• Korean journal of food and cookery science
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• v.19 no.2
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• pp.241-253
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• 2003

Changes in chemical, microbiological quality of pan fried oak mushroom and meat, soy sauce glazed hair tail and roasted dodok in wrap packaging, top sealing, vacuum packaging were evaluated during storage 25$^{\circ}C$, 4$^{\circ}C$, -18$^{\circ}C$ for 5 days. The results were as follows: 1) The cases of chilled and frozen storage, there were small increases in the pH from the first day, with no differences between the different packaging methods, with the exception of the vacuum packaging, which was lower. The pH and Aw of the roasted dodok were lower than those of the other foods. The Aw for all three foods at room temperature significantly decreased in the wrap packaging and top sealing on day one, but the rate of reduction was lower when in chilled storage. The VBN increased with increasing length of storage, and temperatures, but the rate of increase was lower in the top sealing and vacuum packaging. The VBN of roasted dodok was considerably lower than with the other foods. The POV increased significantly on the first day or room temperature storage and the rate or increase was low in chilled End frozen storages, and in the vacuum packaging. 2) SPC of the roasted dodok at room temperature increased significantly within five days of storage. but was inhibited within five days in the vacuum packaging with chilled storage. The SPC of the soy sauce glazed hair tail was low in the top sealing and vacuum packaging when in chilled storage. The coliform of the pan fried oak mushroom and meat. on the fifth day of room temperature storage, was close to hazardous conditions for the wrap packaging. From the third day of chilled storage, few coliform were detected in the pan fried oak mushroom and meat, or the soy sauce glazed hair tail, but not in the vacuum packaging, within five days, for all three foods in frozen storage. The S. spp. had exceeded the standard in the wrap packaging and top sealing with the pan fried oak mushroom and meat on the third day at room temperature, but was not detected in the vacuum packaging within five days, and exceeded the standard in the wrap packaging on the fifth day of chilled storage. S. spp. was not detected in the soy sauce glazed hair tail within five days at all storage temperatures. S. spp. was not detected in the roasted dodok within five days of chilled and frozen storage, but was detected from the third day in the wrap packaging. and the fifth in the top sealing, at room temperature, which exceeded the standard. Sal. spp., V parahaemolyticus, E. coli O157:H7, L. monocytogenes were not detected. 3) The Aw was found to be influenced by storage temperature, period and packaging method, while the VBN was significantly influenced by the storage temperature and period. Regarding the SPC, the pan fried oak mushroom and meat was affected by the storage temperature and period, while the soy sauce glazed hair tail was influenced by the packaging method and storage period. The roasted dodok's microbiological quality was influenced by the method of packaging. The chemical, microbiological quality of home-delivered meals were preserved to be five days in the vacuum packaging, at. chilled and frozen storage.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.