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넓패(Ishige foliacea) 추출물의 피부노화 억제활성
김지윤 ( Ji-youn Kim ),박다빈 ( Da-bin Park ),이연지 ( Yeon-ji Lee ),박선주 ( Sun Joo Park ),김용태 ( Yong-tae Kim ) 한국수산과학회 2023 한국수산과학회지 Vol.56 No.6
In this study, the antioxidant and anti-skin aging properties of the Korean marine algae Ishige foliacea were investigated. Solvent extracts from I. foliacea were prepared with 70% ethanol, 80% methanol, and water. The extraction yields of various solvent extracts ranged from 9.55% to 35.12%. In terms of antioxidant activity, the ethanol extract showed the highest ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt) radical scavenging activity, nitrite oxide scavenging activity, reducing power, and FRAP (ferric reducing antioxidant power). Regarding anti-skin aging activity, evaluation of the skin whitening and anti-wrinkle activities revealed that the methanol, water, and ethanol extracts possessed the highest tyrosinase (IC<sub>50</sub>=0.98 mg/mL), elastase (IC<sub>50</sub>=0.15 mg/ mL), and collagenase (IC<sub>50</sub>=0.06 mg/mL) inhibitory activities, respectively. These results suggest that I. foliacea holds potential as an antioxidant and anti-skin aging substance in food and cosmetic materials.
이영미 ( Young Mi Lee ),배주현 ( Joo Hyun Bae ),박다빈 ( Da Bin Park ) 한국환경과학회 2016 한국환경과학회지 Vol.25 No.4
Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.