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Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.
Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di.
Afiliación
  • Sun LL; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Wang M; Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Zhang HJ; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Liu YN; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Ren XL; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Deng YR; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Qi AD; School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
J Food Drug Anal ; 26(1): 90-99, 2018 01.
Article en En | MEDLINE | ID: mdl-29389593
Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-ß-d-glucoside, emodin-8-O-ß-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-ß-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cromatografía Líquida de Alta Presión / Gastrópodos / Metabolómica Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: J Food Drug Anal Año: 2018 Tipo del documento: Article País de afiliación: China Pais de publicación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cromatografía Líquida de Alta Presión / Gastrópodos / Metabolómica Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: J Food Drug Anal Año: 2018 Tipo del documento: Article País de afiliación: China Pais de publicación: China