• Applied Biological Chemistry
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• v.45 no.1
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• pp.11-17
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• 2002

Fermentation characteristics with/without nitrogen source and quality of the fruit distillate of Opuntia ficus-indica var. saboten were investigated during the manufacturing process of a Korean traditional liquor. As the fermentation period increased, acidity, brix degree, and alcohol concentration increased, whereas pH and contents of reducing sugar decreased. Acidity, pH, and brix degree were higher, whereas the content of reducing sugar lower, in the nitrogen source-added distillate than in the distillate without nitrogen source. The growth of yeast increased, while that of bacteria decreased; this trend was more prominent with the addition of a nitrogen source. Sojues, distilled from two types of mashes and diluted with $H_2O$ and tails of distillate into 22% alcohol concentration, showed pH $3.7{\sim}4.0$, acidity $0.02{\sim}0.10$, and $5.4{\sim}6.1$ $^{\circ}Brix$. Analysis through GC using direct injection methods revealed common volatile flavor compounds in sojues, including acetaldehyde, acetyl acetone, acetic acid ethyl ester, ethyl alcohol, 2-propyl alcohol, acetone, n-propyl alcohol, butanoic acid methyl ester, 2-phenyl ethanol, thymol, acetic acid phenyl ester, and vanillic aldehyde. As revealed through the sensory evaluation, no significant difference (p>0.05) in overall acceptability was shown among four experimental groups, while color and flavor showed significant differences(p<0.05).

• Journal of the Korean Applied Science and Technology
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• v.35 no.1
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• pp.235-246
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• 2018

Paeony has pharmacological activities such as anti-inflammatory, anti-allergic, anti-bacterial, central inhibitory, gastric secretion inhibition, and antispasmodic activities. In addition, its antioxidant activity and whitening effect being reported, thus it is being explored as raw materials for cosmetics. We compared the changes in the contents of paeoniflorin and paeonol in Peony extracts, depending on the changes of extracting solvents, temperature and time. The HPLC method was set up for simultaneous analysis, the system suitabilities were confirmed by using the calibration curves and the QC samples for each assay batch. Paeonol was detected only in roots, and paeoniflorin was higher in leaf and flower than root. Higher concentrations of both ingredients were extracted when the root was used after grinding to a suitable size, and when 30% 1,3-butylene glycol was used as the extraction solvent. Also the concentrations tended to increase at higher temperature and longer time, but the increase was gradual at over $75^{\circ}C$ and 4 hours. The ratio of root, leaf and flower was determined to be 2+2+1g/0.5kg of batch, reaching the contents criteria of paeoniflorin and paeonol. Finally, we selected as the best extraction condition when the raw materials are mixed with 2+2+1g/0.5kg and extracted with 30% 1,3-butylene glycol as an extraction solvent at $75^{\circ}C$ for 4 hours, considering both the concentrations of two components and the cost of raw materials and manufacturing process, The extraction units were scaled up to 10 kg under this condition.

• Journal of Distribution Science
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• v.8 no.3
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• pp.37-47
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• 2010

CRM refers to the operating activities that always maintain and promote good relationship with customers to ultimately maximize the company's profits by understanding the value of customers to meet their demands, establishing a strategy which may maximize the Life Time Value and successfully operating the business by integrating the customer management processes. In our country, many big businesses are introducing CRM initiatively to use it in marketing strategy however, most medium and small sized companies do not understand CRM clearly or they feel difficult to introduce it due to huge investment needed. This study is intended to present CRM promotion strategy and activities plan fit for the medium and small sized companies by analyzing the success factors of the leading companies those have already executed CRM by surveying the precedents to make the distributors out of the industries have close relation with consumers to overcome their weakness in scale and strengthen their competitiveness in such a rapidly changing and fiercely competing market. There are 5 stages to build CRM such as the recognition of the needs of CRM establishment, the establishment of CRM integrated database, the establishment of customer analysis and marketing strategy through data mining, the practical use of customer analysis through data mining and the implementation of response analysis and close loop process. Through the case study of leading companies, CRM is needed in types of businesses where the companies constantly contact their customers. To meet their needs, they assertively analyze their customer information. Through this, they develop their own CRM programs personalized for their customers to provide high quality service products. For customers helping them make profits, the VIP marketing strategy is conducted to keep the customers from breaking their relationships with the companies. Through continuous management, CRM should be executed. In other words, through customer segmentation, the profitability for the customers should be maximized. The maximization of the profitability for the customers is the key to CRM. These are the success factors of the CRM of the distributors in Korea. Firstly, the top management's will power for CS management is needed. Secondly, the culture across the company should be made to respect the customers. Thirdly, specialized customer management and CRM workers should be trained. Fourthly, CRM behaviors should be developed for the whole staff members. Fifthly, CRM should be carried out through systematic cooperation between related departments. To make use of the case study for CRM, the company should understand the customer and establish customer management programs to set the optimal CRM strategy and continuously pursue it according to a long-term plan. For this, according to collected information and customer data, customers should be segmented and the responsive customer system should be designed according to the differentiated strategy according to the class of the customers. In terms of the future CRM, integrated CRM is essential where the customer information gathers together in one place. As the degree of customers' expectation increases a lot, the effective way to meet the customers' expectation should be pursued. As the IT technology improved rapidly, RFID (Radio Frequency Identification) appears. On a real-time basis, information about products and customers is obtained massively in a very short time. A strategy for successful CRM promotion should be improving the organizations in charge of contacting customers, re-planning the customer management processes and establishing the integrated system with the marketing strategy to keep good relation with the customers according to a long-term plan and a proper method suitable to the market conditions and run a company-wide program. In addition, a CRM program should be continuously improved and complemented to meet the company's characteristics. Especially, a strategy for successful CRM for the medium and small sized distributors should be as follows. First, they should change their existing recognition in CRM and keep in-depth care for the customers. Second, they should benchmark the techniques of CRM from the leading companies and find out success points to use. Third, they should seek some methods best suited for their particular conditions by achieving the ideas combining their own strong points with marketing. Fourth, a CRM model should be developed that will promote relationship with individual customers just like the precedents of small sized businesses in Switzerland through small but noticeable events.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.1
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• pp.1-25
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• 1996

The pathogenesis of atherosclerosis can not be summarized as a single process. Lipid infiltration hypothesis and endothelial injury hypothesis have been proposed and investigated. Recent developments show that there are many points of potential interactions between them and that they can actually be regarded as two phases of a single, unifying hypothesis. Among the many risk factors of atherosclerosis, plasma homocysteine and lipoprotein(a) draw a considerable interest because they are independent indicators of atherogenicity. Triglyceride (TG)-rich lipoproteins (chylomicron and VLDL) are not considered to be atherogenic but they are related to the metabolism of HDL cholesterol and indirectly related to coronary heart disease (CHD). LDL can of itself be atherogenic but the oxidative products of this lipoprotein are more detrimental. HDL cholesterol has been considered to be a favorable cholesterol. The so-called 'causalist view' claims that HDL traps excess cholesterol from cellular membranes and transfers it to TG-rich lipoproteins that are subsequently removed by hepatic receptors. In the so-called 'noncausalist view', HDL does not interfere directly with cholesterol deposition in the arterial wall but instead reflects he metabolism of TG-rich lipoproteins and their conversion to atherogenic remnants. Approximately 70-80% of the human population shows an effective feedback control mechanism in cholesterol homeostasis. Type of dietary fat has a significant effect on the lipoprotein cholesterol metabolism and atherosclerosis. Generally, saturated fatty acids elevate and PUFA lower serum cholesterol, whereas MUFA have no specific effect. EPA and DHA inhibit the synthesis of TG, VLDL and LDL, and may have favourable effects on some of the risk factors. Phospholipids, particularly lecithin, have an antiatherosclerotic effect. Essential phospholipids (EPL) may enhance the formation of polyunsaturated cholesteryl ester (CE) which is less sclerotic and more easily dispersed via enhanced hydrolysis of CE in the arterial wall. Also, neutral fecal steroid elimination may be enhanced and cholesterol absorption reduced following EPL treatment. Antioxidants protect lipoproteins from oxidation, and cells from the injury of toxic, oxidized LDL. The rationale for lowering of serum cholesterol is the strong association between elevation of plasma or serum cholesterol and CHD. Cholesterol-lowing, especially LDL cholesterol, to the target level could be achieved using diet and combination of drug therapy. Information on the link between cholesterol and CHD has decreased egg consumption by 16-25%. Some clinical studies have indicated that dietary cholesterol and egg have a significant hypercholesterolemic effect, while others have indicated no effect. These studies differed in the use of purified cholesterol or cholesterol in eggs, in the range of baseline and challenge cholesterol levels, in the quality and quantity of concomitant dietary fat, in the study population demographics and initial serum cholesterol levels, and clinical settings. Cholesterol content of eggs varies to a certain extent depending on the age, breed and diet of hens. However, egg yolk cholesterol level is very resistant to change because of the particular mechanism involved in yolk formation. Egg yolk contains a factor of factors responsible for accelerated cholesterol metabolism and excretion compared with crystalline cholesterol. One of these factors could be egg lecithin. Egg lecithin may not be as effective as soybean lecithin in lowering serum cholesterol level due probably to the differences of fatty acid composition. However, egg lecithin may have positive effects in hypercholesterolemia by increasing serum HDL level and excretion of fecal cholesterol. The association of serum cholesterol with egg consumption has been widely studied. When the basal or control diet contained little or no cholesterol, consumption of 1 or 2 eggs daily increased the concentration of plasma cholesterol, whereas that of the normolipemic persons on a normal diet was not significantly influenced by consuming 2 to 3 eggs daily. At higher levels of egg consumption, the concentration of HDL tends to increase as well as LDL. There exist hyper-and hypo-responders to dietary (egg) cholesterol. Identifying individuals in both categories would be useful from the point of view of nutrition guidelines. Dietary modification of fatty acid composition has been pursued as a viable method of modifying fat composition of eggs and adding value to eggs. In many cases beneficial effects of PUFA enriched eggs have been demonstrated. Generally, consumption of n-3 fatty acids enriched eggs lowered the concentration of plasma TG and total cholesterol compared to the consumption of regular eggs. Due to the highly oxidative nature of PUFA, stability of this fat is essential. The implication of hepatic lipid accumulation which was observed in hens fed on fish oils should be explored. Nutritional manipulations, such as supplementation with iodine, inhibitors of cholesterol biosynthesis, garlic products, amino acids and high fibre ingredients, have met a limited success in lowering egg cholesterol.

• Applied Biological Chemistry
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• v.13 no.3
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• pp.207-218
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• 1970

Studies were carried out to develope the most economical and practical methods of packaging and preservation of kimchi, so commercialization of kimchi manufacture could proceed rapidly. The results obtained may be summarized as following. (1) It is generally established that the acceptable range of lactic acid content of kimchi is between 0.4% and 0.75%. Based on sensory evaluation, kimchi having lactic acid content below 0.4% and above 0.75% was not edible, and the time of optimum taste corresponded to the vicinity of 0.5% of lactic acid content. For the refrigeration storage with or without preservatives, the packaging kimchi in plastic film must be done at the lactic acid content of 0.45%, for lactic acid fermentation will continue slowly after the packaging. However, for the heat sterilized kimchi the packaging should be done at the 0.5% of lactic acid content for the best because lactic acid fermentation is completely stopped after the packaging. (2) Polyethylene, polypropylene, and polycello were chosen as suitable packaging materials. Polyethylene is cheapest among them but kimchi packaged in this film was damaged frequently in handling process and gave off kimchi flavor. On the other hand polypropylene also gave off kimchi flavor, but its higher mechanical strength gave better protection to kimchi and it had superior display effect due to the transparancy. Therefore polypropylene made much better packaging material. Polycello proved to be the best packaging material from the standpoint of physical characteristics but its price is higher than that of other plastic films. To be effective, the thickness of plastic films for packaging kimchi must exceed 0.08mm. (3) Keeping property of kimchi appeared to be excellent by means of freezing. However, by the time the frozen kimchi was thawed out at room temperature, moisture loss due to drip was extensive, rendering the kimchi too stringy. (4) Preservation of kimchi at refrigerated temperatures proved to be the best method and under the refrigerated condition the kimchi remained fresh as long as 3 months. The best results were obtained when kimchi was held at $0^{\circ}C$. (5) In general, preservatives alone were not too elective in preserving kimchi. Among them potassium sorbate appeared to be most effective with the four fold extension of self-life at $20^{\circ}C$ and two fold extension at $30^{\circ}C$. (6) In heat sterilization the thickness of packaged kimchi product had a geat effect upon the rate of heat penetration. When the thickness ranged from 1.5 to 1.8cm, the kimchi in such package could be sterilized at $65^{\circ}C$ for 20 minutes. Kimchi so heat treated could be kept at room temperature as long as one month without apparent changes in quality. (7) Among combination methods, preservation at refrigerated and heat sterilization could be favorably combined. When kimchi was stored at $4^{\circ}C$ after being sterilized at $65^{\circ}C$ for 20 minutes, it was possible to preserve the kimchi for more than 4 months.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Food Science and Preservation
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• v.23 no.2
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• pp.259-266
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• 2016

Bitter melon powder (BMP) was used in the preparation of rice nuruk (RN) and makgeolli as one of raw materials. RN containing BMP (BMP-RN) was prepared by adding 0~2.0% (w/w) BMP into rice. Fermentative microbial, and antioxidant characteristics of makgeolli were determined during the fermentation process. pH during fermentation decreased from 4.52 (0% BMP-RN), 4.93 (0.5%), 4.80 (1.0%), and 4.88 (2.0%) on the initial fermentation day (day 0) to 4.15, 4.30, 4.57, and 4.59, respectively, corresponding to increases in acidity from 0.64~0.70% to 1.17~1.28%, respectively. Soluble solid contents increased from an initial 2.2~4.4 g/L (day 0) to 9.0~9.3 g/L, and alcohol level increased up to 13.0% by the end of fermentation (day 7). Soluble phenolic contents increased from 0.92, 1.01, 1.32, and 1.41 mg/mL on day 0, to 1.85, 2.03, 2.24, and 2.48 mg/mL on day 7, respectively, while the levels of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) diammonium salt (ABTS) radical scavenging activities and ferric reducing/antioxidant power (FRAP) assay increased from 46.95~70.04%, 55.33~74.13%, and 0.629~1.243 on day 0, respectively, to 54.98~83.4%, 70.34~92.39%, and 0.964~1.455 on day 7, respectively. Makgeolli made with BMP-RN had higher soluble phenolic contents and antioxidant activity than those of makgeolli made without BMP-RN. These results suggested that BNP-RN made a functional makgeolli.