Identification of the Anti-oxidants in Flos Chrysanthemi by HPLC-DAD-ESI/MSn and HPLC Coupled with a Post-column Derivatisation System
ABSTRACT INTRODUCTION: Flos Chrysanthemi (Jiju) is a traditional Chinese medicine (TCM) that is known to have anti-oxidant activity; in this study, on-line HPLC-DAD-ESI/MS(n) and HPLC-DAD-DPPH methods have been developed for rapidly screening and identifying free-radical scavengers in Jiju extract. OBJECTIVE: To develop an efficient method for the simultaneous identification and detection of the anti-oxidant components in Flos Chrysanthemi (Jiju). METHODOLOGY: A concentrated methanol extract of Flos Chrysanthemi from Jiaxiang County (Jiju) was first separated into phases soluble in water, petroleum ether and n-butanol. The off-line 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging method was then used to evaluate the anti-oxidant activity of each phase in vitro. The results showed that the n-butanol extract had the highest anti-oxidant activity, and its anti-oxidant compounds were analysed by HPLC-DAD-ESI/MS(n) and HPLC coupled with a post-column derivatisation (PCD) system supplied with DPPH, aluminium chloride or sodium acetate solutions. RESULTS: A total of 17 compounds were separated and identified, three of which were identified in Jiju for the first time, and seven active compounds serve as the chemical basis of the anti-oxidant efficacy of Jiju. CONCLUSION: The methods described here allow rapid separation and convenient identification of the multiple constituents in Jiju, and may be applied to other complex natural matrices. Copyright © 2012 John Wiley & Sons, Ltd.
SourceAvailable from: Jose Antonio Gabaldon[Show abstract] [Hide abstract]
ABSTRACT: In this research, a non-selective extraction procedure followed by a RP-HPLC–ESI–QTOF/MS2 method was applied to evaluate the metabolic profile of these extracts, allowing the identification of a total of 95 compounds belonging to the chemical classes of organic acids, polyphenols, fatty acid derivatives, and others, most of these being identified for the first time in these extracts. These proved far richer in polyphenols, and more specifically in proanthocyanidins. This methodology successfully detected from monomers up to dimers of (epi)catechin, (epi)gallocatechin, and (epi)afzelechin units with one or two galloyl residues. A very high degree of galloylation was found, which may serve in the bioactivity attributed to these extracts. The chromatographic method had sufficient resolving power to separate up to six isomeric forms of several compounds, and the structure of some of these isomeric compounds has been elucidated. Therefore, the methodology applied proved useful for the metabolite profiling of Sclerocarya birrea stem–bark extracts, providing essential information that could be used to explain the plethora of ethnotherapeutic properties and pharmacological actions that have been attributed to this African tree.Industrial Crops and Products 02/2015; DOI:10.1016/j.indcrop.2015.01.068 · 3.21 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Flos Chrysanthemum is a generic name for a particular group of edible plants, which also have medicinal properties. There are, in fact, twenty to thirty different cultivars, which are commonly used in beverages and for medicinal purposes. In this work, four Flos Chrysanthemum cultivars, Hangju, Taiju, Gongju, and Boju, were collected and chromatographic fingerprints were used to distinguish and assess these cultivars for quality control purposes. Chromatography fingerprints contain chemical information but also often have baseline drifts and peak shifts, which complicate data processing, and adaptive iteratively reweighted, penalized least squares, and correlation optimized warping were applied to correct the fingerprint peaks. The adjusted data were submitted to unsupervised and supervised pattern recognition methods. Principal component analysis was used to qualitatively differentiate the Flos Chrysanthemum cultivars. Partial least squares, continuum power regression, and K-nearest neighbors were used to predict the unknown samples. Finally, the elliptic joint confidence region method was used to evaluate the prediction ability of these models. The partial least squares and continuum power regression methods were shown to best represent the experimental results.Analytical Letters 07/2014; 47(12). DOI:10.1080/00032719.2014.893439 · 0.98 Impact Factor