• Applied Biological Chemistry
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• v.17 no.2
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• pp.81-92
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• 1974

In order to prepare the mashing materials for 'Takju', Korean wine, with potatoes, theywere 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 $D40^{\circ}$ 30' 128 W.V. and 13.2 A.U.. 3. The effects of various brewing conditions on the contents of Takju mashes were as 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 were 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 Formol nitrogen contents of the Takju mash brewed with potato flour were less than that with wheat flour, on the contrary, methanol contents and total acidifies 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 Formol 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 bacteria 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.

• 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.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.