Chemical Fingerprint Analysis for Discovering Markers and Identifying by HPLC Coupled with OPLS-DA.

The quality control of has been greatly improved by macroscopic and microscopic identification and chemical profiling described in Chinese Pharmacopoeia since 2005. However, these methods have their own limitations, e.g., their dependence on personal experience and expertise, and it is a huge challenge to identify closely related species that share similar or identical morphological characteristics and chemical profiles. A novel and generally accepted identification strategy is urgently needed as a complement to regulations for protecting the public health interests. In this work, a comprehensive chromatographic fingerprint method was developed and tested on 43 samples from four haplotypes of according to DNA barcoding. Three common patterns consisting of 20, 14, and 7 common peaks were generated by frequency filters of median, upper quartile, and 100%, respectively. Based on two formerly screened patterns, can be effectively identified from its five easily confused snow lotus species, including the most closely related plant () in the orthogonal partial least-squares discriminant analysis (OPLS-DA) models. The model is supported by good R and Q coefficients. In addition, different haplotypes of can be discriminated in the OPLS-DA model using the 20 common peaks. Among them, peaks 9, 11, 16 (zaluzanin C), and 18 (dehydrocostus lactone) have been identified as fingerprint markers of via S-plots and VIP values (>1). Additionally, peaks 19 and 20 were identified as linolenic acid and linoleic acid with anti-inflammatory activity, and they were isolated from the herb for the first time. Collectively, the chromatographic fingerprint of can be an effective and integrated method for the identification of authentic herbs from adulterant species or related plants, and discrimination of its different haplotypes provides an objective and reliable tool for quality control.