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The carbon versus mass diagram to visualize and exploit FTICR-MS data of natural organic matter

机译:碳与质量图,用于可视化和利用天然有机物的FTICR-MS数据

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摘要

A two-dimensional diagram is proposed, in which the carbon number of each formula is plotted against its nominal mass, to visualize large sets of molecular formula data that can be derived from data generated by ultrahigh-resolution Fourier transform ion cyclotron resonance-MS. In such a carbon versus mass (CvM) diagram, each formula (CcHhOo) is unambiguously described by c, its (nominal) mass and the parameter i = c + o. Calculations of chemically allowable formulas illustrate that organic molecules occupy only certain spaces in such a diagram. The extension of these spaces increases with molecular mass in x-direction (hydrogenation) and y-direction (oxygenation). The data sets of molecules determined in natural organic matter(NOM) occupy only a certain range of the allowable space. The intensity of the mass spectrometric signals can be included as the third dimension into a CvM diagram. Separate CvM diagrams can be plotted for NOM molecules that include different heteroatoms. The benefits of the CvM diagram are illustrated by application onto data sets of fulvic acids from riverine and marine origin, of secondary organic aerosol, including organosulfates and organonitrates, as well as of ozonation of fulvic acids. The CvM diagram is a useful tool to visualize the elemental regularities in NOM isolates as well as the differences between isolates. It may also be applicable to large sets of molecular formula data generated in other disciplines such as petroleum biogeochemistry or metabolomics.
机译:提出了一个二维图,其中每个分子式的碳原子数与其标称质量作图,以可视化可以从超高分辨率傅里叶变换离子回旋加速器共振质谱生成的数据中得出的大量分子式数据。在这种碳质量比(CvM)图中,每个式(CcHhOo)均由c,其(标称)质量和参数i = c + o明确描述。化学上允许的式子的计算表明,在这种图中,有机分子仅占据某些空间。这些空间的延伸随着分子量在x方向(氢化)和y方向(氧化)而增加。天然有机物(NOM)中确定的分子数据集仅占据一定范围的允许空间。质谱信号的强度可以作为第三维包含在CvM图中。可以为包含不同杂原子的NOM分子绘制单独的CvM图。通过将CvM图的好处应用于来自河流和海洋来源的富里酸,第二有机气溶胶(包括有机硫酸盐和有机硝酸盐)以及富里酸的臭氧化数据集,可以说明这一点。 CvM图是可视化NOM分离物中的元素规律性以及分离物之间差异的有用工具。它也可能适用于在其他学科(例如石油生物地球化学或代谢组学)中生成的大量分子式数据。

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