赛默飞色谱与质谱分析
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使用LTQ Orbitrap XL混合线性离子阱质谱仪对绿茶和红茶提取物进行代谢组学分析

发布时间: 2012-08-20 09:45 来源: 赛默飞色谱与质谱分析

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使用LTQ Orbitrap XL混合线性离子阱质谱仪对绿茶和红茶提取物进行代谢组学分析

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摘要:

Metabolomics, the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples, marks promising new research territory. The numerous analytes in these samples have diverse chemistries and polarities.In addition, metabolites occur at a range of concentrations within a particular sample. Consequently, comprehensive metabolomics investigations create many analytical challenges that can be addressed using LC-MS/MS.

Tea contains a wide range of components including vitamins, amino acids, and polyphenols, many of which are structurally similar and may differ only in the type and location of a side chain. The use of high resolution chromatography is essential for the separation of such a complex mixture. Furthermore, acquisition of accurate mass data in both full scan and MSn modes enables complete structural characterization.

Here, we highlight an untargeted metabolomic workflow from data acquisition through metabolite ID. The study included differential and structural characterization of polyphenolic catechin (flavan-3-ol) derivatives and theaflavin components of green tea and black tea.

仪器:

Thermo Scientific LTQ-Orbitrap XL组合式高分辨质谱仪

结论:

The analytical metabolomic workflow described here encompasses data acquisition, discovery of differentially abundant metabolites, and metabolite identification.

The LTQ Orbitrap XL coupled to an Accela U-HPLC system afforded fast analysis times while maintaining high chromatographic resolution. Accurate mass measurements increased the confidence in elemental composition assignments and ultimately metabolite identification. SIEVE differential analysis software enabled large-scale evaluation of multiple complex LC-MS data and comparison of metabolite profiles between green and black tea samples.

The spectral database and fragmentation algorithms of Mass Frontier software facilitated structural assignments of metabolites of interest utilizing MSn fragmentation spectra.


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