• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.25 no.4
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• pp.81-96
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• 2020

Berthlot's reaction spectrophotometric method is generally used for the analysis of dissolved ammonium in seawater, but in recent years, a fluorescence method using an orthophthaldialdehyde-sulfite (OPA) fluorescent reagent is actively used internationally. In this study, we investigated the effects of the detection limit between the analysis methods, the reagent refractive index inherent in the spectrophotometric method, and the use of different calibration curves to understand the cause of the difference in dissolved ammonium concentration (about 0.31 𝜇M) observed in the seawater samples and a nutrient reference material between two institutions (KIOST (spectrophotometric method, one-order linear regression gradient only), Australia CSIRO (fluorescence method, quadratic formula)) conducted onboard the Australian R/V Investigator in 2017. The method detection limit (0.063 𝜇M) and the reagent refractive index background value (0.054 𝜇M) of the spectrophotometric method measured in this study could explain the difference in dissolved ammonium concentration values of the two institutes about 20% and 17%, respectively. However, when the concentration of the calibration curve of the spectrophotometric method was calculated using the same quadratic as the fluorescence method or the slope and intercept of linear regression, the difference in the dissolved ammonium concentration between the two institutions was reduced to less than the detection limit of the spectrophotometric method. Therefore, the difference in the concentration of dissolved ammonium between the two institutions, found in the nutrient reference materials and the seawater field sample during the international onboard nutrient inter-comparison experiment, may be attributed to be the effect of the different calibration curves used in the two methods rather than the effect of the difference in two analytical methods. When comparing the dissolved ammonium data from seawater samples in the future, it is recommended to pay attention to the information on the baseline, number of standard solutions, and calibration curve used in the analysis.

• Journal of Food Hygiene and Safety
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• v.31 no.5
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• pp.327-334
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• 2016

This study was conducted to establish the standard method for the contents of biotin in milk formulas. To optimize the method, we compared several conditions for liquid extraction, purification and instrumental measurement using spiked samples and certified reference material (NIST SRM 1849a) as test materials. LC-MS/MS method for biotin was established using $C_{18}$ column and binary gradient 0.1% formic acid/acetonitrile, 0.1% formic acid/water mobile phase is applied for biotin. Product-ion traces at m/z 245.1 ${\rightarrow}$ 227.1, 166.1 are used for quantitative analysis of biotin. The linearity was over $R^2=0.999$ in range of $5{\sim}60{\mu}g/L$. For purification, chloroform was used as a solvent for eliminating lipids in milk formula. The linearity was over 0.999 in range of 5~60 ng/mL. The detection limit and quantification limit were 0.10, 0.31 ng/mL. The accuracy and precision of LC-MS/MS method using CRM were 103%, 2.5% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested milk formulas were acceptable contents of biotin compared with component specification and standards for nutrition labeling. The standard operating procedures were prepared for biotin to provide experimental information and to strengthen the management of nutrient in milk formula.

• Journal of the Korean Society for Marine Environment & Energy
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• v.9 no.4
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• pp.235-242
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• 2006

The precise estimation of total and organic carbon contents in sediments is fundamental to understand the benthic environment. To test the precision and accuracy of CHN analyzer and the procedure to quantify total and organic carbon contents(using in-situ acidification with sulfurous acid($H_2SO_3$)) in the sediment, the reference material s such as Acetanilide($C_8H_9NO$), Sulfanilammide($C_6H_8N_2O_2S$), and BCSS-1(standard estuary sediment) were used. The results indicate that CHN analyzer to quantify carbon and nitrogen content has high precision(percent error=3.29%) and accuracy(relative standard deviation=1.26%). Additionally, we conducted the instrumental comparison of carbon values analyzed using CHN analyzer and Coulometeric Carbon Analyzer. Total carbon contents measured from two different instruments were highly correlated($R^2=0.9993$, n=84, p<0.0001) with a linear relationship and show no significant differences(paired t-test, p=0.0003). The organic carbon contents from two instruments also showed the similar results with a significant linear relationship($R^2=0.8867$, n=84, p<0.0001) and no significant differences(paired t-test, p<0.0001). Although it is possible to overestimate organic carbon contents for some sediment types having high inorganic carbon contents(such as calcareous ooze) due to procedural and analytical errors, analysis of organic carbon contents in sediments using CHN Analyzer and current procedures seems to provide the best estimates. Therefore, we recommend that this method can be applied to measure the carbon content in normal any sediment samples and are considered to be one of the best procedure far routine analysis of total and organic carbon.

• The Korean Journal of Pesticide Science
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• v.19 no.3
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• pp.174-184
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• 2015

In the present study, the metabolism of [$^{14}C$]butachlor was investigated in rice plant according to the OECD test guideline No. 501. [$^{14}C$]Butachlor was treated as granule to paddy water by application of 1.5 kg ingredient (a.i.)/ha at the 3~4 leave stage of rice plant. At 85 days after treatment (DAT), samples of panicle, foliage, and roots were taken for radioactivity analysis. Upon harvest at 126 DAT, rice plants were separated into brown rice, husk, straw, and root parts. Amounts of total radioactivity absorbed by rice plant ranged from 8.6 to 9.8% of applied radioactivity (AR). Total radioactive residues (TRRs) of rice plant at 126 DAT was the highest as 4.0421 mg/kg (7.3% AR) in the straw followed by 1.4595 mg/kg (2.4% AR) in the root, 0.7257 mg/kg (0.1% AR) in the husk. The lowest level recording 0.1020 mg/kg (0.1% AR) was found in brown rice. Each part was extracted with various solvents and solvent/water mixtures. Greater than 70% of TRRs was readily extractable from foliage, panicle, husk and straw. Only 34.0% of the brown rice and 43% of root based on TRRs were extractable showing that the residues were completely assimilated in the plant tissue. The level of non-extractable radioactivity was ranged from 26.2 to 66.0% of TRRs. From this study, five tentative major metabolites (M1, M2, M3, M4 and M5) were observed in rice extracts. Among the metabolites, 2,6-diethylaniline assigned as M4 was identified in rice plant by comparing to retention time of reference standard. Un-metabolized butachlor was not detected in any fractions. In soil extracts, N-(butoxymethyl)-N-(2,6-diethyl phenyl)acetamide, 2,6-diethylaniline, M2, M3 and M5 were observed. And the concentration of butachlor was low level (ca. 0.03 mg/kg).

• Horticultural Science & Technology
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• v.34 no.1
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• pp.172-182
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• 2016

This study was aimed to determine the changes in vitamin $B_5$ and $B_6$ contents compared to fresh materials after parboiling treatment of the main vegetables consumed in Korea. The specificity of accuracy and precision for vitamin $B_5$ and $B_6$ analysis method were validated using high-performance liquid chromatography (HPLC). The recovery rate of standard reference material (SRM) was excellent, and all analysis was under the control line based on the quality control chart for vitamin $B_5$ and $B_6$. The Z-score for vitamin $B_6$ in food analysis performance assessment scheme (FAPAS) proficiency test was -1.0, confirming reliability of analytical performance. The vitamin $B_5$ and $B_6$ contents in a total of 39 fresh materials and parboiled samples were analyzed. The contents of vitamin $B_5$ and $B_6$ ranged from 0.000 to 2.462 and from 0.000 to $0.127mg{\cdot}100g^{-1}$, respectively. The highest contents of vitamin $B_5$ and $B_6$ were $2.462mg{\cdot}100g^{-1}$ in fresh fatsia shoots (stem vegetables), and $0.127mg{\cdot}100g^{-1}$ in fresh spinach beet (leafy vegetables), respectively. Moreover, the vitamin $B_5$ and $B_6$ contents for parboiling treatment in most vegetables were reduced or not detected. In particular, the contents of vitamin $B_5$ in parboiled fatsia shoots and vitamin $B_6$ in parboiled yellow potato and spinach beet were decreased 20- and 4-fold compared with fresh material, respectively. These results can be used as important basic data for utilization and processing of various vegetable crops, information for dietary life, management of school meals, and national health for Koreans.

• Journal of Food Hygiene and Safety
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• v.32 no.5
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• pp.411-417
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• 2017

This study was conducted to establish the analysis method for the contents of choline in infant formulas and follow-up formulas by ion chromatograph (IC). To optimize the method, we compared several conditions for extraction, purification and instrumental measurement using spiked samples and certified reference material (CRM; NIST SRM 1849a) as test materials. IC method for choline was established using Ion Pac CG column and 18 mM $H_2SO_4$ mobile phase. The parameters of validation were specificity, linearity, LOD, LOQ, recovery, accuracy, precision and repeatability. The specificity was confirmed by the retention time and the linearity, $R_2$ was over 0.999 in range of 0.5~10 mg/L. The detection limit and quantification limit were 0.14, 0.43 mg/L. The accuracy and precision of this method using CRM were 95%, 2.1% respectively. Optimized methods were applied in sample analysis to verify the reliability. All the tested products were acceptable contents of choline compared with component specification for nutrition labeling. The standard operating procedures were prepared for choline to provide experimental information and to strengthen the management of nutrient in infant formula and follow-up formula.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.1
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• pp.34-40
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• 2021

Purpose If a new test is introduced or reagents are changed in the laboratory of a medical institution, the characteristics of the test should be analyzed according to the procedure and the assessment of reagents should be made. However, several necessary conditions must be met to perform all required comparative evaluations, first enough samples should be prepared for each test, and secondly, various reagents applicable to the comparative evaluations must be supplied. Even if enough comparative evaluations have been done, there is a limit to the fact that the data variation for the new reagent represents the overall patient data variation, The fact puts a burden on the laboratory to the change the reagent. Due to these various difficulties, reagent changes in the laboratory are limited. In order to introduce a competitive bid, the institute conducted a full investigation of Radioimmunoassay(RIA) reagents for each test and established the range of reagents available in the laboratory through comparative evaluations. We wanted to share this process. Materials and Methods There are 20 items of tests conducted in our laboratory except for consignment tests. For each test, RIA reagents that can be used were fully investigated with the reference to external quality control report. and the manuals for each reagent were obtained. Each reagent was checked for the manual to check the test method, Incubation time, sample volume needed for the test. After that, the primary selection was made according to whether it was available in this laboratory. The primary selected reagents were supplied with 2kits based on 100tests, and the data correlation test, sensitivity measurement, recovery rate measurement, and dilution test were conducted. The secondary selection was performed according to the results of the comparative evaluation. The reagents that passed the primary and secondary selections were submitted to the competitive bidding list. In the case of reagent is designated as a singular, we submitted a explanatory statement with the data obtained during the primary and secondary selection processes. Results Excluded from the primary selection was the case where TAT was expected to be delayed at the moment, and it was impossible to apply to our equipment due to the large volume of reagents used during the test. In the primary selection, there were five items which only one reagent was available.(squamous cell carcinoma Ag(SCC Ag), β-human chorionic gonadotropin(β-HCG), vitamin B12, folate, free testosterone), two reagents were available(CA19-9, CA125, CA72-4, ferritin, thyroglobulin antibody(TG Ab), microsomal antibody(Mic Ab), thyroid stimulating hormone-receptor-antibody(TSH-R-Ab), calcitonin), three reagents were available (triiodothyronine(T3), Tree T3, Free T4, TSH, intact parathyroid hormone(intact PTH)) and four reagents were available are carcinoembryonic antigen(CEA), TG. In the secondary selection, there were eight items which only one reagent was available.(ferritin, TG, CA19-9, SCC, β-HCG, vitaminB12, folate, free testosterone), two reagents were available(TG Ab, Mic Ab, TSH-R-Ab, CA125, CA72-4, intact PTH, calcitonin), three reagents were available(T3, Tree T3, Free T4, TSH, CEA). Reasons excluded from the secondary selection were the lack of reagent supply for comparative evaluations, the problems with data reproducibility, and the inability to accept data variations. The most problematic part of comparative evaluations was sample collection. It didn't matter if the number of samples requested was large and the capacity needed for the test was small. It was difficult to collect various concentration samples in the case of a small number of tests(100 cases per month or less), and it was difficult to conduct a recovery rate test in the case of a relatively large volume of samples required for a single test(more than 100 uL). In addition, the lack of dilution solution or standard zero material for sensitivity measurement or dilution tests was one of the problems. Conclusion Comparative evaluation for changing test reagents require appropriate preparation time to collect diverse and sufficient samples. In addition, setting the total sample volume and reagent volume range required for comparative evaluations, depending on the sample volume and reagent volume required for one test, will reduce the burden of sample collection and planning for each comparative evaluation.