Rhizoma Coptidis (RC) originated from the dried rhizomes of Coptis herbal species is a widely used traditional Chinese medicine in history. In this study, a comprehensive quality assessment for RC medicines from C. chinensis, C. deltoidea, C. omeiensis and C. teeta species was performed based on quantitative and qualitative metabolic profiles obtained from high performance liquid chromatography (HPLC), Fourier transform near-infrared (FT-NIR) and Fourier transform mid-infrared (FT-MIR) combined with multivariate statistical analysis. Eight alkaloids including magnoflorine, groenlandicine, jatrorrhizine, columbamine, epiberberine, coptisine, palmatine and berberine were simultaneously identified and determined. Epiberberine, berberine, magnoflorine and groenlandicine were identified as possible index components. FT-NIR and FT-MIR profiles presented the holistic metabolic characterization of RC medicines. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were successively performed to clearly illustrate the metabolic variation and taxonomic relationship among four RC medicines. Additionally, taking berberine as an example, spectral quantification potential was investigated by referring HPLC data, using a conventional partial least squares regression (PLSR) algorithm. Data fusion strategy exhibited a better prediction for this compound than a single technique. Summary, these techniques can complement each other and provide a comprehensive and effective quality assessment for RC originated from different Coptis plants.
Keywords: FT-MIR; FT-NIR; HPLC; Multivariate statistical analysis; Rhizoma Coptidis.
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