• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.87-93
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• 2019

Revision work on the Codex Classification of Foods and Animal Feeds was undertaken in 2007 and presently, revisions for most food groups have been completed. For vegetables, the work was conducted during 2014-2017, and the final draft revision was adopted by the $40^{th}$ Codex Alimentarius Commission (2017). Here, the revised classification of vegetable commodities is introduced in order to be utilized in various food-related fields, in particular, food safety regulation. The revised classification is briefly summarized as follows: Codex classified vegetables into 10 groups (Group 009-018): bulb vegetables (Group 009), Brassica vegetables (except Brassica leafy vegetables) (Group 010), fruiting vegetables, Cucurbits (Group 011), fruiting vegetables, other than Cucurbits (Group 012), leafy vegetables (including Brassica leafy vegetables) (Group 013), legume vegetables (Group 014), pulses (Group 015), root and tuber vegetables (Group 016), stalk and stem vegetables (Group 017) and edible fungi (Group 018). The groups are further divided into a total of 33 subgroups. In the Classification, 430 different commodity codes are assigned to vegetable commodities. Meanwhile, Korea's Ministry of Food and Drug Safety (MFDS) does not include potatoes, beans and mushrooms within a vegetable group. In addition, the MFDS divides one vegetable group into six subgroups including flowerhead Brassicas, leafy vegetables, stalk and stem vegetables, root and tuber vegetables, fruiting vegetables, Cucurbits, and fruiting vegetables other than Cucurbits. Therefore, care is needed in using the Codex Classification.

• Korean journal of food and cookery science
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• v.22 no.3 s.93
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• pp.299-306
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• 2006

The numerous varieties of coffee beans contain a wide range prices and qualities. While the varieties of green coffee beans can generally be distinguished by their appearance, this visual criterion is impossible after the roasting process. Therefore, we need to develop a classification method or device. In this study, the potential of a new type of electronic nose, fast gas chromatography based on a surface acoustic wave sensor(SAW), was evaluated for the classification of origins and blended commercial brands in roasted coffee beans. Eight blended commercial brands and the origins of four similarly roasted ground coffee beans(with no significant difference of color) were rapidly(90 sec/sample) classified. The reproductive results were easily understandable over the aroma image pattern by $VaporPrint^{TM}$. In conclusion, GC-SAW electronic nose can be applied to the classification of origins and commercial brands in roasted ground coffee beans and to e evaluation of the similarities and differences of volatile pattern between samples.

• Journal of the Korean Society of Food Science and Nutrition
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• v.23 no.3
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• pp.420-428
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• 1994

The purpose of this study was to determine relationship among the observed frequencies of 12 beverages selected by college men and women according to sex, age, race and academic classification and to estimate consumption of milk according to sex, age, race and academic classification. The instrument consisted of a check list and four questions. The sample of 282 subjects, 149 college men and 133 college women, was made by the accidental choice method. Observations occurred in the university center cafeteria at the dinner meal. The significant relationship s were sex and race in association with beverage selections by all subjects. The proportion of men in the distribution who selected regular , carbonated soft drinks and the proportion of white students who selected any of the carobnated soft drinks were the influencies. The result of the study indicated that carbonated soft drinks were the most preferred items followed by milk, water, iced tea, fruit juices, coffee, cocoa, and tea.

• Food Industry
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• s.173
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• pp.104-111
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• 2003

• Food Industry
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• s.173
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• pp.125-131
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• 2003

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.117-124
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• 2023

Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.129-138
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• 2021

To set the estimated energy requirement (EER) in Dietary Reference Intakes for Koreans (KDRI), we need the coefficient by physical activity stage, as determined by the physical activity level(PAL). Thus, there has been demand for a tool to calculate PAL based on the physical activity diary. This study was undertaken to develop a physical activity (PA) classification table for Koreans, using the 2011 Compendium of physical activities in the United States. The PA classification table for Koreans contains 262 codes, and values of the metabolic equivalent of task (MET) for specific activities. Of these, 243 PAs which do not have Korean specific data or information, were selected from the 2011 Compendium of PAs that originated in the United States; another 19 PAs were selected from the previous research data of Koreans. The PA classification table is codified to facilitate the selection of energy values corresponding to each PA. The code for each PA consists of a single letter alphabet (activity category) and four numeric codes that display the activity type (2 digit number), activity intensity (1 digit number), and specific activities (1 digit number). In addition, the intensity (sedentary behavior, low, middle and high) of specific PA and its rate of energy expenditure in MET are presented together. The activity categories are divided into 4 areas: Daily Activity (A), Movement (B), Occupation (C), and Exercise and Sports (D). The developed PA classification table can be applied to quantify the energy cost of PA for adults in research or practice, and to assess energy expenditure and physical activity levels based on self-reported PA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1704-1720
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• 2020

Recognizing food from photographs presents many applications for machine learning, computer vision and dietetics, etc. Recent progress of deep learning techniques accelerates the recognition of food in a great scale. We build a hierarchical structure composed of deep CNN to recognize and classify food from photographs. We build a dataset for Korean food of 18 classes, which are further categorized in 4 major classes. Our hierarchical recognizer classifies foods into four major classes in the first step. Each food in the major classes is further classified into the exact class in the second step. We employ DenseNet structure for the baseline of our recognizer. The hierarchical structure provides higher accuracy and F1 score than those from the single-structured recognizer.

• Food Science of Animal Resources
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• v.21 no.2
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• pp.97-102
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• 2001

The characteristic fitness of food to each Sa-sang constitution and the relationship between Han-Yeoul characteristics and chemical composition after classifying Samgye-tang ingredients and other food into Ohn-Yeoul-Ryang-Han characteristic were assessed. When the suitable constitution to the each characteristic was investigated after classifying Samgye-tang ingredients and other food into Han, Ryang, Pyound, Ohn and Yeoul of which fitness case for Soeumin was 44∼63% but fitness case for Soyangin and Taeyangin was only 0∼18%. When the relationship between Samgye-tang ingredients and other food classified into Ohn-Yeoul-Ryang-Han and chemical composition of fatty acid, amino acid, vitamin and mineral was investigated, the value of correlation coefficient was extremely low. There was not the relationship between chemical composition and Han-Yeoul classification. Therefore it should be further investigated the relationship between characteristic and chemical composition by additional analysis index.

• Proceedings of the Korean Society of Toxicology Conference
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• 2005.05a
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• pp.164-164
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• 2005