• Journal of the Korean Society of Food Culture
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• v.35 no.6
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• pp.613-618
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• 2020

This study evaluated the effects of acidulant treatment on the quality and storage period of Topokkidduck. Two samples of Topokkidduck were prepared, one soaked in 10% acidulant (10SAT) and the other without soaking in the acidulant (NSAT). During the storage period, the two samples were tested for presence of microorganisms (aerobic bacteria, E.coli, and mold) and physicochemical properties (color value, texture profile analysis (TPA)). The 10SAT could be stored for 49 days without detection of E.coli and a mold level of 1.0 log CFU/g. NSAT could be stored for only 21 days. NSAT had an aerobic count of 2.27 log CFU/g as early as 7 days, and E.coli was detected at 21 days at a level of 4.15 log CFU/g. The presence of E.coli is not permitted according to the Ministry of Food and Drug Safety (MFDS). The hardness of the 10SAT increased during the storage period but to a much lesser extent compared to the NSAT. Thus the preparation of Topokkidduck by soaking in the acidulant controlled microbial growth for up to 49 days which is a much longer period compared to the control. Also, Topokkidduck soaked in the acidulant had a softer texture than the control during the storage period.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.1-12
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• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• CELLMED
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• v.11 no.3
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• pp.13.1-13.9
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• 2021

Background and Objective: Nux-vomica based traditional Unani formulation, Habb-e-Azaraqi (HAZ) is an important drug used by Unani physicians since several decades. It possesses Muqawwi-i-A'sab (nervine tonic), Muharrik-i-A'sab (nervine stimulant) properties and is an effective treatment option for diseases like Laqwa (facial palsy), Falij (paralysis), Niqris (gout) and Waja'al-Mafasil (arthritis) etc. The aim of the study is to access and provide information of HAZ for its TLC, HPTLC Fingerprinting defining its clear qualitative perspective and acute oral toxicity evaluation for its safety assessment which was not done earlier, thus contributing in the field of research. Materials and Methods: The chief ingredient, nux-vomica was detoxified as per method mentioned in Unani Pharmacopeia before its use in formulation. TLC and HPTLC was developed under four detection system i.e., UV 366nm, UV 254nm, exposure to iodine vapours and after derivatization with anisaldehyde sulphuric acid. Acute toxicity studies were performed as per OECD Guidelines 425 at a limit dose of 2000 mg/kg. Observations were done for signs of toxicity, body weight, and feed consumption at regular intervals followed by haematological and biochemistry evaluation. Results: The generated data proved the authenticity and established the TLC and HPTLC profile of the formulation. Acute toxicity revealed no significant differences in HAZ-treated animals with respect to body weight gain, feed consumption, haematology, clinical biochemistry evaluation. No significant gross pathological observation was noticed in necropsy. Conclusion: Data of the present study is substantial and scientific proof of HAZ in terms of standardization and toxicity study that can be utilize in future research activities.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.1-6
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• 2022

As the demand for wearable devices increases, many studies have been studied on the development of flexible electrode materials recently. In particular, the development of high-performance flexible electrode materials is very important for wearable sensors for healthcare because it is necessary to continuously monitor and accurately detect body information such as body temperature, heart rate, blood glucose, and oxygen concentration in real time. In this study, we fabricated the nonenzymatic glucose sensor based on polyaniline/carbon nanotube fiber (PANI/CNT fiber) electrode. PANI layer was synthesized on the flexible CNT fiber electrode through electrochemical polymerization process in order to improve the performance of a flexible CNT fiber based electrode material. Surface morphology of the PANI/CNT fiber electrode was observed by scanning electron microscopy. And its electrochemical characteristics were investigated by chronoamperometry, cyclic voltammetry, electrochemical impedance spectroscopy. Compared to bare CNT fiber electrode, this PANI/CNT fiber electrode exhibited small electron transfer resistance, low peak separation potential and large surface area, resulting in enhanced sensing properties for glucose such as wide linear range (0.024~0.39 and 1.56~50 mM), high sensitivity (52.91 and 2.24 ㎂/mM·cm²), low detection limit (2 μM) and good selectivity. Therefore, it is expected that it will be possible to develop high performance CNT fiber based flexible electrode materials using various nanomaterials.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.510-519
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• 2021

Volatile Organic Compounds (VOCs) in sediments, which can cause human health problems, have been monitored in Korea since 2014. Measured VOC concentrations can be affected by matrix type and the volatility of target substances. In this study, (1) VOCs volatility and the influence of matrix interference were confirmed, and (2) internal standards (IS) method was applied to improve analytical method. For these purposes, method detection limit (MDL), calibration linearity, precision and accuracy of VOCs were compared in various matrices using the IS. Some of VOCs in sediments showed different peak areas and reduced rates compared to water matrix. It was suggested that adsorption properties of sediments hindered the migration to vapor during heat pretreatment in headspace method. A calibration curve was created in clean sand. Recovery rates for the calibration curve method and IS applying method were 64.1~83.1% and 99.1~119.3%, respectively. Relative standard deviations ranged from 11.1% to 21.6% for the calibration curve method and those for IS ranged 4.7% to 13.7%. In case of real sediment, calibration curve and 1,2-Dichlorobenzene-d4 (ODCB) among IS were not suitable. The average recovery rate of Fluorobenzene (FBZ) increased by 56.4% and Relative Standard Deviation (RSD) by 4.7%. However, the recovery rate was increased in the samples with large values of igniting intensity. This study confirmed that influence of the matrix of VOCs in sediment, and addition of IS materials improved precision and accuracy. Although IS corrects volatilization and adsorption, it is recommended that more than two types of IS should be added rather than single.

• Journal of Food Hygiene and Safety
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• v.36 no.6
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• pp.474-480
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• 2021

Sodium alginate is the sodium salt of alginic acid, commonly used as a food additive for stabilizing, thickening, and emulsifying properties. A relatively simple and universal analysis method is used to study sodium alginate due to the complex pretreatment process and extended analysis time required during the quantitative method. As for the equipment, HPLC-UVD and Unison US-Phenyl column were used for analysis. For the pretreatment condition, a shaking apparatus was used for extraction at 150 rpm for 180 minutes at room temperature. The calibration curve made from the standard sodium alginate solution in 5 concentration ranges showed that the linearity (R²) is 0.9999 on average. LOD and LOQ showed 3.96 mg/kg and 12.0 mg/kg, respectively. Furthermore, the average intraday and inter-day accuracy (%) and precision (RSD%) were 98.47-103.74% and 1.69-3.08% for seaweed jelly noodle samples and 99.95-105.76% and 0.59-3.63% for sherbet samples, respectively. The relative uncertainty value was appropriate for the CODEX standard with 1.5-7.9%. To evaluate the applicability of the method developed in this study, the sodium alginate concentrations of 103 products were quantified. The result showed that the detection rate is highest from starch vermicelli and instant fried noodles to sugar processed products.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.

• Korean Journal of Optics and Photonics
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• v.33 no.5
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• pp.218-229
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• 2022

We have investigated reliable inspection methods for finding the defects generated during the manufacturing process of lightweight, large-aperture satellite telescope mirrors using silicon carbide, and we have measured the basic physical properties of the mirrors. We applied the advanced ceramic material (ACM) method, a combined method using liquid-silicon penetration sintering and chemical vapor deposition for the carbon molded body, to manufacture four SiC mirrors of different sizes and shapes. We have provided the defect standards for the reflectors systematically by classifying the defects according to the size and shape of the mirrors, and have suggested effective nondestructive methods for mirror surface inspection and internal defect detection. In addition, we have analyzed the measurements of 14 physical parameters (including density, modulus of elasticity, specific heat, and heat-transfer coefficient) that are required to design the mirrors and to predict the mechanical and thermal stability of the final products. In particular, we have studied the detailed measurement methods and results for the elastic modulus, thermal expansion coefficient, and flexural strength to improve the reliability of mechanical property tests.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.2
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• pp.79-94
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• 2022

Due to the highly contagious nature and severity of the respiratory diseases caused by COVID-19, economical and accurate tests are required to better monitor and prevent the spread of this contagion. As the structural and molecular properties of SARS-CoV-2 were being revealed during the early stage of the COVID-19 pandemic, many manufacturers of COVID-19 diagnostic kits actively invested in the design, development, validation, verification, and implementation of diagnostic tests. Currently, diagnostic tests for SARS-CoV-2 are the most widely used and validated techniques for rapid antigen, and immuno-serological assays for specific IgG and IgM antibody tests and molecular diagnostic tests. Molecular diagnostic assays are the gold standard for direct detection of viral RNA in individuals suspected to be infected with SARS-CoV-2. Antibody-based serological tests are indirect tests applied to determine COVID-19 prevalence in the community and identify individuals who have obtained immunity. In the future, it is necessary to explore technical problems encountered in the early stages of global or regional outbreaks of pandemics and provide future directions for better diagnostic tests. This article evaluates the commercially available and FDA-approved molecular and immunological diagnostic assays and analyzes their performance characteristics.