• Journal of Food Hygiene and Safety
• /
• v.33 no.6
• /
• pp.466-473
• /
• 2018

An analytical method of sodium polyacrylate in processed food products was developed and monitored by using size-exclusion chromatography. GF-7M HQ column and UV/VIS detector were selected based on peak shape and linearity. Flow rate, column oven temperature, and mobile phase were selected as 0.6 mL/min, $45^{\circ}C$, and 50 mM sodium phosphate buffer of pH 9.0, respectively. Samples for analysis of sodium polyacrylate were extracted with 50 mM sodium phosphate buffer of pH 7.0 for 3 hr at $20^{\circ}C$ and 150 rpm. Analytical method validation revealed proper selectivity and calibration curve was selected in the range of 50-500 mg/L, and correlation coefficient of calibration curve was more than 0.9985. Limit of detection of sodium polyacrylate was 10.95 mg/kg and limit of quantification was 33.19 mg/kg. Accuracy and coefficient of variation for sodium polyacrylate analysis was 99.6-127.6%, 3.0-8.3% for intra-day and 94.3-121.9%, 1.3-2.6% for inter-day, respectively. Sodium polyacrylate was detected in 40 samples among monitored 125 processed food products. Detected contents were less than 0.2%, limited by the Food Additives Code. Results suggest the established size-exclusion chromatography method could be used to analyze sodium polyacrylate in processed food products.

• Journal of Nutrition and Health
• /
• v.52 no.4
• /
• pp.332-341
• /
• 2019

Purpose: Poor nutrition in hospitalized patients is closely linked to an increased risk of infection, which can result in complications affecting mortality, as well as increased length of hospital stay and hospital costs. Therefore, adequate nutritional support is essential to manage the nutritional risk status of patients. Nutritional support needs to be preceded by nutrition screening, in which accuracy is crucial, particularly for the initial screening. To perform initial nutrition screening of hospitalized patients, we used the Catholic Kwandong University (CKU) Nutritional Risk Screening (CKUNRS) tool, originally developed at CKU Hospital. To validate CKUNRS against the Patient-Generated Subjective Global Assessment (PG-SGA) tool, which is considered the gold standard for nutritional risk screening, results from both tools were compared. Methods: Nutritional status was evaluated in 686 adult patients admitted to CKU Hospital from May 1 to July 31, 2018 using both CKUNRS and PG-SGA. Collected data were analyzed, and the results compared, to validate CKUNRS as a nutrition screening tool. Results: The comparison of CKUNRS and PG-SGA revealed that the prevalence of nutritional risk on admission was 15.6% (n = 107) with CKUNRS and 44.6% (n = 306) with PG-SGA. The sensitivity and specificity of CKUNRS to evaluate nutritional risk status were 98.7% (96.8 ~ 99.5) and 33.3% (28.1 ~ 39.0), respectively. Thus, the sensitivity was higher, but the specificity lower compared with PG-SGA. Cohen's kappa coefficient was 0.34, indicating valid agreement between the two tools. Conclusion: This study found concordance between CKUNRS and PG-SGA. However, the prevalence of nutritional risk in hospitalized patients was higher when determined by CKUNRS, compared with that by PG-SGA. Accordingly, CKUNRS needs further modification and improvement in terms of screening criteria to promote more effective nutritional support for patients who have been admitted for inpatient care.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.2
• /
• pp.36-49
• /
• 2019

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.3
• /
• pp.199-208
• /
• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• Journal of Bio-Environment Control
• /
• v.28 no.3
• /
• pp.243-254
• /
• 2019

With the recent accelerated policy-making and interests in new renewable energy, plans to develop and supply the new renewable energy have been devised across multiple regions in Korea. Solar energy, in particular, is being applied to small-scale power supply in provincial areas, as solar cells are used to convert solar energy into electric energy to produce electric power. Nonetheless, in the case of solar power plants, the need for a large stretch of land and considerable sum of financial support implies that the planning step should take into consideration the most suitable meteorological and geographical factors. In this study, the proxy variables of meteorological and geographical factors associated with solar energy were considered in analyzing the vulnerable areas regarding the photovoltaic power generation facility across the nation. GIS was used in the spatial analysis to develop a map for assessing the optimal location for photovoltaic power generation facility. The final vulnerability map developed in this study did not reveal any areas that exhibit vulnerability level 5 (very high) or 1 (very low). Jeollanam-do showed the largest value of vulnerability level 4 (high), while a large value of vulnerability level 3 (moderate) was shown by several administrative districts including Gwangju metropolitan city, Jeollabuk-do, Chungcheongbuk-do, and Gangwon-do. A value of vulnerability level 2 (low) was shown by the metropolitan cities including Daegu, Ulsan, and Incheon. When the 30 currently operating solar power plants were compared and reviewed, most were found to be in an area of vulnerability level 2 or 3, indicating that the locations were relatively suitable for solar energy. However, the limited data quantity for solar power plants, which is the limitation of this study, prevents the accuracy of the findings to be clearly established. Nevertheless, the significance of this study lies in that an attempt has been made to assess the vulnerability map for photovoltaic power generation facility targeting various regions across the nation, through the use of the GIS-based spatial analysis technique that takes into account the diverse meteorological and geographical factors. Furthermore, by presenting the data obtained for all regions across the nation, the findings of this study are likely to prove useful as the basic data in fields related to the photovoltaic power generation.

• Clean Technology
• /
• v.25 no.2
• /
• pp.107-113
• /
• 2019

Numerical model that considered the shrinking core model and elutriation and degradation of particles was developed to predict selective chlorination of ilmenite and carbo-chlorination of $TiO_2$ in a two stage fluidized bed chlorination furnace. It is possible to analyze the fluidized bed chlorination reaction to be able to reflect particle distribution for mass balances and the chlorination reaction. The numerical model showed an accuracy with error less than 6% compared with fluidized bed experiments. The chlorination degree with particle size change was greater with a smaller particle size, and there was a 100 min difference to obtain a chlorination degree of 1 between $75{\mu}m$ and $275{\mu}m$. This was not shown to such a great extent with variation of temperature ($800{\sim}1000^{\circ}C$), and there was only a 10 min difference to obtain a chlorination degree of 0.9. In the first selective chlorination process, the mass reduction rate approached to the theoretical value of 0.4735 after 180 min, and chlorination changed the Fe component into $FeCl_2$ or $FeCl_3$ and showed nearly 1. In the second carbo-chlorination process, the chlorination degree of $TiO_2$ approached 0.98 and the mass fraction reached 0.02 with conversion into $TiCl_4$. In the first selective chlorination process, 98% of $TiO_2$ was produced at 180 min, and this was changed into 99% of $TiCl_4$ after an additional 90 min. Also the mass reduction rate of $TiO_2$ was reduced to 99% in the second continuous carbo-chlorination process.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.6
• /
• pp.19-25
• /
• 2019

The CleanSYS(Clean SYStem) is operated to monitor air pollutants emitted from specific industrial complexes in Korea. So the industrial complexes without the system are directly monitored by the control officers. For efficient monitoring, studies using various sensors have been conducted to monitor air pollutants emitted from industrial complex. In this study, hyperspectral sensors were used to model and verify the equations for estimating the concentration of $NO_2$(nitrogen dioxide) in air pollutants emitted. For development of the equations, spectral radiance were observed for $NO_2$ at various concentrations with different SZA(Solar Zenith Angle), VZA(Viewing Zenith Angle), and RAA(Relative Azimuth Angle). From the observed spectral radiance, the calculated value of the difference between the values of the specific wavelengths was taken as an absorption depth, and the equations were developed using the relationship between the depth and the $NO_2$ concentration. The spectral radiance mixed gas of $NO_2$ and $SO_2$(sulfur dioxide) was used to verify the equations. As a result, the $R^2$(coefficient of determination) and RMSE(Root Mean Square Error) were different from 0.71~0.88 and 72~23 ppm according to the form of the equation, and $R^2$ of the exponential form was the highest among the equations. Depending on the type of the equations, the accuracy of the estimated concentration with varying concentrations is not constant. However, if the equations are advanced in the future, hyperspectral sensors can be used to monitor the $NO_2$ emitted from the industrial complex.

• Journal of Korean Society of Forest Science
• /
• v.107 no.4
• /
• pp.344-350
• /
• 2018

This study was carried out to improve the forest management method considering the use of high value added timber in the natural broadleaf forests. For this purpose, the criteria for evaluating the quality grade of standing trees were established and applied to the oak stand in the central region of Korea. The evaluation factors of the grade were bending of stem, branch, stem damage, and other defects. If the logs are divided into 2.1 m units and three logs up to 6.3 m are available, they are classified as Grade I (G-I). If two logs are available, they are classified as Grade II (G-II), If only one log is available, it is classified as Grade III (G-III). When any log is not available as timber, it is classified as Grade IV (G-IV). As a result of applying the grade to the oak stand, G-I was 6.7 %, G-II was 28.0 %, G-III was 38.3 %, and G-IV was 27.0 %. The ratio of standing trees by oak species of higher than G-III was 88.2 % for Quercus acutissima, 88.1 % for Q. variabilis, 83.5 % for Q. serrata, 56.3 % for Q. aliena, and 50.3 % for Q. mongolica, respectively. The G-IV ratio for Q. variabilis and Q. mongolica tended to decrease with increasing diameter at breast height. The order of major defect affecting the grading level was bending of stem > branch > stem damage > other defects. Considering the grade level and oak species distribution, it was concluded possible to produce high quality hardwood timber when we concentrate forest tending techniques on Q. acutissima and Q. variabilis stand. In order to improve the accuracy of grading, it is necessary to continuous complement through the monitoring research for evaluation factors.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.39 no.1
• /
• pp.1-8
• /
• 2019

This study was conducted to predict the energy requirements for maintenance and growth of female Korean black goats during their growth and pregnancy phases. Fifty female goats ($18.7{\pm}0.27kg$) in their growth phase with an average age of 5 months were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in metabolic energy (ME) [2.32 (G1), 2.49 (G2), 2.74 (G3), 2.99 (G4), and 3.24 (G5) Mcal/kg] until they were 9-month-old. After natural breeding, 50 female goats ($30.7{\pm}0.59kg$) were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in ME [2.32 (P1), 2.43 (P2), 2.55 (P3), 2.66 (P4), and 2.78 (P5) Mcal/kg]. The average feed intake ranged between 1.5 and 2.0% of the body weight (BW), and there was no significant difference between the treatment groups with goats in growth or pregnancy phases. Average daily gain (ADG) in diet demand during the growth phase increased with an increasing ME density and ranged from 46 to 69 g/d (p<0.01). Feed conversion ratio (FCR) improved with the ME density during the growth phase (p<0.01). The intercept of the regression equation between ME intake and ADG indicated that energy requirement for maintenance of goats during growth and pregnancy phases was $103.53kcal/BW^{0.75}$ and $102.7kcal/BW^{0.75}$, respectively. These results may serve as a basis for the establishment of goat feeding standards in Korea. Further studies are required to assess the nutrient requirement of goats using various methods for improving accuracy.

• Journal of Food Hygiene and Safety
• /
• v.34 no.2
• /
• pp.148-157
• /
• 2019

An optimized analytical method for sodium iron chloriphyllin in foods was established and verified by using high performance liquid chromatography with attached diode array detection. An Inertsil ODS-2 column and methanol-water (80:20 containing 1% acetate) as a mobile phase were employed. The limit of detection and quantitation of sodium iron chloriphyllin were 0.1 and 0.3 mg/kg, respectively, and the linearity of calibration curve was excellent ($R^2=0.9999$). The accuracy and precision were 93.9~104.95% and 2.0~7.7% in both inter-day and intra-day tests. Recoveries for candy and salad dressing were ranged between 93 and 104% (relative standard deviation, (RSD) 0.3~4.3%), and between 83 and 115% (RSD 1.2~2.0%), respectively. Liquid chromatography mass spectrometry was used to verify the main components of sodium iron chlorophyllin which were Fe-isochlorin e4 and Fe-chlorin e4.