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A metabolomic strategy based on integrating headspace gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry to differentiate the five cultivars of Chrysanthemum flower

机译:基于顶空气相色谱-质谱联用和液相色谱-质谱联用的代谢组学策略来区分菊花的五个品种

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The extreme complexity of the chemical composition of plant extracts requires an unbiased and comprehensive detection methodology to improve the potential of metabolomic study. The present work, taking five closely related cultivars of Chrysanthemum flowers as a typical case, attempts to develop a metabolomic strategy to find more markers of metabolites for precise differentiation based on headspace gas chromatography-mass spectrometry (HSGC-MS) and ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). In detail, 53 batches of Chrysanthemum flower samples were collected and analyzed. The fusion of datasets from HSGC-MS and UHPLC-QTOF/MS was done in two different ways. After comparison, the fusion of the total peak area normalized metabolomic data was performed for multivariate statistical analysis. A total of 21 marker compounds (including 14 volatile and 7 nonvolatile metabolites) were identified, and a heatmap was employed for clarifying the distribution of the identified metabolites among the five cultivars. The results indicated that the integrated platform benefited the metabolomic study of medicinal and edible herbs by providing complementary information through fully monitoring functional constituents.
机译:植物提取物化学成分的极端复杂性要求一种无偏见且全面的检测方法,以提高代谢组学研究的潜力。本研究以五个菊花的近缘栽培品种为典型案例,尝试开发一种代谢组学策略,以基于顶空气相色谱-质谱(HSGC-MS)和超高性能液体寻找更多的代谢物标记物,以进行精确区分。色谱结合四极杆飞行时间质谱(UHPLC-QTOF / MS)。详细地,收集和分析了53批次的菊花花样品。来自HSGC-MS和UHPLC-QTOF / MS的数据集的融合以两种不同的方式完成。比较后,对总峰面积归一化代谢组学数据进行融合,以进行多变量统计分析。总共鉴定出21种标记化合物(包括14种挥发性和7种非挥发性代谢物),并使用热图阐明了鉴定出的代谢物在5个品种中的分布。结果表明,通过全面监测功能成分提供补充信息,该集成平台有益于药用和食用草药的代谢组学研究。

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