• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.12
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• pp.1560-1570
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• 2007

The purpose of this study was to investigate the differences according to lifestyle in anthropometric measurement, dietary attitude, health-related behaviors and nutrient intake among the college students. The subjects were 994 nation-wide college students (male: 385, female: 609) and divided into 7 clusters (PEAO: passive economy/appearance-oriented type, NCPR: non-consumption/pursuit of relationship type, PTA: pursuit of traditional actuality type, PAT: pursuit of active health type, UO: utility-oriented type, POF: pursuit of open fashion type, PFR: pursuit of family relations type). A cross-sectional survey was conducted using a self administered questionnaire, and the data were collected via Internet or by mail. The nutrient intake data collected from food record were analyzed by the Computer Aided Nutritional Analysis Program. Data were analyzed by a SPSS 12.0 program. Average age of male and female college students were 23.7 years and 21.6 years, respectively. Most of the college students had poor eating habits. In particular, about 60% of the PEAO group has irregularity in meal time. The students in PAH and POF groups showed significantly higher consumption frequency of fruits, meat products and foods cooked with oil compared to the other groups. As for exercise, drinking and smoking, there were significant differences between PAH and the other groups. Asked for the reason for body weight control, 16.2% of NCPR group answered "for health", but 24.8% of PEAO group and 26.3% of POF group answered "for appearance". Calorie, vitamin A, vitamin $B_2$, calcium and iron intakes of all the groups were lower than the Korean DRIs. Female students in PTA group showed significantly lower vitamin $B_1$ and niacin intakes compared to the PFR group. Therefore, these results provide nation-wide information on health-related behaviors and nutrient intake according to lifestyles among Korean college students.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.1
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• pp.327-353
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• 2017

The issue submitted to the Court of Justice on the merits of case C---573/13 originated from a claim brought in the context of a dispute between Air Berlin and the German Federal Union of Consumer Organisations and Associations. The challenge concerned the way in which air fares were displayed in Air Berlin's computerised booking system. The system was organised in such a way that, after selecting a date and a departure airport, one would find all possible flight connections in a summary table. However, the final price of the ticket was displayed only for the clicked connection, and not for all connections, thus preventing customers from being able to compare such price with the prices of other connections. The German Federal Union took the view that this practice did not meet the requirements laid down by Article 23 of Regulation (EC) No. 1008/2008, which requires transparency in the prices set for air services. This led the German State to bring an injunctive action to cause Air Berlin to discontinue said practice. The claim was upheld at both the application and appeal stage of the relevant proceedings. Subsequently, Air Berlin submitted the matter to the German Federal High Court, which decided to stay the proceedings and ask for a preliminary ruling from the Court of Justice as to 1. whether Article 23 of Regulation (EC) No. 1008/2008 must be interpreted as meaning that, during the computerised booking process, the final price to be paid must be indicated at all times when prices of air services are shown, including when they are shown for the first time; and 2. whether, during the computerised booking process, the final price must be indicated only for the air service specifically selected by the customer or for each air service shown. In a nutshell, the Court, by the here---discussed judgment determined that Article 23 of Regulation (EC) No. 1008/2008 must be interpreted as meaning that, in the context of a computerised air ticket booking system, the final price to be paid must be indicated not only for the air service specifically selected by the customer, but also for each air service in respect of which the fare is shown. Clearly the above judgment will place air companies under an obligation to update and adjust (when needed) their computerised ticket booking and payment systems, in consideration of the primary need for consumers to be aware at all times of the actual price payable for a ticket and be able to compare the price of the service selected with the prices for other air services in respect of which the fare is shown.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.75-95
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• 2020

With the growth of social networks, various forms of SNS have emerged. Based on various motivations for use such as interactivity, information exchange, and entertainment, SNS users are also on the fast-growing trend. Facebook is the main SNS channel, and companies have started using Facebook pages as a public relations channel. To this end, in the early stages of operation, companies began to secure a number of fans, and as a result, the number of corporate Facebook fans has recently increased to as many as millions. from a corporate perspective, Facebook is attracting attention because it makes it easier for you to meet the customers you want. Facebook provides an efficient advertising platform based on the numerous data it has. Advertising targeting can be conducted using their demographic characteristics, behavior, or contact information. It is optimized for advertisements that can expose information to a desired target, so that results can be obtained more effectively. it rethink and communicate corporate brand image to customers through contents. The study was conducted through Facebook advertising data, and could be of great help to business people working in the online advertising industry. For this reason, the independent variables used in the research were selected based on the characteristics of the content that the actual business is concerned with. Recently, the company's Facebook page operation goal is to go beyond securing the number of fan pages, branding to promote its brand, and further aiming to communicate with major customers. the main figures for this assessment are Facebook's 'OK', 'Attachment', 'Share', and 'Number of Click' which are the dependent variables of this study. in order to measure the outcome of the target, the consumer's response is set as a key measurable key performance indicator (KPI), and a strategy is set and executed to achieve this. Here, KPI uses Facebook's ad numbers 'reach', 'exposure', 'like', 'share', 'comment', 'clicks', and 'CPC' depending on the situation. in order to achieve the corresponding figures, the consideration of content production must be prior, and in this study, the independent variables were organized by dividing into three considerations for content production into three. The effects of content material, content structure, and message styles on Facebook's user behavior were analyzed using regression analysis. Content materials are related to the content's difficulty, company relevance, and daily involvement. According to existing research, it was very important how the content would attract users' interest. Content could be divided into informative content and interesting content. Informational content is content related to the brand, and information exchange with users is important. Interesting content is defined as posts that are not related to brands related to interesting movies or anecdotes. Based on this, this study started with the assumption that the difficulty, company relevance, and daily involvement have an effect on the dependent variable. In addition, previous studies have found that content types affect Facebook user activity. I think it depends on the combination of photos and text used in the content. Based on this study, the actual photos were used and the hashtag and independent variables were also examined. Finally, we focused on the advertising message. In the previous studies, the effect of advertising messages on users was different depending on whether they were narrative or non-narrative, and furthermore, the influence on message intimacy was different. In this study, we conducted research on the behavior that Facebook users' behavior would be different depending on the language and formality. For dependent variables, 'OK' and 'Full Click Count' are set by every user's action on the content. In this study, we defined each independent variable in the existing study literature and analyzed the effect on the dependent variable, and found that 'good' factors such as 'self association', 'actual use', and 'hidden' are important. Could. Material difficulties', 'actual participation' and 'large scale * difficulties'. In addition, variables such as 'Self Connect', 'Actual Engagement' and 'Sexual Sexual Attention' have been shown to have a significant impact on 'Full Click'. It is expected that through research results, it is possible to contribute to the operation and production strategy of company Facebook operators and content creators by presenting a content strategy optimized for the purpose of the content. In this study, we defined each independent variable in the existing research literature and analyzed its effect on the dependent variable, and we could see that factors on 'good' were significant such as 'self-association', 'reality use', 'concernal material difficulty', 'real-life involvement' and 'massive*difficulty'. In addition, variables such as 'self-connection', 'real-life involvement' and 'formative*attention' were shown to have significant effects for 'full-click'. Through the research results, it is expected that by presenting an optimized content strategy for content purposes, it can contribute to the operation and production strategy of corporate Facebook operators and content producers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Culinary science and hospitality research
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• v.22 no.7
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• pp.112-130
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• 2016

In this study the moisture content and chromaticity of fresh made lotus leaf powder added Cheongpomuk to utilize various efficacy of lotus leaf for processed food, as well as chromaticity, moisture content change, texture, total phenolic compound content, DPPH radical scavenging ability and preference of lotus leaf powder added Cheongpomuk with different storage period have been measured and analyzed. From the texture of lotus leaf powder added mung bean as per the storage period, the hardness of fresh Cheongpomuk were $0.38g/cm^2$ from control group, $0.40g/cm^2$ from CCD 1% group, $0.42g/cm^2$ from CCD 3% group, $0.37g/cm^2$ from CCD 5% group, $0.42g/cm^2$ from GGD 1% group, $0.39g/cm^2$ from GGD 3% group, $0.35g/cm^2$ from GGD 5% group, $0.39g/cm^2$ from JLD 1% group, $0.33g/cm^2$ from JLD 3% group, and $0.32g/cm^2$ from JLD 5% group. It has shown that JLD 5% group was the lowest, while CCD 3% group and GGD 1% group were the highest, and there were significant differences among sample groups. For DPPH radical scavenging ability, that of GLD 5% group was 22 times higher than that of control group. In addition, the tendency was increasing by increasing the adding rate of lotus leaf powder though there was some tolerance among sample groups. For total phenolic compound content, that of control group was 6.65 mg CE/100 g, and others were 7.48 mg CE/100 g from CCD 1% group, 15.82 mg CE/100 g from CCD 3% group, 20.15 mg CE/100 g from CCD 5% group, 15.55mg CE/100 g from GGD 1% group, 23.02 mg CE/100 g from GGD 3%, 26.95 mg CE/100 g from GGD 5% group, 3.92 mg CE/100 g from JLD 1% group, 16.72 mg CE/100 g from JLD 3%, and 26.58 mg CE/100 from JLD 5% group. From the analyzing result of responses for color and scent, taste, elasticity, and total preference of lotus leaf powder added Cheongpomuk between two panel groups, there was significant difference for the color, higher from professional cooking instructor group, but there were no significant difference between two groups for all other factors among professional cooking instructors and cooking department students. According to the results, it is expected that various functional foods can be developed by utilizing lotus leaf powder, depending on the growth condition and cultural environment of each region by adding 3% of lotus leaf powder, would be the most suitable recipe for Cheongpomuk.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.321-327
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• 2006

In Korea, there is a growing competitive for water resources between industrial, domestic and agricultural consumer, and the environment as many other OECD countries. The demand on water use is also affecting aquatic ecosystems particularly where withdrawals are in excess of minimum environmental needs for rivers, lakes and wetland habits. OECD developed three indicators related to water use by the agriculture in above contexts : the first is a water use intensity indicator, which is expressed as the quantity or share of agricultural water use in total national water utilization; the second is a water stress indicator, which is expressed as the proportion of rivers (in length) subject to diversion or regulation for irrigation without reserving a minimum of limiting reference flow; and the third is a water use efficiency indicator designated as the technical and the economic efficiency. These indicators have different meanings in the aspect of water resource conservation and sustainable water use. So, it will be more significant that the indicators should reflect the intrinsic meanings of them. The problem is that the aspect of an overall water flow in the agro-ecosystem and recycling of water use not considered in the assessment of agricultural water use needed for calculation of these water use indicators. Namely, regional or meteorological characteristics and site-specific farming practices were not considered in the calculation of these indicators. In this paper, we tried to calculate water use indicators suggested in OECD and to modify some other indicators considering our situation because water use pattern and water cycling in Korea where paddy rice farming is dominant in the monsoon region are quite different from those of semi-arid regions. In the calculation of water use intensity, we excluded the amount of water restored through the ground from the total agricultural water use because a large amount of water supplied to the farm was discharged into the stream or the ground water. The resultant water use intensity was 22.9% in 2001. As for water stress indicator, Korea has not defined nor monitored reference levels of minimum flow rate for rivers subject to diversion of water for irrigation. So, we calculated the water stress indicator in a different way from OECD method. The water stress indicator was calculated using data on the degree of water storage in agricultural water reservoirs because 87% of water for irrigation was taken from the agricultural water reservoirs. Water use technical efficiency was calculated as the reverse of the ratio of irrigation water to a standard water requirement of the paddy rice. The efficiency in 2001 was better than in 1990 and 1998. As for the economic efficiency for water use, we think that there are a lot of things to be taken into considerations to make a useful indicator to reflect socio-economic values of agricultural products resulted from the water use. Conclusively, site-specific, regional or meteorogical characteristics as in Korea were not considered in the calculation of water use indicators by methods suggested in OECD(Volume 3, 2001). So, it is needed to develop a new indicators for the indicators to be more widely applicable in the world.

• Food Science of Animal Resources
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• v.29 no.6
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• pp.726-735
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• 2009

This study was conducted to investigate the Warner-Bratzler shear force (WBS), sarcomere length, total collagen contents and consumer tests of different cuts of Hanwoo beef based on quality grade. Five cuts [Cheggt (striploin), Dngsim (loin), Moksim (chuck roll), Udoon (top round), Yanggi (brisket)] were obtained from Hanwoo beef (bulls and steers, 24-30 months old) and grouped by their quality grades ($1^{++}$, $1^+$, 1, 2 and 3). The WBS values of chuck cut in $1^{++}$, $1^+$ and 1 quality grade groups were 5.47 (kg/0.5 $inch^2$), 5.46 (kg/0.5 $inch^2$) and 6.54 (kg/0.5 $inch^2$), respectively, which were significantly lower than those of chuck in quality grade 3, which was 8.41 (kg/0.5 $inch^2$) (p<0.05). The sarcomere length of the quality grade groups were significantly longer for brisket (3.72 ${\mu}m$) of $1^{++}$ quality grade than for that of 3 quality grade brisket (p<0.05). Brisket of $1^{++}$ and 2 quality grade had significantly greater sarcomere lengths than strip loin and top round in the same quality grade (p<0.05). The total collagen contents of strip loins in the $1^{++}$, $1^+$, and 1 quality grade groups (1.17, 1.18 and 1.14 g/100 g, respectively) were significantly lower than those of the 3 quality grade group (1.63 g/100 g) (p<0.05). The overall sensory scores of tenderness and juiciness for the five cuts of $1^{++}$ quality grade were significantly higher than those of the other quality grade (p<0.05). Among the five cuts, loin had the highest tenderness and juiciness scores (p<0.05), whereas top round had the lowest tenderness and juiciness scores (p<0.05). Overall, there were significant differences in the WBS and sensory properties among quality grades and cuts (p<0.05). However, the sarcomere length and total collagen contents of chuck roll, top round and loin did not differ among quality grades.