ArticleJournal of food and drug analysis2018
Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.
Article in Journal of food and drug analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Chemometric Discrimination of the Geographical Origin ofFoods (Basel, Switzerland) · 2024Article
- A multidimensional strategy for uncovering comprehensive quality markers ofFrontiers in plant science · 2024Article
- Polygoni multiflori radix extracts inhibit SARS-CoV-2 pseudovirus entry in HEK293T cells and zebrafish larvae.Phytomedicine : international journal of phytotherapy and phytopharmacology · 2022Article
- Assessing Geographical Origin ofMolecules (Basel, Switzerland) · 2019Article
- Comparative investigation for raw and processed Aconiti Lateralis Radix using chemical UPLC-MS profiling and multivariate classification techniques.Journal of food and drug analysis · 2019Article
- rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea.Journal of food and drug analysis · 2019Article
- Raman spectroscopy coupled with chemometric methods for the discrimination of foreign fats and oils in cream and yogurt.Journal of food and drug analysis · 2019Article
- Quantitative and Chemical Fingerprint Analysis for the Quality Evaluation of Platycodi Radix Collected from Various Regions in China by HPLC Coupled with Chemometrics.Molecules (Basel, Switzerland) · 2018Article
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Authors and funding
7 authors.
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Abstract
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.
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