Chemometrics-Based Approach to Feature Selection of Chromatographic Profiles and its Application to Search Active Fraction of Herbal Medicine

In our previous report (J Pharmaceut Biomed 56 (2011) 443–447), a support vector machine (SVM)‐based pharmacodynamic model was established for predicting active fractions of herbal medicines (HMs), where information contents embedded in the chromatograms of the fractions were represented with the peak areas. However, in this representation the global characteristics of the chromatograms were completely missed, which is definitely contrary to the global and holistic views in theories of HMs and undoubtedly reduce the success rate of this model. To deal with the challenge, two chemometrics methods, that is, minimum redundancy maximum relevance (mRMR) and particle swarm optimizer (PSO), were applied in this article for feature selection of the whole chromatograms, and the PSO was also used to tune the SVM parameters. As a case, a sample HM, that is, Xiangdan injection, was investigated. The predictive accuracy was fully evaluated and compared with those by other popular and reported methods. Furthermore, the confirmation on the independent predicting set exhibited that the predicted bioactivities were well consistent with the experimental values. The important potential application of the present model is to be extended to help search active fractions of other HMs. In this paper, a computational model was proposed to predict the bioactivity of herbal medicine (HM) based on the selected features from its chromatograms. It was verified with good performance and can be extended to help search the active fractions of other HMs.