• Korean Journal of Food Science and Technology
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• v.35 no.5
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• pp.841-848
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• 2003

The sensory characteristics of red wine Gerbong (G), Campbell (C), Moru (M), Gerbong ＋ Moru (70 : 30, GM), Gerbong + Campbell (70 : 30, GC) and French wine (F, Carbernet Sauvignon, 1998) were evaluated. The preferences of color, flavor, taste and total evaluation were determined by a ranking test, and the organoleptic characteristics were evaluated by a quantitative descriptive analysis (QDA) method. The mean color scores of C, GM, F, GC, M and G were 4.74, 3.94, 4.67, 3.70, 2.65 and 1.47, respectively (p<0.001). The order for the mean score for flavor was GM (4.12) = M (3.94) = C (3.76) = F (3.76) ${\geq}$ GC (3.12)>G (2.29) (p<0.01), and the order for taste was F (4.75) ${\geq}$ C (4.25) ${\geq}$ GM (3.37) = GC (3.50) ${\geq}$ G (2.75) = M (2.37) (p<0.001). The total evaluation of mean scores showed G, M, C, GM, GC and F were 237, 2.44, 4.06, 3.87, 3.64 and 4.81, respectively (p<0.001). Influences of sensory characteristics on the total evaluation, in percentages, were 69.3% for taste, 3.7% for color, and 1.5% for flavor. The influences of taste, color, and flavor in red wine were 17% for sweet, acid, bitter and salty taste, 28.9% for purple and red color, and 14.4% for grape flavor. The attributes of the purple and red colors showed a positive correlation with grape flavor, oak flavor, grape taste, and floral tastes, but a negative correlation with $SO_2$, flavor. The attribute of sweet taste showed a positive correlation with grape flavorand floral flavor, but a negative correlation with bitter and astringency tastes, according to Pearsons correlation analysis (p<0.01).

• Journal of Bio-Environment Control
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• v.14 no.2
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• pp.83-88
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• 2005

A study was conducted on three pollination methods on oriental melon(sageageol-ggul) grafting with pumpkin(seongjutozoa) for the labor-saving and to improve fruit set. Fruit weight, flesh thickness and fruit setting rate of oriental melon were greater in growth regulators treatment than those of pollinated by bees. Sugar content and hardness of fruits pollinated by bees were higher than those of by growth regulators. From the last ten days of the February to the first ten days of the March, fruit setting rate was $95\%$ in fruit setting growth regulators, whereas it was $46\%$ and $45\%$ in pollinated by honey and bumble bee, respectively. After the middle of March, the percentage of fruit setting was >$98\%$ in all the pollination methods. The cultivation under plastic houses of oriental melon, suitable fruiting time far the pollination by bees was decided after middle days of the March. Chromaticity and especially the value of 'a' of fruit of oriental melon pollinated by bees were higher than those of growth regulators. The percentage of fermented fruits of bee pollinated and growth regulators treated was $6.7\~9.1\%\;and\;28.1\%$, respectively. The weight of 100 seeds of bees pollinated were higher than that of growth regulators. The more increased the weight of 100 seeds the less appeared the rate of fermented fruits. The percentage of marketable fruits of the honey and bumble bee pollinated and that of growth regulators treated was $82\%,\;80.3\%\;and\;62.5\%$, respectively. The decreasing rate of fruit weight during storage of bees pollinated was less than those of growth regulators. In these results, the introduction of honey bee and bumble bee for the pollination of oriental melon was able to labor-saving of fruit set and increase of fruit quality.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.