• The Korean Journal of Food And Nutrition
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• v.10 no.3
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• pp.302-308
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• 1997

The purpose of this study was to investigate the extraction yield and quality stability as to the oleoresin process with large amount of onion at one time. The first mixed-product is raw onion juice which was reduced the compression and concentrated by Brix 70% mixed together wit the residue which was extracted and concentrated by ethanol, the second product manufactured by the same method above after the autoclaving with onion, and the other product is made by grinding by 50mesh to freeze-dried onion. Each of yields were 7.3, 9.1 and 0.8% and each of total sugar content was 616.4, 712.3 and 150.3mg/g. Therefore the product extracted by ethanol from freeze-dried onion was very low in yield and total sugar content. By the index of the overall odor intensity, contents of total pyruvate were 1,733.7, 520.6, and 2,716.5$\mu\textrm{g}$/g for each product. As a result, oleoresin onion processing that desired to use raw onion was remarkable for odor recovery. For the homogenous mixture with concentrate of onion juice and ethanol extract were emulsified by the addition of 2% of PGDR(polyglycerol condensed ricinoleate) and agitation(10,000rpm, 30 minutes). At this time, interfacial tension was 1.9 dyne/cm and the formation of emulsion was for 96.2% when left over 24hours in 6$0^{\circ}C$. When it was to be centrifuged(2,000$\times$G, 80 minutes) after emulsification, the volume of emulsion level without seperation was 92.6%, and very high in emulsification stability. The induced heating-oxidize with soy bean oil and sesame oil added to 1% of onion oleoresin, induction-time extension effect appeared with antioxidant activity that was applicable for 80.8~82.2% as to the effect of addition of 0.02% BHA.

• Journal of the Korean Chemical Society
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• v.8 no.1
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• pp.20-24
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• 1964

The linear combination of bond orbitals method is used to investigate the reactivity of halomethanes in abstraction reactions by atoms. The activation energy is evaluated on the assumption that, in an activated complex, two electrons in a bond to be broken become completely isolated from the rest of the ${\sigma}$-electron systems. Such a model leads to an intuitively attractive concept that the interactions between the reactive bond and the neighboring bonds govern the reactivity of ${\sigma}$-electron systems. The resulting equation for the activation energy, ${\varepsilon},\;is:\;{\narepsilon}= ${\varepsilon}={\zeta}+$$${\sum}_{i=1}^3$${\eta}c-I,$ c-4 Here, subscript C-4 indicates the bond to be broken, while C-i represents the other three bonds surrounding the reactive bond; ξ is the activation energy of a hypothetical reaction of an isolated C-4 bond and an attacking atom; and ${\eta}$C-i,C-4 stems from the stabilizing interacting of C-4 bond with neighboring C-i bonds. A choie of η′s consistent with bond strength data simplifies the above equation to a form ${\varepsilon}={\zeta}\;+\;N{\eta}c$-H, C-4 where N denotes the number of C-H plus C-F bond in halomethanes. In agreement with this equation, experimental -values increase linearly with increasing N.

• The Journal of the Petrological Society of Korea
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• v.20 no.1
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• pp.23-37
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• 2011

The scaling properties on the length distribution of microcrack populations from Tertiary crystalline tuff are investigated. From the distribution charts showing length range with 15 directional angles and five groups(I~V), a systematic variation appears in the mean length with microcrack orientation. The distribution charts are distinguished by the bilaterally symmetrical pattern to nearly N-S direction. The whole domain of the length-cumulative frequency diagram for microcrack populations can be divided into three sections in terms of phases of the distribution of related curves. Especially, the linear middle section of each diagram of five groups represents a power-law distribution. The frequency ratio of linear middle sections of five groups ranges from 46.6% to 67.8%. Meanwhile, the slope of linear middle section of each group shows the order: group V($N60{\sim}90^{\circ}E$, -2.02) > group IV($N20{\sim}60^{\circ}E$, -1.55) > group I($N60{\sim}90^{\circ}W$, -1.48), group II($N10{\sim}60^{\circ}W$, -1.48) > group III($N10^{\circ}W{\sim}N20^{\circ}E$, -1.06). Five sub-populations(five groups) that closely follow the power-law length distribution show a wide range in exponents( -1.06 - -2.02). These differences in exponent among live groups emphasizes the importance of orientation effect. In addition, breaks in slope in the lower parts of the related curves represent the abrupt development of longer lengths, which is reflected in the decrease in the power-law exponent. Especially, such a distribution pattern can be seen from the diagram with $N10{\sim}20^{\circ}E,\;N10{\sim}20^{\circ}W$ and $N60{\sim}70^{\circ}W$ directional angles. These three directional angles correspond with main directions of faults developed around the study area. The distribution chart showing the individual characteristics of the length-cumulative frequency diagrams for 15 directional angles were made. By arraying above diagrams according to the categories of three groups(A, B and C), the differences in length-frequency distributions among these groups can be easily derived. The distribution chart illustrates the importance of analysing microcrack sets separately. From the related chart, the occurrence frequency of shorter microcracks shows the order: group A > group B > group C. These three types of distribution patterns could reveal important information on the processes occurred during microcrack growth.

• Applied Biological Chemistry
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• v.9
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• pp.9-27
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• 1968

Studies were carried out in order to elucidate chemical components and microflora in three types of soy-sauce, 12-year aged soy-sauce prepared by improved method. 7-year aged and 20-year aged soy-sauce prepared by ordinary method. They results are summarized as follows: 1. The followings are found to be the important factors affecting the quality of soy sauce. a. Organic acids, reducing sugars and free amino acids were increased in the course of storage. b. In the aged soy-sauces under study non-volatile organic acid increased while volatile organic acid decreased and the total acidity was dependent only upon tie latter. c. It was found that suit concentration decreased during the storage. 2. The results of investigation of microflora in the stored soy-sauce are shown as follows. Soy-sauce Improved Ordinary Microbe 12-Y. 20-Y. 7-Y. Aerobic bacteria colony/1ml. 6 123 2 Halophilic lactic acid bacteria colony/1ml. 4 6 10 Osmophilic yeast colony/1ml. $828{\times}10^4$ 248 - b. In the stored soy-sauces, aerobic bacteria are incapable of growing due to drop in pH value and the influence of salt concentration. c. Halophilic lactic acid bacteria are incapable of growing due to drop in pH value, even the salt concentrations decreased during the storage. d. Osmophilic yeast are still growing in low pH value and in the decreasing salt concentration during the strage. 3. The results of amino acid analysis by paper partition chromatographic and calorimetric methods are shown as follows. a. Fourteen kinds of amino acid and thirteen amino acids were detected in the soy-sauce of 12-year aged improved soy-sauce and 7-year aged and 20-year aged ordinary one, respectively. b. The contents of aspartic acid, glutamic acid, serine, valine, leucine, lysine, histidine and methionine increased in the 20-year aged ordinary soy-sauce compared to the 7-year aged one. On the other hand those of alanine, tyrosine, phenylalanine and cystine decreased. 4. The results of sugar analysis by paper chromatography are as follows. a. In the 12-year aged improved soy-sauce, galactose, glucose, arabinose, xylose, rhamnose, maltose and an unknown were detected, and their amounts were in the above order except maltose and an unknown. b. Both in the 7 and 12-year aged ordinary soy-sauces, galactose, arabinose, xylose, glucose and rhamnose were detected and the amounts of the sugars were in the above order. c. In the non-aged ordinary soy-sauce, glucose was not detected but detected from 7-year and 20-year aged ordinary soy-sauce. 5. The results of organic acid analysis by paper chromatography were as follows. a. As volatile acids, acetic, propionic and butyric acids were detected in the 7-year aged ordinary soy-sauce. On the other hand in both the 20-year aged ordinary soy-sauce and the 12-year aged improved ones, only acetic acid was abundant while propionic and butyric acids were round in trace. It was found that propionic and butyric acids, as the unpleasant flavor components, decreased during the storage. b. In the ordinary soy-sauce, citric acid were produced during the storage and lack, malic and tartaric acids increased in the course of aging while succinic, glycolic, fumaric and malonic acids were shown to decrease. Glutaric and oxalic acids disappeared. Citric acid was produced also in the improved soy-sauce, but lactic, tartaric, succinic, malic, and glycolic acids decreased, while both malonic and glutaric acids disappeared. From the above results the citric acid production was considered to be a favorable factor for the taste. c. In the aged soy-sauces, pyruvic, α-ketoglutaric and probably acetoacetic and oxaloacetic acids (both in trace) were present and their amounts were in the above order. All of the α-keto acid abruptly decreased during the storage.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used