• YAKHAK HOEJI
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• v.52 no.2
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• pp.93-100
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• 2008

A liquid chromatographic method with tandom spectrometric detection (LC/MS/MS) for the simultaneous determination of doxifluridine and its active metabolite, 5-fluorouracil (5-FU) was developed over the concentration range of $5{\sim}2000$ ng/ml, respectively. Doxifluridine, 5-FU and internal standard, 5-chlorouracil (5-CU), were extracted from liver and intestine tissue via protein precipitation. Acetonitrile was used as the extraction solvent and the supernatant was evaporated and reconstructed in mobile phase. Optimum chromatographic separation was achieved on a Agilent Zorbax $C_{18}$ ($100\;mm{\times}2.1\;mm$, $3.5\;{\mu}m$) column with mobile phase run in isocratic with methanol : water (20 : 80, v/v). The flow rate was 0.2 ml/min with total cycle time of 5 min. The lower limit of quantification was validated at 5.0 ng/ml of liver and intestine tissue, for both doxifluridine and 5-FU, respectively. The intra-day and inter-day precision and accuracy of quality control (QC) samples were <11% coefficient of variation and <7% relative error from theoretical concentration for both analytes. In addition, the special designed stability study was performed, because the metabolism of doxifluridine occurs spontaneously even in ice bath for monkey liver. The stability of doxifluridine in liver and intestine of monkey and beagle dog was compared. It was found that bioanalytical validation could not be performed for the monkey liver; however, beagle dog's liver has relatively low speed of metabolism compared to monkey liver and instead of monkey liver, beagle dog's liver could be used for the validation. Bioanalytical validation could be performed in monkey intestine. Eventually, this developed method for liver and intestine will be useful in support of the toxicokinetic and pharmacokinetic studies of doxifluridine and 5-FU.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.323-329
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• 2011

BACKGROUND: The conazoles, difenoconazole, diniconazole, hexaconazole, penconazole and tetraconazole are a large class of synthetic fungicides used extensively for foliage and seed treatments in agricultural crops. The extensive use of conazoles has brought concerns on the potentiality of environmental contamination and toxicity. Thus studies on the development of methods for monitoring the conazoles are required. METHODS AND RESULTS: A modified quick, easy, effective, rugged and safe (QuEChERS) method was involved in sample preparation. Quadrapole time of flight mass spectrometer (Q-TOF MS) in electron spray ionization (ESI) mode was employed to determine conazoles in garlic samples. The limit of detection (LOD) and limit of quantification (LOQ) of conazoles by Q-TOF-MS ranged from 0.001 to 0.002 mg/L and 0.002 to 0.005 mg/L, respectively. Q-TOF-MS analysis exhibited less than 2.6 ppm error of accurate mass measurements for the detection of conazoles spiked at 0.05 mg/L in garlic matrix. Recovery values of conazoles fortified in garlic samples at 0.02, 0.05 and 0.1 mg/L were between 79.2 and 106.2% with a maximum 11.8% of standard deviation. No detectable conazoles were found in the domestic market samples by using the Q-TOF-MS method. CONCLUSION(s): High degree of confirmation for conazoles by accurate mass measurements demonstrated that Q-TOF-MS analysis combined with a QuEChERS method may be applicable to simultaneous determination of conazoles in garlic samples.

• Journal of Korean Neurosurgical Society
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• v.60 no.4
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• pp.441-447
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• 2017

Objective : Computed tomography (CT)-based method of three dimensional (3D) analysis ($MIMICS^{(R)}$, Materialise, Leuven, Belgium) is reported as very useful software for evaluation of OPLL, but its reliability and reproducibility are obscure. This study was conducted to evaluate the accuracy of $MIMICS^{(R)}$ system, and inter- and intra-observer reliability in the measurement of OPLL. Methods : Three neurosurgeons independently analyzed the randomly selected 10 OPLL cases with medical image processing software ($MIMICS^{(R)}$) which create 3D model with Digital Imaging and Communication in Medicine (DICOM) data from CT images after brief explanation was given to examiners before the image construction steps. To assess the reliability of inter- and intra-examiner intraclass correlation coefficient (ICC), 3 examiners measured 4 parameters (volume, length, width, and length) in 10 cases 2 times with 1-week interval. Results : The inter-examiner ICCs among 3 examiners were 0.996 (95% confidence interval [CI], 0.987-0.999) for volume measurement, 0.973 (95% CI, 0.907-0.978) for thickness, 0.969 (95% CI, 0.895-0.993) for width, and 0.995 (95% CI, 0.983-0.999) for length. The intra-examiner ICCs were 0.994 (range, 0.991-0.996) for volume, 0.996 (range, 0.944-0.998) for length, 0.930 (range, 0.873-0.947) for width, and 0.987 (range, 0.985-0.995) for length. Conclusion : The medical image processing software ($MIMICS^{(R)}$) provided detailed quantification OPLL volume with minimal error of inter- and intra-observer reliability in the measurement of OPLL.

• Korean Journal of Breeding Science
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• v.42 no.5
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• pp.448-454
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• 2010

The objective of this study was to rapidly evaluate fatty acids in a collection of foxtail millet (Setaria italica (L.) P. Beauv) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (n=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination ($R^2$) of 0.91, 0.89, 0.98 and 0.98 for strearic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values ($r^2=0.97$, 0.91, 0.99 for oleic, linoleic, and total fatty acids, respectively). Standard deviation/standard error of cross-validation (SD/SECV) values were greater than 3 (3.11, 5.45, and 7.50 for oleic, linoleic, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic, linolenic, and total fatty acids characterizing foxtail millet germplasm. Among the samples, IT153491 showed an especially high content of fatty acids ($84.06mg\;g^{-1}$), whereas IT188096 had a very low content ($29.92mg\;g^{-1}$).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.652-658
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• 2019

When IoT is applied to various research experiment fields such as physics, pharmacy, biology and medicine, it can increase the safety and convenience of researchers by intelligently monitoring and controlling research equipment and environment with various sensors and devices. For accurate and convenient record management and the research history and the basis but also for the reverse tracking, real-time reagent measurement and registration should be provided as a research support automation services. Currently, existing methods of reagent management are operated by computerized method, but reagent registration and management are not automated. And also record is managed manually, there are many hassles and problems such as a record error and too much time required for quantification and registration for many reagents. In this paper, we study a real time reagent measuring and registration method based on IoT to resolve the problems aforementioned, by the information of the reagent acquired by image recognition and NFC method.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.183-194
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• 2019

Adulteration of argan oil with some other cheaper oils with similar chemical compositions has resulted in increasing demands for authenticity assurance and quality control. Fast and simple analytical techniques are thus needed for authenticity analysis of high-priced argan oil. Raman spectroscopy is a potent technique and has been extensively used for quality control and safety determination for food products In this study, Raman spectroscopy in combination with a net analyte signal (NAS)-based methodology, i.e., hybrid linear analysis method developed by Goicoechea and Olivieri in 1999 (HLA/GO), was used to predict the different concentrations of olive oil (0 - 20%) added to argan oil. Raman spectra of 90 samples were collected in a spectral range of $400-400cm^{-1}$, and calibration and validation sets were designed to evaluate the performance of the multivariate method. The results revealed a high coefficient of determination ($R^2$) value of 0.98 and a low root-mean-square error (RMSE) value of 0.41% for the calibration set, and an $R^2$ of 0.97 and RMSE of 0.36% for the validation set. Additionally, the figures of merit such as sensitivity, selectivity, limit of detection, and limit of quantification were used for further validation. The high $R^2$ and low RMSE values validate the detection ability and accuracy of the developed method and demonstrate its potential for quantitative determination of oil adulteration.

• Food Science of Animal Resources
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• v.41 no.2
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• pp.312-323
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• 2021

The purpose of this study was to use 1H nuclear magnetic resonance (1H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using 1H-13C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R2=0.945; Q2=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both 1H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.135-143
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• 2009

Recently, in the mobile device, the increase of the need for encoding and decoding of high-resolution images requires an efficient implementation of the image codec. This paper proposes a time-optimized color conversion method for the JPEG decoder, which reduces the number of calculations in the color conversion by the rearrangement of arithmetic operations being possible due to the linearity of the IDCT and the color conversion matrices and brings down the time complexity of the color conversion itself by the integer mapping replacing floating-point operations to the optimal fixed-point shift and addition operations, eventually reducing the time complexity of the JPEG decoder. And the proposed method compensates a decline of image quality incurred by the quantification error of the operation arrangement and the integer mapping by using the multi-mode chrominance reconstruction. The performance evaluation performed on the development platform of embedded systems showed that, compared to previous color conversion methods, the proposed method greatly reduces the image decoding time, minimizing the distortion of decoded images.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.5
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• pp.399-407
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• 2009

Laser induced breakdown spectroscopy(LIBS) is an simple analysis method for directly quantifying many kinds of soil micro-elements on site using a small size of laser without pre-treatment at any property of materials(solid, liquid and gas). The purpose of this study were to find an optimum condition of the LIBS measurement including wavelengths for quantifying soil elements, to relate spectral properties to the concentration of soil elements using LIBS as a simultaneous un-breakdown quantitative analysis technology, which can be applied for the safety assessment of agricultural products and precision agriculture, and to compare the results with a standardized chemical analysis method. Soil samples classified as fine-silty, mixed, thermic Typic Hapludalf(Memphis series) from grassland and uplands in Tennessee, USA were collected, crushed, and prepared for further analysis or LIBS measurement. The samples were measured using LIBS ranged from 200 to 600 nm(0.03 nm interval) with a Nd:YAG laser at 532 nm, with a beam energy of 25 mJ per pulse, a pulse width of 5 ns, and a repetition rate of 10 Hz. The optimum wavelength(${\lambda}nm$) of LIBS for estimating soil and plant elements were 308.2 nm for Al, 428.3 nm for Ca, 247.8 nm for T-C, 438.3 nm for Fe, 766.5 nm for K, 85.2 nm for Mg, 330.2 nm for Na, 213.6 nm for P, 180.7 nm for S, 288.2 nm for Si, and 351.9 nm for Ti, respectively. Coefficients of determination($r^2$) of calibration curve using standard reference soil samples for each element from LIBS measurement were ranged from 0.863 to 0.977. In comparison with ICP-AES(Inductively coupled plasma atomic emission spectroscopy) measurement, measurement error in terms of relative standard error were calculated. Silicon dioxide(SiO2) concentration estimated from two methods showed good agreement with -3.5% of relative standard error. The relative standard errors for the other elements were high. It implies that the prediction accuracy is low which might be caused by matrix effect such as particle size and constituent of soils. It is necessary to enhance the measurement and prediction accuracy of LIBS by improving pretreatment process, standard reference soil samples, and measurement method for a reliable quantification method.