• Journal of Ginseng Research
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• v.37 no.4
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• pp.475-482
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• 2013

The main active components of Panax ginseng are ginsenosides. Ginsenoside Rb1 and Rg1 are accepted as marker substances for quality control worldwide. The analytical methods currently used to detect these two compounds unfairly penalize steamed and dried (red) P. ginseng preparations, because it has a lower content of those ginsenosides than white ginseng. To manufacture red ginseng products from fresh ginseng, the ginseng roots are exposed to high temperatures for many hours. This heating process converts the naturally occurring ginsenoside Rb1 and Rg1 into artifact ginsenosides such as ginsenoside Rg3, Rg5, Rh1, and Rh2, among others. This study highlights the absurdity of the current analytical practice by investigating the time-dependent changes in the crude saponin and the major natural and artifact ginsenosides contents during simmering. The results lead us to recommend (20S)- and (20R)-ginsenoside Rg3 as new reference materials to complement the current P. ginseng preparation reference materials ginsenoside Rb1 and Rg1. An attempt has also been made to establish validated qualitative and quantitative analytical procedures for these four compounds that meet International Conference of Harmonization (ICH) guidelines for specificity, linearity, range, accuracy, precision, detection limit, quantitation limit, robustness and system suitability. Based on these results, we suggest a validated analytical procedure which conforms to ICH guidelines and equally values the contents of ginsenosides in white and red ginseng preparations.

• Journal of Ginseng Research
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• v.37 no.4
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• pp.457-467
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• 2013

A quick and simple method for simultaneous determination of the 30 ginsenosides (ginsenoside Ro, Rb1, Rb2, Rc, Rd, Re, Rf, Rg1, 20(S)-Rg2, 20(R)-Rg2, 20(S)-Rg3, 20(R)-Rg3, 20(S)-Rh1, 20(S)-Rh2, 20(R)-Rh2, F1, F2, F4, Ra1, Rg6, Rh4, Rk3, Rg5, Rk1, Rb3, Rk2, Rh3, compound Y, compound K, and notoginsenoside R1) in Panax ginseng preparations was developed and validated by an ultra performance liquid chromatography photo diode array detector. The separation of the 30 ginsenosides was efficiently undertaken on the Acquity BEH C-18 column with gradient elution with phosphoric acids. Especially the chromatogram of the ginsenoside Ro was dramatically enhanced by adding phosphoric acid. Under optimized conditions, the detection limits were 0.4 to 1.7 mg/L and the calibration curves of the peak areas for the 30 ginsenosides were linear over three orders of magnitude with a correlation coefficients greater than 0.999. The accuracy of the method was tested by a recovery measurement of the spiked samples which yielded good results of 89% to 118%. From these overall results, the proposed method may be helpful in the development and quality of P. ginseng preparations because of its wide range of applications due to the simultaneous analysis of many kinds of ginsenosides.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.81-89
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• 2020

Online reviews we encounter commonly on SNS, although a complex range of assessment information affecting the consumer's preferences are included, it is general that such information is just provided by simple numbers or star ratings. Based on those review types, it is not easy to get specific information that consumers want and use it to make a decision for purchase. Therefore, in this study, we propose a prediction methodology that can provide ratings broken down by evaluation items by performing sentiment analysis on restaurant reviews written in Korean. To this end, we select 'food', 'price', 'service', and 'atmosphere' as the main evaluation items of restaurants, and build a new sentiment dictionary for each evaluation item. It also classifies review sentences by rating item, predicts granular ratings through sentiment analysis, and provides additional information that consumers can use to make decisions. Finally, using MAE and RMSE as evaluation indicators it shows that the rating prediction accuracy of the proposed methodology has been improved than previous studies and presents the use case of proposed methodology.

• YAKHAK HOEJI
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• v.48 no.3
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• pp.207-212
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• 2004

An analytic method was developed for the quantitation of $\Delta$$^{9}-$ tetrahydrocannabinol (THC) and 11-nor-9-carboxy THC (THC-COOH) in human hair. After hair samples were pulverized using Freezer Mill, deuterated internal standards were added and digested in 1 N NaOH at $100^{\circ}C$ water bath for 30 min. Digest solutions were extracted by 5 ml hexane：ethyl acetate (90：10) after acidification with acetic acid. The organic phase was evaporated under N 2 and derivatized by BSTFA (with 1% TMCS) at $85^{\circ}C$ for 45 min. The derivatized solution was separated on HP-5MS column ($30m{\times}0.25mm{\times}0.25mm$) and detected using EI-GC-MS with selective ion monitoring mode. The assay of calibration was ranged from 5 to 100 ng/50 mg hair ($r^2$＞0.99) for THC and THC-COOH. Within and between-run precision were calculated at 6, 30, 60 ng/50 mg hair with coefficients of variation less than 11%. Within and between run accuracies at the same concentrations were$\pm$14% and $\pm$30% of target for both analytes, respectively. Absolute and relative recovery at 10 and 100 ng were 60∼91％. The method was used to detect and quantify THC and THC-COOH in cannabis abuser's hairs (N = 16) and SRM (N=5, THC 1 ng/mg, NIST). We detected THC and THC-COOH in only one hair sample. In SRM, % accuracy was 93% (range 86∼103%) and precision (% CV) was 8.14. We began to set up a quantitative analysis of THC and THC-COOH using EI-GC-MS. Continuously, we need to modify and develop this method in order to apply for identification in cannanbis users' hair.

• Analytical Science and Technology
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• v.34 no.5
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• pp.212-224
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• 2021

The intent of the present work was to develop a simple, sensitive, accurate, precise, rapid and economical UV- spectrophotometric and reverse phase high pressure liquid chromatographic method for the simultaneous estimation of Spironolactone and Furosemide in bulk and combined tablet dosage forms. UV-Spectrophotometry was carried out by simultaneous equation method using 0.02 M potassium dihydrogen phosphate buffer pH 3.5: Acetonitrile (50:50) v/v as a solvent. The linearity range was 2-14 ㎍ mL^-1 for Spironolactone and Furosemide with a correlation coefficient > 0.99. The chromatographic separation was achieved on 250 mm × 4.6 mm, hypersil BDS C18 column with particle size 5 ㎛, by using an isocratic mixture of 0.02 M potassium dihydrogen phosphate buffer pH 3.5: Acetonitrile: tert butyl methyl ether (49:50:1) v/v/v as a solvent at a flow rate of 1 mL min^-1 and UV detection was carried out at 254 nm. The retention time were observed to be 3.666 and 6.661 minutes for Furosemide and Spironolactone respectively. The two developed methods were validated according to the ICH guidelines for accuracy, precision, linearity, LOD, LOQ and were found to be within the limits. It can be concluded that these two methods could be successfully used for the simultaneous estimation of Spironolactone and Furosemide in bulk and combined tablet dosage forms.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.253-263
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• 2021

Machine learning is the process of constructing a cost function using learning data used for learning and an artificial neural network to predict the data, and finding parameters that minimize the cost function. Parameters are changed by using the gradient-based method of the cost function. The more complex the digital signal and the more complex the problem to be learned, the more complex and deeper the structure of the artificial neural network. Such a complex and deep neural network structure can cause over-fitting problems. In order to avoid over-fitting, a weight decay regularization method of parameters is used. We additionally use the value of the cost function in this method. In this way, the accuracy of machine learning is improved, and the superiority is confirmed through numerical experiments. These results derive accurate values for a wide range of artificial intelligence data through machine learning.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.496-503
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• 2021

Background: Considering the expenses of and difficulties in arsenic speciation by high performance liquid chromatography-inductively coupled plasma-mass spectrometry (HPLC-ICP-MS), alternative measurement methods should be useful, especially for large-scale research and projects. Objectives: A measurement method was developed for arsenic speciation using HPLC-atomic fluorescence spectrometry (HPLC-AFS) as an alternative to HPLC-ICP-MS. Methods: Total arsenic and toxic arsenic species in some seafoods were determined by atomic absorption spectrometry coupled with hydride vapor generation (AAS-HVG) and HPLC-AFS, respectively. Recovery rate of arsenic species in seafood was evaluated by ultra sonication, microwave and enzyme (pepsin) for the optimal extraction method. Results: Limits of detection of HPLC-AFS for As³⁺, dimethylarsinate (DMA), monomethylarsonate (MMA) and As⁵⁺ were 0.39, 0.53, 0.60 and 0.64 ㎍/L, respectively. The average accuracy ranged from 97.5 to 108.7%, and the coefficient of variation was in the range of 1.2~16.7%. As³⁺, DMA, MMA and As⁵⁺ were detected in kelp, the sum of toxic arsenic in kelp was 40.4 mg/kg. As³⁺, DMA, MMA and As⁵⁺ were not detected in shrimp and squid, but total arsenic (iAS and oAS) content in shrimp and squid analyzed by AAS-HVG were 18.1 and 24.7 mg/kg, respectively. Conclusions: HPLC-AFS was recommendable for the quantitative analysis method of arsenic species. As toxic arsenic species are detected in seaweeds, further researches are needed for the contribution degree of seafood in arsenic exposure.

• Journal of KIBIM
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• v.11 no.3
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• pp.22-33
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• 2021

In order to maximize the use of BIM, all data related to individual elements in the model must be correctly assigned, and it is essential to check whether it corresponds to the IFC entity classification. However, as the BIM modeling process is performed by a large number of participants, it is difficult to achieve complete integrity. To solve this problem, studies on semantic integrity verification are being conducted to examine whether elements are correctly classified or IFC mapped in the BIM model by applying an artificial intelligence algorithm to the 2D image of each element. Existing studies had a limitation in that they could not correctly classify some elements even though the geometrical differences in the images were clear. This was found to be due to the fact that the geometrical characteristics were not properly reflected in the learning process because the range of the region to be learned in the image was not clearly defined. In this study, the CRF-RNN-based semantic segmentation was applied to increase the clarity of element region within each image, and then applied to the MVCNN algorithm to improve the classification performance. As a result of applying semantic segmentation in the MVCNN learning process to 889 data composed of a total of 8 BIM element types, the classification accuracy was found to be 0.92, which is improved by 0.06 compared to the conventional MVCNN.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.348-355
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• 2021

Asphalt, widely used for road pavement, has different required physical properties depending on the environment to which the road is exposed. Therefore, it is essential to maximize the life of asphalt roads by evaluating the physical properties of asphalt according to additives and selecting an appropriate formulation considering road traffic and climatic environment. Dynamic shear rheometer(DSR) test is mainly used to measure resistance to rutting among various physical properties of asphalt. However, the DSR test has limitations in that the results are different depending on the experimental setting and can only be measured within a specific temperature range. Therefore, in this study, to overcome the limitations of the DSR test, the rheological characteristics were predicted by learning the images collected from atomic force microscopy. Images and rheology properties were trained through EfficientNet, one of the deep learning architectures, and transfer learning was used to overcome the limitation of the deep learning model, which require many data. The trained model predicted the rheological properties of the asphalt binder with high accuracy even though different types of additives were used. In particular, it was possible to train faster than when transfer learning was not used.

• Tuberculosis and Respiratory Diseases
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• v.84 no.3
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• pp.182-187
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• 2021

Background: Fractional exhaled nitric oxide (FeNO) is a non-invasive marker for eosinophilic airway inflammation and a good predictor of response to corticosteroids. There is a need for a reliable and accurate measurement method, as FeNO measurements have been widely used in clinical practice. Our study aimed to compare two FeNO analyzers and derive a conversion equation for FeNO measurements in adults. Methods: We included 99 participants who had chief complaints of chronic cough and difficulty in breathing. The participants underwent concurrent FeNO measurement using NIOX VERO (Circassia AB) and NObreath (Bedfont). We compared the values of the two devices and analyzed their correlation and agreement. We then formulated an equation to convert FeNO values measured by NObreath into those obtained by NIOX VERO. Results: The mean age of the participants was 51.2±17.1 years, with a female predominance (58.6%). Approximately 60% of the participants had asthma. The FeNO level measured by NIOX VERO (median, 27; interquartile range [IQR], 15-45) was significantly lower than that measured by NObreath (median, 38; IQR, 22-58; p<0.001). There was a strong positive correlation between the two devices (r=0.779, p<0.001). Additionally, Bland-Altman plots and intraclass correlation coefficient demonstrated a good agreement. Using linear regression, we derived the following conversion equation: natural log (Ln) (NObreath)=0.728×Ln (NIOX VERO)+1.244. Conclusion: The FeNO values of NIOX VERO and NObreath were in good agreement and had positive correlations. Our proposed conversion equation could help assess the accuracy of the two analyzers.