Quantification and efficient discovery of quality control markers for Emilia prenanthoidea DC. by Fingerprint-Efficacy Relationship Modelling

An effective and comprehensive evaluation method for identifying the origin and assessing the quality of Emilia prenanthoidea DC. was established, based on analysis of high performance liquid chromatography (HPLC) fingerprint combined with the similarity analysis (SA), the hierarchical cluster analysis (HCA) and principal component analysis (PCA). Different data analysis methods drew a similar conclusion that 12 E. prenanthoidea (EP) samples were categorized into two groups. Evaluated the anti-inflammatory effects of different EP samples by analyzing paw edema (PE), serum superoxide dismutase (SOD) activity, concentrations of serum malondialdehyde (MDA), prostaglandin E-2 (PGE(2)) and tumor necrosis factor alpha (TNF-alpha) in the carrageenin-induced acute inflammation mouse model. With the help of Gray Correlation Analysis (GRA), partial least squares regression (PLSR) and artificial neural network (ANN), the relationship between the fingerprints and efficacy of EP was elucidated. As the results, chlorogenic acid, hyperoside and quercitrin could be selected as chemical markers to evaluate the quality of EP from different regions. Thus, a method was established to quantify 7 major bioactive ingredients in the samples under the condition of fingerprint. These results indicated that the HPLC fingerprint coupleing with Profile-Efficacy analysis would have great has potential to identify the medicinal effective components and evaluating the quality of E. prenanthoidea. (C) 2018 Elsevier B.V. All rights reserved.