• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.769-782
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• 2020

Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.

• The Korean Journal of Franchise Management
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• v.14 no.3
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• pp.17-35
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• 2023

Purpose: As more and more food franchise companies want to expand overseas, related research is becoming more and more necessary. This study aims to examine the critical factors for successful overseas expansion according to the stages of overseas expansion, derive vital associations, and examine the success factors of overseas expansion through semantic network analysis. Research Design, Data, and Methodology: This study conducted in-depth interviews with three food franchise companies that have experienced overseas expansion and conducted semantic network analysis among crucial associations. The semantic network analysis was conducted using the Textom program. Results: Based on the results of the in-depth interview analysis, the factors considered when expanding overseas were categorized as 1) standardization and localization strategies of overseas franchisees, 2) physical environment of overseas franchisees, 3) entry types of overseas franchisees, 4) constraints of overseas franchisees, and 5) success criteria of overseas franchisees. The semantic network analysis based on the corresponding keywords showed that the importance of local partners is very high in common. Conclusion: This study examined and re-categorized the important factors to consider when a restaurant franchise company expands overseas in a step-by-step manner. In addition, an attempt was made to examine the keywords derived from the semantic network analysis objectively. The results provided theoretical and practical implications for the successful overseas expansion of franchise companies.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.

• Korean Journal of Food Science and Technology
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• v.31 no.5
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• pp.1171-1177
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• 1999

The changes on microstucture of recombinated gels with different ratio of amylose(A) and amylopectin(AP) which were isolated from nonwaxy rice starch were investigated by scanning electron microscope(SEM) and X-ray diffractometer. As the concentration of amylose was above 3%(1.08% of soluble amylose) in the amylose suspension, gel matrix became like a three-dimensional network. The microstructure of amylose gels showed a network including macroporous structure, but the higher the ratio of amylopectin content were, the firmer network were formed. In case of A/AP mixed gels(15%) with different amylose/amylopectin percent ratios ; 0/5, 5/10, 10/5, 15/0%, as the storage time of gels and the percent ratio of amylose content were increased, network was formed harder with thick films. While X-ray diffractograms of waxy rice starch which contained 100% amylopectin showed A type, those of purified amylose and amylopectin showed V type and amorphous patterns, respectively. Amylose(3%) gels added $2{\sim}3%$ amylopectin and A/AP mixed gels(15%) showed peak at $2{\theta}\;=\;17.0^{\circ}$which were shown B type crystallinity similar af retrograded starches. Also as the percent ratio of amylose content in mixed gels was increased, peak intensity wat increased.

• Korean Journal of Food Science and Technology
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• v.27 no.4
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• pp.525-531
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• 1995

Three quality factors of bread during baking process were measured to develop neural network models for bread baking process. Firstly, volume and browning changes during bread baking process were measured using image processing technique and temperature changes inside the bread during process were measured by K-type thermocouples. Relationships among them showed nonlinearity. Secondly, multilayer perception structure with error back propagation learning was used to construct neural network models. Three neural network models for volume, browning, and bread temperature were developed respectively. Developed models showed good performance with predictive error of 4.62% for volume and browning changes after 30 seconds, 7.38% for volume and browning changes after 2 minutes, and 1.09% for temperature change inside the bread respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.33-42
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• 2022

One of the most pressing socioeconomic problems confronting humanity on a worldwide scale is food security, particularly in light of the expanding population and declining land productivity. These causes have increased the number of people in the world who are at risk of starving and have caused the natural ecosystems to degrade at previously unheard-of speeds. Happily, the Internet of Things (IoT) development provides a glimmer of light for those worried about food security through smart agriculture-a development that is particularly relevant to automating food production operations in order to reduce labor expenses. When compared to conventional farming techniques, smart agriculture has the benefit of maximizing resource use through precise chemical input application and regulation of environmental factors like temperature and humidity. Farmers may make data-driven choices about the possibility of insect invasion, natural disasters, anticipated yields, and even prospective market shifts with the use of smart farming tools. The technical foundation of smart agriculture serves as a potential response to worries about food security. It is made up of wireless sensor networks and integrated cloud computing modules inside IoT.

• The Korean Journal of Food And Nutrition
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• v.37 no.4
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• pp.197-210
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• 2024

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.109-116
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• 2023

This paper presents a network-based automation system for the detection of metallic objects in food and livestock. A metal detector is a core equipment used for the inspection required by HACCP. Since the existing metal detectors are manufactured as a single-body equipment, it is difficult to take into account various user requirements for the system. In order to overcome the drawback, this study presents a network-based automation system for metal detector utilizing an industrial fieldbus and modular components. The proposed system can effectively consider the various customer requirements and control schemes. The proposed system can also achieve the improvement in speed and success rate of detecting metallic objects. The effectiveness of the proposed system is demonstrated through a various experiments.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.121-136
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• 2017

In this paper, using trade data of 57 HS 4 digit based agricultural and food(agro-food) products among 47 countries during 2005 to 2014, the international competitiveness and trade structure have been analyzed from the context of global networks employing the methods of social network analysis. Firstly, the differences in the network structure by agricultural products have been revealed. The number of disconnected groups was significantly lager in order of vegetables, fruits and processed foods. Secondly, the differences in the community structure by agricultural and food products have been also revealed. That is to say, for some commodities, the community structure has been changed dynamically, on the other hand, there are some agricultural products that have not changed its community structure despite the increasing trends of trade volume. Thirdly, even though the international competitiveness of Korea's agricultural products was still very limited in the sense that only 26 items have been included in the top3 network of 57 agricultural products, there has been possibilities of the increasing patterns of the competitiveness.

• Preventive Nutrition and Food Science
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• v.2 no.3
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• pp.259-262
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• 1997

The microstructures of soybean milk curds, prepared by different coagulation methods, were observed by the scaning electron microscope. Th curd coagulated by theaddition of bacerial soybean mil clotting enzyme showed little textural changes and gave smoother gel than those prepared either by lactic acid fermentation using Streptococcus thermophilus or by the addition of CaSO$_4$. The curds obtained by lactic acid fermentation and by the addition of inorganic salt exhibited three dimensional network structure which indicated harder gel than that prepared by soybean mil clotting enzyme.