• Journal of Korean Society for Atmospheric Environment
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• v.6 no.2
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• pp.155-160
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• 1990

During the period from Mar. 1985 to Feb. 1988, airborne particulate matters were collected and size fractionated by the ANdersen high volume air sampler in Seoul. The concentrations of several heavy metals (Pb, Cu, Zn, Fe, Mn) and benzo(a)pyrene were determined to investigate the size distributions and seasonal variations. And with respect to seven components in the total suspended particulate (TSP), the factor analysis was performed for three groups such as the coarse particles (> 2 $\mu$m), fine particles (< $\mu$m) and TSP. As a result of factor analysis by using the varimax method, the chemical components in the TSP were able to characterize with two principal factors. The first factor, F1 was considered to be a factor indicating the contribution of natural sources and the second factor, F2 was a factor indicating the degree of artificial sources. Each components in the TSP was divided into two main groups of components originated from soil and/or road dust and pollutants originated from automobiles and/or human work.

• Journal of environmental and Sanitary engineering
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• v.6 no.1
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• pp.47-62
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• 1991

Yellow Sand Phenomena was observed from April 8 th to 10 th in 1990. During this period particle was collected to investigate the chacteristics of chemical composition of particulate by High Volume Air Sampler and Andersen Air Sa~npler in Seoul. During this period the particle concentration was 350 yg/$m^3$ and the anions, cations, and metal concentrations were increased and the orders of these were $S0_4\;^{-2}>N0_3\;^->Cl^->F^-, Na^+>Ca^{+2}>NH_4\;^+>Mg^{2+}>K^+$, and Fe>Al>Si>Zn>Pb respectively. The principal source of Yellow Sand were identified soil and sea salt. Mn used by the trace element of soil, the persentage of contribution from soil was calculated to be about 81.3% for the particle increased by Yellow Sand Phenomena. Also the principal chemical compounds of particle were estimate metals(Fe, Al, Si, Zn) oxides, $CaSO_4, NaSO_4, MgSO_4, NaC1, MgCl_2$ and $(NH_4)_2SO_4$.

• Journal of Biosystems Engineering
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• v.34 no.2
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• pp.114-119
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• 2009

Field test was conducted to investigate primary factors reducing rapeseed harvesting using a reciprocating cutter-bar of combine. The results showed that the correlation between crop moisture content and yield loss had a U-type, which indicated that the yield reduction increased at too high and too low crop moisture contents. The proper ranges of crop moisture contents were 27${\sim}$35%, 21${\sim}$56%, and 62${\sim}$73% in case of grain, pod and stem, respectively. Crop moisture content was negatively correlated with header loss, but positively correlated with threshing loss. In contrary, stem moisture content showed positive correlations with total loss, threshing loss and separation loss. Working speed was positively correlated with header loss. Total flow rate, pod flow rate and stem flow rate were highly correlated with threshing loss and separation loss. However, grain flow rate did not show any correlation with total loss. According to the principal component analysis, two principal components were derived as components with eigenvalues greater than 1.0. The contribution rates of the first and the second components were 52.7% and 38.9%, which accounted for 91.6% of total variance. As a contributive factor influencing total loss of rapeseed mechanical harvesting, a crop moisture content factor was greater than a crop flow rate factor. The stepwise multiple regression analysis for total loss was conducted using crop moisture content factor, crop flow rate factor and coefficient. However, the model did not show any correlation among independent and dependent factors ($R^2$=0.060).

• Journal of Korean Society on Water Environment
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• v.30 no.2
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• pp.119-127
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• 2014

The objective of this study is to evaluate and analyze Sumjin River Basin water environment system. It was necessary to improve the water quality. The data were collected from 2010 January to 2012 December including Water Temperature, pH, DO, EC, $BOD_5$, COD, TOC, SS, T-N, T-P. The data were used to study were required to; Correlation Analysis; Principle Component Analysis; Factor Analysis. The results were as followed. According to correlation analysis on $BOD_5$ against COD, TOC it revealed that the each value of correlation coefficient were 0.715 and 0.719; this means the strength of the relationship is strong. The same analysis on T-P against $BOD_5$, COD, TOC, SS has revealed that the range of the correlation coefficient value fell between 0.482 and 0.608 which means strength of the relationship between them remained normal. Through correlation analysis, it has been found that all elements except T-N have high correlation. The results of principal component analysis to target the overall water quality was extracted to three main components. The cumulative contribution rate is 68.990%. In the case of seasonal water quality, Spring and Summer are extracted to each of four main components. The cumulative contribution rate is 81.515% and 73.550% respectively. Fall and Winter are extracted to each of three main components. The cumulative contribution rate is 65.072% and 72.721% respectively. There is no seasonality in the case of factor analysis. The first common factor is $BOD_5$, COD, TOC, SS, T-P, which were classified. Totally speaking, Sumjin River Basin water system gets highly affected by the nutrient salts, organic matter and suspended solid at the same time.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.3
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• pp.207-213
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• 1999

Thirty-eight glutinous rice varieties were classified into nine groups on the scatter diagram by the upper two principal components (56% contribution to the total information) based on eleven physicochemical characteristics including the viscograms and physical properties of cooked rice. The first principal component was the factor mainly associated with the viscogram characteristics of rice flour emulsion and the second was the factor chiefly related to the physical properties of cooked rice and water absorbability of rice grain. Indica glutinous rices were clearly distinguished from japonica ones by the first principal component score. Javanica glutinous rices were widely distributed on the intermediate zone between indica and japonica or on several japonica rice groups. Significant positive or negative correlations were found among water absorption rates of rice grain, physical properties of cooked rice, and viscogram characteristics of rice flour. Especially in japonica glutinous rices, the breakdown and setback viscosities of rice flour were closely associated with the alkali digestion value of milled rice and the stickiness of cooked rice. The frequency ratio of short glucose chains (A-chain) to intermediate glucose chains (B-chain), the ratio of B- chains to long glucose chains (C-chain) and the relative frequency of A- or B-chain fractions representing the amylopection structure of rice starch was closely associated with the breakdown and setback viscosities of rice flour.

• Journal of Ginseng Research
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• v.35 no.1
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• pp.31-38
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• 2011

In order to distinguish the cultivation area of Panax ginseng, principal component analysis (PCA) using quantitative and qualitative data acquired from HPLC was carried out. A new HPLC method coupled with evaporative light scattering detection (HPLC-ELSD) was developed for the simultaneous quantification of ten major ginsenosides, namely $Rh_1$, $Rg_2$, $Rg_3$, $Rg_1$, Rf, Re, Rd, $Rb_2$, Rc, and $Rb_1$ in the root of P. ginseng C. A. Meyer. Simultaneous separations of these ten ginsenosides were achieved on a carbohydrate analytical column. The mobile phase consisted of acetonitrile-water-isopropanol, and acetonitrile-water-isopropanol using a gradient elution. Distinct differences in qualitative and quantitative characteristics for ginsenosides were found between the ginseng roots produced in two different Korean cultivation areas, Ganghwa and Punggi. The ginsenoside profiles obtained via HPLC analysis were subjected to PCA. PCA score plots using two principal components (PCs) showed good separation for the ginseng roots cultivated in Ganghwa and Punggi. PC1 influenced the separation, capturing 43.6% of the variance, while PC2 affected differentiation, explaining 18.0% of the variance. The highest contribution components were ginsenoside $Rg_3$ for PC1 and ginsenoside Rf for PC2. Particularly, the PCA score plot for the small ginseng roots of six-year old, each of which was light than 147 g fresh weight, showed more distinct discrimination. PC1 influenced the separation between different sample sets, capturing 51.8% of the variance, while PC2 affected differentiation, also explaining 28.0% of the variance. The highest contribution component was ginsenoside Rf for PC1 and ginsenoside $Rg_2$ for PC2. In conclusion, the HPLC-ELSD method using a carbohydrate column allowed for the simultaneous quantification of ten major ginsenosides, and PCA analysis of the ginsenoside peaks shown on the HPLC chromatogram would be a very acceptable strategy for discrimination of the cultivation area of ginseng roots.

• Korean Journal of Medicinal Crop Science
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• v.16 no.4
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• pp.218-224
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• 2008

This study was conducted to obtain the basic data for using the Artemisia genetic resources as a medicinal crop. 24 taxa including Artemisia capillaris Thunb. were analyzed by principal component analysis of 25 characters and cluster analysis for classification. In Principal components analysis of individuals of taxa using 25 morphological characters of reproductive organ, the first, the second, the third and the fourth components contributed 44.73%, 16.86%, 8.88%, and 7.07% of the variations, respectively. The cumulative contribution from the first to the fourth principal components was 77.56%. In cluster analysis, taxa of Artemisia L. was seperated 3 group by 25 morphological characters of reproductive organ, but it didn't completely coincident with Kitamura classification.

• Korean Journal of Medicinal Crop Science
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• v.10 no.5
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• pp.333-339
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• 2002

This study was conducted to obtain the basic data for practical use of the Polygonatum genetic resources. The 20 collections were analyzed by principal component analysis of 8 characters and cluster analysis. In the principal analysis, the first, the second and the third components contributed 54.10%, 18.95% and 11.62% of the variations, respectively. The cumulative contribution from the first to the third principal components was 84.68%. The first principal component was related to shape and size of plant, and assimilatory, reserve and reproductive organs. The second principal component was related to growth and development of plant, and reserve organ. And the third principal component was related to growth and development of plant. Based on cluster analysis, the 20 collections were classified into 4 distinct groups with the average distance greater than 0.7 between groups. Group I was Polygonatum sibiricum $D_{ELAR}$ and Group II included P. odoratum var. pluriflorum $O_{HWI}$, P. odoratum var. pluriflorum $O_{HWI}$ for 'Variegatum' Y. Lee, for. nov., P. odoratum var. thunbergii $H_{ARA}$ and P. odoratum var. maximowiczii $K_{OIDZ}$. GroupIII was P. involucratum $M_{AXIM}$, P. desoulavyi $K_{OMAROV}$ and P. humile $F_{ISHER}$ ex. $M_{AXIM}$. And GroupIV included P. lasianthum var. coreanum $N_{AKAI}$ and P. inflatum $K_{OMAROV}$.

• Journal of Bio-Environment Control
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• v.17 no.2
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• pp.124-131
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• 2008

To investigate main air temperature factors correlated to difference of fruit characteristics according to cultivating areas, fruit and air temperature characteristics of eight cultivating areas of 'Fuyu' persimmon were analyzed by principle components and multiple regression analysis. The first principal components extracted from 16 air temperature factors was annual mean temperature, mean temperature during October, annual mean minimum extreme temperature, mean temperature during growing period, and so forth. The second principal components was mean temperature during May and June and so forth. And cumulative contribution was 91.4%. The five of eight cultivating area had clearly the difference of main factors or the correlated direction among cultivating areas. In multiple regression analysis between the extracted main factors and fruit characteristics, fruit hight were highly correlated with mean temperature during growing period ($X_8$) and cumulative temperature ($X_6$), and the regression equation was $Y=150.55-5.375X_8+ 0.014X_6(r^2=0.843)$. Also this regression equation was affected by mean minimum temperature during growing period, cumulative temperature, and mean temperature during August. Fruit diameter was negatively correlated with mean temperature during growing period, flesh browning rate and Hunter a value of peel color were positively correlated with mean minimum temperature during growing period and annual minimum air temperature, respectively.

• Journal of Environmental Health Sciences
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• v.26 no.4
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• pp.1-8
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• 2000

The objective of this study was to identify the sources and to estimate the source contributions to the atmospheric TSP(total suspended particulate matter) and PM-10(particulate matter with aerodynamic diameters less than 10$\mu\textrm{m}$) concentration in Taegu area. A total of 84 samples was collected during the January to December 1999. TSP and PM-10 were collected on filters by portable air sampler, and heavy metals in TSP and PM-배 were analyzed by ICO(Inductively Coupled Plasma Spectrometery) after preliminary treatment. The results were follow as : First, annual average of TSP and PM-10 concentration was 123 and 69$\mu\textrm{g}$/㎥ respectively. The concentration of TSP and PM-10 were highest in winter season compared to other seasons. Second, the concentration of Al, Fe, Mn were higher in TSP than in PM-10, indicating that these heavy metals are generally associate with natural contributions. Third, metal combinations showed that a high correlation among concentrations of heavy metals were follows: As Al, Fe and Mn in TSP ; Ni, Cr, Cd and Pb in PM-10. Finally, Statistical analysis was performed using Principal Components Analysis(PCA) in order to find possible sources of the pollutants. The factor analysis was permitted to identify four major sources(soil/road dust resuspension, waste incineration, furl combustion, vehicular emission) in each fraction. These source accounted for at least 83, 85% of variance of TSP and PM-10 concentration in Taegu area.