• Korean Journal of Food Science and Technology
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• v.51 no.5
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• pp.425-431
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• 2019

Selected leafy vegetables, widely used for Korean Namul dishes, were heat-treated in different ways and their folate retention was investigated. The Lactobacillus casei method was applied for folate estimation and validated to ensure reliability of analytical data. The folate content in Namul highly varied, from 29.7 to $293.4{\mu}g/100g$, depending on the heating methods and the types of vegetables. Most of the Namul variants showed increased folate content on heat treatment. Frying yielded higher folate retention than the other cooking methods (blanching, steaming, baking, and panfrying), and pig weed showed the highest folate retention (3.3 times, $293.4{\mu}g/100g$). L. casei assay for folate estimation showed 95.7% recovery and relative standard deviations less than 2% for both reproducibility and repeatability, indicating good accuracy and precision. Quality of the folate assay was assured by monitoring a quality control chart and a proficiency test (z-score= -0.1) during the entire of study.

• Journal of Food Hygiene and Safety
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• v.36 no.4
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• pp.281-288
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• 2021

This study was conducted to analyze and compare the concentrations of heavy metals in 430 different products of 20 types of herbal medicines available in the domestic market in Korea by Graphite Furnace-Atomic Absorption Spectrometry (GF-AAS) and automated mercury analyzer. The accuracy for lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg) was in the range 92.67-102.56%, and the precision was 0.21-6.00 relative standard deviation (RSD%), which was in compliance with the Codex acceptable range. Furthermore, the Food Analysis Performance Assessment Scheme (FAPAS) quality control (QC) material showed a recovery range of 96.7-102.0% and 0.33-4.93 RSD%. The average contents (㎍/kg) of Pb, As, Cd, and Hg in herbal medicines were 254.9 (not detected (N.D.)-2,515.2), 171.0 (N.D.-2,465.2), 99.2 (N.D.-797.1), and 6.0 (N.D.-83.6), respectively. Based on the quantitative analysis results, the heavy metal contents of 20 types of herbal medicines distributed in Korea are within the acceptable range according to the standards issued by the Ministry of Food and Drug Safety (MFDS). By using the manufacturer of herbal products as the standard for QC, the Pb, As, Cd, and Hg contents were investigated in the packaging process just before distribution to determine the actual conditions of residual heavy metals in herbal medicines. Thus, these result may contribute to monitoring the QC of herbal medicines distributed in Korea and could provide basic data for supplying safe herbal medicines to the public.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.4
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• pp.321-330
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• 2022

This study attempted to establish an optimal multi-compound simultaneous analysis method that can secure reliable results for 15 - preservatives, 2 - sun screens and 1 - antioxidants of cosmetics using HPLC-PDA. Since the potential of hydrogen (pH) in the mobile phase affects the acid dissociation constant (pKa) of the preservatives, and the peak retention time shift and area change were observed. The peak separation condition was established by adjusting the pH to 0.1% H₃PO₄ addition (mL) when preparing the mobile phase. As a results of method validation, the linearity correlation coefficient (R²) of above 0.999 were obtained, and accuracy 87.9 ~ 101.1%, 0.1 ~ 7.6% precision for two types of cosmetics (cream and shampoo). It was found that the limit of detection (LOD) was 0.1 ~ 0.2 mg/kg and the limit of quantitation (LOQ) was 2.0 ~ 4.0 mg/kg. In addition, it was possible to simultaneously separate p-anisic acid, a natural compound that was difficult to separate in HPLC due to the small difference from methylparaben, a synthetic preservatives. Through this study, it will be effectively used to secure quality control and safety for compound that need restrictions on use cosmetics.

• Korean Journal of Environmental Agriculture
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• v.42 no.1
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• pp.71-81
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• 2023

An accurate and easy-to-use analytical method for determining isocycloseram and its metabolites (SYN549431 and SYN548569) residue is necessary in various food matrixes. Additionally, this method should satisfy domestic and international guidelines (Ministry of Food and Drug Safety and Codex Alimentarius Commission CAC/GL 40). Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used to determine the isocycloseram and its metabolites residue in foods. To determine the residue and its metabolites, a sample was extracted with 20 mL of 0.1% formic acid in acetonitrile, 4 g magnesium sulfate anhydrous and 1 g sodium chloride and centrifuged (4,700 G, 10 min, 4℃). To remove the interferences and moisture, d-SPE cartridge was performed before LC-MS/MS analysis with C₁₈ column. To verify the method, a total of five agricultural commodities (hulled rice, potato, soybean, mandarin, and red pepper) were used as a representative group. The matrix-matched calibration curves were confirmed with coefficients of determination (R²) ≥ 0.99 at a calibration range of 0.001-0.05 mg/kg. The limits of detection and quantification were 0.003 and 0.01 mg/kg, respectively. Mean average recoveries were 71.5-109.8% and precision was less than 10% for all five samples. In addition, inter-laboratory validation testing revealed that average recovery was 75.4-107.0% and the coefficient of variation (CV) was below 19.4%. The method is suitable for MFDS, CODEX, and EU guideline for residue analysis. Thus, this method can be useful for determining the residue in various food matrixes in routine analysis.

• Analytical Science and Technology
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• v.22 no.1
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• pp.101-108
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• 2009

The bioequivalence of two pioglitazone tablets, Actos$^{(R)}$ tablet (Takeda Chemical Industries, reference drug) and Pioglitazone tablet (Boryung Company, test drug) was evaluated according to the guidelines of Korea Food and Drug Administration. Twenty-eight healthy male Korean volunteers received each medicine (pioglitazone dose of 30 mg) in a $2{\times}2$ crossover study with one week washout interval. After drug administration, blood samples were collected at specific time intervals from 0-36 hours. The plasma concentrations of pioglitazone were determined by high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total chromatographic run time was 5 min and calibration curves were linear over the concentration range of 5-2000 ng/mL for pioglitazone. The method was validated for selectivity, sensitivity, linearity, accuracy and precision. The pharmacokinetic parameters were determined from the plasma concentration-time profiles of both formulations. The primary calculated pharmacokinetic parameters were compared statistically to evaluate bioequivalence between the two preparations. The 90% confidence intervals of the $AUC_t$ ratio and the $C_{max}$ ratio for Pioglitazone tablet and Actos$^{(R)}$ tablet were log0.9422~log1.1040 and log0.9200~log1.1556, respectively. Based on the statistical considerations, we can conclude that the test drug, Pioglitazone tablet was bioequivalent to the reference drug, Actos$^{(R)}$ tablet.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Korean Journal of Environmental Agriculture
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• v.36 no.3
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• pp.154-160
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• 2017

BACKGROUND:This study was carried out to establish an efficient sample preparation for the simultaneous determination of bisphenols (BPs) in river water samples using gas chromatography-mass spectrometry (GC-MS). Sample preparation was examined with conventional extraction methods, such as solid-phase extraction (SPE) and liquid-liquid extraction (LLE), and their efficiency was compared with validation results, including linearity of calibration curve, method detection limit (MDL), limit of quantification (LOQ), accuracy, and precision. METHODS AND RESULTS:The BPs (bisphenol A, BPA; bisphenol B, BPB; bisphenol C, BPC; bisphenol E, BPE; bisphenol F, BPF; bisphenol S, BPS) were analyzed using GC-MS. The range of MDLs by SPE and LLE methods was $0.0005{\sim}0.0234{\mu}g/L$ and $0.0037{\sim}0.2034{\mu}g/L$, and that of LOQs was $0.0015{\sim}0.0744{\mu}g/L$ and $0.0117{\sim}0.6477{\mu}g/L$, respectively. The calibration curve obtained from standard solution of $0.004{\sim}4.0{\mu}g/L$ (SPE) and $0.016{\sim}16{\mu}g/L$ (LLE) showed good linearity with $r^2$ value of 0.9969 over. Accuracy was 93.2~108% and 97.4~120%, and precision was 1.7~4.6% and 0.7~6.5%, respectively. The values of MDL and LOQ resulted from the SPE method were higher than those from the LLE method, particularly those values of BPA were highest among the BPs. Based on the results, the SPE method was applied to determine the BPs in river water samples. Water samples were collected from mainstream, tributary and sewage wastewater treatment plants (SWTPs) in the Yeongsan river basin. The concentration of BPB, BPC, BPE, BPF and BPS were not detected in all sites, whereas BPA was ranged $0.0095{\sim}0.2583{\mu}g/L$, which was $0.0166{\sim}0.0810{\mu}g/L$ for mainstreams, $0.0095{\sim}0.2583{\mu}g/L$ for tributaries, $0.0352{\sim}0.1217{\mu}g/L$ for SWTPs. CONCLUSION: From these results, the SPE method was very effective for the simultaneous determination of BPs in river water samples using GC-MS. We provided that it is a convenient, reliable and sensitive method enough to monitor and understand the fate of the BPs in aquatic ecosystems.