Food safety detection method based on food contact material control

Abstract

The invention belongs to the technical field of food safety management, and particularly relates to a food safety detection method based on food contact material control, which comprises the steps of identifying potential risk factors, calculating an original migration volume, calibrating the migration volume, judging safety risks and tracing and filing data. According to the invention, identification detection is carried out through the contact factor identification module to avoid artificial errors, the migration volume analysis module accurately calculates the original migration volume based on multiple parameters, the migration volume calibration module calculates a calibration coefficient for environmental interference and corrects the original migration volume, and the risk threshold determination module quantifies a risk level coefficient and divides three levels of risks. Health risks caused by migration of harmful substances can be effectively prevented, the data traceability archiving module classifies and stores data in the whole process, allocates unique traceability codes and supports query and export, the requirement for supervision traceability is met, scientific support is provided for compliant production of enterprises and precise management and control of supervision departments, a food contact material safety defense line is practically built, and the safety of food contact materials is improved. The food safety is effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of food safety management technology, specifically a food safety testing method based on the control of food contact materials. Background Technology

[0002] Food contact materials are the core carriers in food production, packaging, and storage. Their safety is directly related to food quality and public health. The migration risk of harmful substances such as plasticizers, heavy metals, and additives has become a key area of ​​food safety supervision. However, traditional testing technologies are still difficult to meet the needs of precise and full-process control, and have many prominent defects, which restrict industry supervision and enterprise compliance production.

[0003] For example, Chinese invention patent CN115015421A discloses "A method for rapid determination of additives in food contact materials." This invention employs thermal desorption-gas chromatography-mass spectrometry to rapidly detect 20 additives in food contact materials, addressing the problems of complex pretreatment, long cycles, and large amounts of organic solvents required by traditional detection methods, thus improving the efficiency of additive detection. However, this method still has significant limitations in practical applications: On the one hand, the detection targets are limited, making it difficult to achieve comprehensive risk screening and reasonably predict the original migration volume. Furthermore, the migration volume calculation does not take into account environmental parameter interference and lacks a targeted calibration mechanism, resulting in deviations between the detection results and the actual migration situation. On the other hand, the operational risks of the detection components are not investigated in advance, resulting in insufficient data credibility and making it difficult to prevent the risk of harmful substance migration from the source, which is not conducive to ensuring food safety.

[0004] In view of the current technical deficiencies, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a food safety testing method based on the control of food contact materials, in order to address the shortcomings of current practical technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a food safety testing method based on food contact materials, comprising the following steps: Step 1: The contact factor identification module collects the physicochemical property parameters of food contact materials and identifies potential risk factors contained in the materials; Step 2: The migration analysis module calculates the original migration amount of risk factors into food by constructing a migration calculation model; Step 3: The migration calibration module calculates calibration coefficients based on environmental interference parameters, and calibrates the original migration amount using these calibration coefficients. Step 4: The risk threshold determination module calculates the risk level coefficient through the risk threshold determination model, classifies the safety risk level based on the risk level coefficient, and generates a risk determination report. Step 5: The data traceability and archiving module receives the risk assessment report, integrates the entire process of testing data for classification and storage, traceability code allocation, and traceability query management.

[0007] Furthermore, the operation process of the contact factor recognition module is as follows: The food contact material sample to be tested is placed on the sample testing stage. A detection beam of a specific wavelength is emitted through the spectral detection component. After the beam penetrates the material sample, the spectral receiver collects the reflected spectral signal. By comparing the spectral signal with the preset risk factor spectral database, the types of risk factors contained in the sample are identified, including plasticizer DEHP, heavy metal lead and fluorescent whitening agent VBL. The thickness detection component measures the thickness parameters of the material sample using the laser ranging principle; it also extracts the sample and measures the initial concentration of risk factors in the material using high-performance liquid chromatography. The parameters, including risk factor type, initial concentration, and material thickness, are integrated into a feature parameter dataset, which is then transmitted to the migration analysis module.

[0008] Furthermore, the migration volume analysis module receives the feature parameter dataset transmitted by the contact factor identification module, verifies the validity of the dataset, and removes abnormal data; it calls the built-in migration volume calculation model, substitutes the relevant parameters into the model for calculation, obtains the original migration volume of the risk factor through calculation, and transmits the original migration volume value to the migration volume calibration module.

[0009] Furthermore, the specific operation process of the migration calibration module includes: The system receives the original migration amount M of the risk factor and simultaneously activates the built-in environmental parameter acquisition component to collect the actual detection environment's temperature T, relative humidity H, and food simulation liquid pH. It calculates the calibration coefficient α using the weighted deviation correction model and calculates the calibrated migration amount M1 using M1=M×α. After calibration, a migration amount calibration report is generated and transmitted to the risk threshold determination module.

[0010] Furthermore, the analysis and calculation process of the calibration coefficient α is as follows: The deviation between the actual temperature and the standard temperature is quantified, and the temperature deviation coefficient ΔT is obtained accordingly. The calculation formula is ΔT=(T-T0) / T0, where T0 is the standard temperature. The degree of deviation between the actual humidity and the standard humidity is quantified, and the humidity deviation coefficient ΔH is obtained accordingly. The calculation formula is ΔH=(H-H0) / H0, where H0 is the standard humidity. The deviation of the actual pH from the standard pH is quantified, and the pH deviation coefficient ΔpH is obtained accordingly. The calculation formula is ΔpH=|pH-pH0| / pH0, where pH0 is the standard pH. Furthermore, the temperature deviation coefficient ΔT, humidity deviation coefficient ΔH, and pH deviation coefficient ΔpH are substituted into the model to calculate the α value.

[0011] Furthermore, the specific operation process of the risk threshold determination module is as follows: The system retrieves the standard migration limit M0 for the corresponding risk factor from the built-in national standard database. Then, it calls the risk threshold determination model, substituting M1 and M0 to calculate the risk level coefficient R1. The specific calculation formula is: R1 = M1 ÷ M0. Based on the R1 value, three risk levels are defined. If R1≤0.5, it is judged as low risk level; if 0.5<R1≤1, it is judged as medium risk level; if R1>1, it is judged as high risk level. After the judgment is completed, a risk judgment report is generated and transmitted to the data traceability and archiving module.

[0012] Furthermore, the data traceability and archiving module receives the risk assessment report, classifies and analyzes the data throughout the process, and divides it into five categories: basic sample data, feature parameter data, raw migration data, calibration data, and risk assessment data; it assigns a unique traceability code to each test report, enabling rapid data location through the traceability code; Furthermore, the classification data is stored in a distributed database, with basic sample data and risk assessment data stored in a relational database, and feature parameter data, raw migration data, and calibration data stored in a time series database; and a built-in data traceability query component that allows users to query data by keywords, with the query results presented in a visual report, and can also be exported to Excel and PDF formats.

[0013] Furthermore, both the risk threshold determination module and the data traceability and archiving module are communicatively connected to the user management control terminal. After receiving the risk determination report, if the risk level is determined to be high, it is recommended to prohibit the use of the food contact material and recall the relevant food; if the risk level is determined to be medium, it is recommended to restrict the use scenarios and contact duration of the food contact material; if the risk level is determined to be low, it is recommended to use it normally.

[0014] Furthermore, the user management control terminal communicates with the credibility risk decision module. Before data collection, the credibility risk decision module analyzes the contact factor identification module. By analyzing the credibility risk of the collected data, it determines whether to generate a credibility alarm signal. When a credibility alarm signal is generated, it is sent to the user management control terminal. When the user management control terminal receives the credibility alarm signal, it issues a corresponding warning.

[0015] Furthermore, the specific analysis process of the credibility risk decision-making module is as follows: All detection components involved in the contact factor recognition module are obtained, the production date of the corresponding detection component is collected, and the time interval between the current date and the production date is marked as the historical duration; and the time of the last maintenance and correction for the corresponding detection component is collected, the time difference between the current time and the maintenance and correction time is calculated, and the ratio of the time difference calculation result to the corresponding preset maintenance and correction standard interval duration is calculated to obtain the maintenance and correction risk value; The system obtains the number of times the corresponding detection component failed in the past three months and marks it as the component failure characteristic value. The component's credible hidden danger value is calculated by weighting and summing the historical duration, maintenance and correction risk value, and component failure characteristic value. The hidden danger characteristic coefficient is calculated by comparing the component's credible hidden danger value with the corresponding preset component credible hidden danger threshold. If the hidden danger characteristic coefficient is greater than 1, the corresponding detection component is marked as an untrusted component. If an untrusted component exists, a credibility alarm signal is generated. If there are no untrusted components, the reliability decision value is calculated by averaging the hazard characteristic coefficients of all detection components. If the reliability decision value exceeds the preset reliability decision threshold, a reliability alarm signal is generated.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, the contact factor identification module is used to identify and detect to avoid human error. The original migration amount is accurately calculated and corrected based on multiple parameters. Finally, the risk level coefficient is quantified and three levels of risk are divided. The detection data can be traced and managed throughout the entire process, providing scientific support for enterprises to comply with production and for regulatory departments to accurately control the situation. This effectively strengthens the safety defense line for food contact materials and effectively protects food safety and public health.

[0017] 2. In this invention, the risk assessment results are transformed into actionable control measures through the user management control terminal, which helps enterprises or regulators respond quickly to risks and prevent the spread of safety hazards. Furthermore, the credibility hazard decision-making module investigates potential operational hazards of the detection components from the source, preventing data distortion caused by component problems. This strengthens the credibility of the entire detection process and further ensures food safety. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 1 of the present invention; Figure 3This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1-2 As shown, the food safety testing method based on food contact materials proposed in this invention includes the following steps: Step 1: The contact factor identification module has built-in spectral detection components, thickness detection components, etc., to collect physicochemical property parameters of food contact materials, quickly and accurately identify potential risk factors contained in the materials, avoid errors in manual identification, and at the same time, the collected multi-dimensional feature parameters provide comprehensive data support for subsequent migration calculations, ensuring the accuracy of subsequent analysis results. Specifically, first, the food contact material sample to be tested is placed on the sample testing stage. A detection beam of a specific wavelength is emitted through the spectral detection component. After the beam penetrates the material sample, the spectral receiver collects the reflected spectral signal. By comparing the spectral signal with a preset risk factor spectral database, the types of risk factors contained in the sample are identified, such as plasticizer DEHP, heavy metal lead, and fluorescent whitening agent VBL. Meanwhile, the thickness detection component measures the thickness parameters of the material sample using the laser ranging principle. These parameters serve as auxiliary characteristic parameters of the risk factors. Subsequently, the concentration detection component extracts the sample and measures the initial concentration of the risk factors in the material using high-performance liquid chromatography. Finally, the risk factor type, initial concentration, material thickness, and other parameters are integrated into a feature parameter dataset, which is then transmitted to the migration analysis module.

[0021] Step 2: The migration amount analysis module, based on the feature parameter dataset transmitted by the contact factor identification module, constructs a migration amount calculation model to calculate the original migration amount of risk factors into food. It fully considers the influence of various factors such as risk factor type, material characteristics, and contact conditions, and solves the problem of insufficient accuracy of existing empirical formulas, so as to achieve accurate quantitative calculation of migration amount. Specifically, the migration amount analysis module receives the feature parameter dataset transmitted by the contact factor identification module. First, it verifies the validity of the dataset, removes abnormal data, and ensures the accuracy of the input parameters. Then, it calls the built-in migration amount calculation model, substitutes parameters such as the initial concentration of risk factors and material thickness into the model for calculation, obtains the original migration amount of risk factors through calculation, and transmits the original migration amount value to the migration amount calibration module. The formula for calculating migration amount is as follows: M = k × C × S × t ÷ d; Where M is the original migration amount of the risk factor; k is the risk factor migration coefficient, which is related to the type of risk factor and the type of material. It can be obtained by consulting the national food safety standards database. For example, the migration coefficient of the plasticizer DEHP in polyethylene plastic is 0.02. C represents the initial concentration of the risk factor in the material, which is measured by the concentration detection component of the contact factor identification module. S represents the contact area between the material and the food. The contact area is calculated by scanning the contact surface of the material sample using the image recognition component built into the module. t represents the contact time between the material and the food, which is input by the user in the system according to the actual usage scenario; d represents the material thickness, which is measured by the thickness detection component of the contact factor recognition module.

[0022] Step 3: The migration calibration module calculates calibration coefficients based on environmental interference parameters, and calibrates the original migration quantity using the calibration coefficients to correct the root cause error of the migration quantity. This provides corrected core data for accurate risk assessment, avoids subsequent judgment deviations caused by environmental interference, and improves the system's anti-interference capability. Specifically, firstly, the migration calibration module receives the raw migration amount M of the corresponding risk factor, and at the same time activates the built-in environmental parameter acquisition components (PT100 thermistor sensor, capacitive humidity sensor, glass electrode pH sensor) to synchronously acquire the actual detection environment temperature T, relative humidity H and food simulation liquid pH. In order to eliminate instantaneous fluctuation errors, the data is acquired in the manner of "1 time / second, continuous acquisition of 5 sets", and the average value is taken as the final environmental parameters. Subsequently, based on the standard testing environment parameters (T0=25℃, H0=60%RH, pH0=7), the deviation coefficient of each environmental parameter is calculated, that is, the degree of deviation between the actual temperature and the standard temperature is quantified, and the temperature deviation coefficient ΔT is obtained accordingly. The calculation formula is ΔT=(T-T0) / T0, where T is the actual testing environment temperature, which is collected in real time by the PT100 thermistor sensor built into the test result calibration module (measurement accuracy ±0.1℃); T0=25℃ is the standard temperature; for example, when the actual temperature T=35℃, ΔT=(35-25) / 25=0.4; The deviation between the actual humidity and the standard humidity is quantified, and the humidity deviation coefficient ΔH is obtained accordingly. The calculation formula is ΔH=(H-H0) / H0, where H is the relative humidity of the actual detection environment, which is collected by the capacitive humidity sensor built into the module (measurement accuracy ±1%RH); H0=60%RH is the standard humidity; for example, when the actual humidity H=75%RH, ΔH=(75-60) / 60=0.25; The deviation of the actual pH from the standard pH is quantified to obtain the pH deviation coefficient ΔpH, calculated as ΔpH=|pH-pH0| / pH0, where pH is the pH of the simulated food liquid in the actual testing environment, acquired by the built-in glass electrode pH sensor (measurement accuracy ±0.01pH); pH0=7 is the standard pH, taken as an absolute value because both excessive acidity (pH<7) and excessive alkalinity (pH>7) will interfere with the migration, and the interference direction is consistent (accelerating the migration of polar risk factors); for example, when the actual pH=5.5, ΔpH=|5.5-7| / 7≈0.214; The calibration coefficient α is calculated using a weighted deviation correction model: the temperature deviation coefficient ΔT, humidity deviation coefficient ΔH, and pH deviation coefficient ΔpH are substituted into the calculation model to obtain the α value, and the calibrated migration amount M1 is calculated using M1=M×α; after calibration, a migration calibration report is generated and transmitted to the risk threshold determination module; the calculation process of the α value is as follows: α=1.0+w1×ΔT+w2×ΔH+w3×ΔpH; Among them, w1, w2, and w3 are the weighting coefficients of temperature, humidity, and pH, respectively, used to characterize the degree of influence of each environmental parameter on the migration detection results. The weighting coefficients are determined by orthogonal experimental design (based on the testing environment requirements of the GB4806 series of food contact material safety standards, fitting analysis was performed on 100 sets of migration detection data under different environmental conditions). The final values ​​are: w1=0.008 (temperature has the largest weight, as temperature directly affects the molecular diffusion rate and has the most significant impact on migration), w2=0.002 (humidity has the second largest weight, indirectly affecting the adsorption of the material surface), and w3=0.01 (pH has a moderate weight). The values ​​satisfy w1+w2+w3=0.02 to ensure that the adjustment range of α is within a reasonable range when a single parameter deviates (avoiding overcalibration).

[0023] For example, the environmental parameters of a certain testing scenario are: T=30℃, H=70%RH, pH=6.2. The calculation process is as follows: Calculate the deviation coefficient: ΔT=(30-25) / 25=0.2; ΔH=(70-60) / 60≈0.167; ΔpH=|6.2-7| / 7≈0.114; Substitute into the α calculation model: α=1.0+0.008×0.2+0.002×0.167+0.01×0.114=1.0+0.0016+0.000334+0.00114≈1.003074, and finally retain four decimal places to take α=1.0031.

[0024] By quantifying the degree of deviation of environmental parameters and combining the weighting coefficients determined by orthogonal experiments, the calculation of the calibration coefficient α is both theoretically based and conforms to the interference patterns of the actual detection environment, avoiding the limitations of traditional "fixed coefficient calibration". At the same time, the parameter acquisition adopts the method of averaging multiple times, which further reduces the impact of sensor error on the calculation result of α, ensuring that α can accurately correct the detection deviation caused by environmental interference, and providing more reliable basic data for subsequent risk level determination.

[0025] Step 4: The risk threshold determination module calculates the risk level coefficient through the risk threshold determination model, classifies the safety risk level according to the risk level coefficient, generates a risk determination report, and realizes the objective quantitative determination of the risk level based on the calibrated data, avoiding the subjectivity of manual assessment and improving the reliability of risk assessment. Specifically, the risk threshold determination module first receives the calibrated migration amount M1 transmitted by the migration amount calibration module, and then retrieves the built-in national standard database to obtain the standard limit migration amount M0 for the corresponding risk factor (for example, the national standard stipulates that the limit migration amount of plasticizer DEHP in food contact plastics is 1.5 mg / kg). Then, the risk threshold determination model is invoked, and M1 and M0 are substituted to calculate the risk level coefficient R1. The specific calculation formula is: R1 = M1 ÷ M0; based on the R1 value, three risk levels are divided: If R1≤0.5, it is judged as low risk level; if 0.5<R1≤1, it is judged as medium risk level; if R1>1, it is judged as high risk level. After the judgment is completed, a risk judgment report is generated and transmitted to the data traceability and archiving module.

[0026] Step 5: The data traceability and archiving module receives the risk assessment report, integrates the entire process of testing data for classification and storage, traceability coding allocation, and traceability query management, realizing full-process traceability management of testing data, meeting the traceability requirements of food safety supervision, and the distributed database design improves the security of data storage and query efficiency, meeting the traceability requirements of supervision.

[0027] Specifically, the data traceability and archiving module receives the risk assessment report and first classifies and analyzes the data throughout the entire process, dividing it into five categories: basic sample data, feature parameter data, raw migration data, calibration data, and risk assessment data. Then, it assigns a unique traceability code to each test report. The traceability code contains information such as sample number, test time, and tester, enabling rapid data location through the traceability code. Furthermore, the system stores categorized data in a distributed database, with basic sample data and risk assessment data stored in a relational database, and feature parameter data, raw migration data, and calibration data stored in a time-series database, meeting the storage needs of different types of data. It also includes a built-in data traceability query component, allowing users to query data using keywords such as traceability code, sample number, and detection time. The query results are presented in a visual report. In addition, it supports data export, allowing users to export detection data to Excel, PDF, and other formats for convenient subsequent data analysis and management.

[0028] Example 2: Figure 3 As shown, the difference between this embodiment and embodiment one is that both the risk threshold determination module and the data traceability archiving module are communicatively connected to the user management control terminal. The administrator can use the user management control terminal to query and modify the stored information in the data traceability archiving module. When the user management control terminal receives a risk determination report, if it is determined to be of a high risk level, it is recommended to prohibit the use of the food contact material and recall the relevant food. If the risk level is determined to be medium, it is recommended to restrict the use of the food contact material and the duration of contact; if the risk level is determined to be low, it is recommended to use it normally. This approach can provide clear recommendations for different risk levels, transform risk assessment results into actionable control measures, help enterprises or regulators respond quickly to risks, prevent the spread of safety hazards, and further ensure food safety.

[0029] Example 3: Figure 3 As shown, the difference between this embodiment and Embodiment 1 and Embodiment 2 is that the user management control terminal communicates with the credibility risk decision module. Before data collection, the credibility risk decision module analyzes the contact factor identification module. By analyzing the credibility risk of the collected data, it determines whether to generate a credibility alarm signal. When a reliability alarm signal is generated, it is sent to the user management control terminal. Upon receiving the reliability alarm signal, the user management control terminal issues a corresponding warning, thus investigating potential operational problems in the detection components from the source and preventing data distortion due to component issues. This strengthens the pre-emptive defense for the reliability of the entire detection process. The specific analysis process of the reliability risk decision module is as follows: The system acquires all detection components involved in the contact factor recognition module (such as spectral detection components, thickness detection components, etc.), collects the production date of the corresponding detection components, marks the time interval between the current date and the production date as the historical duration, and collects the time of the last maintenance and correction performed on the corresponding detection components. The system calculates the time difference between the current time and the maintenance and correction time, and calculates the ratio of the time difference calculation result to the corresponding preset maintenance and correction standard interval duration to obtain the maintenance and correction risk value. The system obtains the number of times the corresponding detection components failed in the past three months and marks them as component failure feature values. The component credible hidden danger value is calculated by weighting and summing the historical duration, maintenance and correction risk value and component failure feature value. Specifically, the historical duration, maintenance and correction risk value and component failure feature value are assigned corresponding preset weight coefficients, and the historical duration, maintenance and correction risk value and component failure feature value are multiplied by the corresponding preset weight coefficients. The sum of the three sets of product results is marked as the component credible hidden danger value. It should be noted that the higher the component's trusted vulnerability value, the higher the overall operational vulnerability of the corresponding detection component. The vulnerability feature coefficient is calculated by comparing the component's trusted vulnerability value with the corresponding preset component trusted vulnerability threshold. If the vulnerability feature coefficient is greater than 1, it indicates that the operational vulnerability of the corresponding detection component is high, and the corresponding detection component is marked as an untrusted component. If untrusted components are present, it indicates that the accuracy of the collected data is not guaranteed, and a reliability alarm signal is generated. If no untrusted components are present, the reliability decision value is calculated by averaging the hazard characteristic coefficients of all detection components. The reliability decision value is then compared with a preset reliability decision threshold. If the reliability decision value exceeds the preset reliability decision threshold, it indicates that the accuracy of the collected data is not guaranteed, and a reliability alarm signal is generated.

[0030] The working principle of this invention is as follows: During use, the contact factor identification module accurately identifies the type of risk factor and its initial concentration, avoiding errors from manual identification. The migration analysis module accurately calculates the original migration amount based on multiple parameters. The migration calibration module calculates calibration coefficients using a weighted deviation correction model to correct errors in the original migration amount, taking into account environmental interference. The risk threshold determination module quantifies the risk level coefficient and classifies risks into three levels, avoiding the subjectivity of manual assessment and effectively preventing health risks caused by the migration of harmful substances. The data traceability and archiving module classifies and stores data throughout the entire process, assigns a unique traceability code, and supports querying and exporting, meeting regulatory traceability requirements. This provides scientific support for compliant production by enterprises and precise control by regulatory departments, effectively strengthening the safety defense line for food contact materials and ensuring food safety and public health.

[0031] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0032] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A food safety testing method based on the control of food contact materials, characterized in that, Includes the following steps: Step 1: The contact factor identification module identifies potential risk factors contained in the material; Step 2: The migration analysis module calculates the original migration amount of risk factors into food. Step 3: The migration calibration module calibrates the original migration amount using calibration coefficients; Step 4: The risk threshold determination module classifies safety risk levels based on the risk level coefficient; Step 5: The data traceability and archiving module receives the risk assessment report, integrates the entire process of testing data for classification and storage, traceability code allocation, and traceability query management.

2. The food safety testing method based on food contact material control according to claim 1, characterized in that, The operation process of the contact factor recognition module is as follows: The spectral detection component emits a detection beam of a specific wavelength. After the beam penetrates the material sample, the spectral receiver collects the reflected spectral signal. By comparing the spectral signal with a preset risk factor spectral database, the type of risk factor contained in the sample is identified. The thickness detection component measures the thickness parameters of the material sample using the laser ranging principle; it also extracts the sample and measures the initial concentration of risk factors in the material using high-performance liquid chromatography. The parameters, including risk factor type, initial concentration, and material thickness, are integrated into a feature parameter dataset, which is then transmitted to the migration analysis module.

3. The food safety testing method based on food contact material control according to claim 2, characterized in that, The migration analysis module receives the feature parameter dataset transmitted by the contact factor recognition module and removes abnormal data. The built-in migration calculation model is invoked to calculate the original migration amount of the risk factor, and the original migration amount value is transmitted to the migration calibration module.

4. The food safety testing method based on food contact material control according to claim 3, characterized in that, The specific operation process of the migration calibration module includes: The system receives the original migration amount M of the risk factor and simultaneously activates the built-in environmental parameter acquisition component to collect the actual detection environment temperature T, relative humidity H, and food simulation liquid pH. The calibration coefficient α is calculated through the weighted deviation correction model, and the calibrated migration amount M1 is calculated through M1=M×α. After calibration, a migration amount calibration report is generated and transmitted to the risk threshold determination module.

5. The food safety testing method based on food contact material control according to claim 4, characterized in that, The analysis and calculation process of the calibration coefficient α is as follows: The deviation of actual temperature from standard temperature is quantified to obtain the temperature deviation coefficient ΔT; the deviation of actual humidity from standard humidity is quantified to obtain the humidity deviation coefficient ΔH; and the deviation of actual pH from standard pH is quantified to obtain the pH deviation coefficient ΔpH. Furthermore, the temperature deviation coefficient ΔT, humidity deviation coefficient ΔH, and pH deviation coefficient ΔpH are substituted into the model to calculate the α value.

6. The food safety testing method based on food contact material control according to claim 4, characterized in that, The specific operation process of the risk threshold determination module is as follows: The system retrieves the standard migration limit M0 for the corresponding risk factor from the built-in national standard database; then, it calls the risk threshold determination model, substituting M1 and M0 to calculate the risk level coefficient R1; and classifies the risk level into three levels based on the R1 value. If R1 ≤ 0.5, it is judged as a low-risk level; If 0.5 < R1 ≤ 1, it is judged as medium risk level; if R1 > 1, it is judged as high risk level. Once the assessment is completed, a risk assessment report is generated and transmitted to the data traceability and archiving module.

7. The food safety testing method based on food contact material control according to claim 6, characterized in that, The data traceability and archiving module receives risk assessment reports, classifies and analyzes the data throughout the process, and divides it into five categories: basic sample data, feature parameter data, original migration data, calibration data, and risk assessment data. It assigns a unique traceability code to each test report, enabling rapid data location through the traceability code. It also stores the classified data in a distributed database and has a built-in data traceability query component.

8. The food safety testing method based on food contact material control according to claim 7, characterized in that, Both the risk threshold determination module and the data traceability and archiving module are connected to the user management control terminal. After receiving the risk determination report, if the risk level is determined to be high, it is recommended to prohibit the use of the food contact material and recall the relevant food; if the risk level is determined to be medium, it is recommended to restrict the use scenarios and contact time of the food contact material; if the risk level is determined to be low, it is recommended to use it normally.

9. The food safety testing method based on food contact material control according to claim 8, characterized in that, The user management control terminal communicates with the credibility hazard decision-making module. Before data collection, the credibility hazard decision-making module analyzes the contact factor identification module. By analyzing the credibility hazards of the collected data, it sends the generated credibility alarm signal to the user management control terminal.

10. The food safety testing method based on food contact material control according to claim 9, characterized in that, The specific analysis process of the credibility hazard decision module is as follows: acquire all detection components involved in the contact factor identification module; if there are untrusted components, generate a credibility alarm signal; if there are no untrusted components, calculate the credibility decision value by averaging the hazard characteristic coefficients of all detection components; if the credibility decision value exceeds the preset credibility decision threshold, generate a credibility alarm signal.

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