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Classification Analysis of Humic Substances Fractionated Using Size Exclusion Chromatography Based on Numerical Descriptors Generated from the Data of Fourier Transform Mass Spectrometryтезисы доклада Тезисы
- Авторы: Perminova I.V., Kudryavtsev A.V., Kazachkov M.A., Konstantinov A.I., Belyaeva E.Yu, Hatfield K., Gaspar A., Schmitt-Kopplin Ph, Abbt-Braun G., Frimmel F.
- Сборник: ACS National Meeting - San Francisco 2017 253rd American Chemical Society National Meeting & Expo
- Тезисы
- Год издания: 2017
- Аннотация: Complex non-living organic matter represented by humic substances (HS) and dissolved organic matter (DOM) occurs throughout the entire Earth’s environment. Its unique molecular diversity has been revealed due to advances in high resolution Fourier Transform Mass Spectrometry, which resolved millions of carbon atoms with distinct chemical environments both in terrestrial and extraterrestrial organic matter. As a result, systematic screening and even simple enumeration of its molecular constituents is beyond any computational capacity: an efficient data reduction is needed. Here, we present a simple tool to quantify and map the chemical space of non-living complex organic matter using cell-based partitioning approach. We have achieved this by binning the compositional space defined by a Van Krevelen diagram into cells, where the relative number of stoichiometries found in each cell serves as a molecular descriptor. A set of these descriptors provides a unique identification for the sample. Furthermore, by assigning molecular tag to each cell, we could describe a supramolecular ensemble of humic materials. We demonstrate here applicability of this approach to classification of a set of peat humic substances and fresh water DOM which was obtained by fractionation of peat humic aicds, peat non-fractionated humic substrances, Suwannee River DOM and Suwannee River humic acids using size exclusion chromatography. The set consisted of 57 fractions as a total stemming from four different samples which differed in molecular weight . The fractions in this set were assigned a priory category “source” and “molecular weight”. The numerical descriptors allowed to classify with 100% validity the samples according to the source, and up to 85% of correct classifications were obtained for molecular weight (the example is shown in Fig. 1). We anticipate, that the proposed data reduction approach might lay grounds for development of molecular systematics of non-living organic matter. These studies will shed new light on evolution of carbon on Earth. The research was supported by the Russian Science Foundation (16-14-00167) in part of FTICR MS anal,ysis of the samples.
- Добавил в систему: Константинов Андрей Иванович