Chapter 18 - Global Characterization of Chemical Constituents of Phellodendri amurensis Cortex

Abstract

Herbal medicine is an essential part of the health care system in most Asian countries, and has even attracted attention in Europe and North America. The therapeutic effects of herbal medicine are caused by the synergistic contribution of multiple constituents. Serum pharmacochemistry of traditional Chinese medicine (TCM) has been proved to be helpful to elucidate the active constituents and metabolites of herbal medicine. Liquid chromatography coupled with tandem mass spectrometry (LC-MS) has become the cornerstone for the analysis of herbal constituents in vivo. However, identification of herbal constituents in biological samples significantly interferes with endogenous species. Inevitably, the conventional method of searching manually and intuitionally for the differences between control and dosed chromatograms may miss certain components, especially some in vivo metabolites. Therefore a robust discrimination method must be developed to facilitate the LC-MS identification of the constituents in the biological samples. Multivariate statistical analysis is concerned with the analysis and interpretation of complex data structures built up by many highly correlated variables. Pattern-recognition approaches, such as principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis, have typically been used to study metabolites and define differences between groups. Applying the pattern-recognition approaches, constituents can be extracted easily with no previous knowledge of the compound structure.