Antioxidant activity of ultrasonic assisted ethanol extract of Ainsliaea acerifolia and prediction of antioxidant activity with machine learning

The antioxidant properties of Ainsliaea acerifolia, a wild edible plant, were examined by ultrasonic-assisted ethanol extraction methods. The primary objective was to optimize the extraction conditions and accurately predict antioxidant activities using advanced machine learning models. The extraction conditions were optimized using Response Surface Methodology (RSM). Various parameters, including temperature, extraction time, and ethanol concentration, were adjusted to maximize antioxidant activity. The optimal conditions identified were a temperature of 68 °C, an extraction time of 86 min, and an ethanol concentration of 57%. Under these conditions, the extracts exhibited the highest antioxidant activity. To enhance the predictive 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 in predicting antioxidant activities based on the given extraction parameters. Additionally, the study developed a graphical user interface (GUI). This GUI allows researchers and industry experts to input extraction conditions and obtain quick, accurate predictions of antioxidant activity.