A traceability detection system and method based on food safety supervision

Abstract

The present invention discloses a traceability detection system and method based on food safety supervision, which relates to the technical field of food safety. The system of the present invention includes a food safety quality inspection module, a traceability safety detection module, and a safety supervision analysis module. The food safety quality inspection module analyzes by chromatography, low-resolution mass spectrometry, and high-resolution mass spectrometry to determine whether the food is abnormal. When the food is abnormal, the traceability safety detection module is used to perform a variety of low-resolution mass spectrometry analyses on the detection of each traceability link of the abnormal food to obtain each abnormal traceability link. The safety supervision analysis module deeply analyzes based on the traceability link correlation data to clarify the low-risk and faulty production links, timely update the detection plan and give an early warning, which helps to clarify the root cause of the problem, improve the traceability efficiency, optimize the detection plan through the analysis of the risk level, realize the reasonable allocation of resources, reduce the detection cost, and enhance the pertinence and effectiveness of supervision.

Description

Technical Field

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

[0002] Food safety issues have increasingly attracted wide public attention. It not only relates to the physical health of the people, but also has a profound impact on economic development. The food supply chain is becoming increasingly complex and globalized, enriching the types and supply channels of food, but also creating more opportunities for the breeding and spread of food safety risks. Therefore, a traceability detection system and method based on food safety supervision are needed.

[0003] The prior art, such as the invention patent application with publication number CN118777547A, discloses a food safety detection method and a food safety detection system, which relates to the technical field of food detection. The invention includes: Step 1, monitoring of food sold in shopping malls and supermarkets; Step 2, analysis of food detection parameters in shopping malls and supermarkets; Step 3, selection of food detection samples in shopping malls and supermarkets; Step 4, food detection in shopping malls and supermarkets; Step 5, analysis of food detection in shopping malls and supermarkets; and Step 6, display processing. This invention ensures the accuracy of the detection quantity and sampling frequency of various foods in shopping malls and supermarkets, thereby improving the reliability of the subsequent safety detection results of various foods in shopping malls and supermarkets, avoiding waste of human and material resources to a certain extent, reducing the risks to the physical health and life safety of consumers, and on the other hand, having a relatively rich dimension for analyzing the sampling quantity and sampling frequency of various foods in shopping malls and supermarkets, ensuring the evaluation of consumers after various foods in shopping malls and supermarkets are sold, reducing the probability of consumer complaints, and thus reducing the probability of shopping malls and supermarkets being punished.

[0004] Regarding the above solution, there are the following technical problems: 1. The above solution mainly focuses on detecting the shopping mall link, starting from sales records, customer feedback, and environmental monitoring. However, there is no effective traceability mechanism for the long supply chain link from the production source of food to before it enters the shopping mall. Once a problem food is found, only the relevant situations during the sales process in the shopping mall can be known, and it is difficult to accurately trace the root causes of problems in the pre - production, processing, transportation, and other early links of food. It is impossible to clarify which production link or supply chain node has potential safety hazards and cannot clearly locate the problem link, which is not conducive to solving food safety problems.

[0005] 2. The above solution focuses on sampling and inspection of foods sold in shopping malls and supermarkets, and the selection of sampling samples is mainly calculated based on comprehensive factors such as sales records, feedback records, and environmental parameters to obtain appropriate sampling quantities and frequencies, lacking in - depth consideration of the characteristics of the food itself. Different types of foods may introduce different types of safety risks during the production process. Only relying on the above external factors to determine the detection method and not using corresponding chromatographic and mass spectrometric analysis cannot comprehensively and accurately detect various potential safety anomalies in foods.

[0006] 3. The above solution calculates the detection risk assessment index of supermarket food, mainly based on factors such as historical detection data, sales records, customer feedback, and environmental parameters. It does not consider the risk changes of different types of safety anomalies in each traceability link of food, does not conduct multi-dimensional analysis, does not consider the association of traceability links, and cannot comprehensively and dynamically evaluate the risk status of food during the entire production and circulation process, resulting in a low accuracy of the detection solution. Summary of the Invention

[0007] Aiming at the above existing technical deficiencies, the purpose of the present invention is to provide a traceability detection system and method based on food safety supervision.

[0008] 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, including the following modules: a food safety quality inspection module for detecting food and judging abnormal food.

[0009] A traceability safety detection module for detecting each traceability link of abnormal food to obtain the risk levels of various types of safety anomalies corresponding to each abnormal traceability link of the abnormal food.

[0010] A safety supervision analysis module for analyzing the risk levels of various types of safety anomalies corresponding to each abnormal traceability link of abnormal food according to the traceability link association data in the database to obtain each low-risk production link and faulty production link, update the detection solution, and give an alarm at the same time.

[0011] Preferably, the analysis of the risk levels of various types of safety anomalies corresponding to each abnormal traceability link of abnormal food is as follows: The traceability link association data is the production sequence of each traceability link. Taking food detection as the benchmark, each traceability link is arranged in the reverse direction of the production sequence to obtain the sequence of each traceability link. According to the sequence of each traceability link, each traceability link with the preset number of previous and subsequent traceability links of 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. The effective traceability links of each abnormal traceability link of abnormal food are fitted with curves according to the type of safety anomaly to obtain the risk level change curves of each abnormal traceability link corresponding to various types of safety anomalies of abnormal food. The change curvature of each abnormal traceability link corresponding to various types of safety anomalies of abnormal food is collected through machine vision, and the trend item correction parameters corresponding to each change curvature are obtained from the database, so as to obtain the trend item change parameters of each abnormal traceability link corresponding to various types of safety anomalies of abnormal food.

[0012] The risk levels of various types of safety anomalies of abnormal food in each traceability link are weighted and calculated to obtain the link risk levels 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 levels, trend item change parameters, and link item correction parameters of various safety anomalies corresponding to each anomaly traceability link of the abnormal food into the corrected risk level calculation formula to obtain the corrected risk levels of various safety anomalies corresponding to each anomaly traceability link of the 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, increase the extraction liquid selection amount in the preset selection quantity unit, and at the same time increase the ion mass spectrometry analysis amount in the preset analysis quantity unit. 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, reduce the extraction liquid selection amount in the preset selection quantity unit, and at the same time reduce the ion mass spectrometry analysis amount in the preset analysis quantity unit.

[0015] If the maximum corrected risk level of a certain type of safety anomaly of the abnormal food is greater than the preset safety correction risk level, give an early warning, and record each anomaly traceability link in the trend item change parameters corresponding to this type of safety anomaly of the abnormal food that is greater than the preset trend change parameter as each problem traceability link, and prompt the staff to detect each problem traceability link.

[0016] On the other hand, the present invention provides a traceability detection method based on food safety supervision, including the following steps: Step 1, food safety quality inspection: Detect the food to judge abnormal food.

[0017] Step 2, traceability safety detection: Detect each traceability link of the abnormal food to obtain the risk levels of various safety anomalies corresponding to each anomaly traceability link of the abnormal food.

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

[0019] The beneficial effects of the present invention are as follows: 1. The present invention first uses the food safety quality inspection module to judge whether the food is abnormal food through chromatography, low-resolution mass spectrometry, and high-resolution mass spectrometry analysis. When the food is abnormal food, the traceability safety detection module is used to perform various low-resolution mass spectrometry analyses on the detection of each traceability link of the abnormal food to obtain each anomaly traceability link. The safety supervision analysis module deeply analyzes based on the traceability link association data to clarify the low-risk and faulty production links, timely update the detection plan and give an early warning, which helps to clarify the root cause of the problem, improve the traceability efficiency, optimize the detection plan through the analysis of the risk level, realize the rational allocation of resources, reduce the detection cost, and enhance the pertinence and effectiveness of supervision.

[0020] 2. In the food detection stage, the present invention accurately identifies the food type through image recognition technology, and then obtains the extraction liquid selection amount matching the food type from the database according to the food type, implements accelerated solvent extraction, provides a reliable extraction liquid sample for subsequent detection, conducts non-target screening, separates compounds using a gas chromatography-mass spectrometry (GC-MS) instrument and performs mass analysis, and accurately determines whether the GC-MS spectrum of the food extraction liquid is abnormal through the cosine similarity analysis of the space vectors of the normal GC-MS spectra in the database. For the initially abnormal food, a low-resolution mass spectrometry detection plan is carefully set according to its initial abnormal type, and then specific detection is carried out to comprehensively and deeply detect various potential safety abnormalities in the food, greatly improving the accuracy and comprehensiveness of the detection, reducing the missed detection rate of problematic foods. First, through the combination of chromatography and high-resolution mass spectrometry, it is analyzed whether there are abnormalities, and then quantitative analysis is performed on the initially abnormal food according to low-resolution mass spectrometry to determine the abnormal level, making use of the characteristics of high-resolution mass spectrometry with strong qualitative ability and low-resolution mass spectrometry with strong quantitative ability to improve the detection accuracy.

[0021] 3. The present invention conducts multi-dimensional and in-depth analysis on the risk levels of various safety abnormalities corresponding to each abnormal traceability link of abnormal foods. According to the associated data of the traceability links, each traceability link is arranged in reverse order according to the production sequence to determine the effective traceability links, and the risk level changes are presented through a fitted curve. At the same time, combining the link risk levels of each traceability link and their corresponding link item correction parameters, the risk level is corrected, enabling dynamic mastery of the risk status of foods throughout the production and circulation process and improving the comprehensiveness of traceability detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

[0024] Figure 2 It is a schematic diagram of the implementation steps flow of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope 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 respectively connected to the food safety quality inspection module and the safety supervision analysis module, 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 detect food and judge abnormal food.

[0029] In a specific embodiment, the detection of food includes the following detection units:

[0030] S1. Obtain the type of food through image recognition technology, obtain the extraction liquid selection amount corresponding to each food type from the database, thereby obtaining the extraction liquid selection amount of the food, and perform accelerated solvent extraction according to the extraction liquid selection amount of the food to obtain each extraction liquid of the food.

[0031] It should be noted that the extraction liquid selection amount is: the staff sets the standard concentration and standard concentration gradient difference of the solution for the extraction liquid selection amount. Taking the standard concentration as the base point and the standard concentration gradient difference as the concentration change unit, each extraction concentration based on the standard concentration is obtained. Taking the standard concentration as the base point, select each extraction concentration greater than the standard concentration by a preset number and each extraction concentration less than the standard concentration by a preset number for extraction. The preset number is recorded as the extraction liquid selection amount. By setting the extraction liquid selection amount, the accuracy of data analysis can be improved.

[0032] S2. Perform non-target screening on each extraction liquid of the food to judge whether it is an initial abnormal food. If the food is an initial abnormal food, obtain various initial abnormalities of the initial abnormal food.

[0033] S3. Set a low-resolution mass spectrometry detection scheme for the initial abnormal food according to various initial abnormalities of the initial abnormal food.

[0034] S4. Perform specific detection on each extraction liquid of the initial abnormal food according to the low-resolution mass spectrometry detection scheme of the initial abnormal food to judge whether it is an abnormal food.

[0035] In a specific embodiment, the non-target screening of each extract of the food is carried out as follows: Each extract of the food is separately injected into a gas chromatography-mass spectrometry (GC-MS) instrument. First, various compounds in the extract are separated by a chromatographic column, and then enter a high-resolution mass spectrometer for mass analysis to obtain various combined technology spectra of each extract of the food. The various combined technology spectra of each extract of the food are vectorized to obtain the spatial vectors of the various combined technology spectra of each extract of the food. The spatial vectors of each normal combined technology spectrum are obtained from the database. The spatial vectors of the various combined technology spectra of each extract of the food are subjected to cosine similarity analysis with the spatial vectors of each normal combined technology spectrum to obtain the normal chromatographic similarities of the various combined technology spectra of each extract of the food. The minimum normal chromatographic similarity is selected as the similarity of the various combined technology spectra of each extract of the food.

[0036] If the similarity of a certain combined technology spectrum of a certain extract of the food is less than the preset similarity, it indicates that the combined technology spectrum of this extract of the food is an abnormal combined technology spectrum. This extract of the food is recorded as the abnormal extract of this combined technology spectrum, and thus the abnormal combined technology spectra of the abnormal extracts of various combined technology spectra of the food are obtained. If all the extracts in each preset unit precision interval of a certain abnormal extract of a certain combined technology spectrum of the food are abnormal combined technology spectra, this abnormal extract of this combined technology spectrum of the food is a problematic extract, and thus the abnormal combined technology spectra of the problematic extracts of various combined technology spectra of the food are obtained.

[0037] The peak area is obtained from the abnormal combined technology spectrum through machine vision calculation, and then the peak areas of the abnormal combined technology spectra of the problematic extracts of various combined technology spectra of the food are obtained. Weight factors are set according to the precision of each extract and weighted calculation is carried out to obtain the peak area of the abnormal combined technology spectrum of various combined technology spectra of the food. If the peak area of the abnormal combined technology spectrum of a certain combined technology spectrum of the food is greater than the preset peak area, it indicates that this combined technology spectrum is an initial abnormal combined technology spectrum. The food is recorded as an initially abnormal food. The initial abnormal types corresponding to various combined technology spectra are obtained from the database, and thus various initial abnormalities of the initially abnormal food are obtained, and at the same time, the peak areas of the abnormal combined technology spectra corresponding to various initial abnormalities of the initially abnormal food are obtained.

[0038] In a specific embodiment, the setting of the low-resolution mass spectrometry detection scheme for the initially abnormal food is carried out as follows: The usage rate, usage frequency, and correct rate of each low-resolution mass spectrometry for various initial abnormalities are obtained from the database. The usage rate, usage frequency, and correct rate of each low-resolution mass spectrometry for various initial abnormalities are substituted into the usage index calculation formula to obtain the usage index of each low-resolution mass spectrometry for various initial abnormalities.

[0039] It should be noted that the use of each low-resolution mass spectrum refers to the use of each low-resolution mass spectrum detection method, and the exponential calculation formula is: , where is the usage index of the m-th type of initial anomaly using the n-th low-resolution mass spectrum, 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, and the value of n is a positive integer. , and are respectively the usage rate, usage frequency, and correct rate of the m-th type of initial anomaly using the n-th low-resolution mass spectrum. , and are respectively the standard usage rate, standard usage frequency, and standard correct rate. , and are respectively the weight factor of the usage rate, the weight factor of the usage frequency, and the weight factor of the correct rate. , , , .

[0040] It should be noted that the usage rate of each low-resolution mass spectrum of each type of initial anomaly is the number of times each low-resolution mass spectrum of each type of initial anomaly is used divided by the total number of times the low-resolution mass spectrum corresponding to the initial anomaly type is used. The usage frequency of each low-resolution mass spectrum of each type of initial anomaly is the number of times each low-resolution mass spectrum of each type of initial anomaly is used divided by the total number of times the low-resolution mass spectrum is used. The correct rate of each low-resolution mass spectrum of each type of initial anomaly is the number of times the usage result of each low-resolution mass spectrum of each type of initial anomaly is correct divided by the total number of times the low-resolution mass spectrum corresponding to the corresponding initial anomaly type is used.

[0041] , and are respectively the usage rate threshold, usage frequency threshold, and correct rate threshold of the normal low-resolution mass spectrum. When the actual value is greater than the threshold, it indicates that when identifying this type of initial anomaly, the usage effect of the corresponding low-resolution mass spectrum is better and the usage priority is higher. The specific value is set by the staff. For example is 0.3, is 0.7, and is 0.6.

[0042] , and are set by the staff. When improving the accuracy, is larger. When improving the effectiveness, is larger. When improving the correctness, is larger. For example is 0.3, is 0.2, and is 0.5.

[0043] Obtain the standard peak areas of the abnormal coupling technology spectra corresponding to various initial abnormalities from the database, divide the peak areas of the abnormal coupling technology spectra corresponding to various initial abnormalities of the initial abnormal food by the standard peak areas to obtain the type abnormality rates of various initial abnormalities of the initial abnormal food, and set the weight factors of various initial abnormalities of the initial abnormal food according to the type abnormality rates of various initial abnormalities of the initial abnormal food, and perform weighted calculation on the usage indices of each low-resolution mass spectrum of various initial abnormalities of the initial abnormal food to obtain the usage indices of each low-resolution mass spectrum of the initial abnormal food.

[0044] It should be noted that the larger the type abnormality rate of various initial abnormalities, the larger the corresponding weight factor.

[0045] Obtain the low-resolution mass spectrometry analysis amounts corresponding to each food type from the database, arrange the usage indices of each low-resolution mass spectrum of the initial abnormal food in descending order to obtain the usage priority sequence of each low-resolution mass spectrum of the initial abnormal food, obtain the low-resolution mass spectrometry analysis amounts corresponding to each food type from the database, and then obtain the low-resolution mass spectrometry analysis amount of the initial abnormal food. Record the low-resolution mass spectra with the first low-resolution mass spectrometry analysis amounts in the usage priority sequence of the initial abnormal food as the used low-resolution mass spectra of the initial abnormal food. The low-resolution mass spectrometry detection scheme for the initial abnormal food is: Detect each extract of the initial abnormal food through each used low-resolution mass spectrum.

[0046] In a specific embodiment, the specific detection process for specifically detecting each extract of the initial abnormal food is as follows: Through the low-resolution mass spectrometry detection scheme of the initial abnormal food, perform multi-mode ion source mass spectrometry analysis to obtain the ion mass spectra of each extract of the initial abnormal food. Obtain the set of characteristic values from each ion mass spectrum of each extract of the initial abnormal food through machine vision respectively. Obtain the set of characteristic values of each abnormal level of each ion mass spectrum from the database. If the set of characteristic values in a certain ion mass spectrum of a certain extract of the initial abnormal food all belong to the set of characteristic values of the corresponding certain abnormal level, it indicates that the ion mass spectrum of this extract of the initial abnormal food is of this abnormal level, and thus obtain the abnormal levels of each extract of various ion mass spectra of the initial abnormal food.

[0047] It should be noted that each ion mass spectrum is obtained through low-resolution mass spectrometry detection.

[0048] Calculate the average anomaly level of each extract of the various ion mass spectra of the initial abnormal food to obtain the average anomaly level of the various ion mass spectra of the initial abnormal food. If the anomaly level of a certain extract of a certain ion mass spectrum of the initial abnormal food is greater than the standard anomaly level, or the average anomaly level of this type of ion mass spectrum of the initial abnormal food is greater than the benchmark anomaly level, it indicates that this type of ion mass spectrum of the initial abnormal food is an abnormal ion mass spectrum. Mark the initial abnormal food as an abnormal food, and obtain the various safety anomalies corresponding to each abnormal ion mass spectrum from the database, so as to obtain the various safety anomalies of the abnormal food.

[0049] The traceability safety detection module is used to detect each traceability link of the abnormal food to obtain the risk levels of the various safety anomalies corresponding to each abnormal traceability link of the abnormal food.

[0050] In a specific embodiment, the detection of each traceability link of the abnormal food, the specific detection unit includes: 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 to obtain each extract of each sample of each traceability link of the abnormal food.

[0051] It should be noted that each sample includes the samples retained by the abnormal food and the samples of each similar food.

[0052] H2. According to the low-resolution mass spectrometry detection scheme of the initial abnormal food, perform specific detection on each extract of each sample of each traceability link of the abnormal food to obtain each abnormal traceability link of the abnormal food.

[0053] H3. Obtain the abnormal data of each abnormal traceability link of the abnormal food from the database, analyze the abnormal data of each abnormal traceability link of the abnormal food to obtain the risk levels of the various safety anomalies corresponding to each abnormal traceability link of the abnormal food.

[0054] In a specific embodiment, the accelerated solvent extraction of the samples of each traceability link of the abnormal food, the specific extraction process is as follows: According to the extraction method of each extract of the food, obtain the extraction liquid selection amount of the food. According to the extraction liquid selection amount of the food, perform accelerated solvent extraction on each sample of each traceability link of the abnormal food to obtain each extract of each sample of each traceability link of the abnormal food.

[0055] In a specific embodiment, the specific detection of each extract of each sample of 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, obtain the use of the low-resolution mass spectrometry detection method for each extract of the initial abnormal food. Use the low-resolution mass spectrometry detection method for each extract of the initial abnormal food to detect each extract of each sample of each traceability link of the abnormal food.

[0056] In a specific embodiment, the analysis of the abnormal data of each abnormal traceability link of the abnormal food is as follows: The abnormal data of each abnormal traceability link of the abnormal food includes the sample food risk level, the historical average risk level of the same type of food, the traceability frequency of the same type of food, and the traceability risk rate of the same type of food corresponding to various safety abnormalities in each abnormal traceability link of the abnormal food. Substitute the sample food risk level, the historical average risk level of the same type of food, the traceability frequency of the same type of food, and the traceability risk rate of the same type of food corresponding to various safety abnormalities in each abnormal traceability link of the abnormal food into the traceability abnormal index calculation formula: , where is the abnormal index of the b-type safety abnormality in the abnormal traceability link a of the abnormal food. a is the number of the abnormal traceability link, and the value of a is a positive integer. b is the number of the safety abnormality type, and the value of b is a positive integer. , , and are respectively the sample food risk level, the historical average risk level of the same type of food, the traceability frequency of the same type of food, and the traceability risk rate of the same type of food corresponding to the b-type safety abnormality in the abnormal traceability link a of the abnormal food. , and are respectively the preset standard food risk level, standard traceability frequency, and standard traceability risk rate. and are respectively the weight factor of the sample food risk level and the weight factor of the historical average risk level preset. , , , and are respectively the weight factor of the traceability frequency and the weight factor of the traceability risk rate preset. , , , to obtain the abnormal index of various safety abnormalities in each abnormal traceability link of the abnormal food. Obtain the abnormal index corresponding to each risk level of various safety abnormalities from the database. If the abnormal index of a certain type of safety abnormality corresponding to a certain abnormal traceability link of the abnormal food belongs to the abnormal index corresponding to a certain risk level, it indicates that the certain type of safety abnormality corresponding to the certain abnormal traceability link of the abnormal food is of the certain risk level. Furthermore, obtain the risk levels of various safety abnormalities corresponding to each abnormal traceability link of the abnormal food.

[0057] It should be noted that the process of obtaining the traceability frequency of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food: divide the traceability times of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food by the traceability times of the corresponding anomaly traceability link to obtain the traceability frequency of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food. The process of obtaining the traceability risk rate of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food: divide the traceability times with the traceability risk level of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food greater than the preset risk level by the traceability times of the corresponding anomaly traceability link to obtain the traceability risk rate of the same type of food corresponding to various safety anomalies in each anomaly traceability link of abnormal food.

[0058] The standard food risk level is the risk level threshold of normal food. When the risk level of the sample food is greater than the standard food risk level, it indicates that the food contains the corresponding type of safety anomaly in the corresponding anomaly traceability link. The standard traceability frequency and the standard traceability risk rate are the traceability frequency and the traceability risk rate of normal food during the detection process. When they are greater than the standard traceability frequency and the standard traceability risk rate, it indicates that the food is prone to the corresponding type of safety anomaly in the corresponding anomaly traceability link. The specific parameters of the standard food risk level, the standard traceability frequency, and the standard traceability risk rate are set by the staff. For example is 1.6, is 0.3, and is 0.15.

[0059] and The setting process is related to the shelf life corresponding to the food type. The longer the shelf life corresponding to the food type, the larger it is. The shorter the shelf life corresponding to the food type, the larger it is. The specific values are set by the staff. For example is 0.4 and is 0.6.

[0060] and The setting process is related to the food production rate. The greater the food production rate, the larger it is. The smaller the food production rate, the larger it is. The specific values are set by the staff. For example is 0.45, is 0.55.

[0061] The safety supervision analysis module is used to analyze the risk levels of various safety anomalies corresponding to each anomaly 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 an alarm at the same time.

[0062] In a specific embodiment, the risk levels of various safety anomalies corresponding to each anomaly tracing link of the abnormal food are analyzed, and the specific analysis process is as follows: The associated data of the tracing link is the production sequence of each tracing link. Based on food detection, each tracing link is arranged in the reverse direction of the production sequence to obtain the sequence of each tracing link. According to the sequence of each tracing link, each tracing link with the number of preset tracing links before and after each tracing link is recorded as each effective tracing link, so as to obtain each effective tracing link of each anomaly tracing link of the abnormal food. The effective tracing links of each anomaly tracing link of the abnormal food are fitted into curves according to different types of safety anomalies to obtain the risk level change curves of each anomaly tracing link corresponding to different types of safety anomalies of the abnormal food. The change curvature of each anomaly tracing link corresponding to different types of safety anomalies of the abnormal food is collected through machine vision, and the trend item correction parameters corresponding to each change curvature are obtained from the database, so as to obtain the trend item change parameters of each anomaly tracing link corresponding to different types of safety anomalies of the abnormal food.

[0063] The risk levels of various safety anomalies of the abnormal food in each tracing link are weighted and calculated to obtain the link risk level of each tracing link, and the link item correction parameters corresponding to each link risk level are obtained from the database.

[0064] Substitute the risk levels, trend item change parameters, and link item correction parameters of various safety anomalies corresponding to each anomaly tracing link of the abnormal food into the corrected risk level calculation formula to obtain the corrected risk levels of various safety anomalies corresponding to each anomaly tracing link of the abnormal food.

[0065] It should be noted that the corrected risk level calculation formula is: , where is the risk level of the b-type safety anomaly corresponding to the anomaly tracing link a of the abnormal food, is the trend item change parameter of the b-type safety anomaly corresponding to the anomaly tracing link a, is the link item correction parameter of the anomaly tracing link a, and are the preset standard trend item change parameter and standard link item correction parameter respectively, and are the weight factors of the trend item change parameter and the weight factors of the link item correction parameter respectively, , , .

[0066] Standard parameter is the threshold value of the trend item change parameter in the traceability link of normal food. 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 likely to deepen the anomaly state of this type of food safety anomaly in the abnormal traceability link, standard parameter is the threshold value of the link item correction parameter in the traceability link of normal food. 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 likely to be converted into abnormal food in this abnormal traceability link, standard parameter and The specific parameters are set by the staff. For example is 0.6 and is 0.8.

[0067] Weight factor and The setting process is related to the number of traceability links of food. The more the number of traceability links of food, the greater, the fewer the number of traceability links of food, the greater. The specific values are set by the staff. For example is 0.3 and is 0.7.

[0068] If the maximum correction risk level of abnormal food is greater than the maximum standard food correction risk level, in the accelerated solvent extraction of the food type corresponding to the abnormal food, increase the extraction liquid selection amount of the preset selection quantity unit, and at the same time increase the ion mass spectrometry analysis amount of the preset analysis quantity unit. If the maximum correction risk level of abnormal food is less than or equal to the minimum standard food correction risk level, in the accelerated solvent extraction of the food type corresponding to the abnormal food, reduce the extraction liquid selection amount of the preset selection quantity unit, and at the same time reduce the ion mass spectrometry analysis amount of the preset analysis quantity unit.

[0069] If the maximum correction risk level of a certain type of food safety anomaly of abnormal food is greater than the preset safety correction risk level, give an alarm, and record each abnormal traceability link in the trend item change parameter corresponding to this type of food safety anomaly of abnormal food that is greater than the preset trend change parameter as each problem traceability link, and prompt the staff to detect each problem traceability link.

[0070] A database for storing associated data in the traceability link, the extraction liquid selection amount corresponding to each food type, the spatial vectors of each normal combined technology spectrum, the initial abnormal types corresponding to each combined technology spectrum, the usage rate of each low-resolution mass spectrum for each initial abnormality, the usage frequency of each low-resolution mass spectrum for each initial abnormality, the correct rate of each low-resolution mass spectrum for each initial abnormality, the accuracy rate of each low-resolution mass spectrum 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 set of characteristic values of each abnormal level of each ion mass spectrum, the class safety abnormality corresponding to each abnormal ion mass spectrum, each traceability link of the abnormal food, the abnormal index corresponding to each risk level of each class of safety abnormality, the trend term correction parameter corresponding to each change curvature, and the link term correction parameter corresponding to each link risk level.

[0071] According to Figure 2 As shown, the present invention provides a traceability detection method based on food safety supervision, including the following steps: Step 1, food safety quality inspection: Detect the food to determine abnormal food.

[0072] Step 2, traceability safety detection: Detect each traceability link of the abnormal food to obtain the risk levels of various safety abnormalities corresponding to each abnormal traceability link of the abnormal food.

[0073] Step 3, safety supervision analysis: According to the associated data of the traceability link in the database, analyze the risk levels of various safety abnormalities corresponding to each abnormal traceability link of the abnormal food to obtain each low-risk production link and faulty production link, update the detection plan, and give an early warning at the same time.

[0074] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all belong to 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 an early warning at the same time; The non-targeted screening of each food extract is specifically carried out as follows: 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 the extract is put into a high-resolution mass spectrometer for mass analysis to obtain various combined technology spectra of each food extract, and various combined technology spectra of each food extract are vectorized to obtain spatial vectors of various combined technology spectra of each food extract, and the spatial vectors of various normal combined technology spectra are obtained from a database, and the spatial vectors of various combined technology spectra of each food extract are analyzed by cosine similarity with 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 the minimum normal chromatographic similarity is selected 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; Obtain peak areas from abnormal combined technology spectra through machine vision calculation, and then obtain peak areas of abnormal combined technology spectra of each problematic extract of each combined technology spectra of food, set weight factors according to the accuracy of each extract, perform weighted calculation, and obtain peak areas of abnormal combined technology spectra of each combined technology spectra of food, if the peak area of ​​abnormal combined technology spectra of a certain type of combined technology spectra of food is greater than the preset peak area, it indicates that this type of combined technology spectra is an initial abnormal combined technology spectra, and the food is recorded as an initial abnormal food, and the initial abnormality types corresponding to each type of combined technology spectra are obtained from the database, so as to obtain various types of initial abnormalities of initial abnormal food, and at the same time obtain the peak areas of abnormal combined technology spectra corresponding to various types of initial abnormalities of initial abnormal food; Setting a low-resolution mass spectrometry detection scheme for initially abnormal foods, the specific setting process is as follows: obtaining the usage rate, usage frequency and correct rate of each low-resolution mass spectrometry for each type of initial abnormality from the database, substituting the usage rate, usage frequency and correct rate of each low-resolution mass spectrometry for each type of initial abnormality into the usage index calculation formula, and obtaining the usage index of each low-resolution mass spectrometry for each type of initial abnormality; 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 amount 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 amount corresponding to each food type is obtained from the database, and then the low-resolution mass spectrometry analysis amount of the initial abnormal food is obtained. Each low-resolution mass spectrometry of the first low-resolution mass spectrometry analysis amount in the usage priority sequence of the initial abnormal food is recorded as each usage low-resolution mass spectrometry of 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 respectively detected by each usage low-resolution mass spectrometry; 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, 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 spectrometry 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 spectrometry 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, thereby obtaining various types of safety anomalies of the abnormal food; The risk levels of various safety anomalies corresponding to various abnormal traceability links of abnormal food are 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, thereby obtaining each effective traceability link of each abnormal traceability link of abnormal food, fitting curves of each effective traceability link of each abnormal traceability link of abnormal food according to the type of safety anomaly, and obtaining the risk level change curve of each abnormal traceability link corresponding to various safety anomalies of abnormal food, collecting the change curvature of each abnormal traceability link corresponding to various safety anomalies of abnormal food through machine vision, and obtaining the trend item correction parameters corresponding to each change curvature from the database, thereby obtaining the trend item change parameters of each abnormal traceability link corresponding to various safety anomalies 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.

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 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.

4. A traceability detection system based on food safety supervision according to claim 3, 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.

5. 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.

6. A traceability detection method using the traceability detection system based on food safety supervision according to any one of claims 1 to 5, 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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