavonoids, anthocyanins, etc.), and these components are the main sources of the flavor and color of the wine; at the same time, according to the information of the carbofuran pesticide used during grape growth, it is determined that the second component is carbofuran pesticide residue.

[0142] In the above example, it has been determined that the first component in the wine is a variety of natural phenols and the second component is carbofuran pesticide residue; in step S147, use the preset pesticide residue matching table to evaluate the content of carbofuran pesticide residue; by comparing the standard residue limit in the matching table with the detection result, it is found that the content of carbofuran pesticide residue in the wine is far lower than the maximum residue limit stipulated by the state; therefore, it is concluded that this batch of wine is safe in terms of pesticide residues and meets national and international safety standards; at the same time, due to the rich content of natural phenols, this batch of wine also has good flavor and color quality.

[0143] In another embodiment of the present application, the pesticide residue matching table is as follows:

[0144] Table 4 Pesticide Residue Matching Table

[0145]

[0146]

[0147] In this pesticide residue matching table, the basic information of three pesticides (carbofuran, dichlorvos, and dimethoate) is listed, including their chemical formulas, standard residue limits, detection methods, and toxicity levels.

[0148] After the hypothetical wine sample was tested, it was determined that the first component was a variety of natural phenolic substances, and the second component was the carbofuran pesticide residue. Now, the specific content of carbofuran in the wine will be determined based on the matching table.

[0149] First, search for relevant information on carbofuran in the matching table. Then, compare the test results with the standard residue limit in the matching table. The hypothetical test results show that the content of carbofuran is 0.03 mg / kg. Since 0.03 mg / kg is lower than the standard residue limit of carbofuran in the matching table, which is 0.05 mg / kg, it is determined that the content of carbofuran pesticide residue in the wine is safe and meets the standard.

[0150] In step S15, determine the level of pesticide residue in the wine based on the content of the components of the pesticide residue in the wine, the service life of the pesticide residue detection equipment, and the type of the wine.

[0151] In the specific implementation process of the present invention, the specific steps are as follows:

[0152] S151: The wine is tested for the corresponding pesticide residue in the pesticide residue detection equipment.

[0153] S152: Determine the service life of the pesticide residue detection equipment based on the traceability of the pesticide residue detection equipment.

[0154] S153: Collect the content of the components of the pesticide residue in the wine and the type of the wine.

[0155] S154: Determine the first level parameter based on the content of the components of the pesticide residue in the wine and the overall content of the wine.

[0156] S155: Determine the second level parameter according to the content of the components of the pesticide residue in the wine and the service life of the pesticide residue detection equipment.

[0157] S156: Determine the level of pesticide residue in the wine based on the first level parameter, the second level parameter, and the type of the wine.

[0158] In the embodiment of the present application, the wine is tested for the corresponding pesticide residue in the pesticide residue detection equipment; the service life of the pesticide residue detection equipment is determined based on the traceability of the pesticide residue detection equipment, and the service life of the pesticide residue detection equipment is introduced.

[0159] At this time, the wine is tested for corresponding pesticide residues in the pesticide residue detection equipment, the pesticide residue detection equipment is controlled, and at the same time, the pesticide residue detection equipment is traced to determine its service life; this usually includes consulting documents such as the equipment's purchase record, maintenance record, calibration record, and user manual to understand information such as the manufacturing date, maintenance history, calibration cycle, and service life of the equipment.

[0160] By tracing this information, the current state and service life of the equipment are evaluated to determine whether it is still suitable for pesticide residue detection; if the equipment has approached or exceeded its service life, or there are serious maintenance records or calibration problems, then it is necessary to consider replacing or repairing the equipment to ensure the accuracy and reliability of subsequent detections.

[0161] Specifically, after the test, the tester begins to trace the service life of the LC-MS equipment used; by consulting the equipment's purchase record and user manual, the tester finds that this LC-MS equipment has been used for 8 years and the last calibration was carried out one year ago; at the same time, the tester also consults the equipment's maintenance record and finds that the equipment has been repaired many times in the past few years, including replacing key components such as the chromatographic column and repairing the mass spectrometry detector.

[0162] Based on this information, the tester evaluates the current state and service life of the equipment; although the equipment can still work normally and give reliable test results, considering that it has been used for a long time and there are multiple maintenance records, the tester believes that this equipment is no longer suitable for high-precision pesticide residue detection; therefore, the tester recommends that the winery replace or repair this equipment as soon as possible to ensure the accuracy and reliability of subsequent detections; at the same time, the tester also records this information in the equipment's maintenance log for subsequent tracking and management.

[0163] Furthermore, the content of the components of pesticide residues in the wine and the type of the wine are collected, and the content of the components of pesticide residues in the wine and the type of the wine are introduced; therefore, the first-level parameter is determined based on the content of the components of pesticide residues in the wine and the overall content of the wine; the second-level parameter is determined according to the content of the components of pesticide residues in the wine and the service life of the pesticide residue detection equipment; the grade of pesticide residues in the wine is determined based on the first-level parameter, the second-level parameter, and the type of the wine, which takes into account the overall consideration of the first-level parameter, the second-level parameter, and the type of the wine, and ensures the accuracy of the grade of pesticide residues in the wine.

[0164] At this time, it is necessary to calculate the ratio between the specific content of pesticide residues in the wine and the overall content of the wine (usually referring to the volume or mass of the wine) to determine an index called the "first-level parameter"; this parameter reflects the concentration level of pesticide residues relative to the overall wine and is an important basis for evaluating the risk of pesticide residues in the wine.

[0165] First, obtain the specific content data of various pesticide residues in the wine from the pesticide residue detection report; at the same time, it is also necessary to know the overall content (such as volume or mass) of the wine sample used for detection; for each detected pesticide residue, calculate its concentration in the wine (pesticide residue content / overall wine content); then, based on these concentration values, combined with factors such as the toxicity of the pesticide and the residue limit standard, comprehensively determine a first-level parameter; this parameter is a specific value and also a classification level (such as low, medium, high).

[0166] Consider the influence of the service life of the pesticide residue detection equipment on the detection results; the longer the service life of the equipment, the more its performance and accuracy will gradually decline, thus affecting the reliability of the detection results; therefore, it is necessary to determine an index called the "second-level parameter" based on the service life of the equipment and the content of pesticide residues.

[0167] Consult documents such as the purchase record, maintenance record, and calibration certificate of the equipment to understand information such as the service life, maintenance history, and calibration cycle of the equipment; based on the service life and maintenance history of the equipment, evaluate the current state and performance of the equipment; analyze the influence of the service life of the equipment on the detection results, such as decreased sensitivity and increased error; combined with the content of pesticide residues and the evaluation results of the equipment, comprehensively determine a second-level parameter; this parameter reflects the credibility or accuracy level of the detection results.

[0168] Integrate the first-level parameter, the second-level parameter, and the information on the type of wine to determine the level of pesticide residues in the wine; this level is a specific classification (such as safe, warning, dangerous, etc.) and also a comprehensive score or rating; integrate the first-level parameter, the second-level parameter, and the information on the type of wine to form a comprehensive evaluation report; based on the integrated information, combined with factors such as the pesticide residue limit standard, the consumption habits of wine, and health risks, comprehensively determine the level of pesticide residues in the wine.

[0169] Specifically, assume that the pesticide residue detection equipment in use has been in service for 10 years and has undergone multiple repairs and calibrations during this period. Based on the equipment's repair records and calibration certificates, the current state of the equipment is evaluated as "good", but its sensitivity has decreased slightly. At the same time, it is known that the residual content of dichlorvos in this batch of wine is 0.02 mg / L. Considering the impact of the equipment's service life on the test results, the second-level parameter is determined as "medium credibility", which means that although the test results are basically reliable, there is a certain error range.

[0170] Assume that the first-level parameter for the dichlorvos residue in a certain batch of wine has been determined as "low risk", the second-level parameter as "medium credibility", and this batch of wine is dry red type. Combining this information, the level of pesticide residue in this batch of wine is comprehensively evaluated as "safe for consumption". This means that although trace amounts of dichlorvos residue have been detected, considering its low concentration and the basically reliable test results of the equipment, the health risk of this batch of wine to consumers is small and it is safe to consume. At the same time, this evaluation result is also informed to consumers and relevant departments to ensure the safety and transparency of the wine market.

[0171] In another embodiment of the present application, an example of level matching is as follows:

[0172] Table 5 Example of level matching representation

[0173]

[0174]

[0175] Suppose there is a pesticide residue test report for wine, in which the first-level parameter is "low risk", the second-level parameter is "medium credibility", and the wine type is "dry red". According to the level matching table, quickly determine that the pesticide residue level of this wine is "safe for consumption".

[0176] Embodiment III

[0177] Please refer to Figure 3 , Figure 3 which is a schematic diagram of the structural composition of the detection system for pesticide residues in wine in an embodiment of the present invention;

[0178] As Figure 3 shown, a detection system for pesticide residues in wine, the detection system for pesticide residues in wine includes:

[0179] A growth data set module 21, configured to determine a growth data set of grapes based on a database of grapes and the picking batches of grapes;

[0180] Quality parameter module 22, configured to determine the quality parameters of wine based on the growth data set of grapes, the surface morphology of grapes, and the wine-making data of grapes;

[0181] Detection parameter model 23, configured to determine the detection parameters of pesticide residues based on the quality parameters of wine, the detection parameters during the detection of wine, and the component data set of grape skins;

[0182] Content model 24, configured to output corresponding component data during the pesticide residue detection of wine, and determine the content of the components of pesticide residues in wine based on this component data, the detection parameters of pesticide residues, and the pesticide products used during the growth of grapes;

[0183] Grade model 25, configured to determine the grade of pesticide residues in wine based on the content of the components of pesticide residues in wine, the service life of the pesticide residue detection equipment, and the type of wine.

[0184] For any combination of the technical features of the above embodiments, for the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

Claims

1. A method for detecting pesticide residues in wine, characterized in that: include: Determine a grape growth data set based on a grape database and grape picking batches; Determine wine quality parameters based on grape growth data sets, grape surface morphology, and grape winemaking data; Determine the detection parameters of pesticide residues based on the quality parameters of wine, the detection parameters of wine during the detection process and the composition data set of grape skin; The wine outputs the corresponding component data in the pesticide residue test, and the content of the pesticide residue components in the wine is determined based on the component data, the pesticide residue test parameters and the pesticide products used in the grape growth process; The level of pesticide residues in wine is determined based on the content of pesticide residues in the wine, the service life of the pesticide residue detection equipment and the type of wine.

2. The method for detecting pesticide residues in wine according to claim 1, characterized in that: The grape growth data set is determined based on the grape database and grape picking batches, including: Determine the growing region and type of the corresponding grapes based on the label information of the wine to be tested; Determine the grape picking batches according to the grape growing area, grape type and winemaking time; Associating grape picking batches with a grape database, and determining data of the grapes at different growth stages according to the grape picking batches and the grape database; A growth data set of the grapes is determined based on the data of the grapes at different growth stages, and the growth data set covers the growth status of the grapes at different times and the corresponding growth environment.

3. The method for detecting pesticide residues in wine according to claim 2, characterized in that: The method of determining the quality parameters of the wine according to the grape growth data set, the surface morphology of the grape and the winemaking data of the grape includes: Collecting grape growth data sets; Determine multiple images of grapes at different positions according to grape picking batches and screening images of grapes before winemaking; determining the surface morphology of the grapes based on multiple images of the grapes at different positions and the storage environment of the grapes; Monitor the grape winemaking process in real time and determine the grape winemaking data based on the dynamic detection of grape winemaking; The quality parameters of the wine are determined based on the grape growth data set, the surface morphology of the grapes, and the winemaking data of the grapes.

4. The method for detecting pesticide residues in wine according to claim 1, characterized in that: The method of determining the detection parameters of pesticide residues based on the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skin includes: Obtain wine quality parameters; The wine is tested online, and the testing parameters of the wine during the testing process are determined based on the online testing of the wine.

5. The method for detecting pesticide residues in wine according to claim 4, characterized in that: The method of determining the detection parameters of pesticide residues based on the quality parameters of the wine, the detection parameters of the wine during the detection process, and the component data set of the grape skin also includes: Control the winemaking process of grapes and conduct random inspections on grape skins; Determine the grape skin composition data set based on random inspection of grape skin; The detection parameters of pesticide residues are determined based on the interaction of the quality parameters of wine, the detection parameters of wine during the detection process and the component data set of grape skin.

6. The method for detecting pesticide residues in wine according to claim 1, characterized in that: The wine outputs corresponding component data in the pesticide residue detection, and the content of the pesticide residue components in the wine is determined according to the component data, the detection parameters of the pesticide residue and the pesticide products used in the growth process of the grapes, including: Testing wine for pesticide residues; The wine outputs corresponding component data in the pesticide residue test; Identify the corresponding grapes based on the wine’s traceability; Determine the pesticides used during the growth of grapes based on the grapes and grape database.

7. The method for detecting pesticide residues in wine according to claim 6, characterized in that: The wine outputs corresponding component data in the pesticide residue detection, and the content of the pesticide residue components in the wine is determined according to the component data, the detection parameters of the pesticide residue and the pesticide products used in the growth process of the grapes, and further includes: Interact with the ingredient data, the detection parameters of pesticide residues, and the pesticides used during the growth of grapes; Determine the first component according to the component data and the detection parameters of the pesticide residue, and determine the second component according to the component data and the pesticide used in the growth process of the grapes; The content of the pesticide residue component in the wine is determined based on the first component, the second component and a preset pesticide residue matching table.

8. The method for detecting pesticide residues in wine according to claim 1, characterized in that: The level of pesticide residues in wine is determined based on the content of the pesticide residue components in the wine, the service life of the pesticide residue detection equipment and the type of wine, including: The wine is tested for corresponding pesticide residues in the pesticide residue testing equipment; Determine the service life of pesticide residue testing equipment based on its traceability; The content of pesticide residues in wine and the type of wine were collected.

9. The method for detecting pesticide residues in wine according to claim 8, characterized in that: The method of determining the level of pesticide residues in wine according to the content of the pesticide residue components in the wine, the service life of the pesticide residue detection equipment and the type of wine also includes: The first level parameters are determined based on the content of pesticide residues in the wine and the overall content of the wine; The second level parameters are determined based on the content of pesticide residues in the wine and the service life of the pesticide residue detection equipment; The level of pesticide residue in the wine is determined based on the first level parameter, the second level parameter and the type of wine.

10. A detection system for pesticide residues in wine, characterized in that: The detection system for pesticide residues in wine is applied to the detection method for pesticide residues in wine as claimed in any one of claims 1 to 9, and the detection system for pesticide residues in wine comprises: A growth data set module, used to determine a growth data set of grapes based on a grape database and a grape picking batch; A quality parameter module, used to determine the quality parameters of wine based on a set of grape growth data, grape surface morphology, and grape winemaking data; A detection parameter model for determining detection parameters for pesticide residues based on a data set of wine quality parameters, detection parameters of the wine during the detection process, and components of grape skin; The content model is used to output the corresponding component data in the pesticide residue detection of wine, and determine the content of the pesticide residue components in the wine based on the component data, the detection parameters of the pesticide residue and the pesticide products used in the growth process of the grapes; The grade model is used to determine the grade of pesticide residues in wine according to the content of the components of pesticide residues in wine, the service life of the pesticide residue detection equipment and the type of wine.

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