• Journal of the Korean Society of Food Culture
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• v.23 no.1
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• pp.97-104
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• 2008

The purpose of this study was to measuring the causal relationships among food involvement, health, mood, convenience, sensory appeal, weight control and vegetable consumption. A total of 290 questionnaires were completed. Structural equation model was used to measure the causal relationships among constructs. Results of the study demonstrated that the structural analysis result for the data also indicated excellent model fit. The effects of food involvement on health, mood, convenience, sensory appeal, weight control and vegetable consumption were statistically significant. As expected, health, mood, sensory appeal and eight control had significant effects on vegetable consumption. Moreover, health, mood, convenience, sensory appeal and weight control played mediating roles in the relationship between food involvement and vegetable consumption. In conclusion, based on structural analysis, a model was proposed of interrelations among food involvement, health, mood, convenience, sensory appeal, weight control and vegetable consumption. It should be noted that the original vegetable consumption model was modified and should, preferably, be alidated in future research. Other variables, such as attitude, subjective norm and perceived behavior control, may be incorporated to form vegetable consumption models that consist of new antecedent and consequence pairs.

• Journal of the FoodService Safety
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• v.2 no.2
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• pp.84-90
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• 2021

This study was performed to assess the microbiological quality and safety of microgreen sampled from harvesting farms and food processing plant in Korea. The samples were analyzed for total viable counts, coliforms, Enterobacteriaceae, Escherichia coli, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Staphylococcus aureus. Total viable counts were highly contaminated in samples collected from farms (7.7~8.2 log CFU/g) and the final products (5.8~7.8 log CFU/g), respectively. B. cereus was detected less than 100 CFU/g, which was satisfied with Korean standards (<1,000 CFU/g) of fresh-cut produce. A predictive model was developed for the changes of total viable counts in microgreens during storage at 5~35℃. The predictive models were developed using the Baranyi model for the primary model and the square root model for the secondary model. The results obtained in this study can be useful to develop the safety management options along the food chain, including fresh-cut produce storage and distribution.

• Journal of the Korean Society of Food Culture
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• v.28 no.3
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• pp.282-292
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• 2013

The purpose of this study is to identify a suitable organizational diagnostic model for analyzing the management efficiency of food service firms. We used the three-iteration Delphi technique on a panel of 19 business employees, 7 industry experts, and 4 employees in the food service industry. A total of 36 assessment indicators were developed through this panel, with numerous major findings. First, it is important to evaluate the leadership qualities of employees in terms of their motivation and competency. Second, it is important to evaluate the ability of employees to interface well with other employees. Third, it is important to evaluate and manage the brand image recognized by customers. Fourth, it is important to evaluate the fairness and regularity of the rewards given for an excellent job performance. Fifth, it is important to evaluate the level of communication and information distribution in the organizational culture. Last, but not least, it is important to evaluate the transparency and fairness of an organization in its human resource management (HRM) and the efficiency of its organizational structure. In conclusion, this study empirically shows how food service firms can develop an organizational diagnostic model to increase their managerial efficiency.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.4
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• pp.558-565
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• 2012

The objective of this study was to develop a predictive growth model for Bacillus cereus in nutrient broth and validate the developed growth model in blanched vegetables. After inoculating B. cereus into nutrient broth, growth of B. cereus was investigated at 13, 17, 24, 30 and $35^{\circ}C$. Lag time (LT) decreased while specific growth rate (SGR) increased with an increase in storage temperature. Growth of B. cereus was not observed at temperatures lower than $12^{\circ}C$. Secondary growth models were developed to describe primary model parameters, including LT and SGR. Model performance was evaluated based on bias factor ($B_f$) and accuracy factor ($A_f$). In addition, we inoculated B. cereus into blanched vegetables stored at 13, 24, $35^{\circ}C$ and observed the growth kinetics of B. cereus in five different blanched vegetables. Growth of B. cereus was most delayed on Doraji at $13^{\circ}C$ and was not observed on Gosari at $13^{\circ}C$. Growth of B. cereus at $35^{\circ}C$ was significantly (p<0.05) slower on Gosari than on other blanched vegetables. The developed secondary LT model for broth in this study was suitable to predict growth of B. cereus on Doraji and Gosari, whereas the SGR model was only suitable for predicting the growth of B. cereus on mung bean sprout.

• Agribusiness and Information Management
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• v.3 no.1
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• pp.1-10
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• 2011

In this exploratory analysis, we investigate the genesis and the evolution of local food-purchasing networks created and operated by consumers. In details, we describe how collecting and sharing information about food-products can become a central activity for some consumers' communities and how these communities are starting to play an active role in the food supply chain. We define this community-based food-purchasing model as collaborative food network (CFN), and we analytically describe its characteristics and differences with respect to the traditional and industrialized agrifood supply chain models. A collaborative food network community in Italy, known as GAS ("Gruppi di Acquisto Solidale" - "Solidarity Purchasing Groups"), is introduced as an example of our analytical model. We will use this empirical example to present the strengths and weaknesses of the CFN model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.51-58
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• 2022

In this paper, we propose a decision tree-based machine learning model that leads to food exchange table renewal by classifying food groups through machine learning for existing food and food data found by web crawling. The food exchange table is the standard for food exchange intake when composing a diet such as diet and diet, as well as patients who need nutritional management. The food exchange table, which is the standard for the composition of the diet, takes a lot of manpower and time in the process of revision through the National Health and Nutrition Survey, making it difficult to quickly reflect food changes according to new foods or trends. Since the proposed technique classifies newly added foods based on the existing food group, it is possible to organize a rapid food exchange table reflecting the trend of food. As a result of classifying food into the proposed model in the study, the accuracy of the food group in the food exchange table was 97.45%, so this food classification model is expected to be highly utilized for the composition of a diet that suits your taste in hospitals and nursing homes.

• The Korean Journal of Food And Nutrition
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• v.29 no.5
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• pp.746-752
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• 2016

In this study, we analyzed the dietary style of Chinese singles; in addition, the effect of convenience food consumption on the quality of life of singles was evaluated through construct model development on the relationship between the frequency of consumption and satisfaction with convenience food and quality of life. A statistical analysis of 153 surveys from Tianj was conducted using SPSS 12.0 for Windows and SEM using AMOS 5.0 statistics package. The reliability of the data was confirmed by an exploratory factor analysis and Cronbach's alpha coefficient. The measurement model was confirmed as appropriate by a confirmatory factor analysis of the measurement model in conjunction with AMOS. The results of a factor analysis were as follows. Dietary style was categorized into four factors. The level of satisfaction with convenience food was categorized into seven factors and quality of life was categorized into four factors. The reliability of these findings was supported by a Cronbach's alpha coefficient of 0.6 and higher for all the factors. For the level of satisfaction with convenience food based on dietary style and the quality of life of singles, a structural equation model was constructed and analyzed. The results of all tests indicated that the model satisfied the recommended level of goodness of fit index and thus, the overall research model was appropriate. The current study highlights the increased interest in eating habits of singles and is necessary for further improvement in nutrition education.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Korean Journal of Food Science and Technology
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• v.23 no.4
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• pp.523-525
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• 1991

The stimulatory effect of oxygen on the browning of a soy sauce model system (glucose-glycine in 18% NaCl solution) with or without added organic acids were investigated. The group incubated aerobically browned at a significantly higher degree than the group incubated anaerobically. Organic acids also positively affected the browning of the soy sauce model system.

• Food Science of Animal Resources
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• v.39 no.3
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• pp.371-378
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• 2019

The objective of this study was to develop mathematical models for describing the kinetic behavior of Staphylococcus aureus (S. aureus) in seasoned beef jerky. Seasoned beef jerky was cut into 10-g pieces. Next, 0.1 mL of S. aureus ATCC13565 was inoculated into the samples to obtain 3 Log CFU/g, and the samples were stored aerobically at $10^{\circ}C$, $20^{\circ}C$, $25^{\circ}C$, $30^{\circ}C$, and $35^{\circ}C$ for 600 h. S. aureus cell counts were enumerated on Baird Parker agar during storage. To develop a primary model, the Weibull model was fitted to the cell count data to calculate Delta (required time for the first decimal reduction) and ${\rho}$ (shape of curves). For secondary modeling, a polynomial model was fitted to the Delta values as a function of storage temperature. To evaluate the accuracy of the model prediction, the root mean square error (RMSE) was calculated by comparing the predicted data with the observed data. The surviving S. aureus cell counts were decreased at all storage temperatures. The Delta values were longer at $10^{\circ}C$, $20^{\circ}C$, and $25^{\circ}C$ than at $30^{\circ}C$ and $35^{\circ}C$. The secondary model well-described the temperature effect on Delta with an $R^2$ value of 0.920. In validation analysis, RMSE values of 0.325 suggested that the model performance was appropriate. S. aureus in beef jerky survives for a long period at low storage temperatures and that the model developed in this study is useful for describing the kinetic behavior of S. aureus in seasoned beef jerky.