A data processing method for food safety

Abstract

This invention relates to the field of data processing technology, specifically to a data processing method for food safety. The method includes: acquiring water quality data, pesticide residue data, and microbial indicators; obtaining a water quality safety factor by comparing the tested water quality with standard water quality; obtaining a residue safety factor by comparing the detected pesticide residues with existing standard pesticide residues; obtaining a microbial presence coefficient based on the microbial indicators; obtaining a potential microbial risk factor based on the presence coefficients of all microorganisms in the same type of food raw material; obtaining a microbial correction factor based on the potential microbial factors before and after processing; obtaining a self-drug overcoming coefficient based on the residue safety factor and potential microbial risk factor before and after processing; obtaining a production safety factor based on this; and ensuring food safety based on the production safety factor. This invention improves the accuracy and real-time performance of food production safety evaluation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a data processing method for food safety. Background Technology

[0002] Food safety refers to food that is non-toxic, harmless, meets the necessary nutritional requirements, and does not cause any acute, subacute, or chronic harm to human health. Food raw materials should undergo laboratory testing before processing. If any food safety indicators are found to be abnormal, the raw materials must not be used; only approved food raw materials should be used. Incoming inspection is the first step in food production. Most food manufacturers do not conduct incoming inspection for every batch, but rather perform random sampling within the same batch. Ensuring food quality and safety primarily relies on process control, workflow control, and the quality control of raw materials and ingredients.

[0003] Current big data processing technologies suffer from low accuracy in food production safety. Because food safety is influenced by numerous factors, the sampling and testing of various food raw materials yields a vast amount of data on food safety. Directly extracting data from this large volume of raw materials can lead to extensive calculations, impacting the efficiency and accuracy of assessments. The resulting evaluations may also differ from actual conditions, lacking reliability and predictability. Furthermore, current risk management for food safety primarily relies on post-event control, a passive and inefficient approach that fails to fundamentally address food safety issues. Summary of the Invention

[0004] To address the technical problem of discrepancies between evaluation results and actual conditions, this invention provides a data processing method for food safety, the specific technical solution of which is as follows:

[0005] This invention proposes a data processing method for food safety, which includes the following steps:

[0006] Obtain water quality data from submitted water samples, and conduct random inspections of pesticide residue data and microbial indicators of food raw materials;

[0007] The water quality data vector and standard vector are obtained from the water quality data of the submitted water samples; the water quality safety factor of the submitted water samples is obtained from the Manhattan distance between the water quality data vector and the standard vector; and the residue safety factor of each sampled food raw material is obtained from the pesticide residue data of the sampled food raw materials.

[0008] The microbial presence coefficient of each microorganism in each sampled food raw material was obtained based on the microbial indicators. The potential microbial hazard factors of each sampled food raw material were obtained based on the microbial presence coefficients of all species of microorganisms in the same category of sampled food raw materials. The microbial correction factors of each sampled food raw material were obtained based on the potential microbial hazard factors before and after processing.

[0009] The self-drug overcome factor of the sampled food raw materials is obtained based on the residual safety factor and potential microbial hazard factor of the sampled food raw materials before and after processing; the production safety factor of the same type of food raw materials is obtained based on the self-drug overcome factor, residual safety factor, potential microbial hazard factor and microbial correction factor of all sampled food raw materials of the same type.

[0010] The food safety coefficients of all food raw materials in the same batch are obtained based on the production safety coefficient and water quality safety factor of the same type of food raw materials. If the food safety coefficients are within the food safety range, they meet the food safety requirements.

[0011] Preferably, the method for obtaining the water quality data vector and standard vector based on the water quality data of the submitted water samples is as follows:

[0012] The detection indicators and ranges of each detection indicator in the submitted water quality sample are obtained through the "Standards for Drinking Water Quality". The upper limit values ​​of the ranges of all detection indicators in the submitted water quality sample are formed into a vector and denoted as the upper limit vector. The lower limit values ​​of the ranges of all detection indicators in the submitted water quality sample are formed into a vector and denoted as the lower limit vector. The upper limit vector and the lower limit vector are collectively referred to as the standard vector. The vector formed by the detection results of all detection indicators in the submitted water quality sample is denoted as the water quality data vector.

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

[0014] The Manhattan distance between the upper limit vector and the lower limit vector is denoted as the first Manhattan distance, the Manhattan distance between the upper limit vector and the water quality data vector is denoted as the second Manhattan distance, the Manhattan distance between the lower limit vector and the water quality data vector is denoted as the third Manhattan distance, the sum of the second Manhattan distance and the third Manhattan distance is denoted as the first distance sum, and the ratio of the first Manhattan distance to the first distance sum is denoted as the first ratio.

[0015] The number of detection indicators in the water quality data vector within the interval is recorded as the first quantity, and the ratio of the first quantity to the total number of all detection indicators in the water quality data vector is recorded as the second ratio.

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

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

[0018] According to the "Maximum Residue Limits for Pesticides in Food", the types of drug indicators that need to be tested for each sampled food raw material are obtained;

[0019] Any random sampled food raw material is designated as the target food raw material. All sampled food raw materials of the same type as the target food raw material are designated as the target class. The average pesticide residue content of the j-th drug indicator of all sampled food raw materials in the target class is designated as the target average. The difference between the pesticide residue content of the j-th drug indicator of the target food raw material and the target average is designated as the first pesticide residue difference.

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

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

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

[0023] The microbial indicators include the number of colonies and the colony area of ​​each microorganism;

[0024] Let the ratio of the total area of ​​all colonies of each microorganism to the number of colonies be denoted as the average colony area of ​​each microorganism. Let the absolute value of the difference between the colony area of ​​each colony of each microorganism and the average colony area be denoted as the first area difference. Let the sum of the first area differences of all colonies be denoted as the microbial existence coefficient of each microorganism.

[0025] Preferably, the method for obtaining the potential microbial risk factors of each sampled food raw material based on the microbial presence coefficients of all species of microorganisms of each sampled food raw material and its counterparts of the same type is as follows:

[0026] The microbial presence coefficients of all microorganisms in each sampled food raw material are constructed into a microbial presence coefficient set. The maximum and minimum presence coefficients are obtained within the microbial presence coefficient set. The difference between the microbial presence coefficient and the minimum presence coefficient of each microorganism is recorded as the first coefficient difference. The difference between the maximum and minimum presence coefficients is recorded as the second coefficient difference. The ratio of the first coefficient difference and the second coefficient difference is recorded as the first coefficient ratio. The average of all first coefficient ratios of the sampled food raw materials is used to obtain the potential microbial hazard factors of the sampled food raw materials.

[0027] Preferably, the method for obtaining the microbial correction factors of the sampled food raw materials based on the potential microbial hazard factors before and after processing is as follows:

[0028] For the same sampled food raw material, the microbial potential risk factors obtained from the initial microbial indicators are recorded as the microbial potential risk factors before processing. After the sampled food raw material has been processed, the same method is used to obtain its microbial potential risk factors after processing.

[0029] All potential microbial hazard factors of the sampled food raw materials before and after processing were used to construct pre-processing hazard factor sequences and post-processing hazard factor sequences, respectively. The DTW distance between the pre-processing and post-processing hazard factor sequences was calculated. The absolute value of the difference between the pre-processing and post-processing potential microbial hazard factors of the sampled food raw materials was recorded as the potential hazard difference, and the mean of the potential hazard differences of all sampled food raw materials was recorded as the mean of potential hazard differences. The ratio of the potential hazard difference of each sampled food raw material to the mean of potential hazard differences was recorded as the first hazard ratio, and the linearly normalized product of the DTW distance and the first hazard ratio was recorded as the microbial correction factor of the sampled food raw material.

[0030] Preferably, the method for obtaining the self-drug overcome factor of the sampled food raw materials based on the residual safety factor and potential microbial hazard factors before and after processing is as follows:

[0031]

[0032] In the formula, YC k,p YC represents the residual safety factor of the k-th sampled food raw material before processing. k,q n represents the residual safety factor of the k-th sampled food raw material after processing. i This represents the quantity of the same type of food raw materials as the i-th sampled food raw material. This represents the potential microbial risk factor of the i-th sampled food raw material before processing. ZK represents the potential microbial hazard 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 represents the potential microbial hazard factor of the i-th sampled food raw material after processing. i This represents the self-drug resistance coefficient of the i-th sampled food raw material.

[0033] Preferably, the method for obtaining the production safety factor of the same type of food raw material based on its own drug resistance factor, residue safety factor, microbial potential hazard factor, and microbial correction factor for all sampled food raw materials of the same type is as follows:

[0034]

[0035] In the formula, YC i ZK represents the residue safety factor of the i-th sampled food raw material. i WCS represents the self-drug resistance coefficient of the i-th sampled food raw material. i WX represents the potential microbial hazard factor of the i-th sampled food raw material. i Let n represent the microbial correction factor for the i-th sampled food raw material. a This represents the quantity of food raw materials sampled from the a-th type of food raw materials. Norm() represents the linear normalization function. SZ a This represents the production safety factor 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 and water quality safety factor of the same type of food raw materials is as follows:

[0037] The production percentage of each food ingredient in the production of tea beverages is recorded as the production percentage. The product of the production percentage of each food ingredient and the production safety factor is recorded as the first safety value. The first safety values ​​of all food ingredients are summed to obtain the food ingredient safety value. The food ingredient safety value is added to the water quality safety factor to obtain the food safety factor.

[0038] The present invention has the following beneficial effects: It analyzes the raw materials for food production and processing. First, it analyzes water quality data to obtain water quality safety factors. Next, it analyzes the drug and microbial indicators of plant-based food raw materials to construct residue safety coefficients and potential microbial hazard factors. Combining the environmental influences on microorganisms and drugs, and integrating the interactions between microorganisms and drugs, it constructs microbial correction factors and self-drug overcoming coefficients, improving the predictability and reliability of the data. Finally, it obtains the production safety coefficients of various food raw materials and, combined with the ingredient ratios of various food raw materials, obtains the production safety coefficients for each batch of food raw materials, improving the accuracy and real-time nature of food production safety evaluation. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a data processing method for food safety provided in one embodiment of the present invention;

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

[0042] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, 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. Furthermore, 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 one of ordinary skill in the art to which this invention pertains.

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

[0045] The following description, in conjunction with the accompanying drawings, details a specific scheme for a data processing method for food safety provided by the present invention.

[0046] Please see Figure 1 The diagram illustrates a data processing method for food safety according to an embodiment of the present invention, which includes the following steps:

[0047] Step S001: Obtain water quality data from the submitted water samples and sample food raw materials for pesticide residue and microbial indicators.

[0048] This embodiment takes tea beverages as an example for food safety processing. The food raw materials for tea beverages include water and related plant-based food raw materials. A portion of the food raw materials from the same batch are randomly selected as sampled food raw materials. The drinking water used to produce tea beverages is sent to a laboratory for testing to obtain water quality data. The tested water quality data includes pH value, iron ion content, and the content of chemicals that cause permanent hardness. The tested drinking water is recorded as the submitted water quality sample. Furthermore, since the food raw materials for tea beverages are generally artificially cultivated, pesticides are inevitably used for treatment. In addition, due to various forms of contact between plants and the environment, plants may be infected by microorganisms. Therefore, the sampled food raw materials are sent to the laboratory for pesticide residue testing and microbial testing to obtain pesticide residue data and microbial indicators of the food raw materials.

[0049] Thus, water quality data of the submitted water samples and pesticide residue data and microbial indicators of the sampled food raw materials were obtained.

[0050] Step S002: Obtain water quality data vector and standard vector based on the water quality data of the submitted water quality samples; obtain the water quality safety factor of the submitted water quality samples based on the Manhattan distance between the water quality data vector and the standard vector; obtain the residue safety coefficient of each sampled food raw material based on the pesticide residue data of the sampled food raw materials.

[0051] Water quality safety factors are calculated by comparing water quality data vectors with national standards. These factors represent the safety of drinking water. Similarly, pesticide residue data from food raw materials is compared with national standards to obtain residue safety coefficients, which represent the safety of food raw materials in terms of pesticide residues. The safety of drinking water and pesticide residues further confirms food safety.

[0052] Given that water is a major component of tea beverages, its quality has a significant impact. Generally speaking, ions such as calcium, magnesium, iron, and chloride in water affect the color and taste of tea, causing turbidity and the formation of tea milk. When the iron ion content in water exceeds 5 ppm (parts per million), the tea will appear black and have a bitter taste; high chloride ion content will give the tea a putrid smell. Tannins in tea leaves can react with various metal ions, producing a variety of colors. Therefore, water quality is crucial for the safety of tea beverage production. Producing high-quality tea beverages requires deionized purified water, with its pH value, iron ion content, and permanent hardness chemical content falling within specified ranges. Water quality data includes several testing indicators. The upper limits of the specified ranges for different testing indicators form an upper limit vector, and the lower limits form a lower limit vector. The specified ranges and the testing indicators included in the water quality data are determined by the "Standards for Drinking Water Quality" (GB5749-2006).

[0053] This yields a water quality data vector composed of the test values ​​of all indicators in the submitted water sample, as well as upper and lower limit vectors composed of all test indicators. Based on the Manhattan distance between each pair of the upper and lower limit vectors, the water quality data vectors, and the number of elements in the water quality data vectors that fall within the specified intervals, the water quality safety factor of the submitted water sample is obtained using the following formula:

[0054]

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

[0056] The Manhattan distance between the upper and lower bound vectors is used to characterize the specified range of difference in water quality data. Then, the sum of the Manhattan distances between the water quality data vector and the upper and lower bound vectors is used to measure the overall difference between the water quality data vector and the specified range. The two are compared; if all elements of the water quality data vector are within the specified range, then... A value of 1 indicates a water quality safety factor of 1; if an element in the water quality data vector is outside the specified range, it indicates that the content of multiple substances in the water exceeds the standard, and the water quality safety factor is less than 1.

[0057] Besides the influence of drinking water on tea beverages, the main factors affecting the taste and function of tea beverages are the raw materials provided by plant foods. In the process of plant production, growers use a lot of pesticides and fertilizers to increase yields or resist pests and diseases. Generally speaking, if pesticides and fertilizers are used in accordance with the prescribed dosage, there will be no pesticide residues. However, if they are used in excess, pesticide residues will appear in the plants. These pesticide residues are recorded as pesticide residues.

[0058] For sampling food raw materials to obtain pesticide residues of different types of drugs, multiple samples are taken from the same food raw material during sampling. The "Maximum Residue Limits for Pesticides in Food" (GB 2763) provides the types of drugs to be tested and the maximum residue limits for each detected drug. The maximum residue limit for each drug type listed in the national standard is recorded as the maximum standard pesticide residue limit. The residue safety factor for each sampled food raw material is obtained based on the difference between the pesticide residue content of each sampled food raw material and the average pesticide residue content of the specified food raw material type, as well as the difference between the maximum pesticide residue content of each food raw material type and the maximum standard pesticide residue limit. The formula is as follows:

[0059]

[0060]

[0061] In the formula, y i,j This represents the pesticide residue content of the j-th drug indicator in the i-th sampled food raw material. This represents the average pesticide residue content of the j-th drug indicator among the food raw material types corresponding to the i-th sampled food raw material. y represents the maximum pesticide residue content of the j-th drug indicator among the food raw material types corresponding to the i-th sampled food raw material. j (i) max YC represents the maximum standard pesticide residue content of the j-th drug indicator among the food raw material types corresponding to the i-th sampled food raw material, δ(x) represents the residue factor, n represents the number of drug indicators sampled, and YC i This represents the residual safety factor of the i-th sampled food raw material.

[0062] Specifically, when the pesticide residue content of each drug indicator in food raw materials is less than the average pesticide residue content in the same type of food raw materials, the pesticide residue content of that drug indicator in the food raw materials is more likely to be within the normal range. At the same time, when the maximum pesticide residue content of each drug indicator in the same type of food raw materials is less than the maximum standard pesticide residue content, the pesticide residue content of that drug in the food raw materials is more likely to be within the normal range, and thus the residue safety factor is greater.

[0063] Thus, the water quality safety factors and the residue safety coefficients of each sampled food raw material were obtained.

[0064] Step S003: Obtain the microbial presence coefficient of each microorganism in each sampled food raw material based on the microbial indicators; obtain the potential microbial hazard factors of each sampled food raw material based on the microbial presence coefficients of all species of microorganisms in each sampled food raw material of the same type as other sampled food raw materials; obtain the microbial correction factors of each sampled food raw material based on the potential microbial hazard factors before and after processing.

[0065] Besides pesticide residues in food raw materials and the quality of drinking water affecting food safety, microorganisms reside on plant surfaces. Because plants frequently come into contact with the environment in various ways, they are susceptible to contamination by microorganisms or their toxins. Some microorganisms can also cause spoilage, all of which affect food safety and, consequently, human health. This study identifies potential microbial hazard factors based on microbial colonies and then uses these potential hazard factors to determine microbial correction factors for each food raw material.

[0066] By sampling and testing food raw materials, the colony counts and areas of various microorganisms in tea were obtained. In this embodiment, Escherichia coli, Salmonella, and Staphylococcus aureus are used as examples. For each microorganism, the colony count and area were obtained. Based on the colony count and area of ​​each microorganism, the potential risk factors of the microorganisms were obtained, as shown in the following formula:

[0067]

[0068]

[0069] In the formula, S i,k,n S represents the colony area of ​​the nth colony of the kth microorganism in the i-th sampled food raw material. i,k m represents the colony area of ​​all colonies of the k-th microorganism in the i-th sampled food raw material. ik WC represents the number of colonies of the k-th microorganism in the i-th sampled food raw material. i,k WC represents the microbial presence coefficient of the k-th microorganism in the i-th sampled food raw material. i Let m represent the set of microbial presence coefficients for the i-th sampled food raw material, Min() denotes the minimum value function, Max() denotes the maximum value function, and m i WCS represents the types of microorganisms sampled from the i-th food raw material. i This represents the potential microbial hazard factor of the i-th sampled food raw material.

[0070] The study discusses the distribution of colony area of ​​various microorganisms in food raw materials. The higher the number of colonies of each microorganism, the stronger the presence of the microorganism, and thus the greater the microbial presence coefficient. Further analysis is conducted on the distribution of the presence coefficient of the same type of microorganism. The greater the presence coefficient of each microorganism, the more microorganisms are on the food raw materials, and thus the greater the potential microbial hazard factors of the food raw materials.

[0071] Before food raw materials undergo processing, the microorganisms in plants mainly originate from water, soil, air, organic fertilizers, and animals. During the processing of food raw materials, personnel and machinery can also influence the microorganisms. Improper cleaning of machinery used in different processing techniques can also generate microorganisms. The abundant nutrients in plants provide a sufficient material basis for microbial growth and reproduction, serving as an excellent culture medium. Microbial growth and reproduction within this medium can cause product spoilage, affect product characteristics, and even produce toxins leading to food poisoning. Therefore, this embodiment estimates the microorganisms introduced during processing due to human contact and machinery based on the results of microbial sampling of food raw materials before and after processing.

[0072] For the same sampled food raw material, the potential microbial hazard factors before and after processing are obtained. The potential microbial hazard factors of all sampled food raw materials before and after processing are used to construct pre-processing hazard factor sequences and post-processing hazard factor sequences, respectively. The difference between the potential microbial hazard factors before and after processing for the same sampled food raw material is used to obtain the potential hazard difference for each sampled food raw material. The average of the potential hazard differences for all sampled food raw materials is used to obtain the average potential hazard difference for the same type of sampled food raw material. Based on the DTW distance between the pre-processing and post-processing hazard factor sequences and the potential hazard difference of the sampled food raw materials, the microbial correction factor for each sampled food raw material is obtained, using the following formula:

[0073]

[0074] In the formula, This represents the potential microbial risk factor of the i-th sampled food raw material before processing. μ represents the potential microbial hazard factor of the i-th sampled food raw material after processing. i WC represents the mean of the potential hazard differences among the types of raw materials used in the i-th sampled food inspection. p WC represents the sequence of hazard factors before processing. q This represents the sequence of hazard factors after processing, DTW() represents the dtw distance, Norm() represents the linear normalization function, and WX... i This represents the microbial correction factor for the i-th sampled food raw material.

[0075] The DTW distance between the pre-processing and post-processing hazard factor sequences can measure the similarity between the pre- and post-processing microbial potential hazard factors. The greater the similarity between the pre- and post-processing microbial potential hazard factors, the greater the likelihood that the influence of personnel and machinery on the microbial indicators during food raw material processing is consistent. The ratio of the difference between the pre- and post-processing microbial potential hazard factors of each food raw material to the mean difference of the same type measures the change of the microbial potential hazard factors before and after processing of each food raw material in relation to the overall trend of change of that type of food raw material. If it is greater than the overall trend, it indicates that the food raw material has been exposed to more microorganisms, and therefore the microbial correction factor should be larger.

[0076] Thus, the potential microbial hazard factors and microbial correction factors of each sampled food raw material were obtained.

[0077] Step S004: Obtain the self-drug resistance coefficient of the sampled food raw materials based on the residual safety coefficient and potential microbial hazard factors before and after processing; obtain the production safety coefficient of the same type of food raw materials based on the self-drug resistance coefficient, residual safety coefficient, potential microbial hazard factors, and microbial correction factors of all sampled food raw materials of the same type.

[0078] Microorganisms in food raw materials can help degrade drugs. Considering that during the artificial and mechanical processing of food raw materials, a small portion of the drug indicators in the plants may be degraded under the action of microorganisms, this study obtains the trend changes of drug degradation in different types of food raw materials based on the changes in pesticide residue content and microbial indicators, and constructs its own drug resistance coefficient, as shown in the following formula:

[0079]

[0080] In the formula, YC k,p YC represents the residual safety factor of the k-th sampled food raw material before processing. k,q n represents the residual safety factor of the k-th sampled food raw material after processing. i This represents the quantity of the same type of food raw materials as the i-th sampled food raw material. This represents the potential microbial risk factor of the i-th sampled food raw material before processing. ZK represents the potential microbial hazard 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 represents the potential microbial hazard factor of the i-th sampled food raw material after processing. i This represents the self-drug resistance coefficient of the i-th sampled food raw material.

[0081] Among them, if the greater the change in the residual safety factor of the sampled food raw materials before and after processing, and the greater the change in the potential risk factors of microorganisms, it indicates that the sampled food raw materials have a better degradation effect on drugs, and thus a greater drug resistance factor.

[0082] The production safety factor for each food raw material is obtained based on the analysis of its effects on drinking water, drug residues, and microorganisms, using the following formula:

[0083]

[0084] In the formula, YC i ZK represents the residue safety factor of the i-th sampled food raw material. i WCS represents the self-drug resistance coefficient of the i-th sampled food raw material. i WX represents the potential microbial hazard factor of the i-th sampled food raw material. i Let n represent the microbial correction factor for the i-th sampled food raw material. a This represents the quantity of food raw materials sampled from the a-th type of food raw materials. Norm() represents the linear normalization function. SZ a This represents the production safety factor of the a-th type of food raw material.

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

[0086] Step S005: Based on the production safety coefficient of the same type of food raw materials and the food safety coefficient of all 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] Based on the production proportion of each food raw material, the production safety coefficient of each food raw material is weighted and matched to obtain the food safety coefficient of the same batch of food raw materials. The formula is as follows:

[0088]

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

[0090] A preset food safety range is defined in this embodiment as [0.6, 1]. If the production safety coefficient of the same batch of food raw materials falls within this range, then the batch of food raw materials meets the food safety standards. If the production safety coefficient of the same batch of food raw materials does not fall within this range, then the batch of food raw materials does not meet the food safety standards. This completes the food safety evaluation. The specific implementation method of the food safety evaluation is as follows: Figure 2 As shown.

[0091] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A data processing method for food safety, characterized by, The method comprises the following steps: Obtaining water quality data of the submitted water quality sample, the water quality data comprising PH value, iron ion content, chemical content of permanent hardness, pesticide residue data and microbial indicators of the sampled food raw materials; Obtaining the detection indexes in the submitted water quality sample and the interval range of each detection index according to the Standards for Drinking Water Health, and taking the vector composed of the upper limit value and the lower limit value of the detection sample as the upper limit vector and the lower limit vector, and collectively as the standard vector; taking the vector composed of the detection results of all detection indexes of the submitted water quality sample as the water quality data vector; calculating the Manhattan distance of the standard vector and the water quality data vector respectively, and then summing to obtain a first distance sum; calculating the Manhattan distance of the upper limit vector and the lower limit vector, and then taking the ratio of the Manhattan distance to the first distance sum as a first ratio; taking the number of detection indexes in the water quality data vector within the interval range as a first number, and taking the ratio of the first number to the number of all detection indexes of the water quality data vector as a second ratio; taking the product of the first ratio and the second ratio as a water quality safety factor; obtaining a residual safety coefficient of each sampled food raw material according to the pesticide residue data of the sampled food raw materials; Obtaining a microbial existence coefficient of each kind of microorganism of each sampled food raw material according to the microbial indicators, and obtaining a microbial potential danger factor of each sampled food raw material according to the microbial existence coefficients of all kinds of microorganisms of the same kind of sampled food raw materials; obtaining a microbial correction factor of the sampled food raw material according to the microbial potential danger factors before and after processing of each sampled food raw material; Obtaining a self-medicine overcoming coefficient of the sampled food raw material according to the residual safety coefficient and the microbial potential danger factor of the sampled food raw material before and after processing; obtaining a production safety coefficient of the food raw material of the same kind according to the self-medicine overcoming coefficients, the residual safety coefficients, the microbial potential danger factors and the microbial correction factors of all sampled food raw materials of the same kind; Obtaining a food safety coefficient of all food raw materials of the same batch according to the production safety coefficient of the food raw material of the same kind and the water quality safety factor, and if the food safety coefficient is within the food safety interval, the food safety coefficient meets the food safety; The method for obtaining the residual safety coefficient of each sampled food raw material according to the pesticide residue data of the sampled food raw material is as follows: Obtaining the types of drug indexes required to be detected for each sampled food raw material according to the Maximum Residue Limits of Pesticides in Food; Taking any one of the sampled food raw materials as a target food raw material, taking all sampled food raw materials of the same kind as the target food raw material as a target class, taking the average value of the pesticide residue content of the jth drug index of all sampled food raw materials of the target class as a target average value, and taking the difference between the pesticide residue content of the jth drug index of the target food raw material and the target average value as a first pesticide residue difference; Taking the difference between the maximum pesticide residue content of the jth drug index of all sampled food raw materials of the target class and the maximum value of the standard pesticide residue content of the jth drug index as a second pesticide residue difference; If the first pesticide residue difference is less than zero, the first residue factor is 0; if the first pesticide residue difference is greater than or equal to zero, the first residue factor is 0.5; the second residue factor is obtained by using the first residue factor obtaining method; the sum of the first residue factor and the second residue factor is recorded as the first residue sum, and the first residue sum of all drug indicators is accumulated to obtain the residual safety factor of the target food raw material; The method for obtaining the microbial presence coefficient of each microorganism of each sampled food raw material according to the microbial indicators is: The microbial indicators include the colony number and colony area of each microorganism; Let the ratio of the colony area of all colonies of each microorganism to the number of colonies be the colony area average of each microorganism, and let the absolute value of the difference between the colony area of each colony of each microorganism and the colony area average be the first area difference; the cumulative sum of the first area difference of all colonies is recorded as the microbial presence coefficient of each microorganism; The method for obtaining the microbial potential risk factor of each sampled food raw material according to the microbial presence coefficients of all microorganisms of each sampled food raw material and the same kind of sampled food raw material is: The microbial presence coefficients of all microorganisms of each sampled food raw material are constructed into a microbial presence coefficient set, the maximum presence coefficient and the minimum presence coefficient are obtained in the microbial presence coefficient set, the difference between the microbial presence coefficient of each microorganism and the minimum presence coefficient is recorded as the first coefficient difference, the difference between the maximum presence coefficient and the minimum presence coefficient is recorded as the second coefficient difference, the ratio of the first coefficient difference to the second coefficient difference is recorded as the first coefficient ratio, and the average of all first coefficient ratios of the sampled food raw material is obtained to obtain the microbial potential risk factor of the sampled food raw material; The method for obtaining 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 is: For the same sampled food raw material, the microbial potential risk factor obtained from the first microbial indicators is recorded as the microbial potential risk factor before processing, and the same method is used to obtain the microbial potential risk factor after processing of the sampled food raw material; The microbial potential risk factors before and after processing of all sampled food raw materials are respectively constructed into a pre-processing risk factor sequence and a post-processing risk factor sequence; the DTW distance of the pre-processing risk factor sequence and the post-processing risk factor sequence is calculated; the absolute value of the difference between the microbial potential risk factor before processing and the microbial potential risk factor after processing is recorded as the potential risk difference, and the average of the potential risk differences of all sampled food raw materials is recorded as the potential risk difference average; the ratio of the potential risk difference of each sampled food raw material to the potential risk difference average is recorded as the first risk ratio, and the product of the DTW distance and the first risk ratio is linearly normalized to obtain the microbial correction factor of the sampled food raw material; The method for obtaining the self-drug overcoming coefficient of the sampled food raw material according to the residual safety factor and the microbial potential risk factor of the sampled food raw material before and after processing is: wherein, represents the residual safety factor of the kth sampled food raw material before processing, represents the residual safety factor of the kth sampled food raw material after processing, represents the number of the same kind of sampled food raw material of the ith sampled food raw material, represents the microbial potential risk factor of the ith sampled food raw material before processing, represents the microbial potential risk factor of the ith sampled food raw material after processing, represents an exponential function with a natural constant as a base, represents a linear normalization function, represents the self-medicine overcoming coefficient of the ith sampled food raw material; The method for obtaining the production safety factor of the same kind of food raw materials according to the self-medicine overcoming coefficient, the residual safety factor, the microbial potential danger factor and the microbial correction factor of all the food raw materials for sampling of the same kind is: wherein, represents the residual safety factor of the i-th sampled food raw material, represents the self-medicine overcoming factor of the i-th sampled food raw material, represents the microorganism potential danger factor of the i-th sampled food raw material, represents the microorganism correction factor of the i-th sampled food raw material, represents the number of the sampled food raw materials in the a-th food raw material, represents a linear normalization function, represents the production safety factor of the a-th food raw material.

2. The data processing method for food safety of claim 1, wherein, The method for obtaining the food safety factor of all the food raw materials of the same batch according to the production safety factor and the water quality safety factor of the same kind of food raw materials is: The proportion of each kind of food raw material in the production of tea beverages is recorded as a production proportion, the product of the production proportion of each kind of food raw material and the production safety factor is recorded as a first safety value, the first safety values of all the food raw materials are accumulated to obtain a food raw material safety value, and the food raw material safety value is added to the water quality safety factor to obtain a food safety factor.

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