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This present study aims to identify the key bioactive components in acorus tatarinowii rhizoma (ATR), a traditional Chinese medicine (TCM) with various bioactivities. Partial least squares regression (PLSR) was employed to describe the relationship between the radical scavenging activity and the volatile components. The PLSR model was improved by outlier elimination and variable selection and was evaluated by 10-fold cross-validation and external validation in this study. Based on the PLSR model, eleven chemical components were identified as the key bioactive components by variable importance in projection. The final PLS regression model with these components has good predictive ability. The Q ² was 0.8284, and the root mean square error for prediction was 2.9641. The results indicated that the eleven components could be a pattern to predict the radical scavenging activity of ATR. In addition, we did not find any specific relationship between the radical scavenging ability and the habitat of the ATRs. This study proposed an efficient strategy to predict bioactive components using the combination of quantitative chromatography fingerprints and PLS regression, and has potential perspective for screening bioactive components in complex analytical systems, such as TCM.

GC-MS Fingerprinting Combined with Chemometric Methods Reveals Key Bioactive Components in Acori Tatarinowii Rhizoma