• Title/Summary/Keyword: different color method

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Studies on Takju Brewing with Potatoes (감자를 이용(利用)한 탁주제조(濁酒製造)에 관(關)한 연구(硏究))

  • Kim, S.Y.;Oh, M.J.;Kim, C.J.
    • Korean Journal of Agricultural Science
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    • v.1 no.1
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    • pp.67-81
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    • 1974
  • In order to prepare the mashing materials for "Takju", Korean wine, with potatoes they were steamed, dryed, and pulverized, and their chemical components were analyzed. As a brewing method of Takju with potatoes, general 2nd stage process with Ipkuk and Bunkuk (enzyme sources), commonly used now, was carried out and the effects of preparing conditions of Ipkuk(koji) with potato flour, mashing materials and brewing conditions on the contents of Takju mash and of storing time on the contents of Takju, were investigated and the results obtained were summarized as follows. 1. Chemical components of steamed potatoes and potato flour were Moisture; 76.2, 10.8%, Total sugar; 16.1, 69.8%, Reducing sugar; 3.45, 13.4%, Crude protein; 2.1, 11.3%, Total acid; 0.012, 0.023% and Volatile acid; 0.0012, 0.0025% respectively 2. The most effective preparing conditions of Ipkuk with potato flour were to incubate the potato flour added 40-50% of water for 48 hours by general preparing process of Koji, and liquefying and saccharogenic amylase activities of Ipkuk incubated at above conditions were $D_{40^{\circ}}{^{30{\prime}}}$ 128 W.V. and 13.2 A. U. 3. The effects of various brewing conditions on the contents of Takju mashes wereas follows; 1) Optimum ratio of mashing water and materials for Takju brewing with potato flour was 140ml of water to 60g of flour in 1st stage and 260ml to 140g in 2nd stage. 2) Optimum fermentating times and temperatures for Takju brewing were at $25^{\circ}C$ for 48 hours in 1st stage and at $30^{\circ}C$ for 48 hours in 2nd stage. 3) Optimum amounts of enzyme sources for Takju brewing 20-30% of Ipkuk and 0.5% of Bunkuk in 1st stage and 1.0% of Bunkuk in 2nd stage. 4) Methanol content of the Takju mash brewed with raw potato flour was much more than that with steamed potato flour. 5) Alcohol fusel oil and Formal nitrogen contents of the Takju mash brewed with potato flour were less than that with wheat flour, on the contrary, methanol contents and total acidities of them were showed conversely above. 4. The changes of chemical components and microflora in the mashes during the brewing potato flour Takju were as follows; 1) The accumulation of ethanol followed rapidly in early stage, being the highest at 72 hours (11.9%) 2) Total sugar content of the mash was decreased considerably within 48-72 hours, being 2.62% at 72 hours, and thereafter slowly. 3) Reducing sugar of the mash had a tendency of decreasing, being 0.29% at 48 hours. 4) Total acidity, volatile acidity and Formal nitrogen content of the mash were increased slowly, being 7.30, 0.20, 2.55 at 48 hours. 5) Total cells of yeast appeared the highest in 72 hours ($2.1{\times}10^8$) and thereafter decreased slowly. 6) Total cells of bacteria appeared the highest in 48 hours ($2.4{\times}10^8$) and thereafter decreased or increased slightly. 5. Takju was made from the fermented mash mixed with water to be 6% of alcohol content, and the change of alcohol content, total acidity, total cells of yeast and bateria during the storing at $30^{\circ}C$ were as follows; 1) Alcohol content of Takju was increased slightly at 24 hours (6.2%), and thereafter decreased slowly. 2) Total acidity of Takju was increased gradually, being 6.1 at 72 hours 3) Total cells of yeast and bacteria appeared the highest at 48 hours ($2.3{\times}10^8$, $1.5{\times}10^8$) and thereafter decreased slowly. 6. Alcohol content, total acidity and Formol nitrogen content of the Takju brewed with potato flour Ipkuk or wheat flour Ipkuk and steamed potatoes(1:5) were 9.8-11.3%, 5.8-7.4, 2.5-3.3 respectively, and the color of the Takju was similar to commercial Takju. 7. The results of sensory test for various experimental Takju, showed that the Takjues brewed with the materials combined with wheat flour and steamed potatoes (4:5 or 3.5:7.5) were not significantly different in color, taste and flavor from commercial Takju, However, those with potato flour and wheat flour (1:1 or 7:3) were significantly different from commercial Takju.

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Emoticon by Emotions: The Development of an Emoticon Recommendation System Based on Consumer Emotions (Emoticon by Emotions: 소비자 감성 기반 이모티콘 추천 시스템 개발)

  • Kim, Keon-Woo;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.24 no.1
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    • pp.227-252
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    • 2018
  • The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.