• Title/Summary/Keyword: better-for-you

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Comparison of Antioxidant Activities of Enzymatic and Methanolic Extracts from Ecklonia cava Stem and Leave (감태(Ecklonia cava) 줄기 및 잎의 효소적 추출물과 메탄올 추출물에 의한 항산화 활성비교)

  • Lee, Seung-Hong;Kim, Kil-Nam;Cha, Seon-Heui;Ahn, Gin-Nae;Jeon, You-Jin
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.35 no.9
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    • pp.1139-1145
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    • 2006

  • In this study, antioxidant activities of enzymatic and methanolic extracts from E. cava stem and leave were evaluated by measuring the scavenging activities on 1,1 diphenyl 2 picrylhydrazyl (DPPH), hydroxyl radical, hydrogen peroxide and the inhibitory effects on DNA damage induced by oxidative stress of cells. Enzymatic extracts were prepared by enzymatic hydrolysis of both stem and leave using food grade five different carbohydrases (Viscozyme, Celluclast, AMG, Termamyl, Ultraflo) and five proteases (Protamex, Kojizyme, Neutrase, Flavourzyme, Alcalase). The enzymatic extracts were lower than methanolic extracts in polyphenol contents, but higher in extraction yield by approximately 30%. The enzymatic extracts were superior to methanolic extracts in DPPH and H2O2 scavenging activities and DNA damage protective effect. There were no significant antioxidant activity difference between stem and leave, but the extracts of leave were relatively better than those of stem. In this study it is suggested that E. cava stem as well as its leave would be a good raw materials for antioxidants compound extraction and enzymatic hydrolysis would be a good strategy to prepare antioxidant extracts from seaweeds.

  • https://doi.org/10.3746/jkfn.2006.35.9.1139 인용 PDF KSCI

A Development of Cholesterol Removed Cheese (콜레스테롤을 제거한 치즈의 개발에 관한 연구)

  • 정청송
    • Proceedings of the Korea Hospitality Industry Research Society Conference
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    • 2002.11a
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    • pp.83-98
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    • 2002

  • The old testament of the Bible has written the milk and curd. God said, I will ive you to how the milk and honey. The present study was designed to examine the effects of different homogenization pressure, homogenization temperature and $\beta$-cyclodextrin concentration on cholesterol removal rate of cheese, and to optimize the factors of cheese manufacture Process. In addition, the characteristics from cholesterol removed cheese and control are compared in the rheological and ensory analysis. The optimized process condition for cholesterol removal was for homogenization pressure, 74$^{\circ}C$ for homogenization temperature and 2% for $\beta$-cyclodextrin concentration, it showed 875% of the highest cholesterol removal rate in milk. Therefore, manufacture conditions of cholesterol removed cheese were chosen 74$^{\circ}C$ for homogenization temperature, for homogenization pressure, and I or 2% for $\beta$-cyclodextrin concentration. Cholesterol removed cheese and control were compared with yield, cholesterol removal, meltability, stretchability, textural properties and sensory analysis. Cholesterol content of control cheese containing 23.8% milk fat was cheese made from milk treated with 2% $\beta$-cyclodextrin and homogenization pressure was cholesterol removal. Yield of cholesterol removed cheese. As the homogenization pressure increased, oiling off reduced with showed better surface appearance. Stretchability of cholesterol removed cheese was lower 5~10cm than over 30cm of control. Meltability of cholesterol removed cheese also was lower than control. The hardness, gumminess, chewiness reduced to respectively. In the result of sensory analysis, treatment of homogenization for cholesterol removed cheese improved appearance and flavor, however texture fell. In addition, the resent result of the study indicated that about 75% of cholesterol in cheese could be removed, and the possibility of development of cholesterol removed cheese was observed. We have hope to research manufacture cheese global wide.

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A study on the classification of research topics based on COVID-19 academic research using Topic modeling (토픽모델링을 활용한 COVID-19 학술 연구 기반 연구 주제 분류에 관한 연구)

  • Yoo, So-yeon;Lim, Gyoo-gun
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.155-174
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    • 2022

  • From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (

    ) were the topic modeling results for each research topic (
    ) was found to be derived from For example, as a result of topic modeling for papers related to 'vaccine', a new topic titled Topic 05 'neutralizing antibodies' was extracted. A neutralizing antibody is an antibody that protects cells from infection when a virus enters the body, and is said to play an important role in the production of therapeutic agents and vaccine development. In addition, as a result of extracting topics from papers related to 'treatment', a new topic called Topic 05 'cytokine' was discovered. A cytokine storm is when the immune cells of our body do not defend against attacks, but attack normal cells. Hidden topics that could not be found for the entire thesis were classified according to keywords, and topic modeling was performed to find detailed topics. In this study, we proposed a method of extracting topics from a large amount of literature using the LDA algorithm and extracting similar words using the Skip-gram method that predicts the similar words as the central word among the Word2vec models. The combination of the LDA model and the Word2vec model tried to show better performance by identifying the relationship between the document and the LDA subject and the relationship between the Word2vec document. In addition, as a clustering method through PCA dimension reduction, a method for intuitively classifying documents by using the t-SNE technique to classify documents with similar themes and forming groups into a structured organization of documents was presented. In a situation where the efforts of many researchers to overcome COVID-19 cannot keep up with the rapid publication of academic papers related to COVID-19, it will reduce the precious time and effort of healthcare professionals and policy makers, and rapidly gain new insights. We hope to help you get It is also expected to be used as basic data for researchers to explore new research directions.

  • https://doi.org/10.13088/jiis.2022.28.1.155 인용 PDF KSCI

    • Clustering Method based on Genre Interest for Cold-Start Problem in Movie Recommendation (영화 추천 시스템의 초기 사용자 문제를 위한 장르 선호 기반의 클러스터링 기법)

    • You, Tithrottanak;Rosli, Ahmad Nurzid;Ha, Inay;Jo, Geun-Sik
      • Journal of Intelligence and Information Systems
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      • v.19 no.1
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      • pp.57-77
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      • 2013

    • Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

    • https://doi.org/10.13088/jiis.2013.19.1.057 인용 PDF KSCI