Data processing method for food safety

Abstract

The invention relates to the technical field of data processing, in particular to a data processing method for food safety. The method comprises the following steps: acquiring water quality data, pesticide residue data and microbial indicators; obtaining a water quality safety factor according to the comparison of the detected water quality and the standard water quality, and obtaining a residue safety factor according to the comparison of the detected pesticide residue and the existing standard pesticide residue; obtaining microorganism existence coefficients according to the microorganism indexes, and obtaining microorganism potential WeChat factors according to all microorganism existence coefficients of the same kind of food raw materials; acquiring a microorganism correction factor according to the microorganism potential factors before and after processing; according to the residual safety coefficient before and after processing and the potential risk factors of microorganisms, the self-drug overcoming coefficient is obtained, the production safety coefficient is obtained based on the self-drug overcoming coefficient, and food safety is guaranteed according to the production safety coefficient. According to the invention, the accuracy and real-time performance of food production safety evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a data processing method for food safety. Background Art

[0002] Food safety means that food is non-toxic, harmless, meets the required nutritional requirements, and does not cause any acute, sub-acute or chronic harm to human health. Food raw materials should be subjected to laboratory tests before processing. If any abnormal indicators related to food safety items are found during the tests, they shall not be used, and only the food raw materials that are determined to be applicable shall be used. Incoming inspection is the first process of food production. Most food manufacturers do not conduct incoming inspections for each batch, but conduct spot checks on products of the same batch. The guarantee of food quality and safety mainly relies on process control, process control and the quality control of raw and auxiliary materials.

[0003] Currently, the existing big data processing technology has the problem of low accuracy in food production safety. Since food safety is affected by many factors, a large amount of data on food safety of various food raw materials will be obtained during spot checks. Directly extracting data from a large amount of food raw material data may lead to a large amount of calculations, thereby affecting the efficiency and accuracy of evaluation. The evaluation results obtained may also be different from the actual situation, lacking reliability and predictability. And currently, its risk control for food safety mainly focuses on post-event control. This control method is not only passive but also has low efficiency, so it cannot fundamentally solve the food safety problem. Summary of the Invention

[0004] In order to solve the technical problem that the evaluation results are different from the actual situation, the present invention provides a data processing method for food safety, and the specific technical solutions adopted are as follows:

[0005] The present invention proposes a data processing method for food safety, and the method includes the following steps:

[0006] Obtain the water quality data of the water quality samples to be tested, and spot check the pesticide residue data and microbial indicators of food raw materials;

[0007] Obtain a water quality data vector and a standard vector according to the water quality data of the water quality samples to be tested; obtain the water quality safety factor of the water quality samples to be tested according to the Manhattan distance between the water quality data vector and the standard vector; obtain the residual safety coefficient of each spot-checked food raw material according to the pesticide residue data of the spot-checked food raw materials;

[0008] Obtain the microbial presence coefficient of each microorganism in each sampled food raw material according to the microbial indicators, and obtain the microbial potential risk factor of each sampled food raw material according to the microbial presence coefficients of all microorganisms in each sampled food raw material and its same-kind sampled food raw materials; obtain the microbial correction factor of the sampled food raw material according to the microbial potential risk factors before and after processing of each sampled food raw material.

[0009] Obtain the self-drug overcoming coefficient of the sampled food raw material according to the residual safety coefficient and the microbial potential risk factor of the sampled food raw material before and after processing; obtain the production safety coefficient of the same-kind food raw materials according to the self-drug overcoming coefficients, residual safety coefficients, microbial potential risk factors, and microbial correction factors of all sampled food raw materials of the same kind.

[0010] Obtain the food safety coefficient of all food raw materials in the same batch according to the production safety coefficient of the same-kind food raw materials and the water quality safety factor. If the food safety coefficient is within the food safety range, it meets the food safety requirements.

[0011] Preferably, the method for obtaining the water quality data vector and the standard vector according to the water quality data of the submitted water quality sample is as follows:

[0012] Obtain the detection indicators in the submitted water quality sample and the range of each detection indicator through the Hygienic Standard for Drinking Water. Form a vector by taking the upper limit values of the range of all detection indicators in the submitted water quality sample and denote it as the upper limit vector. Form a vector by taking the lower limit values of the range of all detection indicators in the submitted water quality sample and denote it as the lower limit vector. The upper limit vector and the lower limit vector are collectively referred to as the standard vector; form a vector by the detection results of all detection indicators in the submitted water quality sample and denote it as the water quality data vector.

[0013] Preferably, the method for obtaining the water quality safety factor of the submitted water quality sample according to the Manhattan distance between the water quality data vector and the standard vector is as follows:

[0014] Denote the Manhattan distance between the upper limit vector and the lower limit vector as the first Manhattan distance, denote the Manhattan distance between the upper limit vector and the water quality data vector as the second Manhattan distance, denote the Manhattan distance between the lower limit vector and the water quality data vector as the third Manhattan distance, denote the sum of the second Manhattan distance and the third Manhattan distance as the first distance sum, and denote the ratio of the first Manhattan distance to the first distance sum as the first ratio;

[0015] Denote the number of detection indicators within the range in the water quality data vector as the first number, and denote the ratio of the first number to the number of all detection indicators in the water quality data vector as the second ratio;

[0016] Denote the product of the first ratio and the second ratio as the water quality safety factor.

[0017] Preferably, the method for obtaining the residual safety coefficient of each sampled food raw material based on the pesticide residue data of the sampled food raw materials is as follows:

[0018] Obtain the types of drug indicators to be detected for each sampled food raw material according to the Maximum Residue Limits of Pesticides in Foods;

[0019] Denote any one sampled food raw material as the target food raw material, denote all sampled food raw materials of the same type as the target food raw material as the target category, denote the average value of the pesticide residue content of the j-th drug indicator of all sampled food raw materials in the target category as the target average value, and denote the difference between the pesticide residue content of the j-th drug indicator of the target food raw material and the target average value as the first pesticide residue difference;

[0020] Denote the difference between the maximum pesticide residue content of the j-th drug indicator of all sampled food raw materials in the target category and the maximum value of the standard pesticide residue content of the j-th drug indicator as the second pesticide residue difference;

[0021] If the first pesticide residue difference is less than zero, the first residue factor is 0; if the first residue difference is greater than or equal to zero, the first residue factor is 0.5; obtain the second residue factor by the method for obtaining the first residue factor; denote the sum of the first residue factor and the second residue factor as the first residue sum, and accumulate the first residue sums of all drug indicators to obtain the residual safety coefficient of the target food raw material.

[0022] Preferably, the method for obtaining the microbial presence coefficient of each type of microorganism in each sampled food raw material based on the microbial indicators is as follows:

[0023] The microbial indicators include the colony count and colony area of each type of microorganism;

[0024] Denote the ratio of the colony area of all colonies of each type of microorganism to the colony count as the average colony area of each type of microorganism, denote the absolute value of the difference between the colony area of each colony of each type of microorganism and the average colony area as the first area difference, and denote the accumulated sum of the first area differences of all colonies as the microbial presence coefficient of each type of microorganism.

[0025] Preferably, the method for obtaining the microbial potential hazard factor of each sampled food raw material based on the microbial presence coefficients of all types of microorganisms in the sampled food raw materials of the same type as the sampled food raw material is as follows:

[0026] Construct a set of microbial presence coefficients for all microorganisms in each randomly inspected food raw material. Obtain the maximum presence coefficient and the minimum presence coefficient within the set of microbial presence coefficients. Denote the difference between the microbial presence coefficient of each microorganism and the minimum presence coefficient as the first coefficient difference, the difference between the maximum presence coefficient and the minimum presence coefficient as the second coefficient difference, the ratio of the first coefficient difference to the second coefficient difference as the first coefficient ratio, and calculate the mean of all the first coefficient ratios of the randomly inspected food raw materials to obtain the microbial potential risk factor of the randomly inspected food raw materials.

[0027] Preferably, the method for obtaining the microbial correction factor of the randomly inspected food raw materials based on the microbial potential risk factors before and after processing of each randomly inspected food raw material is as follows:

[0028] For the same randomly inspected food raw material, denote the microbial potential risk factor obtained from the initial microbial index as the microbial potential risk factor before processing. After the randomly inspected food raw material is processed, use the same method to obtain its microbial potential risk factor after processing;

[0029] Respectively form a pre - processing risk factor sequence and a post - processing risk factor sequence for the microbial potential risk factors before and after processing of all randomly inspected food raw materials; calculate the DTW distance between the pre - processing risk factor sequence and the post - processing risk factor sequence; denote the absolute value of the difference between the microbial potential risk factor before processing and the microbial potential risk factor after processing of the randomly inspected food raw material as the potential risk difference, and denote the mean of the potential risk differences of all randomly inspected food raw materials as the mean potential risk difference; denote the ratio of the potential risk difference of each randomly inspected food raw material to the mean potential risk difference as the first risk ratio, and denote the value obtained by linearly normalizing the product of the DTW distance and the first risk ratio as the microbial correction factor of the randomly inspected food raw material.

[0030] Preferably, the method for obtaining the self - drug overcoming coefficient of the randomly inspected food raw materials based on the residual safety coefficients and microbial potential risk factors before and after processing of the randomly inspected food raw materials is as follows:

[0031]

[0032] In the formula, YC k,p represents the residual safety coefficient of the k - th randomly inspected food raw material before processing, YC k,q represents the residual safety coefficient of the k - th randomly inspected food raw material after processing, n i represents the number of randomly inspected food raw materials of the same type as the i - th randomly inspected food raw material, represents the microbial potential risk factor of the i - th randomly inspected food raw material before processing, Denote the microbial potential risk factor of the i-th sampled food raw material after processing, exp() represents the exponential function with the natural constant as the base, Norm() represents the linear normalization function, and ZK i Denote the self-drug overcoming coefficient of the i-th sampled food raw material.

[0033] Preferably, the method for obtaining the production safety coefficient of food raw materials of the same type based on the self-drug overcoming coefficient, residual safety coefficient, microbial potential risk factor, and microbial correction factor of all sampled food raw materials of the same type is as follows:

[0034]

[0035] In the formula, YC i Denote the residual safety coefficient of the i-th sampled food raw material, ZK i Denote the self-drug overcoming coefficient of the i-th sampled food raw material, WCS i Denote the microbial potential risk factor of the i-th sampled food raw material, WX i Denote the microbial correction factor of the i-th sampled food raw material, n a Denote the number of sampled food raw materials in the a-th type of food raw material, Norm() represents the linear normalization function, SZ a Denote the production safety coefficient of the a-th type of food raw material.

[0036] Preferably, the method for obtaining the food safety coefficient of all food raw materials in the same batch based on the production safety coefficient of food raw materials of the same type and the water quality safety factor is as follows:

[0037] Obtain the proportion of each type of food raw material when producing tea beverages, denoted as the production proportion. Denote the product of the production proportion of each type of food raw material and the production safety coefficient as the first safety value. Accumulate the first safety values of all food raw materials to obtain the food raw material safety value. Add the food raw material safety value and the water quality safety factor to obtain the food safety coefficient.

[0038] The present invention has the following beneficial effects: The present invention considers analyzing the food raw materials to be processed. First, analyze the water quality data to obtain the water quality safety factor; then analyze the drug index and microbial index of plant food raw materials, construct the residual safety coefficient and microbial potential risk factor, combine the influence of the environment on microorganisms and drugs, integrate the mutual influence between microorganisms and drugs, construct the microbial correction factor and self-drug overcoming coefficient, improve the predictability and reliability of data; finally, obtain the production safety coefficient of various food raw materials, and combine the ingredient proportion of various food raw materials to obtain the production safety coefficient of food raw materials in each batch, improving the accuracy and timeliness of food production safety evaluation. Brief Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for description in the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 Flowchart of a data processing method for food safety provided by an embodiment of the present invention;

[0041] Figure 2 Implementation flowchart of a data processing method for food safety provided by an embodiment of the present invention. Detailed Embodiments

[0042] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific embodiments, structures, features and effects of a data processing method for food safety proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0044] An embodiment of a data processing method for food safety:

[0045] The following specifically describes the specific solution of a data processing method for food safety provided by the present invention in conjunction with the drawings.

[0046] Please refer to Figure 1 , which shows a flowchart of a data processing method for food safety provided by an embodiment of the present invention. The method includes the following steps:

[0047] Step S001, obtain the water quality data of the water samples to be inspected, and sample the pesticide residue data and microbial indicators of food raw materials.

[0048] In this embodiment, the treatment of food safety is exemplified by tea beverages. The food raw materials of tea beverages include water and related plant-based food raw materials. A part of the food raw materials is randomly selected from the food raw materials of the same batch as the sampled food raw materials. By sending the drinking water used to produce tea beverages to a laboratory for testing, water quality data of the drinking water is obtained. The water quality data detected includes chemical substance contents such as pH value, iron ion content, and permanent hardness. The detected drinking water is recorded as the water quality sample to be tested. And since the food raw materials of tea beverages are generally artificially cultivated, pesticides are inevitably used for treatment. In addition, due to various ways of contact between plants and the environment, plants are thus infected by microorganisms. Therefore, the sampled food raw materials are sent to a laboratory for pesticide residue detection and microorganism detection to obtain the pesticide residue data and microorganism indicators of the food raw materials.

[0049] So far, the water quality data of the water quality sample to be tested, the pesticide residue data and microorganism indicators of the sampled food raw materials have been obtained.

[0050] Step S002: Obtain a water quality data vector and a standard vector according to the water quality data of the water quality sample to be tested; obtain the water quality safety factor of the water quality sample to be tested according to the Manhattan distance between the water quality data vector and the standard vector; obtain the residue safety coefficient of each sampled food raw material according to the pesticide residue data of the sampled food raw materials.

[0051] By comparing the vector composed of the water quality data of the drinking water with the national standard to calculate the water quality safety factor, the water quality safety factor can represent the safety of the drinking water. By comparing the pesticide residue data of the food raw materials with the national standard to obtain the residue safety coefficient, the residue safety coefficient can represent the safety of the food raw materials in terms of pesticide residues. The food safety can be further determined through the safety of the drinking water and pesticide residues.

[0052] Considering that water is the main component of tea beverages and its quality has a great impact on tea beverages. Generally speaking, ions such as calcium, magnesium, iron, and chlorine in water affect the color and taste of the tea soup, and will make the tea beverage turbid and form tea cream. When the iron ion content in water is greater than 5 ppm (5 parts per million), the tea soup will appear black and have a bitter taste; when the chlorine ion content is high, the tea soup will have a putrid smell. The plant tannins in tea leaves can react with various metal ions and generate various colors. Therefore, water quality is crucial for the production safety of tea beverages. To produce tea beverages with better quality, purified water with removed ions must be used, and its pH value, iron ions, and the content of chemical substances with permanent hardness will be within the specified range. The water quality data includes several detection indicators. The upper limit values of the specified range of different detection indicators form an upper limit vector; the lower limit values form a lower limit vector. The specified range and the detection indicators included in the water quality data are determined by the "Hygienic Standard for Drinking Water" (GB5749-2006).

[0053] The water quality data vector composed of the measured values of all the test indicators of the submitted water quality samples, the upper limit vector and the lower limit vector composed of all the test indicators are obtained. According to the Manhattan distances between the upper limit vector, the lower limit vector and the water quality data vector pairwise, and the number of elements of the test indicators in the water quality data vector within the interval range, the water quality safety factor of the submitted water quality samples is obtained. The formula is as follows:

[0054]

[0055] In the formula, W u represents the upper limit vector, W d represents the lower limit vector, W0 represents the water quality data vector, MH() represents the Manhattan distance between two vectors, count(W0) represents the number of the measured values of the test indicators in the water quality data vector within the interval range, n represents the number of the test indicators in the water quality data vector, and WS represents the water quality safety factor.

[0056] The Manhattan distance between the upper limit vector and the lower limit vector is used to characterize the specified difference range of the water quality data. Then, the sum of the Manhattan distances between the water quality data vector and the upper limit vector and the lower limit vector respectively is used to overall measure the difference between the water quality data vector and the specified interval range. The two are compared. If all the elements of the water quality data vector are within the specified interval range, then the value is 1 and the water quality safety factor is 1; if there are elements in the water quality data vector that are not within the specified interval range, indicating that the contents of multiple substances in the water exceed the standard, then the water quality safety factor is less than 1.

[0057] In addition to the influence of drinking water on tea beverages, the taste and functions of tea beverages are mainly provided by plant food raw materials. During the production and acquisition process of plants, in order to increase the yield or resist pests and diseases, farmers use a large amount of pesticides and fertilizers. Generally speaking, if pesticides and fertilizers are used according to the specified dosage, there will be no pesticide residues. However, if they are used in excess, there will be pesticide residues in the plants. The pesticide residues are recorded as pesticide residues.

[0058] For several sampled food raw materials, the pesticide residues of different types of drugs are obtained. During sampling, multiple samples of the same food raw material will be sampled. Through the "Maximum Residue Limits of Pesticides in Foods" (GB 2763), the types of drugs that need to be detected for the food raw materials during sampling and the maximum values of the pesticide residues of the detected drug types can be obtained. The maximum value of the pesticide residue content of each drug type recorded in the national standard is recorded as the maximum standard pesticide residue content. According to the difference between the pesticide residue content of each sampled food raw material and the average pesticide residue content of the food raw material type, and the difference between the maximum pesticide residue content of the food raw material type and the maximum standard pesticide residue content, the residue safety factor of each sampled food raw material is obtained. The formula is as follows:

[0059]

[0060]

[0061] Wherein, y i,j represents the pesticide residue content of the j-th drug index of the i-th sampled food raw material, represents the average value of the pesticide residue content of the j-th drug index in the type of food raw material corresponding to the i-th sampled food raw material, represents the maximum pesticide residue content of the j-th drug index in the type of food raw material corresponding to the i-th sampled food raw material, y j (i) max represents the maximum value of the standard pesticide residue content of the j-th drug index in the type of food raw material corresponding to the i-th sampled food raw material, δ(x) represents the residue factor, n represents the number of sampled drug indices, YC i represents the residue safety factor of the i-th sampled food raw material.

[0062] Among them, when the pesticide residue content of each drug index in the food raw material is less than the average pesticide residue content in the same kind of food raw material, the greater the possibility that the pesticide residue content of this drug index of this food raw material is a normal content; at the same time, when the maximum pesticide residue content of each drug index in the same kind of food raw material is less than the maximum value of the standard pesticide residue content, the greater the possibility that the pesticide residue content of this drug in this kind of food raw material is normal, and thus the residue safety factor is greater.

[0063] So far, the water quality safety factor and the residue safety factor of each sampled food raw material have been obtained.

[0064] Step S003, obtain the microbial presence coefficient of each microorganism of each sampled food raw material according to the microbial index, and obtain the microbial potential hazard factor of each sampled food raw material according to the microbial presence coefficients of all microorganisms of each sampled food raw material and its same-kind sampled food raw materials; obtain the microbial correction factor of the sampled food raw material according to the microbial potential hazard factors before and after processing of each sampled food raw material.

[0065] In addition to the pesticide residue content of food raw materials and the water quality of drinking water affecting food safety, microorganisms exist on the surface of plants. Because plants often come into contact with the environment in various ways, it may lead to the contamination of microorganisms or their toxins. Some microorganisms can also cause spoilage, all of which will affect food safety and thus human health. Obtain the microbial potential hazard factor according to the microbial colonies, and obtain the microbial correction factor of each food raw material according to the microbial potential hazard factor.

[0066] The colony numbers and colony areas of various microorganisms in tea were obtained through random inspection of food raw materials. In this embodiment, Escherichia coli, Salmonella, and Staphylococcus aureus were taken as examples for description. For each microorganism, its colony number and colony area were obtained, and the potential risk factors of the microorganism were obtained according to the colony number and colony area of each microorganism. The formula is as follows:

[0067]

[0068]

[0069] In the formula, S i,k,n represents the colony area of the nth colony of the kth microorganism in the ith randomly inspected food raw material. S i,k represents the colony area of all colonies of the kth microorganism in the ith randomly inspected food raw material. m ik represents the colony number of the kth microorganism in the ith randomly inspected food raw material. WC i,k represents the microorganism presence coefficient of the kth microorganism in the ith randomly inspected food raw material. WC i represents the set of microorganism presence coefficients of the ith randomly inspected food raw material. Min() represents the minimum value function, Max() represents the maximum value function, m i represents the type of microorganism randomly inspected in the ith randomly inspected food raw material. WCS i represents the potential risk factor of microorganisms in the ith randomly inspected food raw material.

[0070] Among them, the colony area distribution of each microorganism in the food raw material was discussed. The more colonies of each microorganism, the stronger the existence of the microorganism, and thus the greater the microorganism presence coefficient. Further analysis was carried out on the distribution of the presence coefficients of the same type of microorganism. The greater the presence coefficient of each microorganism, the more microorganisms on the food raw material, and thus the greater the potential risk factor of the microorganisms in the food raw material.

[0071] Before the processing of food raw materials, the microorganisms in plants mainly come from water, soil, air, organic fertilizers, animals, etc. During the processing stage of food raw materials, personnel and mechanical equipment will also affect the microorganisms in food raw materials. During processing, mechanical equipment with different processes may also produce microorganisms due to improper cleaning. The rich nutrients in plants provide sufficient material basis for the growth and reproduction of microorganisms, which is an excellent culture medium for microorganisms. The growth and reproduction of microorganisms in it will cause product deterioration, affect the characteristics of the product, and even produce toxins causing food poisoning. Therefore, in this embodiment, according to the microbiological sampling inspection results of food raw materials before and after processing, the microorganisms brought by human contact and mechanical equipment during processing are estimated.

[0072] For the same sampled food raw material, obtain the potential microbial risk factors before and after processing. The potential microbial risk factors of all sampled food raw materials before and after processing respectively constitute the pre-processing risk factor sequence and the post-processing risk factor sequence. Subtract the potential microbial risk factors of the same sampled food raw material before and after processing to obtain the potential risk difference of each sampled food raw material. Calculate the average value of the potential risk differences corresponding to all sampled food raw materials to obtain the average value of the potential risk differences of the same type of sampled food raw materials. Obtain the microbial correction factor of each sampled food raw material according to the DTW distance between the pre-processing risk factor sequence and the post-processing risk factor sequence and the potential risk difference of the sampled food raw material. The formula is as follows:

[0073]

[0074] In the formula, represents the potential microbial risk factor of the i-th sampled food raw material before processing, represents the potential microbial risk factor of the i-th sampled food raw material after processing, μ i represents the average value of the potential risk differences of the type where the i-th sampled food raw material is located, WC p represents the pre-processing risk factor sequence, WC q represents the post-processing risk factor sequence, DTW() represents the dtw distance, Norm() represents the linear normalization function, WX i represents the microbial correction factor of the i-th sampled food raw material.

[0075] The DTW distance between the pre - processing hazardous factor sequence and the post - processing hazardous factor sequence of food raw materials can measure the similarity between the potential microbial hazardous factors before and after processing. The more similar the potential microbial hazardous factors are before and after processing, the greater the possibility that the impact of personnel and machinery on the microbial indicators during food raw material processing is consistent. The ratio of the difference between the potential microbial hazardous factors of each food raw material before and after processing to the average value of the same - type differences measures the comparison between the change of the potential microbial hazardous factors of each food raw material before and after processing and the overall trend of the change of this type of food raw material. If it is greater than the overall trend, it indicates that the more microorganisms the food raw material contacts, and thus the greater the microbial correction factor should be.

[0076] Thus, the potential microbial hazardous factors and microbial correction factors of each randomly - inspected food raw material are obtained.

[0077] Step S004: Obtain the self - drug - overcoming coefficient of the randomly - inspected food raw material according to the residual safety coefficient and potential microbial hazardous factors of the randomly - inspected food raw material before and after processing; obtain the production safety coefficient of the food raw material of the same type according to the self - drug - overcoming coefficient, residual safety coefficient, potential microbial hazardous factors and microbial correction factors of all randomly - inspected food raw materials of the same type.

[0078] Microorganisms in food raw materials can help with the degradation of drugs. Considering that during the processes of manual and mechanical treatment of food raw materials, the drug indicators in the plant body may degrade slightly under the action of microorganisms, the trend change of drug degradation of different types of food raw materials is obtained according to the change of pesticide residue content of the drug indicators and the change of microbial indicators of different types of food raw materials, and the self - drug - overcoming coefficient is constructed. The formula is as follows:

[0079]

[0080] In the formula, YC k,p represents the residual safety coefficient of the k - th randomly - inspected food raw material before processing, YC k,q represents the residual safety coefficient of the k - th randomly - inspected food raw material after processing, n i represents the number of randomly - inspected food raw materials of the same type as the i - th randomly - inspected food raw material, represents the potential microbial hazardous factor of the i - th randomly - inspected food raw material before processing, represents the potential microbial hazardous factor of the i - th randomly - inspected food raw material after processing, exp() represents the exponential function with the natural constant as the base, Norm() represents the linear normalization function, and ZK i represents the self - drug - overcoming coefficient of the i - th randomly - inspected food raw material.

[0081] Among them, if the change in the residual safety factor of the sampled food raw materials before and after processing is greater, and at the same time the change in the potential microbial risk factors is greater, it indicates that the degradation effect of the sampled food raw materials on the drug is better, and thus the self-drug overcoming coefficient is greater.

[0082] Obtain the production safety coefficient of each food raw material based on the analysis of the sampled food raw materials for drinking water, drug residues, and microorganisms. The formula is as follows:

[0083]

[0084] In the formula, YC i represents the residual safety coefficient of the i-th sampled food raw material, ZK i represents the self-drug overcoming coefficient of the i-th sampled food raw material, WCS i represents the potential microbial risk factor of the i-th sampled food raw material, WX i represents the microbial correction factor of the i-th sampled food raw material, n a represents the number of sampled food raw materials in the a-th type of food raw material, Norm() represents the linear normalization function, SZ a represents the production safety coefficient of the a-th type of food raw material.

[0085] Thus, the production safety coefficient of each sampled food raw material is obtained.

[0086] Step S005: According to the production safety coefficients of the food raw materials of the same type, obtain the food safety coefficient of all the food raw materials in the same batch. If the food safety coefficient is within the food safety range, it meets the food safety requirements.

[0087] According to the production proportion of each food raw material, perform weight matching on the production safety coefficients of each food raw material to obtain the food safety coefficient of the food raw materials in the same batch. The formula is as follows:

[0088]

[0089] In the formula, WS represents the water quality safety factor, ρ a represents the proportion of the a-th type of food raw material in the production of tea beverages, SZ a represents the production safety coefficient of the a-th type of food raw material, N represents the types of food raw materials in the production of tea beverages, and SZS represents the food safety coefficient of the food raw materials in the same batch.

[0090] Preset food safety range. In this embodiment, the food safety range is [0.6, 1]. If the production safety coefficient of the food raw materials of the same batch is within the food safety range, the food raw materials of this batch meet the food safety standards. If the production safety coefficient of the food raw materials of the same batch is not within the food safety range, the food raw materials of this batch do not meet the food safety standards. Thus, the food safety evaluation is completed. The specific implementation manner of the food safety evaluation is as Figure 2 shown.

[0091] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A data processing method for food safety, characterized in that, The method includes the following steps: Obtain the water quality data of the water quality sample to be inspected, where the water quality data includes the pH value, the content of iron ions, the content of chemical substances with permanent hardness, the pesticide residue data and the microbial indicators of the randomly inspected food raw materials; Obtain the water quality data vector and the standard vector according to the water quality data of the water quality sample to be inspected; obtain the water quality safety factor of the water quality sample to be inspected according to the Manhattan distance between the water quality data vector and the standard vector; obtain the residue safety coefficient of each randomly inspected food raw material according to the pesticide residue data of the randomly inspected food raw materials; Obtain the microbial presence coefficient of each type of microorganism in each randomly inspected food raw material according to the microbial indicators, and obtain the microbial potential risk factor of each randomly inspected food raw material according to the microbial presence coefficients of all types of microorganisms in the same type of randomly inspected food raw materials as it; obtain the microbial correction factor of the randomly inspected food raw materials according to the microbial potential risk factors before and after processing of each randomly inspected food raw material; Obtain the self-drug overcoming coefficient of the randomly inspected food raw materials according to the residue safety coefficient and the microbial potential risk factor before and after processing of the randomly inspected food raw materials; obtain the production safety coefficient of the food raw materials of the same type according to the self-drug overcoming coefficient, the residue safety coefficient, the microbial potential risk factor and the microbial correction factor of all the randomly inspected food raw materials of the same type; Obtain the food safety coefficient of all the food raw materials in the same batch according to the production safety coefficient and the water quality safety factor of the food raw materials of the same type. If the food safety coefficient is within the food safety range, it meets the food safety requirements.

2. The data processing method for food safety according to claim 1, characterized in that, The method for obtaining the water quality data vector and the standard vector according to the water quality data of the water quality sample to be inspected is as follows: Obtain the detection indicators in the water quality sample to be inspected and the range of each detection indicator through the Hygienic Standard for Drinking Water. Form a vector by taking the upper limit values of the range of all detection indicators of the water quality sample to be inspected, which is denoted as the upper limit vector. Form a vector by taking the lower limit values of the range of all detection indicators of the water quality sample to be inspected, which is denoted as the lower limit vector. The upper limit vector and the lower limit vector are collectively referred to as the standard vector; form a vector by taking the detection results of all detection indicators of the water quality sample to be inspected, which is denoted as the water quality data vector.

3. The data processing method for food safety according to claim 2, characterized in that, The method for obtaining the water quality safety factor of the water quality sample to be inspected according to the Manhattan distance between the water quality data vector and the standard vector is as follows: Denote the Manhattan distance between the upper limit vector and the lower limit vector as the first Manhattan distance, denote the Manhattan distance between the upper limit vector and the water quality data vector as the second Manhattan distance, denote the Manhattan distance between the lower limit vector and the water quality data vector as the third Manhattan distance, denote the sum of the second Manhattan distance and the third Manhattan distance as the first distance sum, and denote the ratio of the first Manhattan distance to the first distance sum as the first ratio; Denote the number of detection indicators within the range in the water quality data vector as the first number, and denote the ratio of the first number to the number of all detection indicators in the water quality data vector as the second ratio; Denote the product of the first ratio and the second ratio as the water quality safety factor.

4. A data processing method for food safety as described in claim 1, characterized in that, The method for obtaining the residue safety coefficient of each randomly inspected food raw material according to the pesticide residue data of the randomly inspected food raw materials is as follows: Obtain the types of drug indicators that need to be detected for each sampled food raw material according to the "Maximum Residue Limits of Pesticides in Foods". Denote any one sampled food raw material as the target food raw material, denote all sampled food raw materials of the same type as the target food raw material as the target category, denote the mean of the pesticide residue content of the j-th drug indicator of all sampled food raw materials in the target category as the target mean, and denote the difference between the pesticide residue content of the j-th drug indicator of the target food raw material and the target mean as the first pesticide residue difference. Denote the difference between the maximum pesticide residue content of the j-th drug indicator of all sampled food raw materials in the target category and the maximum value of the standard pesticide residue content of the j-th drug indicator as the second pesticide residue difference. If the first pesticide residue difference is less than zero, the first residue factor is 0; if the first residue difference is greater than or equal to zero, the first residue factor is 0.5; obtain the second residue factor using the method for obtaining the first residue factor; denote the sum of the first residue factor and the second residue factor as the first residue sum, and accumulate the first residue sums of all drug indicators to obtain the residue safety factor of the target food raw material.

5. The data processing method for food safety according to claim 1, characterized in that, The method for obtaining the microbial presence coefficient of each microorganism in each sampled food raw material according to the microbial indicators is as follows: The microbial indicators include the colony number and colony area of each microorganism. Denote the ratio of the colony area of all colonies of each microorganism to the colony number as the colony area mean of each microorganism, denote the absolute value of the difference between the colony area of each colony of each microorganism and the colony area mean as the first area difference, and denote the accumulated sum of the first area differences of all colonies as the microbial presence coefficient of each microorganism.

6. The data processing method for food safety according to claim 1, wherein, The method for obtaining the microbial potential hazard factor of each sampled food raw material according to the microbial presence coefficients of all microorganisms of the sampled food raw materials of the same type as it is as follows: Construct the microbial presence coefficients of all microorganisms of each sampled food raw material into a microbial presence coefficient set, obtain the maximum presence coefficient and the minimum presence coefficient within the microbial presence coefficient set, denote the difference between the microbial presence coefficient of each microorganism and the minimum presence coefficient as the first coefficient difference, denote the difference between the maximum presence coefficient and the minimum presence coefficient as the second coefficient difference, denote the ratio of the first coefficient difference to the second coefficient difference as the first coefficient ratio, and obtain the microbial potential hazard factor of the sampled food raw material by averaging all the first coefficient ratios of the sampled food raw material.

7. The data processing method for food safety according to claim 1, wherein The method for obtaining the microbial correction factor of each sampled food raw material according to the microbial potential hazard factors before and after processing of each sampled food raw material is as follows: For the same sampled food raw material, denote the microbial potential hazard factor obtained from the initial microbial indicators as the microbial potential hazard factor before processing, and after the sampled food raw material is processed, use the same method to obtain its microbial potential hazard factor after processing. Construct the pre - processing hazard factor sequence and the post - processing hazard factor sequence respectively from the potential microbial hazard factors of all sampled food raw materials before and after processing; calculate the DTW distance between the pre - processing hazard factor sequence and the post - processing hazard factor sequence; record the absolute value of the difference between the potential microbial hazard factors of the sampled food raw materials before processing and after processing as the potential hazard difference, and record the average value of the potential hazard differences of all sampled food raw materials as the average potential hazard difference; record the ratio of the potential hazard difference of each sampled food raw material to the average potential hazard difference as the first hazard ratio, and record the value obtained by linearly normalizing the product of the DTW distance and the first hazard ratio as the microbial correction factor of the sampled food raw materials.

8. The data processing method for food safety according to claim 1, wherein The method for obtaining the self - drug - overcoming coefficient of the sampled food raw materials according to the residual safety coefficient and the potential microbial hazard factors of the sampled food raw materials before and after processing is as follows: Where YC k,p represents the residual safety factor of the k-th randomly inspected food raw material before processing, and YC k,q represents the residual safety factor of the k-th randomly inspected food raw material after processing. n i represents the quantity of randomly inspected food raw materials of the same type as the i-th randomly inspected food raw material. represents the potential microbial hazard factor of the i-th randomly inspected food raw material before processing. represents the potential microbial hazard factor of the i-th randomly inspected food raw material after processing. exp() represents the exponential function with the natural constant as the base, Norm() represents the linear normalization function, and ZK i represents the self-drug overcoming coefficient of the i-th randomly inspected food raw material.

9. The data processing method for food safety according to claim 1, characterized in that, The method for obtaining the production safety coefficient of food raw materials of the same type according to the self - drug - overcoming coefficients, residual safety coefficients, potential microbial hazard factors and microbial correction factors of all sampled food raw materials of the same type is as follows: Where, YC i represents the residual safety factor of the i-th sampled food raw material, ZK i represents the self-drug overcoming factor of the i-th sampled food raw material, WCS i represents the microbial potential risk factor of the i-th sampled food raw material, WX i represents the microbial correction factor of the i-th sampled food raw material, n a represents the number of sampled food raw materials in the a-th kind of food raw material, Norm() represents the linear normalization function, SZ a represents the production safety factor of the a-th kind of food raw material.

10. A data processing method for food safety as claimed in claim 1, wherein, The method for obtaining the food safety coefficient of all food raw materials in the same batch according to the production safety coefficient of food raw materials of the same type and the water quality safety factor is as follows: Obtain the proportion of each food raw material when producing tea beverages, which is recorded as the production proportion. Record the product of the production proportion of each food raw material and the production safety coefficient as the first safety value. Accumulate the first safety values of all food raw materials to obtain the food raw material safety value, and add the food raw material safety value to the water quality safety factor to obtain the food safety coefficient.

Images