Tracing detection system and method based on food safety supervision

Abstract

The invention discloses a traceability detection system and method based on food safety supervision, and relates to the technical field of food safety, the system comprises a food safety quality inspection module, a traceability safety detection module and a safety supervision analysis module, the food safety quality inspection module analyzes through chromatography, low-resolution mass spectrometry and high-resolution mass spectrometry, and the traceability safety detection module detects the traceability of the food safety quality inspection module; whether the food is abnormal food or not is judged, when the food is abnormal food, the traceability safety detection module is adopted, various low-resolution mass spectrometry analysis is conducted on detection of all traceability links of the abnormal food, all abnormal traceability links are obtained, and the safety supervision analysis module conducts deep analysis according to associated data of the traceability links, clarifies low-risk and fault production links, and gives an alarm according to the low-risk and fault production links. According to the method, the detection scheme is updated in time and early warning is carried out, so that the problem source is clear, the tracing efficiency is improved, and reasonable resource allocation, detection cost reduction and supervision pertinence and effectiveness enhancement are realized by analyzing the risk level and optimizing the detection scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety, and in particular to a traceability detection system and method based on food safety supervision. Background Art

[0002] Food safety issues are increasingly receiving widespread public attention. It is not only related to people's physical health, but also has a profound impact on economic development. The increasingly complex and globalized food supply chain has enriched the types and supply channels of food, but also created more opportunities for the breeding and spread of food safety risks. Therefore, a traceability detection system and method based on food safety supervision is needed.

[0003] Prior art, such as the invention patent application with announcement number CN118777547A, discloses a food safety detection method and a food safety detection system, which relate to the field of food detection technology. The invention comprises: step one, monitoring of food sold in supermarkets, step two, analysis of detection parameters of supermarket food, step three, selection of detection samples for supermarket food, step four, detection of supermarket food, step five, detection and analysis of supermarket food, and step six, display processing. The invention ensures the accuracy of the detection quantity and sampling frequency of various types of food in supermarkets, thereby improving the reliability of subsequent safety detection results of various types of food in supermarkets, avoiding waste of manpower and material resources to a certain extent, and reducing risks to consumers' physical health and life safety. On the other hand, the dimensions of the analysis of the sampling quantity and sampling frequency of various types of food in supermarkets are relatively rich, ensuring the subsequent evaluation of various types of food in supermarkets after sale by consumers, reducing the probability of consumer complaints, and thus reducing the probability of supermarkets being punished.

[0004] The above scheme has the following technical problems: 1. The above scheme mainly conducts inspections on supermarkets, starting from sales records, customer feedback and environmental monitoring. However, there is a lack of effective traceability mechanism for the long supply chain from the production source to the entry of food into supermarkets. Once problematic food is found, only the relevant circumstances of the supermarket sales process can be known. It is difficult to accurately trace the root cause of the problem in the early links such as food production, processing and transportation. It is impossible to clarify which production link or supply chain node has a safety hazard, and the problem link cannot be clearly located, which is not conducive to solving food safety problems.

[0005] 2. The above scheme focuses on random inspections of foods sold in supermarkets, and the selection of random inspection samples is mainly based on the appropriate number and frequency of random inspections calculated comprehensively based on sales records, feedback records and environmental parameters. It lacks in-depth consideration of the characteristics of the food itself. Different types of food may introduce different types of safety risks during the production process. It only relies on the above-mentioned external factors to determine the detection method, and does not use corresponding chromatography-mass spectrometry analysis. It is impossible to comprehensively and accurately detect various potential safety anomalies in food.

[0006] 3. The above scheme calculates the inspection risk assessment index of supermarket food, mainly based on historical inspection data, sales records, customer feedback, environmental parameters and other factors. It does not take into account the risk changes of different types of safety anomalies in various food traceability links, does not conduct multi-dimensional analysis, does not take into account the relationship between traceability links, and cannot comprehensively and dynamically assess the risk status of food in the entire production and circulation process, resulting in low accuracy of the inspection scheme. Summary of the invention

[0007] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a traceability detection system and method based on food safety supervision.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a traceability detection system based on food safety supervision, comprising the following modules: a food safety quality inspection module, used to detect food and judge abnormal food.

[0009] The traceability safety detection module is used to detect each traceability link of abnormal food and obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food.

[0010] The safety supervision analysis module is used to analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food according to the traceability link association data in the database, obtain each low-risk production link and faulty production link, update the detection plan, and issue early warning at the same time.

[0011] Preferably, the risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food is analyzed, and the specific analysis process is as follows: the traceability link associated data is the production order of each traceability link, and based on food detection, each traceability link is arranged in the opposite direction of the production order to obtain the order of each traceability link, and according to the order of each traceability link, each traceability link with a preset number of traceability links before and after each traceability link is recorded as each effective traceability link, so as to obtain each effective traceability link of each abnormal traceability link of abnormal food, and each effective traceability link of each abnormal traceability link of abnormal food is fitted with a curve according to the type of safety abnormality to obtain the risk level change curve of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food, and the change curvature of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food is collected by machine vision, and the trend item correction parameter corresponding to each change curvature is obtained from the database, so as to obtain the trend item change parameter of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food.

[0012] The risk levels of various safety anomalies of abnormal foods in each traceability link are weighted and calculated to obtain the link risk level of each traceability link, and the link item correction parameters corresponding to each link risk level are obtained from the database.

[0013] Substitute the risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food, the trend item change parameter and the link item correction parameter into the modified risk level calculation formula to obtain the modified risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food.

[0014] If the maximum corrected risk level of the abnormal food is greater than the maximum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selection quantity unit is increased, and the ion mass spectrometry analysis amount of the preset analysis quantity unit is increased; if the maximum corrected risk level of the abnormal food is less than or equal to the minimum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selection quantity unit is reduced, and the ion mass spectrometry analysis amount of the preset analysis quantity unit is reduced.

[0015] If the maximum corrected risk level of a certain type of safety abnormality of abnormal food is greater than the preset safety corrected risk level, an early warning will be issued, and each abnormal traceability link in the trend item change parameters corresponding to this type of safety abnormality of abnormal food that is greater than the preset trend change parameters will be recorded as the problem traceability links, prompting the staff to conduct inspections on each problem traceability link.

[0016] On the other hand, the present invention provides a traceability detection method based on food safety supervision, comprising the following steps: Step 1, food safety quality inspection: testing food and determining abnormal food.

[0017] Step 2: Traceability safety testing: Test each traceability link of abnormal food to obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food.

[0018] Step 3: Safety supervision analysis: Based on the traceability link-related data in the database, analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food, obtain each low-risk production link and faulty production link, update the detection plan, and issue an early warning at the same time.

[0019] The beneficial effects of the present invention are: 1. The present invention first uses a food safety quality inspection module to determine whether the food is abnormal food through chromatography, low-resolution mass spectrometry and high-resolution mass spectrometry analysis. When the food is abnormal food, a traceability safety detection module is used to perform a variety of low-resolution mass spectrometry analyses on each traceability link of the abnormal food to obtain each abnormal traceability link. The safety supervision analysis module conducts in-depth analysis based on the traceability link correlation data, clarifies the low-risk and faulty production links, and timely updates the detection plan and issues warnings, which helps to clarify the root cause of the problem and improve the traceability efficiency. By analyzing the risk level, the detection plan is optimized, and reasonable resource allocation is achieved, the detection cost is reduced, and the pertinence and effectiveness of supervision are enhanced.

[0020] 2. In the food detection stage, the present invention accurately identifies the type of food through image recognition technology, and then obtains the amount of extract liquid selected from the database according to the food type, implements accelerated solvent extraction, provides reliable extract liquid samples for subsequent detection, conducts non-targeted screening, uses chromatography-mass spectrometry to separate compounds and conducts quality analysis, and accurately determines whether the combined technology spectrum of the food extract is abnormal through cosine similarity analysis with the normal combined technology spectrum space vector in the database. For the initial abnormal food, the low-resolution mass spectrometry detection scheme is carefully set according to its initial abnormal type, and then specific detection is performed to comprehensively and deeply detect various potential safety abnormalities in the food, which greatly improves the accuracy and comprehensiveness of the detection and reduces the missed detection rate of problematic food. First, through the combination of chromatography and high-resolution mass spectrometry, it is analyzed whether it contains abnormalities, and then the initial abnormal food is quantitatively analyzed according to the low-resolution mass spectrometry to determine the abnormality level, which utilizes the strong qualitative ability of high-resolution mass spectrometry and the strong quantitative ability of low-resolution mass spectrometry to improve the detection accuracy.

[0021] 3. The present invention conducts a multi-dimensional and in-depth analysis of the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food. According to the traceability link-related data, each traceability link is arranged in reverse order according to the production sequence, and the effective traceability link is determined. The change in risk level is presented through a fitting curve. At the same time, the risk level is corrected in combination with the link risk level of each traceability link and its corresponding link item correction parameter. This can dynamically grasp the risk status of food in the entire production and circulation process, and improve the comprehensiveness of traceability detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0024] Figure 2 The present invention is a schematic flow chart of the steps for implementing the method. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] according to Figure 1 As shown, the present invention provides a traceability detection system based on food safety supervision, including the following modules: a food safety quality inspection module, a traceability safety detection module, a safety supervision analysis module and a database.

[0027] The traceability safety detection module is connected to the food safety quality inspection module and the safety supervision analysis module respectively, and the food safety quality inspection module, the traceability safety detection module, and the safety supervision analysis module are all connected to the database.

[0028] The food safety quality inspection module is used to test food and identify abnormal food.

[0029] In a specific embodiment, the food is tested, including the following testing units: S1. The type of food is obtained through image recognition technology, and the selected amount of extract corresponding to each food type is obtained from the database to obtain the selected amount of extract of the food. According to the selected amount of extract of the food, accelerated solvent extraction is performed to obtain each extract of the food.

[0030] It should be noted that the amount of extraction liquid selected is: the standard concentration and the standard concentration gradient difference of the solution set by the staff for the amount of extraction liquid selected, with the standard concentration as the base point and the standard concentration gradient difference as the unit of concentration change, to obtain various extraction concentrations based on the standard concentration, and with the standard concentration as the base point, select various extraction concentrations greater than the preset number of standard concentrations and extraction concentrations less than the preset number of standard concentrations for extraction. The preset number is recorded as the amount of extraction liquid selected. By setting the amount of extraction liquid selected, the accuracy of data analysis can be improved.

[0031] S2. Perform non-targeted screening on each extract of the food to determine whether it is an initial abnormal food. If the food is an initial abnormal food, obtain various initial abnormalities of the initial abnormal food.

[0032] S3. According to various types of initial abnormalities of the initial abnormal foods, a low-resolution mass spectrometry detection scheme for the initial abnormal foods is set.

[0033] S4. According to the low-resolution mass spectrometry detection scheme of the initial abnormal food, each extract of the initial abnormal food is specifically detected to determine whether it is an abnormal food.

[0034] In a specific embodiment, the non-targeted screening of each food extract is performed, and the specific screening process is: each food extract is respectively injected into a chromatographic mass spectrometer, firstly various compounds in the extract are separated by a chromatographic column, and then enter the high-resolution mass spectrometer for mass analysis to obtain various combined technology spectra of each food extract, the various combined technology spectra of each food extract are vectorized to obtain the spatial vectors of various combined technology spectra of each food extract, the spatial vectors of each normal combined technology spectra are obtained from a database, the spatial vectors of various combined technology spectra of each food extract are subjected to cosine similarity analysis with the spatial vectors of various normal combined technology spectra to obtain the normal chromatographic similarities of various combined technology spectra of each food extract, and the minimum normal chromatographic similarity is selected as the similarity of various combined technology spectra of each food extract.

[0035] If the similarity of a certain type of combined technology spectrum of a certain extract of food is less than a preset similarity, it indicates that the combined technology spectrum of the extract of food is an abnormal combined technology spectrum, and the extract of food is recorded as an abnormal extract of the type of combined technology spectrum, so as to obtain the abnormal combined technology spectrum of each abnormal extract of each type of combined technology spectrum of food; if the extracts of each preset unit precision interval of a certain abnormal extract of a certain type of combined technology spectrum of food are all abnormal combined technology spectrums, the abnormal extract of the type of combined technology spectrum of food is a problematic extract, so as to obtain the abnormal combined technology spectrum of each problematic extract of each type of combined technology spectrum of food.

[0036] The peak area is obtained from the abnormal combined technology spectrum through machine vision calculation, and then the peak area of ​​the abnormal combined technology spectrum of each problematic extract of each type of combined technology spectrum of the food is obtained. The weight factor is set according to the accuracy of each extract, and weighted calculation is performed to obtain the peak area of ​​the abnormal combined technology spectrum of each type of combined technology spectrum of the food. If the peak area of ​​the abnormal combined technology spectrum of a certain type of combined technology spectrum of the food is greater than the preset peak area, it indicates that this type of combined technology spectrum is an initial abnormal combined technology spectrum. The food is recorded as an initial abnormal food, and the initial abnormality type corresponding to each type of combined technology spectrum is obtained from the database, so as to obtain various types of initial anomalies of the initial abnormal food, and at the same time, the peak area of ​​the abnormal combined technology spectrum corresponding to each type of initial abnormality of the initial abnormal food is obtained.

[0037] In a specific embodiment, the low-resolution mass spectrometry detection scheme for initially abnormal foods is set up, and the specific setting process is as follows: the usage rate, usage frequency and accuracy of each low-resolution mass spectrometer used for each type of initial abnormality are obtained from a database, and the usage rate, usage frequency and accuracy of each low-resolution mass spectrometer used for each type of initial abnormality are substituted into the usage index calculation formula to obtain the usage index of each low-resolution mass spectrometer for each type of initial abnormality.

[0038] It should be noted that the use of each low-resolution mass spectrometer is to use each low-resolution mass spectrometry detection method, and the index calculation formula is: ,in, The usage index of using n low-resolution mass spectra for m types of initial anomalies, m is the number of the initial anomaly, the value of m is a positive integer, n is the number of the low-resolution mass spectrum, the value of n is a positive integer, , and are the usage rate, usage frequency and accuracy of n low-resolution mass spectrometers for m types of initial anomalies, , and They are standard usage rate, standard usage frequency and standard accuracy rate. , and They are the weight factors of usage rate, usage frequency and accuracy, , , , .

[0039] It should be noted that the usage rate of each low-resolution mass spectra for each type of initial anomaly is the number of times each low-resolution mass spectra for each type of initial anomaly are used divided by the total number of times the low-resolution mass spectra of the corresponding initial anomaly type are used, the usage frequency of each low-resolution mass spectra for each type of initial anomaly is the number of times each low-resolution mass spectra for each type of initial anomaly are used divided by the total number of times the low-resolution mass spectra of the corresponding initial anomaly type are used, and the accuracy rate of each low-resolution mass spectra for each type of initial anomaly is the number of correct times the low-resolution mass spectra of each type of initial anomaly are used divided by the total number of times the corresponding low-resolution mass spectra of the corresponding initial anomaly type are used.

[0040] , and They are the usage rate threshold, usage frequency threshold and accuracy threshold of normal low-resolution mass spectrometry. When the actual value is greater than the threshold, it indicates that when identifying this type of initial anomaly, the corresponding low-resolution mass spectrometry has a better usage effect and a higher usage priority. The specific value is set by the staff, for example is 0.3, is 0.7 and is 0.6.

[0041] , and The staff sets the accuracy. Larger, increasing effectiveness, When larger, the accuracy is improved. Larger, e.g. is 0.3, is 0.2 and is 0.5.

[0042] The standard peak areas of the abnormal combined technology spectra corresponding to each type of initial abnormalities are obtained from the database, and the peak areas of the abnormal combined technology spectra corresponding to each type of initial abnormalities of the initial abnormal foods are divided by the standard peak areas to obtain the type abnormality rates of each type of initial abnormalities of the initial abnormal foods, and according to the type abnormality rates of each type of initial abnormalities of the initial abnormal foods, weight factors of each type of initial abnormalities of the initial abnormal foods are set, and the usage indexes of each low-resolution mass spectra of each type of initial abnormalities of the initial abnormal foods are weightedly calculated to obtain the usage indexes of each low-resolution mass spectra of the initial abnormal foods.

[0043] It should be noted that the greater the type anomaly rate of each type of initial anomaly, the greater the corresponding weight factor.

[0044] The low-resolution mass spectrometry analysis quantity corresponding to each food type is obtained from the database, and the usage index of each low-resolution mass spectrometry of the initial abnormal food is arranged in descending order to obtain the usage priority sequence of each low-resolution mass spectrometry of the initial abnormal food. The low-resolution mass spectrometry analysis quantity corresponding to each food type is obtained from the database, and then the low-resolution mass spectrometry analysis quantity of the initial abnormal food is obtained. The low-resolution mass spectra of the first low-resolution mass spectrometry analysis quantity in the usage priority sequence of the initial abnormal food are recorded as the low-resolution mass spectra used by the initial abnormal food. The low-resolution mass spectrometry detection scheme of the initial abnormal food is: each extract of the initial abnormal food is detected by using each low-resolution mass spectrometry.

[0045] In a specific embodiment, the specific detection of each extract of the initial abnormal food is as follows: a multi-mode ion source mass spectrometry analysis is performed through a low-resolution mass spectrometry detection scheme for the initial abnormal food to obtain each ion mass spectrometry of each extract of the initial abnormal food, and a feature value set is obtained from each ion mass spectrometry of each extract of the initial abnormal food through machine vision, and a feature value set of each abnormal level of each ion mass spectrometry is obtained from a database. If the feature value sets in a certain ion mass spectrum of a certain extract of the initial abnormal food all belong to the feature value set corresponding to a certain abnormal level, it indicates that the ion mass spectrum of the extract of the initial abnormal food is of the abnormal level, thereby obtaining the abnormal level of each extract of each type of ion mass spectrometry of the initial abnormal food.

[0046] It should be noted that the mass spectra of each ion are obtained by low-resolution mass spectrometry.

[0047] The abnormality levels of each extract of each type of ion mass spectrum of the initial abnormal food are averaged to obtain the average abnormality level of each type of ion mass spectrum of the initial abnormal food. If the abnormality level of a certain extract of a certain type of ion mass spectrum of the initial abnormal food is greater than the standard abnormality level, or the average abnormality level of this type of ion mass spectrum of the initial abnormal food is greater than the benchmark abnormality level, it indicates that this type of ion mass spectrum of the initial abnormal food is an abnormal ion mass spectrum. The initial abnormal food is recorded as an abnormal food, and the class safety anomaly corresponding to each abnormal ion mass spectrum is obtained from the database to obtain various types of safety anomalies of the abnormal food.

[0048] The traceability safety detection module is used to detect each traceability link of abnormal food and obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food.

[0049] In a specific embodiment, the traceability links of abnormal food are detected, and the specific detection unit includes: H1, obtaining the traceability links of abnormal food from the database, performing accelerated solvent extraction on each sample of each traceability link of abnormal food, and obtaining each extract of each sample of each traceability link of abnormal food.

[0050] It should be noted that each sample includes samples retained from abnormal foods and samples of similar foods.

[0051] H2. According to the low-resolution mass spectrometry detection scheme of the initial abnormal food, specific detection is performed on each extract of each sample in each traceability link of the abnormal food to obtain each abnormal traceability link of the abnormal food.

[0052] H3. Obtain the abnormal data of each abnormal traceability link of abnormal food from the database, analyze the abnormal data of each abnormal traceability link of abnormal food, and obtain the risk level of various safety abnormalities corresponding to each abnormal traceability link of abnormal food.

[0053] In a specific embodiment, the samples of each traceability link of the abnormal food are subjected to accelerated solvent extraction, and the specific extraction process is as follows: according to the extraction method of each extract of the food, a selected amount of the extract of the food is obtained, and according to the selected amount of the extract of the food, each sample of each traceability link of the abnormal food is subjected to accelerated solvent extraction to obtain each extract of each sample of each traceability link of the abnormal food.

[0054] In a specific embodiment, the specific detection is performed on each extract of each sample in each traceability link of the abnormal food. The specific specific detection is as follows: according to the specific detection scheme of each extract of the initial abnormal food, a low-resolution mass spectrometry detection method is used to detect each extract of each sample in each traceability link of the abnormal food.

[0055] In a specific embodiment, the abnormal data of each abnormal traceability link of abnormal food is analyzed, and the specific analysis process is as follows: the abnormal data of each abnormal traceability link of abnormal food includes the sample food risk level corresponding to various safety abnormalities of each abnormal traceability link of abnormal food, the historical average risk level of similar food, the traceability frequency of similar food and the traceability risk rate of similar food, and the sample food risk level corresponding to various safety abnormalities of each abnormal traceability link of abnormal food, the historical average risk level of similar food, the traceability frequency of similar food and the traceability risk rate of similar food are substituted into the calculation formula of traceability abnormality index: ,in, is the abnormal index of the safety abnormality of type b in the abnormal traceability link a of the abnormal food, a is the number of the abnormal traceability link, the value of a is a positive integer, b is the number of the safety abnormality type, the value of b is a positive integer, , , and They are the sample food risk level corresponding to the safety abnormality of type b in the abnormal traceability link a of abnormal food, the historical average risk level of similar foods, the traceability frequency of similar foods, and the traceability risk rate of similar foods. , and They are the preset standard food risk level, standard traceability frequency and standard traceability risk rate. and are the weight factors of the preset sample food risk level and the weight factors of the historical average risk level, , , , and They are the preset weight factor of the traceability frequency and the weight factor of the traceability risk rate, , , , obtain the abnormal index of various types of safety abnormalities in each abnormal traceability link of abnormal food, obtain the abnormal index corresponding to each risk level of various types of safety abnormalities from the database, if the abnormal index of a certain type of safety abnormality corresponding to a certain abnormal traceability link of abnormal food belongs to the abnormal index corresponding to a certain risk level, it indicates that the type of safety abnormality corresponding to the abnormal traceability link of the abnormal food is the risk level, and then obtain the risk level of various types of safety abnormalities corresponding to each abnormal traceability link of abnormal food.

[0056] It should be noted that the process of obtaining the traceability frequency of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods is as follows: the number of traceability times of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods is divided by the number of traceability times of the corresponding abnormal traceability link to obtain the traceability frequency of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods; the process of obtaining the traceability risk rate of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods is as follows: the number of traceability times of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods whose traceability risk level is greater than the preset risk level is divided by the number of traceability times of the corresponding abnormal traceability link to obtain the traceability risk rate of similar foods corresponding to various types of safety abnormalities in each abnormal traceability link of abnormal foods.

[0057] The standard food risk level is the risk level threshold of normal food. When the sample food risk level is greater than the standard food risk level, it indicates that the food contains the corresponding safety abnormality type in the corresponding abnormal traceability link. The standard traceability frequency and standard traceability risk rate are the traceability frequency and traceability risk rate of normal food in the detection process. When it is greater than the standard traceability frequency and standard traceability risk rate, it indicates that the food is prone to the corresponding safety abnormality type in the corresponding abnormal traceability link. The specific parameters of the standard food risk level, standard traceability frequency and standard traceability risk rate are set by the staff, such as 1.6 is 0.3, and is 0.15.

[0058] and The setting process is related to the shelf life of the food type. The longer the shelf life of the food type, The larger it is, the shorter the shelf life of the food type. The larger the value, the specific value is set by the staff, e.g. is 0.4 and is 0.6.

[0059] and The setting process of is related to the food production rate. The greater the food production rate, The larger it is, the smaller the food production rate is. The larger the value, the specific value is set by the staff, e.g. 0.45 is 0.55.

[0060] The safety supervision analysis module is used to analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food according to the traceability link association data in the database, obtain each low-risk production link and faulty production link, update the detection plan, and issue early warning at the same time.

[0061] In a specific embodiment, the risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food is analyzed, and the specific analysis process is as follows: the traceability link associated data is the production order of each traceability link. Based on food detection, each traceability link is arranged in the opposite direction of the production order to obtain the order of each traceability link. According to the order of each traceability link, each traceability link with a preset number of traceability links before and after each traceability link is recorded as each effective traceability link, so as to obtain each effective traceability link of each abnormal traceability link of abnormal food, and each effective traceability link of each abnormal traceability link of abnormal food is fitted with a curve according to the type of safety abnormality to obtain the risk level change curve of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food, and the change curvature of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food is collected by machine vision, and the trend item correction parameter corresponding to each change curvature is obtained from the database to obtain the trend item change parameter of each abnormal traceability link corresponding to each type of safety abnormality of abnormal food.

[0062] The risk levels of various safety anomalies of abnormal foods in each traceability link are weighted and calculated to obtain the link risk level of each traceability link, and the link item correction parameters corresponding to each link risk level are obtained from the database.

[0063] Substitute the risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food, the trend item change parameter and the link item correction parameter into the modified risk level calculation formula to obtain the modified risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food.

[0064] It should be noted that the modified risk level calculation formula is: ,in, is the risk level of Class B safety anomaly corresponding to abnormal traceability link a of abnormal food, is the trend item change parameter of the b-type security anomaly corresponding to the anomaly tracing link a, Correction parameters for the link item of abnormal tracing link a. and They are respectively the preset standard trend item change parameters and the standard link item correction parameters. and are the preset weight factors of the trend item change parameter and the link item correction parameter, respectively. , , .

[0065] Standard parameters is the trend item change parameter threshold of normal food in the traceability link. When the trend item change parameter of a certain type of food safety anomaly is greater than the trend item change parameter threshold, it indicates that the food is prone to deepen the abnormal state of this type of safety anomaly in the abnormal traceability link. The standard parameter It is the parameter threshold for correcting the link item of normal food in the traceability link. When the trend item change parameter of a certain abnormal traceability link of food is greater than the trend item change parameter threshold, it indicates that the food is easy to be transformed into abnormal food in the abnormal traceability link. The standard parameter and The specific parameters are set by the staff, such as is 0.6 and is 0.8.

[0066] Weight Factor and The setting process is related to the number of traceability links of food. The more traceability links there are, The larger it is, the fewer food traceability links there are. The larger the value, the specific value is set by the staff, e.g. is 0.3 and is 0.7.

[0067] If the maximum corrected risk level of the abnormal food is greater than the maximum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selection quantity unit is increased, and the ion mass spectrometry analysis amount of the preset analysis quantity unit is increased; if the maximum corrected risk level of the abnormal food is less than or equal to the minimum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selection quantity unit is reduced, and the ion mass spectrometry analysis amount of the preset analysis quantity unit is reduced.

[0068] If the maximum corrected risk level of a certain type of safety abnormality of abnormal food is greater than the preset safety corrected risk level, an early warning will be issued, and each abnormal traceability link in the trend item change parameters corresponding to this type of safety abnormality of abnormal food that is greater than the preset trend change parameters will be recorded as the problem traceability links, prompting the staff to conduct inspections on each problem traceability link.

[0069] A database is used to store traceability-related data, the amount of extract selected for each food type, the spatial vector of each normal combined technology spectrum, the initial abnormality type corresponding to each combined technology spectrum, the usage rate of each low-resolution mass spectrometer for each initial abnormality, the usage frequency of each low-resolution mass spectrometer for each initial abnormality, the correctness of each low-resolution mass spectrometer for each initial abnormality, the accuracy of each low-resolution mass spectrometer for each initial abnormality, the standard peak area of ​​abnormal combined technology spectrum corresponding to each initial abnormality, the low-resolution mass spectrometry analysis amount corresponding to each food type, the characteristic value set of each abnormal level of each ion mass spectrum, the class safety abnormality corresponding to each abnormal ion mass spectrum, each traceability link of abnormal food, the abnormality index corresponding to each risk level of each safety abnormality, the trend item correction parameter corresponding to each change curvature, and the link item correction parameter corresponding to each link risk level.

[0070] according to Figure 2 As shown, the present invention provides a traceability detection method based on food safety supervision, comprising the following steps: Step 1, food safety quality inspection: testing food and determining abnormal food.

[0071] Step 2: Traceability safety testing: Test each traceability link of abnormal food to obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food.

[0072] Step 3: Safety supervision analysis: Based on the traceability link-related data in the database, analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food, obtain each low-risk production link and faulty production link, update the detection plan, and issue an early warning at the same time.

[0073] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A traceability detection system based on food safety supervision, characterized in that: Includes the following modules: Food safety quality inspection module, used to test food and identify abnormal food; The traceability safety detection module is used to detect each traceability link of abnormal food and obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food; The safety supervision analysis module is used to analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food according to the traceability link association data in the database, obtain each low-risk production link and faulty production link, update the detection plan, and issue early warning at the same time.

2. A traceability detection system based on food safety supervision according to claim 1, characterized in that: The food detection includes the following detection units: S1. Obtain the type of food by image recognition technology, obtain the selected amount of extract corresponding to each food type from the database, thereby obtaining the selected amount of extract of the food, and perform accelerated solvent extraction according to the selected amount of extract of the food to obtain each extract of the food; S2. Perform non-targeted screening on each extract of the food to determine whether it is an initial abnormal food. If the food is an initial abnormal food, obtain various initial abnormalities of the initial abnormal food; S3. According to various types of initial abnormalities of the initial abnormal food, a low-resolution mass spectrometry detection scheme for the initial abnormal food is set; S4. According to the low-resolution mass spectrometry detection scheme of the initial abnormal food, each extract of the initial abnormal food is specifically detected to determine whether it is an abnormal food.

3. A traceability detection system based on food safety supervision according to claim 2, characterized in that: The non-targeted screening of each food extract is carried out, and the specific screening process is as follows: Injecting each food extract into a chromatographic mass spectrometer respectively, firstly separating various compounds in the extract by a chromatographic column, and then entering a high-resolution mass spectrometer for mass analysis to obtain various combined technology spectra of each food extract, performing vectorization processing on various combined technology spectra of each food extract to obtain spatial vectors of various combined technology spectra of each food extract, obtaining spatial vectors of various normal combined technology spectra from a database, performing cosine similarity analysis on the spatial vectors of various combined technology spectra of each food extract and the spatial vectors of various normal combined technology spectra to obtain various normal chromatographic similarities of various combined technology spectra of each food extract, and selecting the minimum normal chromatographic similarity as the similarity of various combined technology spectra of each food extract; If the similarity of a certain type of combined technology spectrum of a certain extract of a food is less than a preset similarity, it indicates that the combined technology spectrum of the extract of the food is an abnormal combined technology spectrum, and the extract of the food is recorded as an abnormal extract of the combined technology spectrum, thereby obtaining abnormal combined technology spectrums of each abnormal extract of each type of combined technology spectrum of the food; if all preset unit precision interval extracts of a certain abnormal extract of a certain type of combined technology spectrum of the food are abnormal combined technology spectrums, the abnormal extract of the combined technology spectrum of the food is a problematic extract, thereby obtaining abnormal combined technology spectrums of each problematic extract of each type of combined technology spectrum of the food; The peak area is obtained from the abnormal combined technology spectrum through machine vision calculation, and then the peak area of ​​the abnormal combined technology spectrum of each problematic extract of each type of combined technology spectrum of the food is obtained. The weight factor is set according to the accuracy of each extract, and weighted calculation is performed to obtain the peak area of ​​the abnormal combined technology spectrum of each type of combined technology spectrum of the food. If the peak area of ​​the abnormal combined technology spectrum of a certain type of combined technology spectrum of the food is greater than the preset peak area, it indicates that this type of combined technology spectrum is an initial abnormal combined technology spectrum. The food is recorded as an initial abnormal food, and the initial abnormality type corresponding to each type of combined technology spectrum is obtained from the database, so as to obtain various types of initial anomalies of the initial abnormal food, and at the same time, the peak area of ​​the abnormal combined technology spectrum corresponding to each type of initial abnormality of the initial abnormal food is obtained.

4. A traceability detection system based on food safety supervision according to claim 3, characterized in that: The low-resolution mass spectrometry detection scheme for setting up the initial abnormal food is specifically set up as follows: Obtaining the usage rate, usage frequency and correct rate of each low-resolution mass spectrum used for each type of initial anomaly from the database, substituting the usage rate, usage frequency and correct rate of each low-resolution mass spectrum used for each type of initial anomaly into the usage index calculation formula to obtain the usage index of each low-resolution mass spectrum for each type of initial anomaly; Obtaining the standard peak areas of the abnormal combined technology spectra corresponding to various types of initial abnormalities from the database, dividing the peak areas of the abnormal combined technology spectra corresponding to various types of initial abnormalities of the initial abnormal foods by the standard peak areas, to obtain the type abnormality rates of various types of initial abnormalities of the initial abnormal foods, setting weight factors of various types of initial abnormalities of the initial abnormal foods according to the type abnormality rates of various types of initial abnormalities of the initial abnormal foods, performing weighted calculation on the usage indexes of various low-resolution mass spectra of various types of initial abnormalities of the initial abnormal foods, to obtain the usage indexes of various low-resolution mass spectra of the initial abnormal foods; The low-resolution mass spectrometry analysis quantity corresponding to each food type is obtained from the database, and the usage index of each low-resolution mass spectrometry of the initial abnormal food is arranged in descending order to obtain the usage priority sequence of each low-resolution mass spectrometry of the initial abnormal food. The low-resolution mass spectrometry analysis quantity corresponding to each food type is obtained from the database, and then the low-resolution mass spectrometry analysis quantity of the initial abnormal food is obtained. The low-resolution mass spectra of the first low-resolution mass spectrometry analysis quantity in the usage priority sequence of the initial abnormal food are recorded as the low-resolution mass spectra used by the initial abnormal food. The low-resolution mass spectrometry detection scheme of the initial abnormal food is: each extract of the initial abnormal food is detected by using each low-resolution mass spectrometry.

5. A traceability detection system based on food safety supervision according to claim 2, characterized in that: The specific detection of each extract of the initial abnormal food is performed, and the specific detection process is as follows: Through the low-resolution mass spectrometry detection scheme of the initial abnormal food, a multi-mode ion source mass spectrometry analysis is performed to obtain each ion mass spectrogram of each extract of the initial abnormal food, and each feature value set is obtained from each ion mass spectrogram of each extract of the initial abnormal food through machine vision, and a feature value set of each abnormal level of each ion mass spectrogram is obtained from the database. If the feature value sets in a certain ion mass spectrogram of a certain extract of the initial abnormal food all belong to the feature value set of a corresponding abnormal level, it indicates that the ion mass spectrogram of the extract of the initial abnormal food is of the abnormal level, thereby obtaining the abnormal level of each extract of each type of ion mass spectrometry of the initial abnormal food; The abnormality levels of each extract of each type of ion mass spectrum of the initial abnormal food are averaged to obtain the average abnormality level of each type of ion mass spectrum of the initial abnormal food. If the abnormality level of a certain extract of a certain type of ion mass spectrum of the initial abnormal food is greater than the standard abnormality level, or the average abnormality level of this type of ion mass spectrum of the initial abnormal food is greater than the benchmark abnormality level, it indicates that this type of ion mass spectrum of the initial abnormal food is an abnormal ion mass spectrum. The initial abnormal food is recorded as an abnormal food, and the class safety anomaly corresponding to each abnormal ion mass spectrum is obtained from the database to obtain various types of safety anomalies of the abnormal food.

6. A traceability detection system based on food safety supervision according to claim 2, characterized in that: The detection of each traceability link of abnormal food, the specific detection units include: H1. Obtain each traceability link of the abnormal food from the database, perform accelerated solvent extraction on each sample of each traceability link of the abnormal food, and obtain each extract of each sample of each traceability link of the abnormal food; H2. According to the low-resolution mass spectrometry detection scheme of the initial abnormal food, each extract of each sample of each traceability link of the abnormal food is specifically detected to obtain each abnormal traceability link of the abnormal food; H3. Obtain the abnormal data of each abnormal traceability link of abnormal food from the database, analyze the abnormal data of each abnormal traceability link of abnormal food, and obtain the risk level of various safety abnormalities corresponding to each abnormal traceability link of abnormal food.

7. A traceability detection system based on food safety supervision according to claim 6, characterized in that: The abnormal data of each abnormal traceability link of abnormal food is analyzed, and the specific analysis process is as follows: The abnormal data of each abnormal traceability link of abnormal food include the risk level of sample food corresponding to each type of safety abnormality in each abnormal traceability link of abnormal food, the historical average risk level of similar food, the traceability frequency of similar food and the traceability risk rate of similar food. The risk level of sample food corresponding to each type of safety abnormality in each abnormal traceability link of abnormal food, the historical average risk level of similar food, the traceability frequency of similar food and the traceability risk rate of similar food are substituted into the traceability abnormality index calculation formula to obtain the abnormal index of each type of safety abnormality in each abnormal traceability link of abnormal food, and the abnormal index corresponding to each risk level of each type of safety abnormality is obtained from the database. If the abnormal index of a certain type of safety abnormality corresponding to an abnormal traceability link of abnormal food belongs to the abnormal index corresponding to a certain risk level, it indicates that the type of safety abnormality corresponding to the abnormal traceability link of the abnormal food is the risk level, and then the risk level of each type of safety abnormality corresponding to each abnormal traceability link of abnormal food is obtained.

8. A traceability detection system based on food safety supervision according to claim 1, characterized in that: The risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food are analyzed, and the specific analysis process is as follows: The traceability link associated data is the production sequence of each traceability link. Based on food testing, each traceability link is arranged in the opposite direction of the production sequence to obtain the order of each traceability link. According to the order of each traceability link, each traceability link with a preset number of traceability links before and after each traceability link is recorded as each valid traceability link, thereby obtaining each valid traceability link of each abnormal traceability link of abnormal food. Each valid traceability link of each abnormal traceability link of abnormal food is fitted with a curve according to the type of safety anomaly to obtain the risk level change curve of each abnormal traceability link corresponding to each type of safety anomaly of abnormal food. The change curvature of each abnormal traceability link corresponding to each type of safety anomaly of abnormal food is collected through machine vision, and the trend item correction parameter corresponding to each change curvature is obtained from the database to obtain the trend item change parameter of each abnormal traceability link corresponding to each type of safety anomaly of abnormal food. Perform weighted calculation on the risk levels of various safety anomalies of abnormal foods in each traceability link to obtain the link risk level of each traceability link, and obtain the link item correction parameters corresponding to the risk level of each link from the database; Substitute the risk level of each type of safety anomaly corresponding to each abnormal traceability link of abnormal food, the trend item change parameter and the link item correction parameter into the modified risk level calculation formula to obtain the modified risk level of each type of safety anomaly corresponding to each abnormal traceability link of abnormal food; If the maximum corrected risk level of the abnormal food is greater than the maximum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selected quantity unit is increased, and the ion mass spectrometry analysis amount of the preset analysis amount unit is increased; if the maximum corrected risk level of the abnormal food is less than or equal to the minimum corrected risk level of the standard food, in the accelerated solvent extraction of the food type corresponding to the abnormal food, the extraction liquid selection amount of the preset selected quantity unit is reduced, and the ion mass spectrometry analysis amount of the preset analysis amount unit is reduced; If the maximum corrected risk level of a certain type of safety abnormality of abnormal food is greater than the preset safety corrected risk level, an early warning will be issued, and each abnormal traceability link in the trend item change parameters corresponding to this type of safety abnormality of abnormal food that is greater than the preset trend change parameters will be recorded as the problem traceability links, prompting the staff to conduct inspections on each problem traceability link.

9. A traceability detection system based on food safety supervision according to claim 1, characterized in that: It also includes a database for storing traceability-related data, the amount of extract selected for each food type, the spatial vector of each normal combined technology spectrum, the initial abnormality type corresponding to each combined technology spectrum, the usage rate of each low-resolution mass spectrometer for each initial abnormality, the usage frequency of each low-resolution mass spectrometer for each initial abnormality, the correctness of each low-resolution mass spectrometer for each initial abnormality, the accuracy of each low-resolution mass spectrometer for each initial abnormality, the standard peak area of ​​the abnormal combined technology spectrum corresponding to each initial abnormality, the low-resolution mass spectrometry analysis amount corresponding to each food type, the characteristic value set of each abnormal level of each ion mass spectrum, the class safety abnormality corresponding to each abnormal ion mass spectrum, each traceability link of abnormal food, the abnormality index corresponding to each risk level of each safety abnormality, the trend item correction parameter corresponding to each change curvature and the link item correction parameter corresponding to each link risk level.

10. A traceability detection method using the traceability detection system based on food safety supervision according to any one of claims 1 to 9, characterized in that: The steps include: Step 1: Food safety quality inspection: Test food and identify abnormal food; Step 2: Traceability safety testing: Test each traceability link of abnormal food to obtain the risk level of various safety anomalies corresponding to each abnormal traceability link of abnormal food; Step 3: Safety supervision analysis: Based on the traceability link-related data in the database, analyze the risk levels of various safety anomalies corresponding to each abnormal traceability link of abnormal food, obtain each low-risk production link and faulty production link, update the detection plan, and issue an early warning at the same time.

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