A food safety testing method for food production

Abstract

The present invention provides a food safety detection method for food production, which belongs to the technical field of food safety detection. The method comprises the following steps: obtaining raw materials for food production and classifying them; performing basic processing steps on the raw materials according to the classification results; sampling, observing and culturing the raw materials after the basic processing; and judging the surface purity of the raw materials after the basic processing; constructing an environmental model of the food production site based on site environmental data of the food production site and environmental impact data generated by food production machines during operation; determining the sampling detection time at different production links in the food production process according to the environmental model and the food production process, and judging the production purity of the corresponding production link according to all surface purity of the same production line and the production purity of all production links involved in the same production line, which not only avoids the roughness of food detection, but also effectively ensures the reliability of food safety detection.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety detection, and in particular to a food safety detection method for food production. Background Art

[0002] Food is an important support for human survival and development, and the quality of food directly affects the health of the human body. At the same time, with the emergence of an endless variety of food, food safety issues are gradually emerging. For food safety issues, food testing should be a prerequisite to discover problems in a timely manner, so as to effectively deal with the problems and prevent problematic foods from entering the market.

[0003] At present, the most common food testing method is to manually select the materials before production and manually select the products after production. In most cases, only rough filtering can be done to remove the imperfect things, but the filtering in the true sense is not strict, resulting in reduced reliability of safety testing.

[0004] Therefore, the present invention provides a food safety detection method for food production. Summary of the invention

[0005] The present invention provides a food safety detection method for food production, which is used to determine the surface purity of food production raw materials by classifying and sampling them for culturing, obtain site environmental data and environmental impact data to construct an environmental model of the food site, determine the production purity, and determine the food safety by combining the surface purity and the production purity, which not only avoids the roughness of food detection, but also effectively ensures the reliability of food safety detection.

[0006] The present invention provides a food safety detection method for food production, comprising:

[0007] Step 1: Obtain food production raw materials and classify them, perform basic processing steps on the raw materials according to the classification results, sample, observe and culture the raw materials after basic processing, and determine the surface purity of the raw materials after basic processing;

[0008] Step 2: Based on the site environment data of the food production site and the environmental impact data generated by the food production machines during operation, an environmental model of the food production site is constructed. According to the environmental model and the food production process, the sampling and testing time of different production links in the food production process is determined, and the production cleanliness of the corresponding production link is judged;

[0009] Step 3: Determine food production safety and implement food safety testing based on the surface cleanliness of all layers on the same production line and the production cleanliness of all production links involved in the same production line.

[0010] The present invention provides a food safety detection method for food production, which determines the surface cleanliness of raw materials after basic treatment, comprising:

[0011] Conduct bacterial culture on instruments that undergo basic processing steps to determine the cleanliness of the instruments;

[0012] Classify food production raw materials according to their shelf life and types of meat and vegetables, and perform basic processing steps on the raw materials based on the classification results;

[0013] Sampling, observing and culturing each step in the basic processing steps under each classification result is performed respectively, and a strain culture vector of each raw material under the corresponding classification is established according to the observed culture results, wherein the strain culture vector is composed of the culture information of the same raw material in different steps;

[0014] According to the bacterial strain culture vectors of all raw materials of the same classification, the primary bacterial strain matrix of the corresponding classification is constructed by row arrangement;

[0015] The surface cleanliness of each step in the corresponding classification is determined according to the primary strain matrix, and the surface cleanliness of the corresponding raw materials is determined according to the surface cleanliness of all steps and the cleanliness of the equipment.

[0016] The present invention provides a food safety detection method for food production, characterized in that the surface cleanliness of each step in the corresponding classification is judged according to the primary bacterial species matrix, and the surface cleanliness of the corresponding raw material is determined according to the surface cleanliness of all steps and the cleanliness of the equipment, including:

[0017] The cleanliness of the instruments corresponding to each raw material in each step under the same classification is obtained to construct an instrument matrix, wherein each row of the instrument matrix is ​​the cleanliness of the instruments used in different steps for the same raw material in the corresponding classification;

[0018] Obtaining standard information of different raw materials in different steps to construct a standard matrix, performing a first comparative analysis on the standard matrix and a corresponding primary strain matrix to obtain a first difference matrix, and performing element purity standardization on the first difference matrix to obtain a second difference matrix;

[0019] Performing a second comparative analysis on the primary bacterial species matrix and the instrument matrix to obtain a third difference matrix;

[0020] Extract the same row vectors from the first difference matrix and the third difference matrix to obtain a double row matrix, and obtain the first difference absolute mean of the first row vectors in the double row matrix and the first clarity variance , the absolute mean of the second difference of the second row vector and the second clarity variance ;

[0021] Extract the same column vectors from the first difference matrix and the third difference matrix to obtain a double-column matrix, and obtain the third difference absolute mean of the first column row vectors in the double-column matrix and the third clarity variance , the fourth difference absolute mean of the second column vector and the fourth clarity variance ;

[0022] According to the third difference absolute mean and the third clarity variance , calculate the surface cleanliness of the corresponding step ,in, represents the individual step clarity function;

[0023] Calculate the surface cleanliness of the raw material based on the surface cleanliness of the steps and combined with the cleanliness of the equipment:

[0024] ;in, Process cleanliness functions representing the basic processing steps corresponding to the corresponding raw materials; a device cleanliness function representing the basic processing steps corresponding to the corresponding raw materials; represents the device cleanliness function before each step of processing corresponding to the corresponding raw material; Indicates the initial purity of the raw material; J1 indicates the surface purity of the corresponding raw material; represents the net weight corresponding to the i1th step; represents the step surface clarity corresponding to the i1th step; Indicates the cleanliness of the i2th instrument in the i1th step; represents the minimum cleanliness of the equipment under the corresponding i1th step; It represents the current instrument cleanliness of the corresponding raw material in the i1th step, N1 is the total number of steps; N2 is the total number of instrument cleanliness under the i1th step.

[0025] The present invention provides a food safety detection method for food production, characterized in that the method comprises: determining the production cleanliness of the corresponding production link, comprising:

[0026] Obtaining site environmental data of each representative site point in the food production site, and deriving the point cleanliness of the corresponding representative site point at different operating time points based on the site environmental data, and constructing an initial model, wherein the site environmental data includes the temperature, humidity, air quality and sterilization and disinfection value of the corresponding representative site point;

[0027] Obtaining environmental impact data generated by food production machines during operation, and constructing an impact diffusion model based on the site structure at different operating times based on the site structure based on the environmental impact data, the site structure of the site where the food production machines are located, the location layout of the food production machines, and the operation schedule, wherein the environmental impact data includes: energy efficiency, waste and by-products, noise data, emissions of food production machines, and residual chemicals in the cleaning, disinfection, and maintenance processes of food production machines;

[0028] According to the impact diffusion model at different operation time points, the cleanliness of the points at the corresponding operation time points is adjusted, the lowest cleanliness site point of the food production site at the same operation time point is determined and marked first, and the site points with a cleanliness lower than the preset cleanliness are marked second;

[0029] Performing training supervision on the initial model based on the first labeling result and the second labeling result to obtain an environment model;

[0030] Obtaining the production timeline of each production link in the food production process, and integrating and analyzing the production timeline with the environmental model to determine the sampling and testing time of each production timeline;

[0031] Based on the test results at each sampling test time in each production link, the production purity of the corresponding production link is determined.

[0032] The present invention provides a food safety detection method for food production, characterized in that the production cleanliness of the corresponding production link is determined based on the detection results at each sampling detection time in each production link, including:

[0033] Conduct sampling bacterial culture, chemical reagent and image testing on the sampled samples at each sampling and testing time;

[0034] Based on the culture results of the sampled bacterial species, the first bacterial species with the slowest reproduction cycle, the second bacterial species with the fastest reproduction cycle, and the third bacterial species with the second fastest reproduction cycle are obtained, and a first contamination degree is determined;

[0035] Based on the chemical reagent test results, the disinfection residue and additive content of the sampled sample are obtained to determine the second contamination degree;

[0036] Performing contaminated pixel analysis based on the image detection result to determine a third contamination degree;

[0037] Based on the first contamination degree, the second contamination degree and the third contamination degree, the production cleanliness of the corresponding production link is obtained.

[0038] The present invention provides a food safety detection method for food production, characterized in that the image detection comprises: image detection under natural light, image detection under infrared light and image detection under ultrasound.

[0039] The present invention provides a food safety detection method for food production, characterized in that determining a first contamination degree comprises:

[0040] ; Wherein, W1 represents the corresponding first pollution degree; T1 represents the corresponding fastest reproduction cycle; It indicates that it corresponds to the second fastest reproductive cycle; Indicates the fastest historical reproduction cycle of the corresponding link; represents the breeding area that matches T1; S2 represents the breeding area that matches T2; Indicates the fastest historical reproduction rate of the corresponding link; Indicates the corresponding slowest reproduction cycle; represents the conversion factor based on the period; represents the area-based conversion factor; represents the conversion coefficient based on rate; e is a constant with a value of 2.7; ln is the sign of the logarithmic function.

[0041] The present invention provides a food safety detection method for food production, characterized in that the food production safety is determined according to the surface cleanliness of all the surfaces of the same production line and the production cleanliness of all the production links involved in the same production line, and the food safety detection is realized, comprising:

[0042] According to the surface clarity and material properties of the raw materials in each production link in the same production line, the clarity is correlated with the production clarity of the corresponding production link to determine the first sub-clarity of the corresponding raw material after production in the corresponding production link;

[0043] Based on obtaining all first sub-purities of the same raw material in different production links, the second sub-purity of the corresponding production raw material is determined;

[0044] The second sub-purity of all raw materials involved is comprehensively calculated to obtain the food production safety. When the food production safety is greater than the preset safety, the food is determined to be safe.

[0045] Compared with the prior art, the beneficial effects of the present application are as follows: by classifying the raw materials for food production and sampling and culturing to determine the surface purity of the raw materials, obtaining site environmental data and environmental impact data to build an environmental model of the food site, determining the production purity, and combining the surface purity and production purity to determine the food safety, it not only avoids the roughness of food testing, but also effectively ensures the reliability of food safety testing.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 The present invention is a flowchart of a food safety detection method for food production according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0051] Embodiment 1:

[0052] The embodiment of the present invention provides a food safety detection method for food production, such as Figure 1 As shown, including:

[0053] Step 1: Obtain food production raw materials and classify them, perform basic processing steps on the raw materials according to the classification results, sample, observe and culture the raw materials after basic processing, and determine the surface purity of the raw materials after basic processing;

[0054] Step 2: Based on the site environment data of the food production site and the environmental impact data generated by the food production machines during operation, an environmental model of the food production site is constructed. According to the environmental model and the food production process, the sampling and testing time of different production links in the food production process is determined, and the production cleanliness of the corresponding production link is judged;

[0055] Step 3: Determine food production safety and implement food safety testing based on the surface cleanliness of all layers on the same production line and the production cleanliness of all production links involved in the same production line.

[0056] In this embodiment, the classification results include protein sources, carbohydrate sources and fat sources.

[0057] In this embodiment, the basic processing operations are primary cleaning, secondary peeling, and tertiary cutting.

[0058] In this embodiment, the sampling, observation and cultivation process is to collect samples of raw materials, and the sample selection is random to cover various areas of the raw materials. The observation process is based on molecular biology techniques, such as PCR to detect DNA or RNA. The cultivation process is the process of promoting the growth and reproduction of microorganisms under appropriate environmental conditions, wherein the appropriate environmental conditions include temperature, humidity, atmosphere and culture medium.

[0059] In this embodiment, the surface cleanliness is determined according to the type and quantity of bacterial strains.

[0060] In this embodiment, the site environment data includes temperature, humidity, air quality and sterilization data.

[0061] In this embodiment, the environmental impact data includes energy efficiency, waste and byproducts, noise data, emissions from the food production machines, and residual chemicals from the food production machines during cleaning, disinfection, and maintenance.

[0062] In this embodiment, the environmental model is constructed based on environmental impact data, the input data is the relevant data of the production machine in the food production process, and the output data is the environmental impact data generated by the corresponding food production machine.

[0063] In this embodiment, the food production process is raw material procurement, raw material inspection, storage, processing and manufacturing, packaging, quality control, labeling and distribution, storage and transportation and waste disposal.

[0064] In this embodiment, production purity is to sample food in different production processes, and culture the sampled bacteria and test the chemical reagent content on the sampling results, and determine the corresponding production link based on the culture results and the test results.

[0065] In this embodiment, the sampling inspection time is determined based on the low point mark in the environmental data and the relevant environmental data in the production process. For example, when the sampling point is located at the center of the low point mark and the temperature and humidity in the environmental data are the most suitable environmental data for microbial growth, the sampling inspection time for the corresponding point is 3 days.

[0066] In this embodiment, all surface cleanliness refers to the surface cleanliness of all kinds of food raw materials, that is, the cleanliness reflection of basic processing operations.

[0067] In this embodiment, food production safety is the food safety ultimately obtained by combining all surface cleanliness and all production cleanliness. For example, if the average surface cleanliness of all food raw materials of food A is 8 and the average of all production cleanliness is 8, the final food safety is 8.

[0068] The working principle and beneficial effects of the above technical solution are: by classifying the raw materials for food production and sampling and culturing them to determine the surface purity of the raw materials, obtaining site environmental data and environmental impact data to build an environmental model of the food site, determining the production purity, and combining the surface purity and production purity to determine food safety, it not only avoids the roughness of food testing, but also effectively ensures the reliability of food safety testing.

[0069] Embodiment 2:

[0070] The embodiment of the present invention provides a food safety detection method for food production, which determines the surface cleanliness of raw materials after basic treatment, including:

[0071] Conduct bacterial culture on instruments that undergo basic processing steps to determine the cleanliness of the instruments;

[0072] Classify food production raw materials according to their shelf life and types of meat and vegetables, and perform basic processing steps on the raw materials based on the classification results;

[0073] Sampling, observing and culturing each step in the basic processing steps under each classification result is performed respectively, and a strain culture vector of each raw material under the corresponding classification is established according to the observed culture results, wherein the strain culture vector is composed of the culture information of the same raw material in different steps;

[0074] According to the bacterial strain culture vectors of all raw materials of the same classification, the primary bacterial strain matrix of the corresponding classification is constructed by row arrangement;

[0075] The surface cleanliness of each step in the corresponding classification is determined according to the primary strain matrix, and the surface cleanliness of the corresponding raw materials is determined according to the surface cleanliness of all steps and the cleanliness of the equipment.

[0076] In this embodiment, ,in, Indicates the raw material The bacterial culture vector corresponding to each bacterial species; Indicates the raw material The growth rate of each bacterial species; Indicates the raw material The degree of food contamination by the metabolites of each bacterial species; Indicates the raw material The growth cycle of each bacterial species; Indicates the raw material The suitable growth environment function of each bacterial species;

[0077] In this embodiment, the basic processing steps include primary cleaning, secondary peeling and tertiary cutting. The primary cleaning includes physical removal of dirt and residues, such as food debris, grease and protein, and a standardized cleaning procedure, typically including pre-rinsing, cleaning, rinsing and disinfection steps. The secondary peeling includes removing the skin, shell or skin of the food and removing the seeds. The tertiary cutting is to cut the product into different shapes and sizes that meet the specifications.

[0078] In this embodiment, .

[0079] In this embodiment, the surface cleanliness of the raw material is the average of the surface cleanliness of the device and the surface cleanliness of the step.

[0080] The working principle and beneficial effects of the above technical solution are as follows: after basic treatment of the raw materials, bacterial strains are cultured on the surface of the raw materials and the surface of the equipment respectively, and the corresponding bacterial strain culture data is obtained for purity calculation to obtain the purity of the surface of the raw materials, and the purity of the raw materials and equipment after the basic operation steps is detected, the corresponding purity problem location is determined, and disinfection is carried out in time to ensure food safety.

[0081] Embodiment 3:

[0082] The embodiment of the present invention provides a food safety detection method for food production, which determines the surface cleanliness of each step in the corresponding classification according to the primary bacterial species matrix, and determines the surface cleanliness of the corresponding raw material according to the surface cleanliness of all steps and the cleanliness of the equipment, including:

[0083] The cleanliness of the instruments corresponding to each raw material in each step under the same classification is obtained to construct an instrument matrix, wherein each row of the instrument matrix is ​​the cleanliness of the instruments used in different steps for the same raw material in the corresponding classification;

[0084] Obtaining standard information of different raw materials in different steps to construct a standard matrix, performing a first comparative analysis on the standard matrix and a corresponding primary strain matrix to obtain a first difference matrix, and performing element purity standardization on the first difference matrix to obtain a second difference matrix;

[0085] Performing a second comparison analysis on the primary bacterial species matrix and the instrument matrix to obtain a third difference matrix;

[0086] Extract the same row vectors from the first difference matrix and the third difference matrix to obtain a double row matrix, and obtain the first difference absolute mean of the first row vectors in the double row matrix and the first clarity variance , the absolute mean of the second difference of the second row vector and the second clarity variance ;

[0087] Extract the same column vectors from the first difference matrix and the third difference matrix to obtain a double-column matrix, and obtain the third difference absolute mean of the first column row vectors in the double-column matrix and the third clarity variance , the fourth difference absolute mean of the second column vector and the fourth clarity variance ;

[0088] According to the third difference absolute mean and the third clarity variance , calculate the surface cleanliness of the corresponding step ,in, represents the individual step clarity function;

[0089] Calculate the surface cleanliness of the raw material based on the surface cleanliness of the steps and combined with the cleanliness of the equipment:

[0090] ;in, Process cleanliness functions representing the basic processing steps corresponding to the corresponding raw materials; a device cleanliness function representing the basic processing steps corresponding to the corresponding raw materials; represents the device cleanliness function before each step of processing corresponding to the corresponding raw material; Indicates the initial purity of the raw material; J1 indicates the surface purity of the corresponding raw material; represents the net weight corresponding to the i1th step; represents the step surface clarity corresponding to the i1th step; Indicates the cleanliness of the i2th instrument in the i1th step; represents the minimum cleanliness of the equipment under the corresponding i1th step; It represents the current instrument cleanliness of the corresponding raw material in the i1th step; N1 is the total number of steps; N2 is the total number of instrument cleanliness under the i1th step.

[0091] In this embodiment, .

[0092] In this embodiment, .

[0093] In this embodiment, the first comparative analysis is a process of generating a corresponding standard matrix based on the strain data of the strain cultured from the corresponding raw materials under standard conditions and subtracting the matrix from the primary strain matrix.

[0094] In this embodiment, the first difference matrix is ​​a difference matrix between the standard matrix and the corresponding primary strain matrix.

[0095] In this embodiment, the element clarity standardization is to subtract the average value of the column where the corresponding clarity is located, and then divide it by the standard deviation of the column to obtain the clarity standardization value of the corresponding clarity.

[0096] In this embodiment, the second comparative analysis obtains the actual purity of the corresponding positions based on the primary strain matrix to form an actual purity matrix, which is subtracted from the instrument matrix to obtain a third difference matrix.

[0097] In this embodiment, the comparison process is to calculate the difference between the instrument clarity and the standard instrument clarity, and the step surface clarity and the standard step clarity, respectively, expand the low point mark of the difference, and finally obtain the comparison result.

[0098] In this embodiment, the contaminated position is a position point in the comparison result that does not meet the standard value. For example, if the difference is 0.5 but the corresponding difference position point is a low point mark point, it is expanded to 0.8. The standard value is 0.7, and the position corresponding to this point is the contaminated position.

[0099] In this embodiment, the bacterial species settings corresponding to the disinfection methods and disinfection operations include Escherichia coli and Salmonella of the bacteria type, the disinfection methods include heat treatment, ultraviolet radiation, and chemical disinfectants, and the disinfection operations include maintaining an appropriate storage temperature and soaking or spraying the food contact surface with a suitable disinfectant; for molds and yeasts of the fungi type, the disinfection methods include generally preventing fungal contamination by controlling humidity, temperature and moisture content of food, and using antifungal chemicals.

[0100] Sterilization operation: keep food dry, avoid excessive storage time, and check food quality regularly

[0101] The working principle and beneficial effects of the above technical solution are: by comparing the standard purity with the actual purity, judging whether there is a contaminated location based on the comparison result, and locking and disinfecting the contaminated location, to achieve purity detection of raw materials and equipment after basic operation steps, determine the corresponding purity problem location, and disinfect in time to ensure food safety.

[0102] Embodiment 4:

[0103] The embodiment of the present invention provides a food safety detection method for food production, which is based on judging the production cleanliness of corresponding production links, including:

[0104] Obtaining site environmental data of each representative site point in the food production site, and deriving the point cleanliness of the corresponding representative site point at different operating time points based on the site environmental data, and constructing an initial model, wherein the site environmental data includes the temperature, humidity, air quality and sterilization and disinfection value of the corresponding representative site point;

[0105] Obtaining environmental impact data generated by food production machines during operation, and constructing an impact diffusion model based on the site structure at different operating times based on the site structure based on the environmental impact data, the site structure of the site where the food production machines are located, the location layout of the food production machines, and the operation schedule, wherein the environmental impact data includes: energy efficiency, waste and by-products, noise data, emissions of food production machines, and residual chemicals in the cleaning, disinfection, and maintenance processes of food production machines;

[0106] According to the impact diffusion model at different operation time points, the cleanliness of the points at the corresponding operation time points is adjusted, the lowest cleanliness site point of the food production site at the same operation time point is determined and marked first, and the site points with a cleanliness lower than the preset cleanliness are marked second;

[0107] Performing training supervision on the initial model based on the first labeling result and the second labeling result to obtain an environment model;

[0108] Obtaining the production timeline of each production link in the food production process, and integrating and analyzing the production timeline with the environmental model to determine the sampling and testing time of each production timeline;

[0109] Based on the test results at each sampling test time in each production link, the production purity of the corresponding production link is determined.

[0110] In this embodiment, the point cleanliness is obtained based on the corresponding environmental data of all points in the site environment.

[0111] In this embodiment, the site structure of the site where the food production machine is located includes work area division, equipment placement, passages and corridors, material storage area, equipment maintenance area, ventilation and air quality control area, and safety facilities.

[0112] In this embodiment, the impact diffusion model is a model of the impact diffusion of the site environment on the purity of the entire production process. It can be directly simulated according to the environmental impact data. The subsequent input data is the site environment data, and the output data is the purity of the corresponding point at the corresponding time.

[0113] In this embodiment, the lowest cleanliness site point is the corresponding lowest environmental cleanliness point in the entire food production site, and there may be multiple points.

[0114] In this embodiment, the first marking is the marking of the lowest clarity field point.

[0115] In this embodiment, the second mark is to compare the lowest clarity site point with the preset clarity, and the second mark is performed on the points lower than the preset clarity. When all the lowest clarity site points are lower than the preset clarity, a step-by-step comparison will be performed until the clarity of the remaining site points is higher than the preset point.

[0116] In this embodiment, the environmental model is a model of the impact of the site environment on the cleanliness of the food production process. The input data is the site environment data, and the output data is the cleanliness impact factor corresponding to the entire environment. The environment at different time points on the production timeline may be different. Therefore, it is integrated with the environmental model to accurately determine the subsequent sampling and detection time of Huqiu.

[0117] In this embodiment, the sampling and testing time is the time interval for sampling food materials in the production process.

[0118] The working principle and beneficial effects of the above technical solution are: constructing an initial model through the site environment data corresponding to the food production site, constructing an impact diffusion model according to the environmental impact data, determining the site cleanliness of all points and performing first marking and second marking on the corresponding points, and training and supervising the initial model based on the marking results to obtain an environmental model, determining the sampling inspection time, determining the production cleanliness based on the inspection results, and determining food safety by combining surface cleanliness and production cleanliness, which not only avoids the roughness of food inspection, but also effectively ensures the reliability of food safety inspection.

[0119] Embodiment 5:

[0120] The embodiment of the present invention provides a food safety detection method for food production, which determines the production cleanliness of the corresponding production link based on the detection results at each sampling detection time in each production link, including:

[0121] Conduct sampling bacterial culture, chemical reagent and image testing on the sampled samples at each sampling and testing time;

[0122] Based on the culture results of the sampled bacterial species, the first bacterial species with the slowest reproduction cycle, the second bacterial species with the fastest reproduction cycle, and the third bacterial species with the second fastest reproduction cycle are obtained, and a first contamination degree is determined;

[0123] Based on the chemical reagent test results, the disinfection residue and additive content of the sampled sample are obtained to determine the second contamination degree;

[0124] Performing contaminated pixel analysis based on the image detection result to determine a third contamination degree;

[0125] Based on the first contamination degree, the second contamination degree and the third contamination degree, the production cleanliness of the corresponding production link is obtained.

[0126] In this embodiment, the first contamination degree is the contamination degree corresponding to the contamination caused by the bacteria species.

[0127] In this embodiment, the second contamination level is a contamination level corresponding to the contamination caused by the chemical reagent.

[0128] In this embodiment, the pollution pixel analysis is performed by acquiring image information, smoothing, denoising and enhancing the image information, performing threshold segmentation, determining pollution source pixels, extracting features and classifying the pollution source pixels, and finally obtaining the corresponding pollution situation.

[0129] In this embodiment, the third pollution level is a pollution level corresponding to the pollution situation obtained through image information.

[0130] The working principle and beneficial effects of the above technical solution are: determining the sampling time interval through the culture results of the sampled bacterial strains, and determining the purity of the corresponding production process according to the corresponding experiments, so as to achieve timely disinfection of the entire production process and ensure food safety.

[0131] Embodiment 6:

[0132] An embodiment of the present invention provides a food safety detection method for food production, wherein the image detection includes: image detection under natural light, image detection under infrared light, and image detection under ultrasound.

[0133] In this embodiment, the image information obtained under natural light conditions uses color analysis technology combined with the texture and texture changes of the corresponding food during the production process to determine the type and corresponding content of the first additive;

[0134] In this embodiment, the image information obtained under infrared light is used to draw a corresponding spectrum, lock the absorption peak of the spectrum, and match the type and content of the second additive according to the infrared-characteristic table;

[0135] In this embodiment, ultrasonic imaging is used to determine the structural changes inside the food, the presence of foreign matter inside the food, and the type and content of the third additive;

[0136] In this embodiment, the type and content of the first additive, the type and content of the second additive, and the type and content of the third additive are comprehensively considered to obtain the type and content of the additive in the sampling result.

[0137] In this embodiment, the color analysis technology is to check the appearance and color consistency of the food by measuring the color parameters of the food, such as hue, saturation and brightness, usually using CIE Lab or CIE Lab* color space to describe the color, and determine the first additive.

[0138] In this embodiment, the changes in texture and grain include changes in moisture content, changes caused by processing methods, changes caused by formulation changes, and changes caused by storage conditions and processing time.

[0139] In this embodiment, the infrared-signature table can determine the chemical composition of the substance and the second additive by detecting the interaction between the substance and infrared light of different wavelengths through infrared spectroscopy technology.

[0140] The working principle and beneficial effects of the above technical solution are: through corresponding setting experiments, the corresponding types of additives and corresponding contents in food production are determined, the food quality is controlled in time, and the food safety is determined by comprehensively combining the surface cleanliness and production cleanliness, effectively ensuring the reliability of food safety testing and ensuring food safety.

[0141] Embodiment 7:

[0142] An embodiment of the present invention provides a food safety detection method for food production, which determines a first contamination degree, including:

[0143] ; Wherein, W1 represents the corresponding first pollution degree; T1 represents the corresponding fastest reproduction cycle; It indicates that it corresponds to the second fastest reproductive cycle; Indicates the fastest historical reproduction cycle of the corresponding link; represents the breeding area that matches T1; S2 represents the breeding area that matches T2; Indicates the fastest historical reproduction rate of the corresponding link; Indicates the corresponding slowest reproduction cycle; represents the conversion factor based on the period; represents the area-based conversion factor; represents the conversion coefficient based on rate; e is a constant with a value of 2.7; ln is the sign of the logarithmic function.

[0144] The working principle and beneficial effect of the above technical solution are: by calculating the first contamination degree based on the reproduction cycle data of the bacterial species, the roughness of food testing can be avoided and the reliability of food safety testing can be effectively guaranteed.

[0145] Embodiment 8:

[0146] The embodiment of the present invention provides a food safety detection method for food production, which determines the food production safety according to the surface cleanliness of all the surfaces of the same production line and the production cleanliness of all the production links involved in the same production line, and realizes food safety detection, including:

[0147] According to the surface clarity and material properties of the raw materials in each production link in the same production line, the clarity is correlated with the production clarity of the corresponding production link to determine the first sub-clarity of the corresponding raw material after production in the corresponding production link;

[0148] Based on obtaining all first sub-purities of the same raw material in different production links, the second sub-purity of the corresponding production raw material is determined;

[0149] The second sub-purity of all raw materials involved is comprehensively calculated to obtain the food production safety. When the food production safety is greater than the preset safety, the food is determined to be safe.

[0150] In this example, the material properties are corrosion resistance, temperature stability, and food and material compatibility.

[0151] In this embodiment, the clarity association is the relationship between the surface clarity and the production clarity of the same raw material. For example, if the surface clarity of raw material 1 is 8 and the production clarity is 7, the clarity association is a negative association. If the surface clarity of raw material 2 is 8 and the production clarity is 9, the clarity association is a positive association.

[0152] In this embodiment, the first sub-clarity is the clarity of the same raw material after the production stage, which corresponds to the weighted combination of the surface clarity and the production clarity of the raw material.

[0153] In this embodiment, the second sub-clarity is a normalized value of the first sub-clarity.

[0154] The working principle and beneficial effect of the above technical solution are: by determining the purity of raw materials in different food production links, the purity of production raw materials is determined, thereby determining the safety of food production, which not only avoids the roughness of food testing, but also effectively ensures the reliability of food safety testing.

[0155] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A food safety detection method for food production, characterized in that: include: Step 1: Obtain food production raw materials and classify them, perform basic processing steps on the raw materials according to the classification results, sample, observe and culture the raw materials after basic processing, and determine the surface purity of the raw materials after basic processing; Step 2: Based on the site environment data of the food production site and the environmental impact data generated by the food production machines during operation, an environmental model of the food production site is constructed. According to the environmental model and the food production process, the sampling and testing time of different production links in the food production process is determined, and the production cleanliness of the corresponding production link is judged; Step 3: Determine food production safety and implement food safety testing based on the surface cleanliness of all surfaces on the same production line and the production cleanliness of all production links involved in the same production line; Among them, judging the surface cleanliness of the raw materials after basic treatment includes: Conduct bacterial culture on instruments that undergo basic processing steps to determine the cleanliness of the instruments; Classify food production raw materials according to their shelf life and types of meat and vegetables, and perform basic processing steps on the raw materials based on the classification results; Sampling, observing and culturing each step in the basic processing steps under each classification result is performed respectively, and a strain culture vector of each raw material under the corresponding classification is established according to the observed culture results, wherein the strain culture vector is composed of the culture information of the same raw material in different steps; According to the bacterial strain culture vectors of all raw materials of the same classification, the primary bacterial strain matrix of the corresponding classification is constructed by row arrangement; Determine the surface cleanliness of each step in the corresponding classification according to the primary strain matrix, and determine the surface cleanliness of the corresponding raw materials according to the surface cleanliness of all steps and the cleanliness of the equipment; Among them, the surface cleanliness of each step in the corresponding classification is determined according to the primary strain matrix, and the surface cleanliness of the corresponding raw materials is determined according to the surface cleanliness of all steps and the cleanliness of the equipment, including: The cleanliness of the instruments corresponding to each raw material in each step under the same classification is obtained to construct an instrument matrix, wherein each row of the instrument matrix is ​​the cleanliness of the instruments used in different steps for the same raw material in the corresponding classification; Obtaining standard information of different raw materials in different steps to construct a standard matrix, performing a first comparative analysis on the standard matrix and the corresponding primary strain matrix to obtain a first difference matrix, and performing element purity standardization on the first difference matrix to obtain a second difference matrix; Performing a second comparative analysis on the primary bacterial species matrix and the instrument matrix to obtain a third difference matrix; Extract the same row vectors from the first difference matrix and the third difference matrix to obtain a double row matrix, and obtain the first difference absolute mean of the first row vectors in the double row matrix and the first clarity variance , the absolute mean of the second difference of the second row vector and the second clarity variance ; Extract the same column vectors from the first difference matrix and the third difference matrix to obtain a double-column matrix, and obtain the third difference absolute mean of the first column row vectors in the double-column matrix and the third clarity variance , the fourth difference absolute mean of the second column vector and the fourth clarity variance ; According to the third difference absolute mean and the third clarity variance , calculate the surface cleanliness of the corresponding step ,in, represents the individual step clarity function; Calculate the surface cleanliness of the raw material based on the surface cleanliness of the steps and combined with the cleanliness of the equipment: ;in, Process cleanliness functions representing the basic processing steps corresponding to the corresponding raw materials; a device cleanliness function representing the basic processing steps corresponding to the corresponding raw materials; represents the device cleanliness function before each step of processing corresponding to the corresponding raw material; Indicates the initial purity of the raw material; J1 indicates the surface purity of the corresponding raw material; represents the net weight corresponding to the i1th step; represents the step surface clarity corresponding to the i1th step; Indicates the cleanliness of the i2th instrument in the i1th step; represents the minimum cleanliness of the equipment under the corresponding i1th step; Indicates the current instrument cleanliness of the corresponding raw material in the i1th step, N1 is the total number of steps; N2 is the total number of instrument cleanliness under the i1th step; Among them, judging the production cleanliness of the corresponding production link includes: Obtaining site environmental data of each representative site point in the food production site, and constructing an initial model based on the site environmental data to obtain the point cleanliness of the corresponding representative site point at different operating time points, wherein the site environmental data includes the temperature, humidity, air quality and sterilization and disinfection value of the corresponding representative site point; Obtaining environmental impact data generated by food production machines during operation, and constructing an impact diffusion model based on the site structure at different operating times based on the site structure based on the environmental impact data, the site structure of the site where the food production machines are located, the location layout of the food production machines, and the operation schedule, wherein the environmental impact data includes: energy efficiency, waste and by-products, noise data, emissions of food production machines, and residual chemicals in the cleaning, disinfection, and maintenance processes of food production machines; According to the impact diffusion model at different operation time points, the cleanliness of the points at the corresponding operation time points is adjusted, the lowest cleanliness site point of the food production site at the same operation time point is determined and marked first, and the site points with a cleanliness lower than the preset cleanliness are marked second; Performing training supervision on the initial model based on the first labeling result and the second labeling result to obtain an environment model; Obtaining the production timeline of each production link in the food production process, and integrating and analyzing the production timeline with the environmental model to determine the sampling and testing time of each production timeline; Based on the test results at each sampling test time in each production link, determine the production cleanliness of the corresponding production link; Among them, based on the test results at each sampling test time in each production link, the production cleanliness of the corresponding production link is determined, including: Conduct sampling bacterial culture, chemical reagent and image testing on the sampled samples at each sampling and testing time; Based on the culture results of the sampled bacterial species, the first bacterial species with the slowest reproduction cycle, the second bacterial species with the fastest reproduction cycle, and the third bacterial species with the second fastest reproduction cycle are obtained, and a first contamination degree is determined; Based on the chemical reagent test results, the disinfection residue and additive content of the sampled sample are obtained to determine the second contamination degree; Performing contaminated pixel analysis based on the image detection result to determine a third contamination degree; Based on the first contamination degree, the second contamination degree and the third contamination degree, obtaining the production cleanliness of the corresponding production link; Wherein, determining the first pollution degree includes: ; Wherein, W1 represents the corresponding first pollution degree; T1 represents the corresponding fastest reproduction cycle; It indicates that it corresponds to the second fastest reproductive cycle; Indicates the fastest historical reproduction cycle of the corresponding link; represents the breeding area that matches T1; S2 represents the breeding area that matches T2; Indicates the fastest historical reproduction rate of the corresponding link; Indicates the corresponding slowest reproduction cycle; represents the conversion factor based on the period; represents the area-based conversion factor; represents the conversion coefficient based on the rate; e is a constant with a value of 2.7; ln is the sign of the logarithmic function; Among them, the food production safety is determined based on the surface cleanliness of all the production links involved in the same production line and the production cleanliness of all the production links involved in the same production line, and the food safety detection is realized, including: According to the surface clarity and material properties of the raw materials in each production link in the same production line, the clarity is correlated with the production clarity of the corresponding production link to determine the first sub-clarity of the corresponding raw material after production in the corresponding production link; Based on obtaining all first sub-purities of the same raw material in different production links, the second sub-purity of the corresponding production raw material is determined; The second sub-purity of all raw materials involved is comprehensively calculated to obtain the food production safety. When the food production safety is greater than the preset safety, the food is determined to be safe.

2. A food safety detection method for food production according to claim 1, characterized in that: The image detection includes: image detection under natural light, image detection under infrared light and image detection under ultrasound.

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