首页|Researchers at Gyeongsang National University Report Research in Machine Learnin g (Antioxidant Activity of Ultrasonic Assisted Ethanol Extract of Ainsliaea acer ifolia and Prediction of Antioxidant Activity with Machine Learning)
Researchers at Gyeongsang National University Report Research in Machine Learnin g (Antioxidant Activity of Ultrasonic Assisted Ethanol Extract of Ainsliaea acer ifolia and Prediction of Antioxidant Activity with Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from JinJu, South Korea, by NewsRx correspondents, research stated, “The antioxidant properties of Ainsli aea acerifolia, a wild edible plant, were examined by ultrasonic-assisted ethano l extraction methods. The primary objective was to optimize the extraction condi tions and accurately predict antioxidant activities using advanced machine learn ing models.” Our news journalists obtained a quote from the research from Gyeongsang National University: “The extraction conditions were optimized using Response Surface Me thodology (RSM). Various parameters, including temperature, extraction time, and ethanol concentration, were adjusted to maximize antioxidant activity. The opti mal conditions identified were a temperature of 68 °C, an extraction time of 86 min, and an ethanol concentration of 57%. Under these conditions, t he extracts exhibited the highest antioxidant activity. To enhance the predictiv e accuracy of antioxidant activity, an XGBoost (XGB) model was employed. The XGB model performance was evaluated and compared with the RSM model. The XGB model achieved an R² value of 94.71%, significantly outperforming the RSM model by 12.8%. This highlights the superiority of the XGB model i n predicting antioxidant activities based on the given extraction parameters.”
Gyeongsang National UniversityJinJuS outh KoreaAsiaAlcoholsCyborgsEmerging TechnologiesEthanolEthanolamin esMachine Learning
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