• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.11
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• pp.1554-1558
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• 2012

Method development and validation of ellagic acid for the standardization of Gochang Bokbunja as a functional ingredient and health food were accomplished. A Symmetry$^{(R)}$ (C18, $4.6{\times}250mm$, $5.0{\mu}m$) column was used with a gradient elution system of 1% formic acid in water and acetonitrile. This method was validated according to specificity, linearity, accuracy, precision test, and recovery test. Specificity was confirmed with identical retention time, and calibration curves of ellagic acid showed good linear regression ($R^2$ >0.9996). Relative standard deviations (RSD) of data from the intra- and inter-day experiments were less than 2.28% and 2.84%, except in the low limit of quality control (LLOQ, $1{\mu}g/mL$) sample. The results of the recovery test were from 89.17% to 97.92% with RSD values from 0.05 to 0.14%. Therefore, we performed analysis of ellagic acid as a marker compound in Gochang Bokbunja extracts. The amount of ellagic acid in Gochang Bukbunja was about $1.918{\mu}g/mg$ (0.192%) in the three times analysis, and RSD was less than 2.36% by the validated method. These results suggest that the developed HPLC method is simple, efficient, and could contribute to the quality control of Gochang Bokbunja extract as a functional ingredient.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.2
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• pp.210-219
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• 2017

This study aimed to establish an optimal extraction process and high-performance liquid chromatography (HPLC)-photodiode array (PDA) analytical method for determination of marker compounds, dihydrokaempferol (DHK) and 3-O-methylquercetin (3-MeQ), as a part of materials standardization for the development of health functional foods from stems of Opuntia ficus-indica var. saboten (OFS). The quantitative determination method of marker compounds was optimized by HPLC analysis, and the correlation coefficient for the calibration curve showed very good linearity. The HPLC-PDA method was applied successfully to quantification of marker compounds in OFS after validation of the method in terms of linearity, accuracy, and precision. Ethanolic extracts from stems of O. ficus-indica var. saboten (OFSEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 50, 70, and 80% ethanol for 3, 4, 5, and 6 h. Among OFSEs, OFS70E at $80^{\circ}C$ showed the highest contents of DHK and 3-MeQ of $26.42{\pm}0.65$ and $3.88{\pm}0.29mg/OFS100g$, respectively. Furthermore, OFSEs were determined for their antioxidant activities by measuring 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging and lipid peroxidation (LPO) inhibitory activities in rat liver homogenate. OFS70E at $70^{\circ}C$ showed the most potent antioxidant activities with $IC_{50}$ values of $1.19{\pm}0.11$ and $0.89{\pm}0.09mg/mL$ in the DPPH radical scavenging and LPO inhibitory assays, respectively. To identify active components of OFS, various chromatographic separation of OFS70E led to isolation of 11 flavonoids: dihydrokaempferol, dihydroquercetin, 3-O-methylquercetin, quercetin, isorhamnetin 3-O-glucoside, isorhamnetin 3-O-galactoside, narcissin, kaempferol 7-O-glucoside, quercetin 3-O-galactoside, isorhamnetin, and kaempferol 3-O-rutinoside. The results suggest that standardization of DHK in OFSEs using HPLC-PDA analysis would be an acceptable method for the development of health functional foods.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.11
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• pp.1610-1616
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• 2016

This study aimed to establish an optimal extraction process and high performance liquid chromatography-ultraviolet (HPLC-UV) analytical method for determination of 3,5-dicaffeoylquinic acid (3,5-DCQA) as a part of materials standardization for the development of a xanthine oxidase inhibitor as a health functional food. The quantitative determination method of 3,5-DCQA as a marker compound was optimized by HPLC analysis using a Luna RP-18 column, and the correlation coefficient for the calibration curve showed good linearity of more than 0.9999 using a gradient eluent of water (1% acetic acid) and methanol as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 320 nm. The HPLC-UV method was applied successfully to quantification of the marker compound (3,5-DCQA) in Aster glehni extracts after validation of the method with linearity, accuracy, and precision. Ethanolic extracts of A. glehni (AGEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 30, 50, 70, and 80% ethanol for 3, 4, 5, and 6 h, respectively. Among AGEs, 70% AGE at $70^{\circ}C$ showed the highest content of 3,5-DCQA of $52.59{\pm}3.45mg/100g$ A. glehni. Furthermore, AGEs were analyzed for their inhibitory activities on uric acid production by the xanthine/xanthine oxidase system. The 70% AGE at $70^{\circ}C$ showed the most potent inhibitory activity with $IC_{50}$ values of $77.01{\pm}3.13{\sim}89.96{\pm}3.08{\mu}g/mL$. The results suggest that standardization of 3,5-DCQA in AGEs using HPLC-UV analysis would be an acceptable method for the development of health functional foods.

• Journal of Animal Environmental Science
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• v.18 no.sup
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• pp.81-90
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• 2012

This study was evaluated high end research grade Near infrared spectrophotometer (NIRS) to low end popular field grade multiple Near infrared spectrophotometer (NIRS) for rapid analysis at forage quality at sight with 241 samples of Italian ryegrass silage during 3 years collected whole country for evaluate accuracy and precision between instruments. Firstly collected and build database high end research grade NIRS using with Unity Scientific Model 2500X (650 nm~2,500 nm) then trim and fit to low end popular field grade NIRS with Unity Scientific Model 1400 (1,400 nm~2,400 nm) then build and create calibration, transfer calibration with special transfer algorithm. The result between instruments was 0.000%~0.343% differences, rapidly analysis for chemical constituents, NDF, ADF, and crude protein, crude ash and fermentation parameter such as moisture, pH and lactic acid, finally forage quality parameter, TDN, DMI, RFV within 5 minutes at sight and the result equivalent with laboratory data. Nevertheless during 3 years collected samples for build calibration was organic samples that make differentiate by local or yearly bases etc. This strongly suggest population evaluation technique needed and constantly update calibration and maintenance calibration to proper handling database accumulation and spread out by knowledgable control laboratory analysis and reflect calibration update such as powerful control center needed for long lasting usage of forage analysis with NIRS at sight. Especially the agriculture products such as forage will continuously changes that made easily find out the changes and update routinely, if not near future NIRS was worthless due to those changes. Many research related NIRS was shortly study not long term study that made not well using NIRS, so the system needed check simple and instantly using with local language supported signal methods Global Distance (GD) and Neighbour Distance (ND) algorithm. Finally the multiple popular field grades instruments should be the same results not only between research grade instruments but also between multiple popular field grade instruments that needed easily transfer calibration and maintenance between instruments via internet networking techniques.

• Journal of Food Hygiene and Safety
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• v.29 no.2
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• pp.123-130
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• 2014

A simultaneous determination was developed for 9 aminoglycoside antibiotics (amikacin, apramycin, dihydrostreptomycin, gentamicin, hygromycin B, kanamycin, neomycin, spectinomycin, and streptomycin) in meat by liquid chromatography tandem mass spectrometry (LC-MS/MS). Each parameter was established by multiple reaction monitoring in positive ion mode. The developed method was validated for specificity, linearity, accuracy, and precision based on CODEX validation guideline. Linearity was over 0.98 with calibration curves of the mixed standards. Recovery of 9 aminoglycosides ranged on 60.5~114% for beef, 60.1~112% for pork and 63.8~131% for chicken. The limit of detection (LOD) and limit of quantification (LOQ) were 0.001~0.009 mg/kg and 0.006~0.03 mg/kg, respectively in livestock products including beef, pork and chicken. This study also performed survey of residual aminoglycoside antibiotics for 193 samples of beef, pork and chicken collected from 9 cities in Korea. Aminoglycosides were not found in any of the samples.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.1
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• pp.69-75
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• 2019

Preservatives of cosmetics is managed by positive list in Korea. Positive list requires a proper quantitative analysis method, but the analysis method is still insufficient. In this study, gas chromatography with flame ionization detector was used to simultaneously analyze 14 preservatives in cosmetics. As a result of method validation, the specificity was confirmed by the calibration curves of 14 preservatives showing good linearity correlation coefficient of above 0.9997 except dehydroacetic acid (0.9891). The limits of detection (LOD) and quantification (LOQ) of 14 preservatives were 0.0001 mg/mL ~ 0.0039 mg/mL and 0.0003 mg/mL ~ 0.0118 mg/mL, respectively, but they were 0.0204 mg/mL, 0.0617 mg/mL for dehydroacetic acid, respectively. The precision (Repeatability) of the values was less than 1.0%, but 7.1% for dehydroacetic acid. The Accuracy (% recovery) of 14 preservatives in cosmetics showed 96.9% ~ 109.2%. Finally, this method was applied to 50 cosmetics available in market. Results showed that the commonly used preservatives were chlorophene, phenoxyethanol, benzyl alcohol and parabens. However, the amount of the detected preservatives was within maximum allowed limits established by KFDA.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.604-617
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• 2022

Sea surface temperature (SST) is a key variable that can be used to understand ocean-atmosphere phenomena and predict climate change. Satellite microwave remote sensing enables the measurement of SST despite the presence of clouds and precipitation in the sensor path. Therefore, considering the high utilization of microwave SST, it is necessary to continuously verify its accuracy and analyze its error characteristics. In this study, the validation of the microwave global precision measurement (GPM)/GPM microwave imager (GMI) SST around the Northwest Pacific and Korean Peninsula was conducted using surface drifter temperature data for approximately eight years from March 2014 to December 2021. The GMI SST showed a bias of 0.09K and an average root mean square error of 0.97K compared to the actual SST, which was slightly higher than that observed in previous studies. In addition, the error characteristics of the GMI SST were related to environmental factors, such as latitude, distance from the coast, sea wind, and water vapor volume. Errors tended to increase in areas close to coastal areas within 300 km of land and in high-latitude areas. In addition, relatively high errors were found in the range of weak wind speeds (<6 m s^-1) during the day and strong wind speeds (>10 m s^-1) at night. Atmospheric water vapor contributed to high SST differences in very low ranges of <30 mm and in very high ranges of >60 mm. These errors are consistent with those observed in previous studies, in which GMI data were less accurate at low SST and were estimated to be due to differences in land and ocean radiation, wind-induced changes in sea surface roughness, and absorption of water vapor into the microwave atmosphere. These results suggest that the characteristics of the GMI SST differences should be clarified for more extensive use of microwave satellite SST calculations in the seas around the Korean Peninsula, including a part of the Northwest Pacific.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.61-71
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• 2024

In this study, we analyzed chlorogenic acid indicator components in preparation for the additional listing of green coffee bean extract in the Health Functional Food Code and optimized caffeine for simultaneous analysis. We extracted chlorogenic acid and caffeine using 30% methanol, phosphoric acid solution, and acetonitrile-containing phosphoric acid and analyzed them at 330 and 280 nm, respectively, using liquid chromatography. Our analysis validation results yielded a correlation coefficient (R²) revealing a significance level of at least 0.999 within the linear quantitative range. The chlorogenic acid and caffeine detection and quantification limits were 0.5 and 0.2 ㎍/mL and 1.4, and 0.4 ㎍/mL, respectively. We confirmed that the precision and accuracy results were suitable using the AOAC validation guidelines. Finally, we developed a simultaneous chlorogenic acid and caffeine analysis approach. In addition, we confirmed that our analysis approach could simultaneously quantify chlorogenic acid and caffeine by examining the applicability of each formulation through prototypes and distribution products. In conclusion, the results of this study demonstrated that the standardized analysis would expectably increase chlorogenic acidcontaining health functional food quality control reliability.

• Journal of Pharmaceutical Investigation
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• v.38 no.4
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• pp.261-267
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• 2008

The aim of the present study was to evaluate the bioequivalence of two gabapentin preparations. We used Neurontin tablet 800 mg (Pfizer Korea Inc.) as a reference drug for bioequivalence of Gabalep tablet 800 mg (Chong Kun Dang Pharmaceutical Co., Korea), and performed this whole study according to the guidelines of Korea Food and Drug Administration (KFDA). Twenty five healthy male volunteers were administered with each drug in a randomized $2{\times}2$ cross-over study with one week washout interval. After drug administration, blood was taken at predetermined time intervals ($0{\sim}24$ hours) and the concentrations of gabapentin in serum were determined using an high performance liquid chromatography-tandem mass spectrometer (LC-MS/MS) employing electrospray ionization technique and operating in multiple reaction mornitoring (MRM). The analytical method was validated in specificity, accuracy, precision and linearity. The phar-macokinetic parameters such as AUCt and Cmax were calculated and ANOVA test was utilized for the statistical analysis of the parameters using logarithmically transformed AUCt and Cmax. $Mean{\pm}SD$. of AUCt and Cmax value for reference drug and test drug were $29.94{\pm}9.23\;({\mu}g/mL{\cdot}hr)$ and $3.12{\pm}1.11\;({\mu}g/mL{\cdot}hr)$, and $31.48{\pm}9.77\;({\mu}g/mL{\cdot}hr)$ and $3.15{\pm}1.03\;({\mu}g/mL)$, respectively. The 90% confidence intervals using logarithmically transformed data were within the acceptance range of log(0.8) to log(1.25) for AUCt and Cmax, respectively. These results indicate that Gabalep tablet 800 mg is bioequivalent to Neurontin tablet 800 mg.