Food safety traceability detection method and system based on the Internet of Things

Abstract

The present invention discloses a food safety traceability detection method and system based on the Internet of Things, which relates to the field of food safety traceability detection. The method and system include a data acquisition module, a finished product module, a raw material module, a processing module, a storage and transportation module, and a judgment module. The method and system include the following steps: analyzing a finished meat dish to obtain abnormal values ​​of the finished meat dish, sending a corresponding abnormal safety and quality traceability signal of the pre-prepared dish, judging and analyzing the raw materials of the meat dish to obtain abnormal safety and quality values ​​of the raw materials of the meat dish, judging and analyzing the processing environment, preservatives, and cleaning status of the meat dish to obtain the residual value of the meat dish after spoilage, judging and analyzing the storage and transportation of abnormal meat dish to obtain abnormal storage and transportation values ​​of the meat dish, analyzing the pre-prepared dish processing in each process of the pre-prepared dish to obtain abnormal processes that affect food problems of the meat dish, and being able to analyze pre-prepared dishes in abnormal states from multiple dimensions and directions, thereby reducing the cost of the enterprise in the production of pre-prepared dishes.

Description

Technical Field

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

[0002] Traceability testing of meat pre-prepared food safety is very important. Traditional meat pre-prepared food safety traceability testing relies too much on manual monitoring and cannot monitor and trace the quality and safety status of pre-prepared food in real time.

[0003] When running, traditional food safety traceability detection methods are unable to accurately identify abnormal safety conditions of meat pre-prepared dishes in real time, and are even more unable to analyze abnormal pre-prepared dishes from multiple dimensions and directions. As a result, the image of the company's pre-prepared dishes is greatly reduced in the eyes of many consumers, increasing the company's cost of pre-prepared dish production. At the same time, it is also unable to detect violations and safety hazards of pre-prepared dishes, increasing the company's potential losses and operating risks.

[0004] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0005] In order to solve the technical problems raised by the above background technology, the present invention is proposed. The embodiments of the present invention provide a food safety traceability detection method and system based on the Internet of Things.

[0006] The purpose of the present invention can be achieved by the following technical solution: A food safety traceability detection method based on the Internet of Things comprises the following steps:

[0007] S1: The data acquisition module collects information on the finished product, raw materials, processing, storage and transportation of pre-prepared meat dishes, and sends it to the finished product module, raw materials module, processing module and storage and transportation module;

[0008] S2: The finished product module analyzes the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of the meat pre-prepared dish products, obtains abnormal values ​​of the pre-prepared dish products, compares them with the set comparison range, and issues a traceability signal for abnormal safety and quality of the pre-prepared dish;

[0009] S3: The raw material module receives raw material information and pre-prepared food safety and quality anomaly traceability signals, and conducts judgment and analysis on the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials to obtain the safety and quality anomaly values ​​of the meat pre-prepared food raw materials;

[0010] S4: The processing module receives the meat pre-prepared dish processing information and the pre-prepared dish safety and quality anomaly traceability signal, and analyzes the meat pre-prepared dish processing environment, preservatives, and pre-prepared dish cleaning status to obtain the meat pre-prepared dish spoilage residual value;

[0011] S5: The storage and transportation module receives the storage and transportation information of prepared meat dishes and the abnormal traceability signal of the safety and quality of the prepared meat dishes, performs judgment and analysis on the storage and transportation of abnormal prepared meat dishes, and obtains the abnormal value of storage and transportation of the prepared meat dishes;

[0012] S6: The judgment module receives the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes, and the abnormal values ​​of storage and transportation of pre-prepared meat dishes, and analyzes the pre-prepared dish processing of each process to obtain abnormal processes that affect the food problems of pre-prepared meat dishes.

[0013] Furthermore, the steps for analyzing the signal for tracing the source of abnormal quality of prepared dishes are as follows:

[0014] Normalize the oil oxidation value, egg change value, carbohydrate loss value and fungal disease value of the pre-prepared food finished product, and obtain the abnormal value of the pre-prepared food finished product through graph construction.

[0015] Compare the abnormal value of the pre-prepared food product with the set comparison intervals CX1 and CX2, where the comparison interval CX1>CX2. When the abnormal value of the pre-prepared food product is in the comparison interval CX1, the quality of the corresponding pre-prepared food product is abnormal, and a pre-prepared food safety and quality abnormality traceability signal is issued and sent to the raw material module, processing module and storage and transportation module. When the abnormal value of the pre-prepared food product is in the comparison interval CX2, the quality of the corresponding pre-prepared food product is normal.

[0016] Furthermore, the steps for analyzing the oil oxygenation value, egg change value, carbohydrate loss value and fungal disease value of the prepared dish product are as follows:

[0017] The oil chemical composition is the oil peroxide value, oil acid value and oil carbonyl value of the meat pre-prepared dish product to obtain the oil peroxide value of the pre-prepared dish product. The protein structure refers to the sum of the low and high molecular weight values ​​of the band, the band width value and the band tailing degree to obtain the protein change value of the pre-prepared dish product. The protein of the pre-prepared dish product is extracted by protein extraction buffer, and the gel electrophoresis experiment is carried out. According to the molecular weight value of the c1 and c2 markers, the area with a molecular weight less than c1 in the gel electrophoresis experiment corresponds to the low molecular weight area, and the area greater than c2 corresponds to the high molecular weight area. The number of bands and the optical density integral value of the low molecular weight area and the high molecular weight area are counted and summed to obtain the low and high molecular weight values ​​of the band. The sharpness value on the band is lower than the set value. When the sharpness threshold is set, it corresponds to tailing. The distance from the end of the normal part of the strip to the last position of the tail identified by the cursor is counted and marked as the tailing length of the strip. The tailing length of each strip is counted and marked as the tailing degree of the strip. The carbohydrate content refers to the reaction of distilled water and anthrone reagent in the pre-prepared dish product, and the absorbance is measured by a spectrophotometer at a certain wavelength. The organic acid in the mixture of distilled water of the pre-prepared dish product is titrated with a standard sodium hydroxide solution, and the consumption of sodium hydroxide is counted. The measured absorbance is added to the consumption of sodium hydroxide, and multiplied by the correction factor coefficient to obtain the carbohydrate reduction value of the pre-prepared dish product. The microbial index refers to the total number of colonies, the number of coliform bacteria, and the content of pathogenic bacteria to obtain the bacteriopathic value of the pre-prepared dish product.

[0018] Furthermore, the steps for analyzing the safety and quality of raw materials for prepared meat dishes are as follows:

[0019] Obtain the sensory quality value, bioelectric special value, and protein carbonyl content in the meat pre-prepared dish raw material information, mark them as Gz, Tb, and Db, normalize them with the muscle fiber value JQz of the meat pre-prepared dish raw material, and substitute them into the set formula , the safety and quality abnormality values ​​AZZ of meat pre-prepared food raw materials are calculated, where g1, g2, g3, g4, g5, g6, g7 and g8 are the set weight factor coefficients, and njz refers to the cohesion index of the pre-prepared food raw materials.

[0020] Furthermore, the steps for analyzing the muscle fiber value of the raw materials of the prepared meat dishes are as follows:

[0021] Step 4: Divide the three-dimensional model of the muscle fiber bundle into several cubic units, which are marked as voxels. The ratio of the number of voxels occupied by the fiber bundle to the total number of voxels is counted and marked as the muscle fiber bundle space filling value. The muscle fiber bundle fractal value, muscle fiber bundle dispersion value, muscle fiber bundle neighbor value, and muscle fiber bundle space filling value are marked as fz, js, sl, and tc, respectively, and normalized with the muscle fiber bundle cocircularity value Tyz and substituted into the set formula The muscle fiber value JQz of the raw materials of pre-prepared meat dishes is calculated, where th1, th2, th3, th4, th5, th6, th7, th8 and th9 are the set influencing factor coefficients, and e is a natural constant.

[0022] Furthermore, the muscle fiber bundle cocircularity value analysis steps are as follows:

[0023] Step 3: The three-dimensional model of the muscle fiber bundle is fitted with a cylinder by the least squares method to obtain its central axis. The distance from each point on the fiber bundle to the central axis is obtained and marked as the median value of each point in the fiber bundle. The absolute value of the median difference between the adjacent points of the fiber bundle is counted. When the absolute value of the median difference is less than or equal to the set threshold, the adjacent points are classified into a concentric circle structure. The number of concentric circle structures in the muscle fiber bundle is counted, and the three-dimensional coordinates of the starting point of each concentric circle structure (xs n , ys n , zs n ) and the end point coordinates (xz n , yz n ,zz n ), n is the serial number of the concentric circle structure, according to the formula, the pitch value jj of each concentric circle structure is obtained n According to the formula, the co-circular distance value Txz of the muscle fiber bundle is obtained, the distance from each point in the concentric circle structure to the central axis is obtained and the average value is taken to obtain the median distance of the concentric circle structure, and the sum of the median distances of each concentric circle structure is counted and divided by the number of concentric circle structures N to obtain the median co-circular distance of the muscle fiber bundle Jzz. The median co-circular distance of the muscle fiber bundle Jzz, the number of concentric circle structures N, and the co-circular distance value Txz of the muscle fiber bundle are calculated to obtain the co-circular value Tyz of the muscle fiber bundle.

[0024] Furthermore, the muscle fiber bundle fractionation value, muscle fiber bundle dispersion value and muscle fiber bundle neighbor value analysis steps are as follows:

[0025] Step 1: 3D data of muscle fiber bundles of meat pre-cooked food ingredients is obtained through a 3D microscope, and 3D reconstruction of the muscle fiber bundles is performed to obtain the 3D shape of the muscle fiber bundles. The 3D shape of the fiber bundles is refined into a center line through a thinning algorithm, and the center line is marked as the skeleton of the fiber bundle. When the neighborhood of each point of the fiber bundle skeleton exceeds the connection in two directions, the point is determined to be a branch point. All points of the skeleton of each fiber bundle are traversed, and the total number of branch points is counted to obtain the muscle fiber bundle fractionation value;

[0026] Step 2: Obtain the angles between the branches of each branch point, and count the branch angles of all branch points in the muscle three-dimensional model to obtain the branch angle data, calculate the standard deviation of the branch angle data, and mark it as the muscle fiber bundle dispersion value. If the distance between adjacent fiber bundles is less than the set threshold, the two fiber bundles are classified into the same group. If the distance between a fiber bundle and each adjacent fiber bundle is greater than or equal to the set threshold, the fiber bundle is grouped together separately, and the fiber bundles in the three-dimensional model are traversed to obtain the grouping status of the muscle fiber bundles, and the number of independent groups is counted, which is marked as the muscle fiber bundle neighbor value.

[0027] Furthermore, the abnormal process analysis steps affecting the prepared meat food problem are as follows:

[0028] The meat pre-prepared dish raw material safety and quality abnormality value AZZ, the meat pre-prepared dish added spoilage residual value JFZ, and the meat pre-prepared dish storage and transportation abnormality value CYZ are calculated according to the formula to obtain the meat pre-prepared dish raw material abnormality value YYZ. When the meat pre-prepared dish raw material abnormality value is greater than or equal to the set raw material abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormal meat pre-prepared dish raw materials. When the meat pre-prepared dish raw material abnormality value is less than the set raw material abnormality value threshold, it means that the meat pre-prepared dish added abnormality value JYZ is calculated according to the formula. When the meat pre-prepared dish added abnormality value is greater than or equal to the set pre-prepared dish added abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormality in the meat pre-prepared dish processing process. When the meat pre-prepared dish added abnormality value is less than the set pre-prepared dish added abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormality in the meat pre-prepared dish storage and transportation process.

[0029] Furthermore, the steps for analyzing the residual value of the prepared meat dishes after spoilage are as follows:

[0030] The processing information of meat pre-prepared dishes is obtained by obtaining the processing ring micro-residue value, preservative dosage, and meat clear value, and marking them as jhc, ffj, and rqg respectively. They are normalized according to the formula JFZ=[h1×jhc+h2×(ffj-A2)] 2 ] / (h3×rqg), and the residual value of meat pre-prepared dishes JFZ is calculated, where h1, h2 and h3 are the set influencing factor coefficients, and A2 is the set reference amount of preservatives.

[0031] Furthermore, the steps for analyzing the outliers in the storage and transportation of prepared meat dishes are as follows:

[0032] The storage abnormality value, transportation vibration impulse value, and transportation temperature control abnormality value in the storage and transportation information of pre-prepared meat dishes are obtained, marked as cyz, yzc, and kyw respectively. According to the set formula CYZ=t1×cyz+t2×yzc+t3×kyw, the storage and transportation abnormality value CYZ of pre-prepared meat dishes is calculated, where t1, t2, and t3 are all set weight factor coefficients.

[0033] The food safety traceability detection system based on the Internet of Things is characterized by including:

[0034] The data acquisition module is used to collect information about the finished product, raw materials, processing, storage and transportation of pre-prepared meat dishes, and send it to the finished product module, raw materials module, processing module and storage and transportation module;

[0035] The finished product module is used to analyze the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of meat pre-prepared dishes, obtain abnormal values ​​of the pre-prepared dishes, compare them with the set comparison range, and issue a traceability signal for abnormal quality of the pre-prepared dishes;

[0036] The raw material module is used to receive raw material information and pre-prepared food safety and quality anomaly traceability signals, and conduct judgment and analysis on the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials to obtain the safety and quality anomaly values ​​of the meat pre-prepared food raw materials;

[0037] The processing module is used to receive the processing information of the prepared meat dishes and the traceability signal of the safety and quality anomalies of the prepared meat dishes, judge and analyze the processing environment, preservatives, and cleaning status of the prepared meat dishes, and obtain the residual value of the prepared meat dishes;

[0038] The storage and transportation module is used to receive the storage and transportation information of prepared meat dishes and the traceability signal of abnormal quality of prepared meat dishes, conduct judgment and analysis on the storage and transportation of abnormal prepared meat dishes, and obtain the abnormal value of storage and transportation of prepared meat dishes;

[0039] The judgment module is used to receive the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes, and the abnormal values ​​of storage and transportation of pre-prepared meat dishes, and analyze the pre-prepared dish processing in each process to obtain abnormal processes that affect the food problems of pre-prepared meat dishes.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention analyzes the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of the finished meat pre-prepared dish through the finished product module to obtain the abnormal value of the finished pre-prepared dish, compares it with the set comparison interval, and sends a pre-prepared dish safety and quality abnormality tracing signal. The raw material module receives the raw material information and the pre-prepared dish safety and quality abnormality tracing signal, and makes judgments and analyses on the muscle fiber bundles, sensory state, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared dish raw materials to obtain the abnormal value of the meat pre-prepared dish raw materials. The processing module receives the meat pre-prepared dish processing information and the pre-prepared dish safety and quality abnormality tracing signal, and makes judgments and analyses on the processing environment, preservatives, and pre-prepared dish cleaning state to obtain the meat pre-prepared dish spoilage residual value. The storage and transportation module receives the meat pre-prepared dish storage and transportation information and the pre-prepared dish safety and quality abnormality tracing signal, and makes judgments and analyses on the storage and transportation of abnormal meat pre-prepared dishes to obtain the meat pre-prepared dish storage and transportation abnormal value. The present invention can accurately identify the safety abnormality of meat pre-prepared dishes in real time, and can analyze pre-prepared dishes in abnormal states from multiple dimensions and directions.

[0042] 2. The present invention receives the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes and the abnormal values ​​of storage and transportation of pre-prepared meat dishes through the judgment module, and analyzes the pre-prepared dish processing in each process to obtain abnormal processes that affect the food problems of pre-prepared meat dishes. It can improve the image of the enterprise in the eyes of many consumers, reduce the cost of the enterprise in the production of pre-prepared dishes, and at the same time can discover violations and safety hazards of pre-prepared dishes, reducing the potential losses and operating risks of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0044] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of the present invention.

[0046] like Figure 1 As shown in FIG, the food safety traceability detection system based on the Internet of Things includes a data acquisition module, a finished product module, a raw material module, a processing module, a storage and transportation module, and a judgment module.

[0047] The data acquisition module is used to collect information on finished products, raw materials, processing, storage and transportation of pre-prepared meat dishes, and send it to the finished product module, raw material module, processing module and storage and transportation module;

[0048] The finished product module is used to determine the quality and safety of pre-prepared meat dishes by analyzing their oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators. It obtains abnormal values ​​for the pre-prepared dishes, compares them with the set comparison interval, and issues a traceability signal for abnormal quality of the pre-prepared dishes. The specific analysis is as follows:

[0049] The quality and safety of meat pre-prepared dishes are judged and analyzed by their oil and fat chemical composition, protein structure, carbohydrate content and microbial indicators. The oil and fat chemical composition is the oil peroxide value, oil acid value and oil carbonyl value of meat pre-prepared dishes added together to get the oil peroxide value of pre-prepared dishes. The protein structure refers to the sum of the low and high molecular weight values ​​of the band, the band width value and the band tailing degree to get the protein change value of pre-prepared dishes. It should be noted that the protein of the pre-prepared dishes is extracted by protein extraction buffer, and through gel electrophoresis experiment, according to the molecular weight value in c1 and c2 markers, the area with molecular weight less than c1 in the gel electrophoresis experiment corresponds to the low molecular weight area, and the area greater than c2 corresponds to the high molecular weight area. The number of bands and the optical density integral value of the low molecular weight area and the high molecular weight area are counted and summed to get the low and high molecular weight values ​​of the band, where the optical density is The integral value is obtained by optical density analysis software. When the sharpness value on the band is lower than the set sharpness threshold, it corresponds to tailing. The distance from the end of the normal part of the band to the last position of the tailing identified by the cursor is counted and marked as the tailing length of the band. The tailing length of each band is counted and marked as the tailing degree of the band. The carbohydrate content refers to the reaction of distilled water and anthrone reagent in the pre-prepared dish product, and the absorbance is measured by spectrophotometer at a certain wavelength. The organic acid in the mixture of distilled water of the pre-prepared dish product is titrated with sodium hydroxide standard solution, and the consumption of sodium hydroxide is counted. The measured absorbance is added to the consumption of sodium hydroxide and multiplied by the correction factor coefficient to obtain the carbohydrate reduction value of the pre-prepared dish product. The microbial index refers to the total number of colonies, the number of coliform bacteria, and the content of pathogenic bacteria to obtain the bacteriopathic value of the pre-prepared dish product, among which the pathogenic bacteria are specifically Staphylococcus aureus, Salmonella, and Listeria monocytogenes.

[0050] Normalize the oil content and oxygen value of the pre-prepared dish product, the egg content and change value of the pre-prepared dish product, the carbohydrate content and fungal disease value of the pre-prepared dish product, use the oil content and oxygen value of the pre-prepared dish product as the length of the cuboid, the egg content and change value of the pre-prepared dish product as the width of the cuboid, and the carbohydrate content and change value of the pre-prepared dish product as the height of the cuboid. Build a sphere with the center point of the upper surface of the cuboid as the center of the sphere and the fungal disease value of the pre-prepared dish product as the radius of the sphere. Identify the irregular volume formed by the sphere and the cuboid, and mark it as the abnormal value of the pre-prepared dish product.

[0051] Compare the abnormal value of the pre-prepared meal product with the set comparison intervals CX1 and CX2, where the comparison interval CX1>CX2. When the abnormal value of the pre-prepared meal product is within the comparison interval CX1, the quality of the corresponding pre-prepared meal product is abnormal, and a pre-prepared meal safety and quality abnormality traceability signal is issued and sent to the raw material module, processing module, and storage and transportation module for raw material, processing, storage and transportation analysis of the pre-prepared meal. When the abnormal value of the pre-prepared meal product is within the comparison interval CX2, the quality of the corresponding pre-prepared meal product is normal and no corresponding operation is performed;

[0052] The raw material module receives raw material information and pre-prepared food safety and quality anomaly traceability signals, and analyzes the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials to obtain the safety and quality anomaly values ​​of the meat pre-prepared food raw materials. The specific analysis is as follows:

[0053] Step 1: 3D data of muscle fiber bundles of meat pre-cooked food ingredients is obtained through a 3D microscope, and 3D reconstruction of the muscle fiber bundles is performed to obtain the 3D shape of the muscle fiber bundles. The 3D shape of the fiber bundles is refined into a center line through a thinning algorithm, and the center line is marked as the skeleton of the fiber bundle. When the neighborhood of each point of the fiber bundle skeleton exceeds the connection in two directions, the point is determined to be a branch point. All points of the skeleton of each fiber bundle are traversed, and the total number of branch points is counted to obtain the muscle fiber bundle fractionation value;

[0054] Step 2: Obtain the angles between the branches of each branch point, and count the branch angles of all branch points in the muscle three-dimensional model to obtain the branch angle data, calculate the standard deviation of the branch angle data, and mark it as the muscle fiber bundle dispersion value. If the distance between adjacent fiber bundles is less than the set threshold, the two fiber bundles are classified into the same group. If the distance between a fiber bundle and each adjacent fiber bundle is greater than or equal to the set threshold, the fiber bundle is grouped separately, and each fiber bundle in the three-dimensional model is traversed to obtain the grouping status of the muscle fiber bundles. The number of independent groups is counted and marked as the muscle fiber bundle neighbor value. When the muscle fiber bundle neighbor value is smaller, the cell structure and muscle integrity can be better maintained during freezing and storage.

[0055] Step 3: The three-dimensional model of the muscle fiber bundle is fitted with a cylinder by the least squares method to obtain its central axis. The distance from each point on the fiber bundle to the central axis is obtained and marked as the median value of each point in the fiber bundle. The absolute value of the median difference between the adjacent points of the fiber bundle is counted. When the absolute value of the median difference is less than or equal to the set threshold, the adjacent points are classified into a concentric circle structure. The number of concentric circle structures in the muscle fiber bundle is counted, and the three-dimensional coordinates of the starting point of each concentric circle structure (xs n , ys n , zs n ) and the end point coordinates (xz n , yz n ,zz n ), n is the serial number of the concentric circle structure, n=1, 2, ..., N, N is the maximum serial number of the concentric circle structure, according to the formula , get the pitch value jj of each concentric circle structure n , according to the formula , the muscle fiber bundle cocircular distance value Txz is obtained, λ is the set correction factor coefficient, the distance from each point in the concentric circle structure to the central axis is obtained and the average is taken to obtain the median distance of the concentric circle structure, and the sum of the median distances of each concentric circle structure is divided by the number of concentric circle structures N to obtain the median distance of the muscle fiber bundle cocircular distance Jzz, the median distance of the muscle fiber bundle cocircular distance Jzz, the number of concentric circle structures N, and the muscle fiber bundle cocircular distance value Txz are normalized according to the set formula Tyz=(a2×N+a3×Txz) / [a1×(Jzz-A1) 2 +1.025], and calculate the muscle fiber bundle cocircular value Tyz, where a1, a2, and a3 are respectively the median of the muscle fiber bundle cocircular distance, the number of concentric circle structures, and the set weight factor coefficient of the muscle fiber bundle cocircular distance value. The specific values ​​are determined by professionals in this field. A1 is the set standard value of the muscle fiber bundle cocircular distance. It should be noted that the more concentric circle structures there are, the larger the muscle fiber bundle cocircular distance value is, the more uniform the force is when bearing, and the more it can increase the overall mechanical stability of the muscle;

[0056] Step 4: Divide the three-dimensional model of the muscle fiber bundle into several cubic units, which are marked as voxels. The ratio of the number of voxels occupied by the fiber bundle to the total number of voxels is counted and marked as the muscle fiber bundle space filling value. The muscle fiber bundle fractal value, muscle fiber bundle dispersion value, muscle fiber bundle neighbor value, and muscle fiber bundle space filling value are marked as fz, js, sl, and tc, respectively, and normalized with the muscle fiber bundle cocircularity value Tyz and substituted into the set formula The muscle fiber value (JQz) of meat pre-cooked dish ingredients was calculated, where th1, th2, th3, th4, th5, th6, th7, th8, and th9 are the set influencing factor coefficients. Specifically, th1>th6, th2>th7, th3>th8, th9>th5, and e is a natural constant with a value of 2.718. When the muscle fiber bundle space filling value is between 40% and 60%, the freezing process can reduce the damage of ice crystals to muscle tissue. At the same time, within this range, there are more gaps between muscle fiber bundles, which is conducive to water evaporation and air circulation, inhibiting the growth of microorganisms.

[0057] Obtain the sensory quality value, bioelectric special value, and protein carbonyl content in the meat pre-prepared dish raw material information, mark them as Gz, Tb, and Db, normalize them with the muscle fiber value JQz of the meat pre-prepared dish raw material, and substitute them into the set formula , the safety and quality abnormality values ​​AZZ of meat pre-prepared food ingredients are calculated, where g1, g2, g3, g4, g5, g6, g7, and g8 are all set weight factor coefficients, and g1>g5>g2>g6>g3>g7>g4>g8. njz refers to the cohesion index of the pre-prepared food ingredients. It can be seen from the formula that when the cohesion index of the pre-prepared food ingredients is greater than or equal to 70%, the internal structure is tight and the cohesion is good;

[0058] It should be noted that the sensory texture concentration value of the pre-prepared food raw material is obtained by subjecting the pre-prepared food raw material to two consecutive compression cycles using a texture analyzer, and respectively establishing compression curves of force versus displacement. The area under the second compression curve is divided by the area under the first compression curve to obtain the cohesiveness index of the pre-prepared food raw material. The maximum peak force in the first compression cycle is marked as the hardness peak of the pre-prepared food raw material, and the ratio of the height recovered by the sample after the first compression to the original height is calculated and marked as the spring height value of the pre-prepared food raw material. The cohesiveness index, hardness peak value and spring height value of the pre-prepared food raw material are added together to obtain the sensory texture concentration value of the pre-prepared food raw material. The bioelectric characteristic value of the sensory texture concentration value of the pre-prepared food raw material refers to the conductivity of the pre-prepared food raw material divided by the sum of the electrical impedance, capacitance value and charge transfer coefficient to obtain;

[0059] The processing module receives meat pre-prepared dish processing information and pre-prepared dish safety and quality anomaly traceability signals, and analyzes the processing environment, preservatives, and cleaning status of the meat pre-prepared dish to obtain the meat pre-prepared dish spoilage residual value. The specific analysis is as follows:

[0060] The processing information of meat pre-prepared dishes is obtained by obtaining the processing ring micro-residue value, preservative dosage, and meat clear value, and marking them as jhc, ffj, and rqg respectively. They are normalized according to the formula JFZ=[h1×jhc+h2×(ffj-A2)] 2] / (h3×rqg), and the spoilage residual value JFZ of pre-prepared meat dishes is calculated, where h1, h2 and h3 are the set influencing factor coefficients of the processing ring micro-residue value, the amount of preservatives used, and the meat clear value, respectively, and A2 is the set reference amount of preservatives used;

[0061] It should be noted that the micro-residue value of the meat pre-prepared dish processing ring refers to the sum of the diversity value of the microbial community in the processing environment, the average particle size of the residual oil aggregates on the surface of the processing environment, the particle size distribution width of the residual oil aggregates, and the order of the residual oil molecular layer, divided by the concentration of negative air ions, wherein the diversity value of the microbial community in the processing environment is the maximum percentage difference in nucleotides between different individuals of the microbial community in the processing environment; the meat clearness value refers to the glossiness and cleanliness of the surface of the meat pre-prepared dish after cleaning divided by the arithmetic mean roughness. The cleanliness is obtained by graying the collected surface image of the cleaned meat pre-prepared dish, dividing the surface grayscale image into several areas, and when the grayscale value is greater than the set grayscale threshold, it corresponds to the meat part, and the regional quantity proportion of the meat part is counted;

[0062] The storage and transportation module receives the storage and transportation information of prepared meat dishes and the traceability signal of abnormal quality of prepared meat dishes, and analyzes the storage and transportation of abnormal prepared meat dishes to obtain the abnormal value of storage and transportation of prepared meat dishes. The specific analysis is as follows:

[0063] Obtain the storage abnormality value, transportation vibration impulse value, and transportation temperature control abnormality value from the storage and transportation information of prepared meat dishes, marked as cyz, yzc, and kyw, respectively. Calculate the meat pre-prepared dish storage and transportation abnormality value CYZ according to the set formula CYZ=t1×cyz+t2×yzc+t3×kyw, where t1, t2, and t3 are all set weight factor coefficients. The specific value range is determined by professionals in this field.

[0064] It should be noted that the storage abnormality value of meat pre-prepared dishes is obtained by dividing the humidity of the storage environment by the ambient temperature and the sum of the bag damage value. The bag damage value refers to the total value of damage, cracks and pinholes in the entire bag. The transportation vibration impact value is obtained by adding the vibration acceleration, impact number and impact intensity of the meat pre-prepared dishes during transportation. The transportation temperature control abnormal stability value refers to the temperature fluctuation amplitude of the meat pre-prepared dishes within a certain period of time during transportation.

[0065] The judgment module receives the abnormal values ​​of safety and quality of pre-prepared meat ingredients, the residual value of pre-prepared meat, and the abnormal values ​​of storage and transportation of pre-prepared meat, and analyzes the pre-prepared food processing process to obtain abnormal processes that affect pre-prepared meat food problems. The specific analysis is as follows:

[0066] Normalize the meat pre-prepared dish raw material safety and quality abnormality value AZZ, meat pre-prepared dish spoilage residual value JFZ, and meat pre-prepared dish storage and transportation abnormality value CYZ. According to the set formula model YYZ=Ψ×AZZ / (JFZ+CYZ), the meat pre-prepared dish raw material abnormality value YYZ is obtained. Ψ is the correction factor coefficient. When the meat pre-prepared dish raw material abnormality value is greater than or equal to the set raw material abnormality threshold, it corresponds to the meat pre-prepared dish safety and quality abnormality due to the meat pre-prepared dish raw material abnormality. When the meat pre-prepared dish raw material abnormality value is less than When the raw material abnormality threshold is set, the set formula JYZ=Y×JFZ / CYZ is used to obtain the meat pre-prepared dish abnormality value JYZ, where Y is the correction factor coefficient. When the meat pre-prepared dish abnormality value is greater than or equal to the set pre-prepared dish abnormality threshold, the corresponding meat pre-prepared dish safety and quality abnormality is due to an abnormality in the meat pre-prepared dish processing process. When the meat pre-prepared dish abnormality value is less than the set pre-prepared dish abnormality threshold, the corresponding meat pre-prepared dish safety and quality abnormality is due to an abnormality in the meat pre-prepared dish storage and transportation process.

[0067] The food safety traceability detection method based on the Internet of Things includes the following steps:

[0068] S1: The data acquisition module collects information on the finished product, raw materials, processing, storage and transportation of pre-prepared meat dishes, and sends it to the finished product module, raw materials module, processing module and storage and transportation module;

[0069] S2: The finished product module analyzes the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of the meat pre-prepared dish products, determines the quality and safety of the meat pre-prepared dish products, obtains the abnormal value of the pre-prepared dish products, and compares it with the set comparison range, and issues a traceability signal for the abnormal quality of the pre-prepared dish.

[0070] S3: The raw material module receives raw material information and pre-prepared food safety and quality anomaly traceability signals, and conducts judgment and analysis on the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials to obtain the safety and quality anomaly values ​​of the meat pre-prepared food raw materials;

[0071] S4: The processing module receives the meat pre-prepared dish processing information and the pre-prepared dish safety and quality anomaly traceability signal, and analyzes the meat pre-prepared dish processing environment, preservatives, and pre-prepared dish cleaning status to obtain the meat pre-prepared dish spoilage residual value;

[0072] S5: The storage and transportation module receives the storage and transportation information of prepared meat dishes and the abnormal traceability signal of the safety and quality of the prepared meat dishes, performs judgment and analysis on the storage and transportation of abnormal prepared meat dishes, and obtains the abnormal value of storage and transportation of the prepared meat dishes;

[0073] S6: The judgment module receives the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes, and the abnormal values ​​of storage and transportation of pre-prepared meat dishes, and analyzes the pre-prepared dish processing of each process to obtain abnormal processes that affect the food problems of pre-prepared meat dishes.

[0074] The above is an illustration of the present invention and should not be considered as limiting thereof. Although several exemplary embodiments of the present invention have been described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the above is an illustration of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A food safety traceability detection method based on the Internet of Things, characterized in that: The following steps are involved: S1: Collect information on finished meat dishes, raw materials, processing, storage and transportation; S2: Analyze the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of the finished meat pre-prepared dishes, obtain abnormal values ​​of the finished pre-prepared dishes, compare them with the set comparison range, and issue a traceability signal for abnormal quality of the pre-prepared dishes; The steps for analyzing outliers of the prepared meal products are as follows: Normalize the oil content and oxygen value of the pre-prepared dish product, the egg content and change value of the pre-prepared dish product, the carbohydrate content and fungal disease value of the pre-prepared dish product, use the oil content and oxygen value of the pre-prepared dish product as the length of the cuboid, the egg content and change value of the pre-prepared dish product as the width of the cuboid, and the carbohydrate content and change value of the pre-prepared dish product as the height of the cuboid. Build a sphere with the center point of the upper surface of the cuboid as the center of the sphere and the fungal disease value of the pre-prepared dish product as the radius of the sphere. Identify the irregular volume formed by the sphere and the cuboid, and mark it as the abnormal value of the pre-prepared dish product. S3: Receives raw material information and pre-prepared food safety and quality anomaly traceability signals, conducts judgment and analysis on the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials, and obtains the safety and quality anomaly values ​​of the meat pre-prepared food raw materials; S4: Receive meat pre-prepared dish processing information and pre-prepared dish safety and quality anomaly traceability signals, determine and analyze the meat pre-prepared dish processing environment, preservatives, and pre-prepared dish cleaning status, and obtain the meat pre-prepared dish spoilage residual value; S5: Receive the storage and transportation information of prepared meat dishes and the abnormal traceability signal of the safety and quality of the prepared meat dishes, conduct judgment and analysis on the storage and transportation of abnormal prepared meat dishes, and obtain the abnormal value of storage and transportation of prepared meat dishes; S6: Receive the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes, and the abnormal values ​​of storage and transportation of pre-prepared meat dishes, and analyze the pre-prepared dish processing in each process to obtain the abnormal processes that affect the food problems of pre-prepared meat dishes.

2. The food safety traceability detection method based on the Internet of Things according to claim 1, characterized in that: The steps for analyzing the signal for tracing the source of abnormalities in the safety and quality of prepared dishes are as follows: Compare the abnormal value of the pre-prepared food product with the set comparison intervals CX1 and CX2. When the abnormal value of the pre-prepared food product is in the comparison interval CX1, the quality of the corresponding pre-prepared food product is abnormal, and a pre-prepared food safety and quality abnormality traceability signal is issued. When the abnormal value of the pre-prepared food product is in the comparison interval CX2, the quality of the corresponding pre-prepared food product is normal.

3. The food safety traceability detection method based on the Internet of Things according to claim 2, characterized in that: The steps for analyzing the oil oxidation value, egg change value, carbohydrate loss value and fungal disease value of the prepared dish finished product are as follows: The oil chemical composition is the oil peroxide value, oil acid value and oil carbonyl value of the meat pre-prepared dish product to obtain the oil peroxide value of the pre-prepared dish product. The protein structure refers to the sum of the low and high molecular weight values ​​of the band, the band width value and the band tailing degree to obtain the protein change value of the pre-prepared dish product. The protein of the pre-prepared dish product is extracted by protein extraction buffer, and the gel electrophoresis experiment is carried out. According to the molecular weight value of the c1 and c2 markers, the area with a molecular weight less than c1 in the gel electrophoresis experiment corresponds to the low molecular weight area, and the area greater than c2 corresponds to the high molecular weight area. The number of bands and the optical density integral value of the low molecular weight area and the high molecular weight area are counted and summed to obtain the low and high molecular weight values ​​of the band. The sharpness value on the band is lower than the set value. When the sharpness threshold is set, it corresponds to tailing. The distance from the end of the normal part of the strip to the last position of the tail identified by the cursor is counted and marked as the tailing length of the strip. The tailing length of each strip is counted and marked as the tailing degree of the strip. The carbohydrate content refers to the reaction of distilled water and anthrone reagent in the pre-prepared dish product, and the absorbance is measured by a spectrophotometer at a certain wavelength. The organic acid in the mixture of distilled water of the pre-prepared dish product is titrated with a standard sodium hydroxide solution, and the consumption of sodium hydroxide is counted. The measured absorbance is added to the consumption of sodium hydroxide, and multiplied by the correction factor coefficient to obtain the carbohydrate reduction value of the pre-prepared dish product. The microbial index refers to the total number of colonies, the number of coliform bacteria, and the content of pathogenic bacteria to obtain the bacteriopathic value of the pre-prepared dish product.

4. The food safety traceability detection method based on the Internet of Things according to claim 1, characterized in that: The steps for analyzing the safety and quality abnormalities of the raw materials of prepared meat dishes are as follows: Obtain the sensory quality value, bioelectric special value, and protein carbonyl content in the meat pre-prepared dish raw material information, mark them as Gz, Tb, and Db, normalize them with the muscle fiber value JQz of the meat pre-prepared dish raw material, and substitute them into the set formula , the safety and quality abnormality values ​​AZZ of meat pre-prepared food ingredients are calculated, where g1, g2, g3, g4, g5, g6, g7 and g8 are all set weight factor coefficients, and njz refers to the cohesion index of pre-prepared food ingredients.

5. The food safety traceability detection method based on the Internet of Things according to claim 4 is characterized in that: The steps for analyzing the muscle fiber value of the raw materials of the prepared meat dishes are as follows: Step 4: Divide the three-dimensional model of the muscle fiber bundle into several cubic units, which are marked as voxels. The ratio of the number of voxels occupied by the fiber bundle to the total number of voxels is counted and marked as the muscle fiber bundle space filling value. The muscle fiber bundle fractal value, muscle fiber bundle dispersion value, muscle fiber bundle neighbor value, and muscle fiber bundle space filling value are marked as fz, js, sl, and tc, respectively, and normalized with the muscle fiber bundle cocircularity value Tyz and substituted into the set formula The calculation is performed to obtain the muscle fiber value JQz of the raw materials for pre-prepared meat dishes, where th1, th2, th3, th4, th5, th6, th7, th8 and th9 are the set influencing factor coefficients, and e is a natural constant.

6. The food safety traceability detection method based on the Internet of Things according to claim 5 is characterized in that: The steps for analyzing the cocircularity value of muscle fiber bundles are as follows: Step 3: The three-dimensional model of the muscle fiber bundle is fitted with a cylinder by the least squares method to obtain its central axis. The distance from each point on the fiber bundle to the central axis is obtained and marked as the median value of each point in the fiber bundle. The absolute value of the median difference between the adjacent points of the fiber bundle is counted. When the absolute value of the median difference is less than or equal to the set threshold, the adjacent points are classified into a concentric circle structure. The number of concentric circle structures in the muscle fiber bundle is counted, and the three-dimensional coordinates of the starting point of each concentric circle structure (xs n , ys n , zs n ) and the end point coordinates (xz n , yz n ,zz n ), n is the serial number of the concentric circle structure, according to the formula, the pitch value jj of each concentric circle structure is obtained n According to the formula, the co-circular distance value Txz of the muscle fiber bundle is obtained, the distance from each point in the concentric circle structure to the central axis is obtained and the average value is taken to obtain the median distance of the concentric circle structure, and the sum of the median distances of each concentric circle structure is counted and divided by the number of concentric circle structures N to obtain the median co-circular distance of the muscle fiber bundle Jzz. The median co-circular distance of the muscle fiber bundle Jzz, the number of concentric circle structures N, and the co-circular distance value Txz of the muscle fiber bundle are calculated to obtain the co-circular value Tyz of the muscle fiber bundle.

7. The food safety traceability detection method based on the Internet of Things according to claim 5, characterized in that: The steps for analyzing the muscle fiber bundle fractionation value, the muscle fiber bundle dispersion value, and the muscle fiber bundle neighbor value are as follows: Step 1: 3D data of muscle fiber bundles of meat pre-cooked food ingredients is obtained through a 3D microscope, and 3D reconstruction of the muscle fiber bundles is performed to obtain the 3D shape of the muscle fiber bundles. The 3D shape of the fiber bundles is refined into a center line through a thinning algorithm, and the center line is marked as the skeleton of the fiber bundle. When the neighborhood of each point of the fiber bundle skeleton exceeds the connection in two directions, the point is determined to be a branch point. All points of the skeleton of each fiber bundle are traversed, and the total number of branch points is counted to obtain the muscle fiber bundle fractionation value; Step 2: Obtain the angles between the branches of each branch point, and count the branch angles of all branch points in the muscle three-dimensional model to obtain the branch angle data, calculate the standard deviation of the branch angle data, and mark it as the muscle fiber bundle dispersion value. If the distance between adjacent fiber bundles is less than the set threshold, the two fiber bundles are classified into the same group. If the distance between a fiber bundle and each adjacent fiber bundle is greater than or equal to the set threshold, the fiber bundle is grouped together separately, and the fiber bundles in the three-dimensional model are traversed to obtain the grouping status of the muscle fiber bundles, and the number of independent groups is counted, which is marked as the muscle fiber bundle neighbor value.

8. The food safety traceability detection method based on the Internet of Things according to claim 1, characterized in that: The steps for analyzing abnormal processes that affect prepared meat food problems are as follows: The meat pre-prepared dish raw material safety and quality abnormality value AZZ, the meat pre-prepared dish added spoilage residual value JFZ, and the meat pre-prepared dish storage and transportation abnormality value CYZ are calculated according to the formula to obtain the meat pre-prepared dish raw material abnormality value YYZ. When the meat pre-prepared dish raw material abnormality value is greater than or equal to the set raw material abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormal meat pre-prepared dish raw materials. When the meat pre-prepared dish raw material abnormality value is less than the set raw material abnormality value threshold, it means that the meat pre-prepared dish added abnormality value JYZ is calculated according to the formula. When the meat pre-prepared dish added abnormality value is greater than or equal to the set pre-prepared dish added abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormality in the meat pre-prepared dish processing process. When the meat pre-prepared dish added abnormality value is less than the set pre-prepared dish added abnormality value threshold, it means that the meat pre-prepared dish safety and quality abnormality is due to abnormality in the meat pre-prepared dish storage and transportation process.

9. The food safety traceability detection method based on the Internet of Things according to claim 8, characterized in that: The steps for analyzing the residual value of pre-prepared meat dishes are as follows: The processing information of meat pre-prepared dishes is obtained by obtaining the processing ring micro-residue value, preservative dosage, and meat clear value, and marking them as jhc, ffj, and rqg respectively. They are normalized according to the formula JFZ=[h1×jhc+h2×(ffj-A2)] 2 ] / (h3×rqg), and the residual value of meat pre-prepared dishes plus spoilage JFZ is calculated, where h1, h2 and h3 are the set influencing factor coefficients, and A2 is the set reference amount of preservatives; The steps for analyzing the outliers in the storage and transportation of prepared meat dishes are as follows: The storage abnormality value, transportation vibration impulse value, and transportation temperature control abnormality value in the storage and transportation information of pre-prepared meat dishes are obtained, marked as cyz, yzc, and kyw respectively. According to the set formula CYZ=t1×cyz+t2×yzc+t3×kyw, the storage and transportation abnormality value CYZ of pre-prepared meat dishes is calculated, where t1, t2, and t3 are all set weight factor coefficients.

10. The food safety traceability detection system based on the Internet of Things is characterized by: The method for food safety traceability detection based on the Internet of Things is applied to implement any one of claims 1 to 9, comprising: The data acquisition module is used to collect information about the finished product, raw materials, processing, storage and transportation of pre-prepared meat dishes, and send it to the finished product module, raw materials module, processing module and storage and transportation module; The finished product module is used to analyze the oil and fat chemical composition, protein structure, carbohydrate content, and microbial indicators of meat pre-prepared dishes, obtain abnormal values ​​of the pre-prepared dishes, compare them with the set comparison range, and issue a traceability signal for abnormal quality of the pre-prepared dishes; The raw material module is used to receive raw material information and pre-prepared food safety and quality anomaly traceability signals, and conduct judgment and analysis on the muscle fiber bundles, sensory status, bioelectric characteristics, and protein carbonyl groups of the meat pre-prepared food raw materials to obtain the safety and quality anomaly values ​​of the meat pre-prepared food raw materials; The processing module is used to receive the processing information of the prepared meat dishes and the traceability signal of the safety and quality anomalies of the prepared meat dishes, judge and analyze the processing environment, preservatives, and cleaning status of the prepared meat dishes, and obtain the residual value of the prepared meat dishes; The storage and transportation module is used to receive the storage and transportation information of prepared meat dishes and the traceability signal of abnormal quality of prepared meat dishes, conduct judgment and analysis on the storage and transportation of abnormal prepared meat dishes, and obtain the abnormal value of storage and transportation of prepared meat dishes; The judgment module is used to receive the abnormal values ​​of safety and quality of raw materials of pre-prepared meat dishes, the residual value of pre-prepared meat dishes, and the abnormal values ​​of storage and transportation of pre-prepared meat dishes, and analyze the pre-prepared dish processing in each process to obtain abnormal processes that affect the food problems of pre-prepared meat dishes.

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