• Journal of Food Hygiene and Safety
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• v.33 no.4
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• pp.296-305
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• 2018

Spiroxamine, one of fungicides, is used to control powdery mildew in various crops and black yellow sigatoka in bananas. The major strength of spiroxamine is to control powdery mildew in various crops and bananas yellow sigatoka in bananas. The compound has shown a high level of activity, good persistence and crop tolerance. Besides powdery mildew, good control of rust, net blotch and Rhynchosporium diseases been indicated in cereals, together with a complementary activity against Septoria diseases. In 2017, the maximum residue limit (MRL) of spiroxamine established in Korea. According to Ministry of ood and rug afety) regulations, spiroxamine residues defined only parent compound. Thus, a analytical method is needed to estimate the residue level of the parent compound. The objective of this study was to develop and validate analytical method for spiroxamine in representative agricultural commodities. Samples were extracted with acetonitrile and partitioned with dichloromethane to remove the interfering substances. The analyte were quantified and confirmed liquid chromatograph-tandem mass spectrometer (LC-MS/MS) in positive-ion mode using multiple reaction monitoring (MRM). Matrix matched calibration curves were linear over the calibration ranges ($0.0005{\sim}0.1{\mu}g/mL$) for the analyte in blank extract with coefficient of determination ($r^2$) > 0.99. For validation purposes, recovery studies will be carried out at three different concentration levels (LOQ, 10LOQ, and 50LOQ) performing five replicates at each level. The recoveries 70.6~104.6% with relative standard deviations (RSDs) less than 10%. All values were consistent with the criteria ranges in the Codex guidelines (CAC/GL40, 2003) and MFDS guidelines. proposed analytical method be used as an official analytical method in the Republic of Korea.

• Korean Journal of Soil Science and Fertilizer
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• v.32 no.2
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• pp.95-108
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• 1999

Field experiments were conducted in the 101 tobacco fields(51 fields in 1985 and 50 fields in 1986) of chief tobacco producing counties of Chungbuk province(Jincheon, Eumseong, Goesan, and Joongweon counties), Chungnam province(Cheonweon county), and Kyongbuk province (Cheongdo, Seongju, and Andong counties) for two years from 1985 to 1986 in order to evaluate soil fertility using chemical properties and soil map database. Pot experiments also on the same soils were conducted and the results were compared to those of field experiments. The yield of tobacco in the plots of no fertilization was considered as a basic factor representing the soil fertility and was evaluated by nineteen independent variables, that was 9 chemical properties and 10 soil map databases. These independent variables were classified into two groups, 11 quantitative indexes and 9 qualitative indexes, and were analyzed by multiple linear regression(MLR) of SAS by REG and GLM models. The yield of tobacco in the plot of no fertilization showed high variations, e.g. the difference between minimum and maximum yields was about 5.0-5.5 times in the pot experiment and 8.2-14.9 times in the field experiment. The indexes indicating close link between yield of tobacco and soil chemical indexes, was selected but it was not well matched by the years or between pot and field experiments. Also, the standardized partial regression coefficients of quantitative indexes for the yield of field were less than 1.0, suggesting that it is difficult to develop an available single index for the evaluation of soil fertility. Evaluation for the soil fertility of field by MLR was better than that of single regression and it was gradually improved by adding chemical properties, quantitative indexes, and qualitative indexes of soil map. For example, the coefficient of determination ($R^2$) of MLR for the yield of 1985 was increased to 0.422 with chemical indexes, 0.503 by addition of quantitative indexes, and 0.633 by the additional adding of qualitative indexes of soil map, compared to 0.244 of single index, $NO_3-N$ content of soil. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes including chemical properties and soil map databases was available as an evaluation model of soil fertility for tobacco field.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.441-448
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• 2015

Lack of agricultural information for food supply and demand in Democratic People's republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS $NDVI_{max}$ in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.

• Journal of Genetic Medicine
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• v.4 no.1
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• pp.45-52
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• 2007

Purpose : A simple, rapid, and highly sensitive analytical method for Gb3 in plasma was developed without labor-ex tensive pre-treatment by electrospray ionization MS/ MS (ESI-MS/MS). Measurement of globotriaosy lceramide (Gb3, ceramide trihex oside) in plasma has clinical importance for monitoring after enzyme replacement therapy in Fabry disease patients. The disease is an X-linked lipid storage disorder that results from a deficiency of the enzyme ${\alpha}$-galactosidase A (${\alpha}$-Gal A). The lack of ${\alpha}$-Gal A causes an intracellular accumulation of glycosphingolipids, mainly Gb3. Methods : Only simple 50-fold dilution of plasma is necessary for the extraction and isolation of Gb3 in plasma. Gb3 in diluted plasma was dissolved in dioxane containing C17:0 Gb3 as an internal standard. After centrifugation it was directly injected and analyzed through guard column by in combination with multiple reaction monitoring mode of ESI-MS/MS. Results : Eight isoforms of Gb3 were completely resolved from plasma matrix. C16:0 Gb3 occupied 50% of total Gb3 as a major component in plasma. Linear relationship for Gb3 isoforms w as found in the range of 0.001-1.0 ${\mu}g$/mL. The limit of detection (S/N=3) was 0.001 ${\mu}g$/mL and limit of quantification was 0.01 ${\mu}g$/mL for C16:0 Gb3 with acceptable precision and accuracy. Correlation coefficient of calibration curves for 8 Gb3 isoforms ranged from 0.9678 to 0.9982. Conclusion : This quantitative method developed could be useful for rapid and sensitive 1st line Fabry disease screening, monitoring and/or diagnostic tool for Fabry disease.

• Korean Journal of Environmental Biology
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• v.25 no.4
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• pp.303-312
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• 2007

In order to investigate the dynamics of phytoplankton standing crops affecting by environmental factors, biological and environmental factors, this study was examined in the marine ranching ground of Tongyeong coastal waters from 2000 to 2007. During the study, mean water temperature and salinity were 16.7$^{\circ}C$ and 32.9 psu, respectively. pH, DO and SS varied from 7.81$\sim$8.09, 3.02$\sim$8.97 mg $L^{-1}$ and 2.7$\sim$32.2 mg $L^{-1}$, respectively. Mean concentrations of dissolved inorganic nitrogen, phosphate and silicate were 21.75 ${\mu}M$, 0.90 ${\mu}M$ and 14.38 ${\mu}M$, respectively. Chlorophyll a concentrations varied from 0.02 ${\mu}g$ $L^{-1}$ to 25.29 ${\mu}g$ $L^{-1}$ with mean a value of 2.0 ${\mu}g$ $L^{-1}$. These factors did show significant differences on each layer and season, while did not show on the sampling stations. Phytoplankton standing crops varied from $4.21\times10^3$ cells $L^{-1}$ to $1.44\times10^6$ cells $L^{-1}$ with a mean value of $1.92\times10^5$ cells $L^{-1}$. Especially, variations of phytoplankton standing crops had an unimodal pattern as only bloomed in autumn rather than a bimodal pattern as generally bloomed in spring and autumn. In results of stepwise multiple regression analysis, the coefficient of determination $(R^2)$ for total standing crops was 0.35 and the standing crops were affected by water temperature, salinity, phosphate and silicate. The factors affected were different seasonally; water temperature in spring, salinity in summer, water temperature, salinity and silicate in autumn and water temperature, salinity and suspended solids in winter. Therefore, the results from the statistical analysis showed that the environmental factors influencing on the variations of the phytoplankton standing crops were predominantly water temperature and salinity.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1195-1205
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• 2018

In the study, the water shortage of Boryeong Dam watershed ($163.6km^2$) was evaluated under future climate change scenario. The Soil and Water Assessment Tool (SWAT) was used considering future dam release derived from multiple linear regression (MLR) analysis. The SWAT was calibrated and verified by using daily observed dam inflow and storage for 12 years (2005 to 2016) with average Nash-Sutcliffe efficiency of 0.59 and 0.91 respectively. The monthly dam release by 12 years MLR showed coefficient of determination ($R^2$) of above 0.57. Among the 27 RCP 4.5 scenarios and 26 RCP 8.5 scenarios of GCM (General Circulation Model), the RCP 8.5 BCC-CSM1-1-M scenario was selected as future extreme drought scenario by analyzing SPI severity, duration, and the longest dry period. The scenario showed -23.6% change of yearly dam storage, and big changes of -34.0% and -24.1% for spring and winter dam storage during 2037~2047 period comparing with 2007~2016 period. Based on Runs theory of analyzing severity and magnitude, the future frequency of 5 to 10 years increased from 3 in 2007~2016 to 5 in 2037~2046 period. When considering the future shortened water shortage return period and the big decreases of winter and spring dam storage, a new dam operation rule from autumn is necessary for future possible water shortage condition.

• Korean Journal of Ecology and Environment
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• v.39 no.3 s.117
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• pp.340-351
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• 2006

To reveal physicochemical factors contributing to variation of phytoplankton communities, the study was carried out biweekly at 6 stations from Feb. 2004 to Feb. 2005 in the lower part of the Han River, Korea. As results, water temperature was changed from $0.3^{\circ}C$ to $26.6^{\circ}C$, pH: 6.6${\sim}$9.1, DO: 1.89${\sim}$22.23 mg $L^{-1}$, BOD: 0.38${\sim}$9.20 mg $L^{-1}$, COD: 1.4${\sim}$15.2 mg $L^{-1}$, Conductivity: $62.5{\sim}500.0\;{\mu}s\;cm^{-1}$, SS: 3.00${\sim}$159.3 mg $L^{-1}$, and Chl a $1.7{\sim}71.3\;{\mu}g\;L^{-1}$. Phytoplankton standing crops ranged from min. $3.6{\times}10^2\;cells\;mL^{-1}$ (July 2004, St. 3) to max. $2.3{\times}10^4\;cells\;mL^{-1}$ (Feb. 2005, St. 6), and mean of those varied from $5.9{\times}10^3\;cells\;mL^{-1}$in spring, $2.1{\times}10^3\;cells\;mL^{-1}$ in summer, $4.1{\times}10^3\;cells\;mL^{-1}$ in autumn and $8.5{\times}10^3\;cells\;mL^{-1}$ in winter, respectively. In order to investigate factors influencing the total phytoplankton standing crops a multiple regression analysis was adopted for the correlation between standing crops and environmental factors. The coefficient of determination ($R^2$) value of the regression was 0.465, it showed that environmental factors which predominantly influenced phytoplankton standing crops were water temperature, COD, $NO_2-N$, $PO_4-N$, Discharge and pH. six stations could be divided into 3 groups based on similarity index in terms of environmental factors. In ANOVA analysis for physicochemical and biological factors, water temperature, chlorophyll a, silicate, phytoplankton standing crops were the same group differed little from stations. However, Station 1and 2 were grouped followed in dissolved oxygen, conductivity, COD, nitrite, nitrate, ammonia and phosphate, and Station 3, 4 and 5 were followed in dissolved oxygen, conductivity, pH and phosphate.