KR20200103904A - Method and device for food risk assessment - Google Patents

Abstract

Disclosed are a food risk assessment system capable of reflecting an exposure amount at a current time point and an assessment method thereof. According to one embodiment of the present invention, the food risk assessment system comprises: a concentration calculation unit calculating a reference representative concentration for each material and food to be assessed; a food intake amount calculation unit calculating a food intake amount of a total population group and an intake population group; an exposure amount calculation unit calculating an exposure amount based on the concentration of the material to be assessed and the food intake amount for each population group and calculating an exposure contribution ratio for each population group based on the exposure amount; a reference exposure value calculation unit calculating a reference exposure amount for each population group based on the exposure amount and the exposure contribution ratio; a risk level calculation unit calculating a risk level for each population group based on data calculated through the calculation units; a food determination unit setting a group of food contaminated by the material to be assessed and determining food whose exposure possibility to the material to be assessed based on the intake amount is equal to or greater than the reference value from the set food group; and a big data generation unit integrating the data of the calculation units and the food determination unit to generate big data for assessing risk of food.

Description

Food risk assessment system and evaluation method {Method and device for food risk assessment}

The present invention relates to a food risk assessment system and evaluation method, and more particularly, to a food risk assessment system and evaluation method capable of reflecting the exposure amount at the present time.

Recently, people's interest in food safety and harmfulness has increased, and as food-related accidents continue to occur, interest in food safety has been greatly increased. In addition, public interest in harmful substances including regulated or non-regulated substances containing toxins in food is also continuously increasing.

Hazardous substances contained in these foods may be generated during the intermediate process of manufacturing food or during chemical processing for food production, but may also occur during the production, storage, distribution, and cooking of agricultural raw materials, which are the main raw materials.

In particular, among the harmful substances, if the toxic effect of the regulated substance is found after permission, or if it is a non-degradable substance with a long half-life, there is a concern that it may be exposed to the human body through the food chain after contaminating the soil or water. Therefore, it is desirable to consider not only the regulated substances at the present time, but also the regulated substances before the half-life and non-regulated substances having toxins.

In general, the risk (risk) of food is determined by toxicity x exposure, and the exposure is calculated as the concentration of chemical substances in food x food intake. At this time, the concentration is determined through monitoring by a government agency, and the amount of food intake is calculated using the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. The risk is to control the risk of the public through the establishment of standards and standards for foods, which are the limit values of concentration, after risk assessment is performed on foods with low/high frequency or high content for each chemical substance.

However, the National Health and Nutrition Survey data on the amount of food used in calculating the risk above require a lot of time to process a vast amount of data, and generally rely on historical data from two years ago, so that changes in the external environment at this point can be observed. There is a limit to reflecting.

In addition, big data analysis is required because the main factors in calculating risk are to combine thousands or tens of thousands of data by population, consumption level, and food.

Republic of Korea Patent Publication No. 10-2008-0088011 Republic of Korea Patent Publication No. 10-2012-0134462

Accordingly, the present invention has been devised to solve the above problems, and the object to be solved of the present invention is to use a set evaluation system and consider all of the regulated substances, non-regulated substances, and past regulated substances at the present time. It is to provide a food risk assessment system and assessment method that can predict the rate of change in exposure doses.

In addition, the present invention is to provide a food risk assessment system and evaluation method capable of analyzing and processing big data based on a combination of data by population group, consumption level, and food.

However, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned are clearly to those of ordinary skill in the technical field to which the present invention belongs from the following description. It will be understandable.

As a technical means for achieving the above object, the risk assessment system for food according to an embodiment of the present invention includes: a concentration calculation unit for calculating a reference representative concentration for each substance and food to be evaluated; A food intake calculation unit that calculates the food intake of the entire population and ingestion population; An exposure amount calculation unit that calculates the exposure amount based on the concentration of the substance to be evaluated and the amount of food intake by the population group, and calculates the exposure contribution rate for each population group based on the exposure amount; An exposure reference value calculation unit that calculates an exposure reference value for each population group based on the exposure amount and exposure contribution rate; A risk calculation unit for calculating a risk for each population group based on the data calculated through the calculation unit; A food discrimination unit configured to set a food group contaminated with the substance to be evaluated, and to determine a food whose exposure possibility of the substance to be evaluated based on an intake of the set food group is greater than or equal to a reference value; And a big data generation unit that generates big data for risk assessment of food by synthesizing the data of the calculation unit and the food identification unit.

In one embodiment, the food risk assessment system includes a food detection unit that detects a food in which the food intake is increased or decreased by using the food intake among big data; And a food intake predictor for predicting the food intake of the detected food after discriminating whether the increase or decrease of the food detected by the food detection unit is a simple increase or decrease.

In one embodiment, the food detection unit detects foods with an increase or decrease in food intake based on at least one analysis model of a constant average model, a simple linear regression model, a quadratic linear model, and an exponential growth model.

In one embodiment, the food intake predictor classifies whether the detected increase or decrease of food is a simple increase or decrease based on a constant average model.

In one embodiment, the food intake calculation unit recalculates food intake by reflecting the predicted data of the food intake prediction unit, the exposure amount calculation unit recalculates the exposure by reflecting the recalculated food intake, and the risk calculation unit recalculates the exposure amount. The risk is recalculated by reflecting it.

In one embodiment, the concentration calculation unit calculates the average, median, minimum, and maximum standard representative concentration for each substance and food to be evaluated.

In one embodiment, the food intake calculation unit calculates the food intake amount of the entire population and the food intake amount of the ingestion population, including age and sex.

As a technical method for achieving the above object, the risk assessment method for food according to an embodiment of the present invention includes: calculating a reference representative concentration for each substance and food to be evaluated by a concentration calculation unit; Calculating the food intake of the entire population and the intake population by the food intake calculation unit; Determining, by the food discrimination unit, foods in which the exposure possibility of the substance to be evaluated based on intake amount among the food groups that may be contaminated with the substance to be evaluated is greater than or equal to a reference value; Calculating an exposure contribution rate for each population group based on the exposure amount and exposure amount based on the concentration of the substance to be evaluated and the food intake amount for each population group by the exposure amount calculation unit; Calculating an exposure reference value for each population group based on the exposure amount and the exposure contribution rate by the exposure reference value calculation unit; Calculating a risk for each population group based on the data calculated through the calculation unit by the risk calculation unit; And generating big data for risk assessment of food by synthesizing the data of the calculation unit and the food identification unit by the big data generation unit.

In one embodiment, the step of detecting the food intake amount increased or decreased by using the food intake amount among big data by the food detection unit; And predicting the food intake amount of the detected food after discriminating whether the increase or decrease of the food detected by the food detection unit is a simple increase or decrease by the food intake predictor.

The risk assessment system and evaluation method for a product according to the present invention has the advantage of being able to quickly and sensitively reflect changes in the external environment since it is possible to predict not only the change rate of the exposure amount at the present time, but also the change rate of the exposure amount at the future time point.

Since the food risk assessment system and evaluation method according to the present invention simultaneously contains regulated substances, unregulated substances and past regulated substances as target hazardous substances, a more systematic evaluation can be performed by considering the potential risks of substances. There is an advantage to be able to.

The food risk assessment system and evaluation method according to the present invention has the advantage that big data analysis processing is possible.

However, the effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description. I will be able to.

The following drawings appended in the present specification illustrate preferred embodiments of the present invention, and serve to further understand the technical idea of the present invention together with the detailed description of the present invention to be described later, so the present invention is described in such drawings. It is limited to and should not be interpreted.
1 is a schematic diagram of the configuration of a food risk assessment system according to an embodiment of the present invention.
2 is a diagram illustrating a process of selecting a food monitoring system of a food monitoring DB by a control device according to an embodiment of the present invention.
3 is a format table of a food monitoring DB cited by a concentration calculator according to an embodiment of the present invention.
4 is a table showing a detection range set by a concentration calculator according to an embodiment of the present invention to determine a representative concentration.
5 is an example of a graph of a detection range set by a concentration calculator according to an embodiment of the present invention.
6 is a table of reference representative concentrations for each substance and food to be evaluated calculated by a concentration calculator according to an embodiment of the present invention.
7 is one end view of a process of selecting food intake amount data of a food intake amount DB by a control device according to an embodiment of the present invention.
8 is one side of food intake data selected by the control device according to an embodiment of the present invention.
9 is a table showing the total population group and the amount of food intake of the ingestion population calculated by the food intake calculation unit according to an embodiment of the present invention.
10 is a table showing an exposure amount calculated by an exposure amount calculator according to an embodiment of the present invention.
11 is a table showing an exposure contribution rate calculated by an exposure amount calculator according to an embodiment of the present invention.
12 is a table of reference exposure values calculated by an exposure reference value calculation unit according to an embodiment of the present invention.
13 is a table of the risk calculated by the risk calculation unit according to an embodiment of the present invention.
14 is one end view of a process in which food groups that can be contaminated are set by the control device according to an embodiment of the present invention.
15 is a graph in which a food discrimination unit according to an embodiment of the present invention determines a food whose exposure possibility of a substance to be evaluated is greater than or equal to a reference value.
16 is one end view of a result of detecting a food with an increase or decrease in food intake by evaluating a fitness by a food detection unit according to an embodiment of the present invention.
17 is an example of a graph of a result of determining the increase or decrease of food detected by the food intake amount prediction unit according to an embodiment of the present invention.
18 is a step flow diagram of a risk assessment method according to an embodiment of the present invention.
19 is a step flow diagram of a method for correcting big data for risk assessment of food generated according to the steps shown in FIG. 18.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The detailed description to be disclosed hereinafter together with the accompanying drawings is intended to describe exemplary embodiments of the present invention, and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details to provide a thorough understanding of the present invention. However, those of ordinary skill in the art to which the present invention pertains knows that the present invention may be practiced without these specific details. In addition, throughout the specification, when a part is said to "comprising or including" a certain component, this does not exclude other components, but may further include other components unless otherwise stated. Means that. In addition, when it is determined that detailed descriptions of known functions or configurations may unnecessarily obscure the subject matter of the present invention, detailed descriptions thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions in an embodiment of the present invention, which may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout this specification.

Evaluation system

Hereinafter, a food risk assessment system (hereinafter referred to as "evaluation system") according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

As shown in FIG. 1, the evaluation system according to an embodiment of the present invention includes a control device 50, a concentration calculation unit 100, a food intake calculation unit 200, an exposure amount calculation unit 300, and an exposure reference value calculation. Consisting of a unit 400, a risk calculation unit 500, a food determination unit 600, a big data generation unit 700, a food detection unit 800, a food intake prediction unit 900, and a database 1000 do. The evaluation system according to an embodiment of the present invention may further include a configuration required in addition to the above configuration.

The control device 50 includes the above-described concentration calculation unit 100, food intake calculation unit 200, exposure amount calculation unit 300, exposure reference value calculation unit 400, risk calculation unit 500, food determination unit ( 600), the big data generation unit 700, the food detection unit 800, the food intake prediction unit 900, and the database 1000 are connected to control the above configurations. A typical embodiment of the control device 50 may be a microcomputer, a CPU, etc., and it is preferable to be configured with a computer including the same.

The concentration calculation unit 100 is stored in the food monitoring DB 1010 of the database 1000 after the evaluation target material (eg, mycotoxin) is selected by the control device 50 as shown in FIGS. 2 to 6. When one of the food monitoring systems is selected, the standard representative concentration for each substance and food to be evaluated is calculated. To this end, the concentration calculation unit 100 sets a detection range for calculating a representative concentration, and calculates a standard representative concentration for each substance and food to be evaluated for each average, median, minimum, and maximum. In addition, the concentration calculation unit 100 does not detect the concentration according to the substance and food to be evaluated. The concentration calculated by the concentration calculation unit 100 is stored in the concentration DB 1020 of the database 1000. Meanwhile, the operation of the concentration calculation unit 100 may be performed by the control device 50.

As shown in Figs. 7 to 9, the food intake amount calculation unit 200 selects the food intake amount data stored in the food intake amount DB 1030 of the database 1000 by the control device 50, the selected food intake amount data Calculate the food intake of the whole population and the intake population using. The food intake calculation unit 200 calculates the food intake amount of the entire population group and the food intake amount of the ingestion population as an intake ratio, average weight, average intake amount, and standard deviation. Furthermore, the food intake calculation unit 200 calculates the food intake amount of the entire population group and the food intake amount of the ingestion population group, including age and sex. Meanwhile, the operation of the food intake calculation unit 200 may be performed by the control device 50.

As shown in Figs. 10 to 11, the exposure amount calculation unit 300 is based on the concentration and food intake of substances to be evaluated for each population group calculated through the concentration calculation unit 100 and the food intake calculation unit 200. The exposure amount is calculated, and the exposure amount is calculated through the following [Equation 1].

Here, E is the amount of exposure, and the amount of exposure (E) refers to the amount of exposure of a chemical substance (c) to a specific food (f) at a specific time point (t) in the population (p) by sex, age or sex and age. . The exposure amount (E) is determined according to the concentration of chemical substances in the food (C) and the food intake (I) and body weight consumed by the subject in the population (p).

In addition, the exposure amount calculation unit 300 calculates an exposure contribution rate for each population group based on the calculated exposure amount E. The exposure amount and the exposure contribution rate calculated by the exposure amount calculation unit 300 are stored in the exposure amount DB 1040 and the exposure contribution rate DB 1050 of the database 1000, respectively. Meanwhile, the operation of the exposure amount calculation unit 300 may be performed by the control device 50.

As illustrated in FIG. 12, the reference exposure value calculation unit 400 calculates an exposure reference value for each population group based on the exposure amount and exposure contribution rate calculated by the exposure amount calculation unit 300. Here, the exposure reference value includes the chemical field, the number of data sources, the number of chemical substances, the number of items, and the number of records. In addition, the reference exposure value calculated by the reference exposure value calculation unit 400 is stored in the reference exposure value DB 1060 of the database 1000. Meanwhile, when the exposure is referenced, the operation of the calculation unit 400 may be performed by the control device 50.

As shown in FIG. 13, the risk calculation unit 500 calculates the risk for each population group based on the data calculated through the calculation units 100, 200, 300, and 400 described above. Here, the risk is data calculated as toxicity x exposure, and is calculated through [Equation 2] below.

Here, R is the risk, and the risk (R) is determined according to the exposure amount (E) and the human exposure safety standard (H). In addition, it would be desirable for the risk calculation unit 500 to calculate the risk only when data is calculated by the calculation units 100, 200, 300, and 400 described above, and the calculated risk is the risk of the database 1000. It is also stored in DB 1070. In addition, the operation of the risk calculation unit 500 may be performed by the control device 50.

As shown in FIGS. 14 to 15, the food discrimination unit 600 sets a food group that can be contaminated with the material to be evaluated by the control device 50, and the possibility of exposure of the material to be evaluated based on the intake of the set food group is Foods that are above the standard value are identified. Through this food determination unit 600, it is possible to determine a food group that may be excessively exposed to the material to be evaluated. Meanwhile, the operation of the food determination unit 600 may be performed by the control device 50.

The big data generation unit 700 generates big data for risk assessment of food by synthesizing the data of the calculation units 100, 200, 300, 400, 500 and the food identification unit 600 described above. It would be desirable for the big data generation unit 700 to generate big data by receiving data from DBs 1020, 1030, 1040, 1050, 1060, and 1070 of the database 1000. In addition, the big data generated by the big data generation unit 700 includes data such as population group, chemical substances, food (group, individual), non-detection, food intake, and time difference, and is stored in a separate database for big data. Meanwhile, the operation of the big data generation unit 700 may be performed by the control device 50. Furthermore, the big data generation unit 700 provides the generated big data to a user (preferably a terminal), so that the user prepares the exposure amount by population group and gender, age or gender and age.

On the other hand, the evaluation system according to an embodiment of the present invention is configured to include the above-described food detection unit 800 and food intake prediction unit 900 to correct the big data generated by the big data generation unit 700.

The food detection unit 800 detects food in which the food intake amount is increased or decreased by using the food intake amount among big data generated by the big data generator 700. At this time, the food detection unit 800 pre-processes and excludes food in which the increase or decrease in food intake is '0'. The food detection unit 800 having completed the pre-processing process is based on at least one analysis model of a constant average model, a simple linear regression model, a quadratic linear model, and an exponential growth model stored in the analysis model DB 1080 of the database 1000. Evaluate food suitability. Meanwhile, the food detection unit 800 evaluates the suitability of the food through the following [Equation 3].

Here, BIC is the goodness of fit, and the goodness of fit (BIC) evaluates the fit of the food based on the maximum value (L) of the likelihood function of the estimated food and the number of data points (N). In addition, in the fitness rate evaluated by the food detection unit 800, the year is composed of an independent variable, and the average intake by year is a dependent variable, and when the fitness of the increased or decreased food is less than or equal to the reference value, it is excluded from the increased or decreased food. In addition, the food detection unit 800 detects foods that have increased or decreased by more than a reference value (90%) in the graph shown in FIG. 16 as increased or decreased foods. Meanwhile, the operation of the food detection unit 800 may be performed by the control device 50.

The food intake predictor 900 predicts the food intake amount of food detected by the food detector 800. At this time, the food intake prediction unit 900 applies the constant average model stored in the analysis model DB 1080 to predict whether the increase or decrease of the detected food (eg, oats) is a simple increase or decrease and the increase or decrease starts before predicting the food intake. Determine the year. This determination process is the same as the graph shown in FIG. 17, and the food intake predictor 900 makes a difference between a food intake predicting method for a simple increase or decrease and a food intake predicting method for a sudden increase or decrease. The food intake predicted data predicted by the food intake predictor 900 is stored in the predictive DB 1090 of the database 1000. Meanwhile, the operation of the food intake prediction unit 900 may be performed by the control device 50.

Meanwhile, when the food intake predictor 900 predicts the food intake, the food intake calculator 200 recalculates the food intake by reflecting the predicted data stored in the prediction DB 1090.

In addition, the exposure amount calculation unit 300 recalculates the exposure amount by reflecting the food intake amount recalculated from the food intake amount calculation unit 200. Accordingly, the reference exposure value for each population group of the reference exposure value calculation unit 400 is also recalculated.

Further, the risk calculation unit 500 recalculates the risk by reflecting the recalculated data.

In this way, the evaluation system according to an embodiment of the present invention generates big data for risk assessment of food, and by configuring the food detection unit 800 and the food intake prediction unit 900, the predicted data is evaluated for risk of food. It is possible to correct big data by reflecting it in the big data for use. Accordingly, there is an advantage that it is possible to perform a re-evaluation for food.

Assessment Methods

Hereinafter, a food risk assessment method (hereinafter referred to as "evaluation method") according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

First, an evaluation target material is selected by the control device 50 (S100).

Then, the food monitoring DB 1010 is selected by the control device 50. Here, the food monitoring DB 1010 stores the MIMS/MAP (monitoring system operated by the Ministry of Food and Drug Safety, and may be selected and executed by the control device 50. Here, the MIMS is the hazardous substance monitoring DB operated by the Ministry of Food and Drug Safety. And MAP is an integrated food risk assessment platform.

Then, a reference representative concentration for each substance and food to be evaluated is calculated by the concentration calculating unit 100 (S200). Here, the concentration calculation unit 100 calculates the average, median, minimum, and maximum concentrations of the substance to be evaluated and the standard representative concentration for each food by using the monitoring system selected from the food monitoring DB 1010 by the control device 50. In addition, the concentration calculating unit 100 also performs non-detection processing of the concentration according to the substance to be evaluated and each food. In addition, the concentration calculating unit 100 stores the calculated concentration in the concentration DB 1020.

Then, the food intake DB 1030 is selected by the control device 50.

Then, the food intake amount of the entire population and the intake population is calculated by the food intake calculation unit 200 (S300). Here, when the food intake amount data stored in the food intake DB 1030 is selected by the control device 50, the food intake calculation unit 200 uses the data to calculate the food intake amount of the entire population group and the intake population group. , Average weight, intake average, standard deviation, etc. Furthermore, the food intake calculation unit 200 calculates the food intake amount of the entire population group and the food intake amount of the ingestion population group, including age and sex.

Then, the food discrimination unit 600 determines a food whose exposure possibility of the substance to be evaluated is greater than or equal to the reference value, based on the intake amount among food groups that can be contaminated with the substance to be evaluated (S400). Here, the reference value may be set to 90% food intake. In this way, discriminating food is to discriminate a food group that may be excessively exposed to the material to be evaluated.

At this time, if it is determined by the food determination unit 600 that the exposure possibility of the substance to be evaluated is less than the reference value (S400-NO), it is determined whether the exposure possibility of the substance to be evaluated is more than the reference value.

On the contrary, if it is determined by the food determination unit 600 that the exposure possibility of the substance to be evaluated is more than the reference value (S400-YES), the concentration of the substance to be evaluated by the population group and the food intake by the exposure amount calculation unit 300 Based on the exposure amount and the exposure contribution rate by population group is calculated (S500).

Then, the exposure amount and the exposure contribution rate calculated by the exposure amount calculating unit 300 are stored in the exposure amount DB 1040 and the exposure contribution rate DB 1050 of the database 1000 (S550).

Then, the reference exposure value for each population group is calculated by the exposure reference value calculation unit 400 based on the exposure amount and the exposure contribution rate (S600). Here, the exposure reference value calculated by the exposure reference value calculation unit 400 includes a field of chemical substances, the number of data sources, the number of chemical substances, the number of items, and the number of records.

Then, the exposure reference value calculated by the exposure reference value calculation unit 400 is stored in the exposure reference value DB 1060 of the database 1000.

The risk for each population group is also calculated based on the data calculated through the calculation units 100, 200, 300, and 400 described above in the risk calculation unit 500 (S700). Here, the risk is data calculated as toxicity x exposure.

Then, the risk calculated by the risk calculation unit 500 is stored in the risk DB 1070 of the database 1000.

Then, the data of the calculation units 100, 200, 300, 400, 500 and the food identification unit 600 described above are synthesized by the big data generation unit 700, and risk assessment of food by the synthesis of the data Big data for use is generated (S800). Here, the big data generated by the big data generation unit 700 includes data such as population group, chemical substances, food (group, individual), non-detection, food intake, and time difference, and is stored in a separate database for big data. .

Meanwhile, the evaluation method according to an embodiment of the present invention further includes a step of correcting the big data generated by the big data generator 700.

Looking at the correction step in detail, food in which the food intake amount is increased or decreased by using the food intake amount among big data is detected by the food detection unit 800 (S1000). Here, the food detection unit 800 pre-processes and excludes food in which the increase or decrease in food intake is '0'. The food detection unit 800 having completed the pre-processing process is based on at least one analysis model of a constant average model, a simple linear regression model, a quadratic linear model, and an exponential growth model stored in the analysis model DB 1080 of the database 1000. Evaluate suitability. At this time, the year is composed of the independent variable, and the average intake by year is composed of the dependent variable. At this time, if the suitability of the increased or decreased food is less than the standard value, it is excluded from the increased or decreased food, and the increased or decreased food above the reference value is detected as the increased or decreased food.

Then, the food intake amount of the detected food is predicted after determining whether the increase or decrease of the food detected by the food detection unit 800 by the food intake predictor 900 is a simple increase or decrease (S1010). Here, the food intake predictor 900 distinguishes whether the increase or decrease is a simple increase or decrease before predicting the food intake. At this time, the food intake predictor 900 applies the constant average model stored in the analysis model DB 1080 to distinguish whether the increase or decrease of food is a simple increase or decrease, and when the increase or decrease is a simple increase or decrease, the food intake prediction method and the rapid increase or decrease. Make a difference in how food intake is predicted. The food intake data predicted and calculated in this way is stored in the prediction DB 1090 of the database 1000.

In this way, when the food intake predictor 900 predicts the food intake (S1010), the food intake calculator 200 recalculates the food intake by reflecting the predicted data stored in the predictive DB 1090 (S1020). .

When the food intake is recalculated by the food intake calculation unit 200 (S1020), the exposure calculation unit 300 recalculates the exposure by reflecting the recalculated food intake (S1030). At this time, the exposure reference value calculation unit 400 also recalculates the exposure reference value for each population group as the exposure amount is recalculated (S1040).

Then, the risk calculation unit 500 recalculates the risk by reflecting the recalculated data (S1050).

Then, the big data generation unit 700 corrects the big data for risk assessment of food by reflecting the data recalculated in steps S1020 to S1050 (S1060).

As described above, the evaluation method according to an embodiment of the present invention generates big data for risk assessment of food, and by configuring the food detection unit 800 and the food intake prediction unit 900, the predicted data is evaluated for risk of food. It is possible to correct big data by reflecting it in the big data for use. Accordingly, there is an advantage that it is possible to perform a re-evaluation for food.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains can understand that it is possible to easily transform it into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

50: control device,
100: concentration calculation unit,
200: food intake calculation unit,
300: exposure amount calculation unit,
400: exposure reference value calculation unit,
500: risk calculation unit,
600: food identification unit,
700: big data generation unit,
800: food detection unit,
900: food intake prediction unit,
1000: database.

Claims (9)

A concentration calculation unit that calculates a reference representative concentration for each substance and food to be evaluated;
A food intake calculation unit that calculates the food intake of the entire population and ingestion population;
An exposure amount calculation unit that calculates an exposure amount based on the concentration of the substance to be evaluated and the food intake by the population group, and calculates an exposure contribution rate for each population group based on the exposure amount;
An exposure reference value calculator for calculating an exposure reference value for each population group based on the exposure amount and the exposure contribution rate;
A risk calculation unit for calculating the risk for each population group based on the data calculated through the calculation unit;
A food discrimination unit configured to set a food group contaminated with the material to be evaluated, and to determine a food whose exposure possibility of the material to be evaluated is greater than or equal to a reference value based on an intake of the set food group; And
And a big data generation unit for generating big data for risk assessment of food by synthesizing the data of the calculation unit and the food identification unit. The method of claim 1,
A food detector configured to detect food in which the food intake amount is increased or decreased by using the food intake amount among the big data; And
And a food intake predictor for predicting the food intake amount of the detected food after determining whether the increase or decrease of the food detected by the food detection unit is a simple increase or decrease. The method of claim 2,
The food detection unit,
A food risk assessment system, characterized in that, based on at least one analysis model of a constant average model, a simple linear regression model, a quadratic linear model, and an exponential growth model, the food intake increases or decreases. The method of claim 2,
The food intake predictor,
A risk assessment system for food, characterized in that for distinguishing whether the detected increase or decrease of food is a simple increase or decrease based on a constant average model. The method of claim 2,
The food intake calculation unit recalculates the food intake by reflecting the predicted data of the food intake prediction unit,
The exposure amount calculation unit recalculates the exposure amount by reflecting the recalculated food intake amount,
The risk assessment system for food, characterized in that the risk calculation unit recalculates the risk by reflecting the recalculated exposure amount. The method of claim 1,
The concentration calculation unit,
A food risk assessment system, characterized in that calculating the average, median, minimum, and maximum concentrations of the substance to be evaluated and the standard representative concentration for each food. The method of claim 1,
The food intake calculation unit,
Food risk assessment system, characterized in that calculating the food intake amount of the entire population group and the food intake amount of the ingesting population including age and sex. Selecting a substance to be evaluated by a control device;
Calculating a reference representative concentration for each substance and food to be evaluated by the concentration calculation unit;
Calculating the food intake of the entire population and the intake population by the food intake calculation unit;
Determining, by a food discrimination unit, a food whose exposure possibility of the substance to be evaluated is greater than or equal to a reference value based on an intake amount among food groups that may be contaminated with the substance to be evaluated;
Calculating an exposure contribution rate for each population group based on the exposure amount and the exposure amount based on the concentration of the substance to be evaluated for each population group and the food intake by an exposure amount calculation unit;
Calculating an exposure reference value for each population group based on the exposure amount and the exposure contribution rate by an exposure reference value calculation unit;
Calculating a risk for each population group based on the data calculated through the calculation unit by a risk calculation unit; And
And generating big data for risk assessment of food by synthesizing the data of the calculation unit and the food identification unit by a big data generation unit. The method of claim 8,
Detecting, by a food detection unit, the food intake amount increased or decreased by using the food intake amount among the big data; And
Predicting the food intake amount of the detected food after discriminating whether the increase or decrease of the food detected by the food detection unit is a simple increase or decrease by a food intake predictor; .

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