Multivariate statistical analysis based on a chromatographic fingerprint for the evaluation of important environmental factors that affect the quality of Angelica sinensis
Abstract
Multiple components in traditional Chinese medicines (TCMs) have a synergistic action on the therapeutic effects of TCMs and their contents may vary substantially with environmental changes. In this study, an ultra-performance liquid chromatographic (UPLC) fingerprint was established to choose the optimum environmental conditions for the cultivation of Angelica sinensis (A. sinensis). Optimum separation was achieved on a C18 column (50 × 2.1 mm i.d., 1.7 μm particles) with a 25 min gradient. The method was applied to establish the chromatographic fingerprint of A. sinensis by analyzing 109 samples cultivated under controlled environmental conditions. A representative standard fingerprint chromatogram was obtained using professional software, in which 30 common peaks were marked. The common peaks for all samples were subjected to principal component analysis with partial least squares discriminant analysis to screen out the peaks related to specific environmental factors. Peaks with areas that showed significant differences under different environmental conditions were screened out and used to obtain the optimum environmental conditions. Our method of integrating the advantages of chromatographic fingerprinting and multivariate statistical analysis can reveal the integral characteristics of herbal medicines. Consequently, it is a comprehensive, scientific method that provides a technical safeguard for the cultivation of herbal medicines.
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