Combining Online HPLC-ABTS Screening With Machine Learning to Predict the Bioactivity of Antioxidant Components in Pandani Tectorii Radix et Rhizoma. (PubMed, Biomed Chromatogr) - Jun 16, 2026 - "Six common constituents were screened and assigned by standards: neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C. Using these six components as variables, four regression models were constructed to predict ABTS radical scavenging rate: support vector regression (SVR), decision tree regression, K-nearest neighbors (KNN) regression, and extreme gradient boosting (XGBoost) regression...After final tuning (C = 100, gamma = 0.1, and epsilon = 0.5), it achieved R2 = 0.9000, RMSE = 4.0602, and MAE = 2.2986 on the hold-out set. This efficient and accurate method enables simultaneous screening of antioxidant constituents and prediction of radical scavenging activity, providing a new technical tool and scientific basis for PT quality evaluation."
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Dong-Xiao Li; Hong-Liang Ma; Ji-Wen Wang; Lian-Lin Yang; Qian-Qian Hu; Shu-Zhen Chen; Yong-Lin Ma
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