Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods

• Published in: Yàowu shi͡p︡in fenxi 2018-01, Vol.26 (1), p.90-99
• Main Authors: Sun, Li-Li, Wang, Meng, Zhang, Hui-Jie, Liu, Ya-Nan, Ren, Xiao-Liang, Deng, Yan-Ru, Qi, Ai-Di
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Antioxidants; Artificial neural networks; Biological products; chemometric; Chromatography; Classification; Clusters; counter-propagation artificial neural network; Emodin; Fingerprints; Functional foods & nutraceuticals; Geographical distribution; Herbal medicine; High performance liquid chromatography; Liquid chromatography; Markers; Mass spectrometry; Mass spectroscopy; Materials handling; Methods; multivariate curve resolution-alternating least squares; Neural networks; Organic chemistry; Original; Origins; Polygoni Multiflori Radix; Principal components analysis; Quadrupoles; Quality; Stilbene; ultra-high-performance liquid chromatograph fingerprints
• ISSN: 1021-9498; 2224-6614
• DOI: 10.1016/j.jfda.2016.11.009

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-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6′-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. [Display omitted] •The multivariate curve resolution–alternating least squares (MCR–ALS) model resolved problems of UPLC fingerprints.•Principal component analysis (PCA) and Ward's method were applied to cluster samples.•Chemical markers were obtained by Ward's method.•Counter-propagation artificial neural network (CP-ANN) studied the effect of chemical markers in clusters.•UPLC–quadrupole time-of-flight mass spectrometer (UPLC–Q-TOF-MS) data were used to identify chemical markers.