KR20180078391A - Apparatus And Method For Predicting Dynamic Microbial Growth, a Computer-readable Storage Medium for executing the Method, and System For Managing Food Safety - Google Patents

Abstract

The present invention relates to an apparatus and a method for predicting dynamic microbial growth in fresh vegetables, a computer-readable medium for recording a program to execute the method, and a system for managing food safety of the fresh vegetables using the same. According to the present invention, the method for predicting dynamic microbial growth in fresh vegetables comprises: a step in which a first model for microbial growth is determined based on data related to a growth pattern of microorganisms in fresh vegetables stored at each storage temperature, and parameters with respect to the number of the initial bacteria, the maximum number of bacteria, the maximum growth rate, and a lag phase duration of the microorganisms stored at each storage temperature are calculated; a step in which a second model according to a variation in the storage temperatures of the calculated parameters is derived; and a step in which a dynamic model for predicting the growth of microorganisms in the fresh vegetables under a temperature changing condition is derived by using parameter values calculated in the first model and the second model. According to the present invention, it is possible to accurately predict the growth of microorganisms even under the temperature changing condition. Moreover, it is possible to provide a safety management standard and food risk evaluation information for safe intake of the fresh vegetables.

Description

FIELD OF THE INVENTION [0001] The present invention relates to an apparatus and method for predicting dynamic microorganism growth in fresh vegetables, a computer-readable recording medium on which a program for executing the method is recorded, and a safety management system for fresh vegetables using the same. -readable Storage Medium for executing the Method, and System for Managing Food Safety}

The present invention relates to an apparatus and method for predicting dynamic microorganism growth in fresh vegetables, a computer-readable recording medium on which a program for executing the method is recorded, and a safety management system for fresh vegetables using the same. More specifically, A computer-readable recording medium recording a program for executing the method, and a safety management system for fresh vegetables using the same. 2. Description of the Related Art

Recently, as food safety accidents are frequent, public concern about food safety is increasing. As the causes and behavior of food safety accidents become complicated due to the increase of food additives, the emergence of new harmful substances, the development of distribution network and the increase of international trade, the need for systematic and efficient management of food safety is emerging.

Food poisoning accidents caused by food are caused by hygiene control of group foodservice and restaurants, but there are also a few cases where contamination problems arise in the production and distribution of raw materials. According to the data from 2002 to 2013, accidents caused by harmful microorganisms in domestic food safety accidents account for about 60% of the total, and it is known that food poisoning accidents due to agricultural products consumed freshly in the world are increasing . Especially, as the weather of Korea gradually changes into a tropical climate, the importance of safety management during storage and transportation is increasing.

On the other hand, the model for predicting the microbial growth is based on factors such as Hazard Analysis Critical Control Points (HACCP), risk assessment, determination of shelf life of food, microbial management in industry, microbial growth, survival and death It is very important for food safety management.

However, conventionally developed microbial growth prediction models are assumed to be constant temperature conditions, and are not applicable to the temperature change conditions in which the storage temperature is changed in actual distribution or sales, and thus the prediction accuracy is poor.

In order to solve the above problems, the present invention provides a method for predicting the growth of dynamic microorganisms in fresh vegetables that can predict the growth of microorganisms under a temperature-change condition, and a computer-readable recording medium recording a program for executing the method .

In addition, safety management information on storage temperature and storage time for safe management of fresh vegetables by predicting dynamic microorganism growth under such temperature change conditions, and safety management system of fresh vegetables that can provide risk assessment information of fresh vegetables The purpose is to provide.

The above object is achieved by a method for predicting dynamic microbial growth in fresh vegetables, comprising the steps of: determining a first model of microbial growth based on data on microbial growth patterns in fresh vegetables at different storage temperatures; Calculating parameters relating to number, maximum number of bacteria, maximum growth rate, and inducer; Deriving a quadratic model according to a storage temperature change of the calculated parameter; And deriving a dynamic model for predicting the microbial growth of fresh vegetables under the thermocompression conditions using the parameter values calculated in the primary model and the secondary model, wherein the dynamic model predicts the dynamic microbial growth in fresh vegetable ≪ / RTI >

The above object can also be achieved by a computer-readable recording medium on which a program for executing a method for predicting dynamic microorganism growth in fresh vegetables is recorded.

The above object is also achieved by a device for predicting dynamic microorganism growth in fresh vegetables, comprising: a user input unit for receiving a selection of fresh vegetables and microorganisms from a user; A model storage unit for storing information on a first model and a second model related to microbial growth of at least one fresh vegetable; And information on a model corresponding to the selection information on the type of fresh vegetables and microorganisms input through the user input unit from the model storage unit and stores profile information on the storage temperature according to the storage time of fresh vegetables inputted from the user To the information on the extracted model to derive a dynamic model for predicting microbial growth of fresh vegetables under the thermocompression condition can be achieved by an apparatus for predicting dynamic microbial growth in fresh vegetables .

The present invention also provides a safety management system for a fresh vegetable, comprising: a data collection server for collecting data on temperature changes in storage and distribution of fresh vegetables; Microbial growth prediction which has a first model and a second model concerning microbial growth of fresh vegetables and calculates a dynamic model for predicting microbial growth by applying the temperature data collected from the data collection server to the corresponding model of fresh vegetables Device; And a safety management information providing server for providing safety management information on storage time and storage temperature of the fresh vegetables based on the dynamic model.

As described above, the method for predicting the growth of dynamic microorganisms in fresh vegetables according to the present invention, the computer-readable recording medium on which the program for executing the method is recorded, and the safety management system for fresh vegetables using the same, It is possible to predict the growth of microorganisms, and furthermore, safety management standards for safe intake of fresh vegetables can be provided and risk assessment information can be provided.

FIG. 1 is a flowchart illustrating a method for developing a dynamic microorganism growth prediction model in fresh vegetables according to a first embodiment of the present invention and a method for predicting microbial growth using the same.
Fig. 2 shows the data obtained by measuring the product temperature during the distribution process and the probability distribution model statistically suitable for the product temperature data in the production of fresh vegetables such as lettuce, assorted buds, and buttercups.
Figures 3a to 3c is 4 ^o C, 10 ^o C, 15 ^o C, 25 ^o C, 30 ^o lettuce, assorted according to the change of time at storage temperatures of C sprout, and the number of changes in the E. coli found in parsley , And Figs. 4A to 4C are graphs respectively showing the number of Staphylococcus aureus.
FIG. 5 shows a second model of the maximum growth rate of pathogenic Escherichia coli in lettuce, assorted sprouts and buttercups, calculated using the values of the maximum growth rate obtained in the primary model using the values of the secondary model.
Fig. 6 shows a second model of the maximum growth rate of Staphylococcus aureus in lettuce, assorted sprouts and buttercups, calculated using the values of the maximum growth rate obtained in the first-order model using the variables in the second-order model.
FIG. 7 shows the results of the assay for evaluating the fitness of the growth-promoting model of pathogenic Escherichia coli of fresh vegetables, and FIG. 8 shows the results of the assays for Staphylococcus aureus.
FIG. 9 is a graph comparing a value obtained by predicting the growth of pathogenic E. coli of fresh vegetables under actual temperature conditions using the dynamic model of the present invention, and FIG. 10 is a graph showing a dynamic model of Staphylococcus aureus .
FIG. 11 is a schematic view of a safety management system for fresh vegetables according to an embodiment of the present invention, and FIG. 12 is a conceptual diagram of an apparatus for predicting dynamic microbial growth in fresh vegetables according to an embodiment of the present invention.

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings.

The present invention relates to the prediction of microbial growth in fresh vegetables. Fresh vegetables can include various vegetables belonging to fresh vegetables, including lettuce, sprouts, parsley, cabbage, broccoli, cucumber, kale, , And the microorganism includes pathogenic Escherichia coli, Staphylococcus aureus, salmonella, Vibrio, or cholera, and the like. Hereinafter, the growth prediction of pathogenic Escherichia coli and Staphylococcus aureus against lettuce, assorted buds, and buttercups among fresh vegetables will be described as an embodiment of the present invention. However, the present invention should not be construed as limited to lettuce, assorted buds, parsley, pathogenic Escherichia coli, and Staphylococcus aureus.

FIG. 1 is a flowchart illustrating a method for developing a dynamic microorganism growth prediction model in fresh vegetables according to a first embodiment of the present invention and a method for predicting microbial growth using the same.

Referring to FIG. 1, in order to develop a microbial growth prediction model, data on the production of fresh vegetables, the distribution environment, and the microbial growth pattern at each storage temperature are collected (S10).

The data on the production and distribution environment of fresh vegetables may be the time taken from the production site through intermediate distribution to the point of sale, and the storage temperature at that time. As a result of the data collection about the storage temperature during the production and distribution process of fresh vegetables, the foodstuffs supplied to the school meals took about 14 hours from the processing plant to the intermediate customer, about 2 hours from the intermediate customer to the school, 5.7 ^o C.

Fig. 2 shows the data of the measured product temperature during the distribution process and the probability distribution model statistically suitable for the product temperature data in the production of lettuce, assorted sprouts and buttercups, which are part of fresh vegetables, A representative value of the assorted bud, and the temperature of the dropwort.

Data on storage temperatures during the production and distribution of these fresh vegetables can be used to calculate dynamic models for microbial growth prediction under later temperature conditions. Therefore, in order to improve the accuracy of the dynamic model, it is desirable to collect data on various storage environments, i.e., storage temperatures of fresh vegetables in various places. It is needless to say that the present invention is not limited to the temperature distribution shown in FIG.

In order to collect data on microbial growth patterns at different storage temperatures, the test organisms were inoculated into fresh vegetables to obtain a certain number of bacteria, and then the bacteria were cultured at a constant storage temperature .

Figures 3a to 3c is 4 ^o C, 10 ^o C, 15 ^o C, 25 ^o C, 30 ^o lettuce, assorted according to the change of time at storage temperatures of C sprout, and the number of changes in the E. coli found in parsley , And Figs. 4A to 4C are graphs respectively showing the number of Staphylococcus aureus.

Next, a primary model related to the microbial growth prediction is determined according to the characteristics of the collected data (S11). The microorganism growth prediction model includes an exponential model, a logistic model, a Gompertz model, and a Baranyi model. In the present invention, Considering the microbial growth characteristics, we will develop the first model using Baranyi model.

 The expression of the Varani model is shown in Equation (1) below.

(Where mu _max : maximum growth rate of microorganisms, N ₀ : number of initial microorganisms, Nt : Number of microorganisms over time, q ₀ : parameter defining initial physiological ecology of the cell, t: time, A: time variable)

The data collected in step S10 are applied to a barani model, which is a primary model, and the number of initial bacteria (N ₀ ), maximum number of bacteria (N _max ), maximum growth rate (μ _max ) An LPD (Lag Phase Duration), and the like are calculated (S12).

Table 2 below shows the parameters of the first-order model for the growth prediction of pathogenic Escherichia coli at storage temperatures of 4 ^o C, 10 ^o C, 15 ^o C, 25 ^o C and 30 ^o C in lettuce, assorted buds, The maximum growth rate of microorganisms, the number of initial bacteria and the maximum number of bacteria, and Table 3 shows the parameters of Staphylococcus aureus.

Induction (LPD) of Escherichia coli in lettuce 25, 30 ^o C showed a 1.63, 1.76 h in the storage temperature, the maximum growth rate of 4, 10, 15, 25, 30 in ^o C respectively 0.004, 0.018, 0.016, 0.294 , 0.316 log CFU / g / h, indicating that the pathogenic Escherichia coli was able to grow even at low temperature (10 ^° C). The maximum growth rate was also increased with increasing storage temperature.

In the assorted buds, the pathogenic Escherichia coli (LPD) was found to be 3.36 and 3.46 h at storage temperatures of 25 and 30 ^° C. The maximum growth rates were 0.003, 0.310 and 0.336 log CFU / g at 15, 25 and 30 ^o C, respectively / h, indicating that the growth of E. coli was poor.

The pathogenic Escherichia coli (LPD) was found to be 1.49 h at a storage temperature of 30 ^° C. and the maximum growth rate was 0.002, 0.015, 0.150, and 0.428 log CFU / g at 10, 15, 25 and 30 ^o C, respectively. h, and the maximum growth rate was also found to increase with increasing storage temperature.

Induction machine (LPD) of Staphylococcus aureus in lettuce 4, 10 ^o C was shown as 52.16, 12.06 h at storage temperatures, the maximum growth rate was 4, 10, 15, 25, 30 in ^o C, respectively 0.003, 0.004, 0.006, 0.015 and 0.024 log CFU / g / h, respectively, and no active growth of yellow staphylococci was observed in lettuce stored at 15 ^o C or lower.

Induction machine (LPD) of Staphylococcus aureus in assorted sprout is 4, 10 ^o C was shown as 33.60, 15.29 h at storage temperatures, the maximum growth rate was 4, 10, 15, 25, 30 in ^o C, respectively 0.001, 0.089, 0.133 , 0.103 and 0.006 log CFU / g / h, respectively, indicating that the growth of Staphylococcus aureus was poor.

The maximum induction rate of the Staphylococcus aureus (LPD) was 45.47, 33.6 and 0.90 h at 4, 10 and 25 ^o C storage temperature, respectively. The maximum growth rate was 0.002 at 4, 10, 15, 25 and 30 ^o C, 0.003, 0.010, 0.021, and 0.010 log CFU / g / h, indicating that Staphylococcus aureus grows even at low temperatures (10 ^° C).

Next, in order to predict how the parameters calculated in the first model change according to the internal and external environmental factors, a second model according to the environmental change of the parameters calculated in the first model is developed (S13).

Secondary models for microbial growth prediction include the Polynomial model, the Arrhenius model, the square root model, the Gamma concept, and the Ratkowski model. In the present invention, a second-order model is developed to calculate the maximum growth rate of pathogenic Escherichia coli according to the storage temperature change using a square root model.

Equation (2) below represents the expression of the square root model.

Where μ _{max is the} maximum growth rate, α is the slope, T is the storage temperature, T _{min is the} theoretical minimum temperature,

FIG. 5 shows a second-order model of the maximum growth rate of pathogenic Escherichia coli in lettuce, assorted sprouts and buttercups, calculated using the values of the maximum growth rate obtained in the first-order model, The second model equation for

The T _min value for the maximum growth _rate of the pathogenic Escherichia coli of lettuce was 3.618, and it was predicted that the pathogenic Escherichia coli could grow at a storage temperature of 3.6 ^o C or higher in lettuce. In the case of the induction value, the LPD value calculated in the first- 1.76 h, indicating that the need for the development of a second model for LPD is low, and that the average value of LPD calculated in applying the predictive model is 0.68 h.

The T _min value for the maximum growth _rate of pathogenic Escherichia coli in assorted buds was 7.876, and the pathogenic Escherichia coli in assorted buds was predicted to be able to grow at a storage temperature of 7.8 ^o C or higher. For inducer values, LPD Value is 0.00-3.46 h, the need for the development of the second model for LPD is low, and it is also acceptable to apply 1.42 h, which is the average value of LPD calculated when applying the predictive model.

The T _min value for the maximum growth _rate of pathogenic Escherichia coli in watercress was 7.097, and it was predicted that pathogenic Escherichia coli could grow at a storage temperature of about 7.1 ^o C or higher in parsley. In the case of the inducer value, 1.49 h, indicating that the need for the development of a second model for LPD is low and it is acceptable to apply 0.30 h, which is the average value of LPD calculated in applying the predictive model.

In the present invention, a second-order model is developed to calculate the maximum growth rate of Staphylococcus aureus according to the storage temperature change using a polynomial model.

Equation (4) below expresses the formula of the polynomial model.

(Where, _max : maximum growth rate,?, B, c: constant, T: storage temperature)

6 shows a second-order model of the maximum growth rate of Staphylococcus aureus in lettuce, assorted sprouts, and buttercups, using the values of the maximum growth rate obtained in the first-order model using the variables of the second-order model, The second model equation is shown.

The maximum growth rate of Staphylococcus aureus in lettuce showed a tendency to increase with increasing storage temperature. As a result of the development of the second model of lettuce, it was found that the experimental data were included in the 95% confidence interval.

The maximum growth rate of Staphylococcus aureus of the assorted buds showed no significant relation with the change of the storage temperature. As a result of the development of the secondary model of assorted buds, the accuracy of the data was higher in the 95% confidence interval.

The maximum growth rate of S. staphylococcus aureus showed a slight increase with increasing storage temperature. As a result of the development of a second model of parsley, the accuracy of the results was higher in the 95% confidence interval.

FIG. 7 is a graph showing the results of evaluating the suitability of the growth prediction model of pathogenic Escherichia coli of fresh vegetables, which have been developed through the above-described processes. The statistical fitness of the developed model was calculated by calculating the root mean square error (RMSE) . Figure 8 shows the results of the assays for Staphylococcus aureus.

Additional experiments were carried out at 12 and 20 ^o C storage temperatures, which were not used for the model validation, and the number of pathogenic E. coli or S. aureus at each temperature was estimated using the developed first and second models Mean root mean square deviation between pathogenic Escherichia coli or S. aureus was calculated. The average root mean square deviation values for the pathogenic Escherichia coli were 0.489 (lettuce), 0.398 (assorted bud) and 0.520 (miscellaneous), respectively. The mean square root deviation values for S. aureus were 0.361 (lettuce) and 0.425 Buds), 0.304 (dropwort), these values correspond to those that are comparable to the results of previous studies. Therefore, it is verified that the first and second models developed above are statistically suitable models.

Next, a dynamic model for predicting the microbial growth of fresh vegetables under the temperature change condition is derived using the first and second models developed through the above-described process (S14). In the present invention, a dynamic model for predicting the microbial growth under the thermocompression condition has been developed in consideration of the continuous change of the storage temperature at the stage of sale and consumption of fresh vegetables. In the present invention, a first-order model is obtained by using a Barani's model, a second-order model is developed using the calculated parameters, and the microorganisms are grown at different temperatures Predict microbial growth under conditions.

FIG. 9 is a graph comparing a value obtained by predicting the growth of pathogenic E. coli of fresh vegetables under actual temperature conditions using the dynamic model of the present invention, and FIG. 10 is a graph showing a dynamic model of Staphylococcus aureus . The dynamic model of Figs. 9 and 10 shows that the storage temperature of lettuce was changed to 4.2, 21.2, and 10.5 ^o C at intervals of 6 h, based on the results of distribution temperature and time at the time of investigation of the distribution environment of lettuce To predict the increase of the number of pathogenic Escherichia coli and Staphylococcus aureus, a dynamic model was developed using differential concept.

Referring to FIGS. 9 and 10, the accuracy of the prediction can be confirmed once again that the predicted value of microorganism growth in the dynamic model according to the present invention closely matches the observed value.

Through the result of the dynamic model thus derived, risk assessment information and / or safety management information of fresh vegetables is derived (S15). The risk assessment information is information for assessing the risk of human ingestion based on the predicted microbial growth of the fresh vegetables. The safety management information is information about the appropriate distribution temperature or storage temperature for the safe distribution and consumption of fresh vegetables, .

9, the maximum growth rate of pathogenic Escherichia coli in lettuce was calculated as 0.004, 0.018 and 0.016 log CFU / g / h at 4, 10 and 15 ^o C, respectively, indicating that E. coli was grown at a low temperature of 10 ^o C It is shown that the average temperature of the store is 10.5 ^o C and it should be distributed and sold at a lower temperature. It can be seen that it must be stored at a temperature of less than 10 ^o C when it is stored in a domestic refrigerator to ensure safety. The maximum growth rates of pathogenic Escherichia coli inoculated on lettuce at 25 and 30 ^o C were 0.294 and 0.316 log CFU / g / h, respectively, and LPD was 1.63 and 1.76 h at 25 and 30 ^o C storage temperature, It can be concluded that there is a risk of food poisoning caused by E. coli when exposed to a high room temperature for a long time in the process of being transported and stored by E. coli.

9, the growth of pathogenic E. coli in assorted sprout is 10 ^o C at temperatures below was not observed, 15 ^o The maximum growth rate value at C is a 0.003 log CFU / g / h, 10 ^o C and out from the sales floor - the maximum growth rate of pathogenic Escherichia coli at 25 and 30 ^o C, Values were 0.310 and 0.336 log CFU / g / h, respectively. However, the increase from the lowest growth to the maximum growth was less than 1 log CFU / g, suggesting that the growth of pathogenic Escherichia coli in assorted sprouts is relatively low .

The maximum growth rate value of pathogenic E. coli in buttercup 10, 15 in ^o C, respectively 0.002, 0.015 log CFU / g / are calculated as h shown to the E. coli growth at low temperature more as the average temperature of the shop 12.3 ^o C being It should be stored at a temperature of less than 10 ^° C when stored in a domestic refrigerator after consumption. In addition, as the pathogenic E. coli is also actively growing at a storage temperature of 15 ^° C, it should be avoided to store at room temperature in winter, and the maximum growth rate of pathogenic Escherichia coli inoculated at 25 and 30 ^o C, respectively 0.150 and 0.428 log CFU / g / h, respectively. The induction unit (LPD) is 1.49 h at 30 ^o C storage temperature, and is exposed to high room temperature for a long time during transportation and storage by consumers. . Therefore, it is necessary to minimize the exposure time to room temperature when storing after consumption, such as taking into consideration the temperature inside the vehicle when transporting it to the home after consumption.

10, the maximum growth rate of S. aureus in lettuce is 4, 10, 15 ^o is calculated from C to 0.003, 0.004, 0.006 log CFU / g / h , respectively 10 ^o yellow even at low temperatures staphylococcal the growth of C The average temperature of the store is 10.5 ^o C, so it should be distributed and sold at a lower temperature. It should be stored at a temperature of less than 10 ^o C when stored in a domestic refrigerator after consumption. In addition, the maximum growth rate of Staphylococcus aureus inoculated at lettuce at 25 and 30 ^o C was 0.015 and 0.024 log CFU / g / h, respectively. When exposed to high room temperature for long periods during transportation and storage by consumers, There is a risk of food poisoning caused by staphylococci. Therefore, it is necessary to minimize the exposure time to room temperature when storing after consumption, such as taking into consideration the temperature inside the vehicle when transporting it to the home after consumption.

Referring back to Figure 10, 4, 10 in ^o C maximum growth rate value of a Staphylococcus aureus assorted buds maximum growth rate value at 0.001, 0.089 log CFU / g / h, 15 o C is 0.133 log CFU / g / h, it should be possible to distribute the assorted sprouts at a temperature of around 10 ^o C in the shop. - The _maximum value of Staphylococcus aureus at 25 and 30 ^o C was 0.103 and 0.006 log CFU / g / h, and the risk of food poisoning due to Staphylococcus aureus at the time of exposure to high room temperature for a long time during transportation and storage by consumers It is considered to be exposed to room temperature for a long time when it is transported to home after consumption, and room temperature storage after consumption should be avoided.

10, the maximum growth rate of Staphylococcus aureus was estimated to be 0.002, 0.003, and 0.010 log CFU / g / h at 4, 10, and 15 ^o C, respectively, indicating that Staphylococcus aureus grows even at low temperatures. The average temperature of 12.3 ^o C should be distributed and sold at a lower temperature. It should be stored at a temperature of less than 10 ^o C when stored in a domestic refrigerator after consumption. The maximum growth rate of Staphylococcus aureus inoculated with lettuce at 25 and 30 ^o C was 0.021 log CFU / g / h, respectively. It is considered that there is a risk of food poisoning caused by Staphylococcus aureus. Therefore, it is necessary to minimize the exposure time to room temperature when storing after consumption, such as taking into consideration the temperature inside the vehicle when transporting it to the home after consumption.

The present invention can provide information on proper storage temperature and storage time so that the user can easily understand based on the analysis result of the dynamic model.

In the above-mentioned embodiments, development of a dynamic growth prediction model of pathogenic Escherichia coli and Staphylococcus aureus for lettuce, assorted sprouts and buttercups in fresh vegetables has been described. However, this is only an illustrative example, and a model for more fresh vegetables and more food poisoning bacteria Can be developed and further databases can be constructed.

FIG. 11 is a schematic view of a safety management system for fresh vegetables according to an embodiment of the present invention, and FIG. 12 is a conceptual diagram of an apparatus 100 for predicting dynamic microbial growth in fresh vegetables according to an embodiment of the present invention. The description overlapping with the above embodiment will be omitted as necessary.

11 and 12, a system for managing the safety of fresh vegetables according to an embodiment of the present invention includes a data collection server 1, a microorganism growth prediction apparatus 100, and a safety management information providing server 200 The microorganism growth prediction apparatus 100 includes a user input unit 10, a model storage unit 20, and a model calculation unit 30.

The data collection server 1 is for collecting data on temperature changes due to storage or distribution during distribution and sale of fresh vegetables. The collected data is used to develop the dynamic model. For the temperature data collection of fresh vegetables, it is possible to collect the measured values of the actual fresh vegetables according to the temperature. However, by using the equation about the correlation between the temperature of the storage place of fresh vegetables and the temperature of each fresh vegetables, The temperature data of each of fresh vegetables may be estimated from the corresponding data.

The microorganism growth prediction apparatus 100 has a primary model and a secondary model for microbial growth of fresh vegetables and applies the temperature data collected by the data collection server 1 to the corresponding model of fresh vegetables to determine microbial growth Referring to FIG. 12, the dynamic model includes a user input unit 10, a model storage unit 20, and a model calculation unit 30.

The user input unit 10 is for receiving a user input, and includes a keyboard, a mouse, a touch screen, a GUI, and the like. A user can select a desired fresh vegetable or a type of food poisoning bacteria through the user input unit 10 to derive a dynamic model that can predict the growth of the food poisoning bacteria. The user may select the storage temperature profile of the fresh vegetables collected from the data collection server 1 through the user input unit 10 or input the stored temperature profile data measured separately from the outside into the microorganism growth prediction apparatus 100 It is possible.

The model storage unit 20 stores the first and second models for predicting the growth of microorganisms according to the fresh vegetables. The first and second predictive models are used to predict the growth of each food poisoning bacteria Develop and save the model. The more precise the growth prediction is possible, the more various fresh vegetable types and microbial species models are constructed and updated.

The model calculator 30 extracts models suitable for the fresh vegetables and the microorganism species from the model storage unit 20 when the user selects fresh vegetable species and microorganism species from the user, To calculate the model. The model calculation unit 30 differentiates the parameters calculated in the primary model and the secondary model with respect to the temperature to predict the growth of microorganisms in fresh vegetables under the thermocompression conditions. The user can confirm the growth prediction value of the microorganism through the value of the dynamic model calculated by the model calculation unit 30. [

The apparatus for predicting microbial growth 100 may further include a database unit 40 for storing data on storage temperatures of fresh vegetables collected by the data collection server 1. [ The database unit 40 stores data and statistics about the storage temperature and storage temperature for each fresh vegetable.

If profile data relating to the storage temperature of the fresh vegetables is not input from the user, the dynamic model is derived according to the statistics regarding the storage temperature of the fresh vegetables stored in the database unit 40, , The dynamic model is derived by applying the corresponding data. If the temperature profile data inputted by the user has only data relating to a part of the storage period of the fresh vegetables, the model calculating unit 30 calculates the temperature profile data Extract temperature data and utilize it.

The safety management information providing server 200 provides safety management information on storage time and storage temperature of the fresh vegetables based on the dynamic model derived from the model calculation unit 30. [ Furthermore, the safety management information providing server 200 can provide the risk assessment information of the fresh vegetables based on the calculated dynamic model. Here, the safety management information of the fresh vegetables provided by the safety management information providing server 200 includes information about proper storage temperature, storage period, etc. so that the user can easily understand the analysis result of the derived dynamic model.

Although the microorganism growth prediction apparatus 100 and the safety management information providing server 200 are separately configured in the above-described embodiment, the two configurations may be implemented as one apparatus.

According to another embodiment of the present invention, the microorganism growth prediction apparatus 100 may further include a safety management information calculation unit 50. [ The safety management information calculation unit 50 is for calculating safety management information on the storage time and the storage temperature of the fresh vegetables based on the dynamic model derived by the model calculation unit 30, The predicted value or trend of the number of microorganisms in the vegetable is interpreted, and data on the appropriate storage temperature or storage time is calculated and provided in the future.

According to another embodiment of the present invention, the microbial growth prediction apparatus 100 may further include a risk evaluation unit 60. [ The risk assessment unit (60) calculates risk assessment information of the fresh vegetables based on the dynamic model derived by the model calculation unit (30). The risk assessment information for fresh vegetables includes information on the risk of human health if the fresh vegetable is consumed at the present stage.

As described above, the microbial growth prediction method of fresh vegetables according to the present invention, and the safety management system of fresh vegetables according to the present invention, can construct a prediction model for each microorganism according to fresh vegetables to predict the microbial growth under the temperature- It is possible to increase the accuracy of the prediction.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. The codes and code segments constituting the computer program may be easily deduced by those skilled in the art. Such a computer program can be stored in a computer-readable storage medium, readable and executed by a computer, thereby realizing an embodiment of the present invention. As the storage medium of the computer program, a magnetic recording medium, an optical recording medium, a carrier wave medium, or the like may be included.

Furthermore, the terms "comprises", "comprising", or "having" described above mean that a component can be implanted unless otherwise specifically stated, But should be construed as including other elements. All terms, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used terms, such as predefined terms, should be interpreted to be consistent with the contextual meanings of the related art, and are not to be construed as ideal or overly formal, unless expressly defined to the contrary.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

1: Data collection server 10: User input part
20: Model storage unit 30: Model calculation unit
40: Database part 50: Safety management information calculating part
60: Risk Assessment Unit 100: Microorganism Growth Prediction Apparatus
200: Safety management information providing server

Claims (18)

In a method for predicting dynamic microbial growth in fresh vegetables,
Based on the data on the microbial growth pattern in fresh vegetables according to storage temperature, a first-order model for microbial growth was determined, and the number of initial microbial bacteria, the maximum number of microbial cells, the maximum growth rate, ;
Deriving a quadratic model according to a storage temperature change of the calculated parameter; And
And deriving a dynamic model for predicting microbial growth of fresh vegetables under the thermocompression conditions using the parameter values calculated in the primary model and the secondary model. .
The method according to claim 1,
Wherein the step of deriving the dynamic model derives the dynamic model for predicting microbial growth of fresh vegetables under a temperature-change condition by differentiating the parameters calculated in the primary model and the secondary model with respect to temperature. A Method for Predicting Dynamic Microorganism Growth in Vegetables.
3. The method according to claim 1 or 2,
Wherein the temperature-change condition is based on profile data on the temperature change of the fresh vegetables with time.
3. The method according to claim 1 or 2,
Wherein the primary model is derived using a Baranyi Model. ≪ RTI ID = 0.0 > 8. < / RTI >
3. The method according to claim 1 or 2,
Wherein the secondary model is derived using either a square root model of Equation (2) or a polynomial model of Equation (4).
&Quot; (2) "

(Where _max : maximum growth rate,: slope, T: storage temperature, T _min : theoretical minimum temperature)
&Quot; (4) "

(Where _max : maximum growth rate,, b, c: constant, T: storage temperature)
3. The method according to claim 1 or 2,
Further comprising extracting safety management information at a storage temperature and storage time of the fresh vegetables based on the dynamic model.
3. The method according to claim 1 or 2,
Further comprising the step of deriving the risk assessment information of the fresh vegetables based on the dynamic model.
3. The method according to claim 1 or 2,
Wherein the dynamic model is derived for each of lettuce, sprouts, parsley, cabbage, broccoli, cucumber, kale, cheongchaejang, or red pepper.
9. The method of claim 8,
Wherein the dynamic model is derived for each of pathogenic Escherichia coli, Staphylococcus aureus, Salmonella, Vibrio, or Cholera.
A computer-readable recording medium having recorded thereon a program for executing a method for predicting dynamic microorganism growth in fresh vegetables according to claim 1 or 9.
An apparatus for predicting dynamic microorganism growth in fresh vegetables,
A user input unit for receiving a selection of fresh vegetables and microorganisms from a user;
A model storage unit for storing information on a first model and a second model related to microbial growth of at least one fresh vegetable; And
Information on the model corresponding to the selection information on the type of fresh vegetables and microorganisms input through the user input unit is extracted from the model storage unit and profile information about the storage temperature according to the storage time of fresh vegetables inputted from the user And a model calculation unit for deriving a dynamic model that is applied to the information on the extracted model and predicts microbial growth of fresh vegetables under the thermocompression condition.
12. The method of claim 11,
Wherein the model calculation unit differentiates the parameters calculated in the primary model and the secondary model with respect to the temperature to predict the growth of microorganisms in fresh vegetables under the thermocompression condition.
12. The method of claim 11,
Further comprising a database section for storing statistical data on the storage temperature during distribution and sale of at least one fresh vegetable,
Wherein the model calculator compares the missing temperature information with the temperature information of the statistical data in the profile information about the storage temperature according to the storage time of the fresh vegetables inputted from the user to calculate the dynamic model. Dynamic Microorganism Growth Predictor of.
14. The method of claim 13,
Further comprising a safety management information calculation unit for calculating safety management information on the storage time and storage temperature of the fresh vegetables based on the dynamic model.
14. The method of claim 13,
And a risk evaluation unit for calculating the risk assessment information of the fresh vegetables based on the dynamic model.
In a safety management system for fresh vegetables,
A data collection server for collecting data on temperature changes of fresh vegetables in accordance with storage time during storage and sale;
Microbial growth prediction which has a first model and a second model concerning microbial growth of fresh vegetables and calculates a dynamic model for predicting microbial growth by applying the temperature data collected from the data collection server to the corresponding model of fresh vegetables Device; And
And a safety management information providing server for providing safety management information on storage time and storage temperature of the fresh vegetables based on the dynamic model.
17. The method of claim 16,
And the safety management information providing server provides the risk assessment information of the fresh vegetables based on the dynamic model.
17. The method of claim 16,
Wherein the microbial growth prediction apparatus differentiates the parameters calculated in the primary model and the secondary model with respect to temperature to predict the microbial growth of fresh vegetables under the thermocompression condition.

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