Low-Level Fusion of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Data Sets for the Characterization of Nitrogen and Sulfur Compounds in Vacuum Gas Oils.,Analytical Chemistry

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Low-Level Fusion of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Data Sets for the Characterization of Nitrogen and Sulfur Compounds in Vacuum Gas Oils.
Analytical Chemistry ( IF 6.7 ) Pub Date : 2020-01-23 , DOI: 10.1021/acs.analchem.9b05263
Julie Guillemant ₁ , Alexandra Berlioz-Barbier ₁ , Florian Albrieux ₁ , Luis P de Oliveira ₁ , Marion Lacoue-Nègre ₁ , Jean-François Joly ₁ , Ludovic Duponchel ₂

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A total of 18 vacuum gas oils have been analyzed by Fourier transform ion cyclotron resonance mass spectrometry considering six replicates in three different ionization modes (electrospray ionization (ESI)(+), ESI(-), and atmospheric pressure photoionization (APPI)(+)) to characterize the nitrogen and sulfur compounds contained in these samples. Classical data analysis has been first performed on generated data sets using double bond equivalents (DBE) versus number of carbon atoms (#C) plots in order to observe similarities and differences within the nitrogen and sulfur-containing molecular classes from samples produced by different industrial processes. In a second step, three-way arrays have been generated for each ionization mode considering three dimensions: DBE related to aromaticity, number of carbon atoms related to alkylation, and sample. These three-way arrays have then be concatenated using low-level data fusion strategy to obtain a new tensor with three new modes: aromaticity, alkylation, and sample. The PARAFAC method has then been applied for the first time to this three-way data structure. A two components decomposition has allowed us to highlight unique samples with unexpected reactivity behaviors throughout hydrotreatment. The obtained loadings led to the identification of the variables responsible for this specific character. This original strategy has provided a fast visualization tool able to highlight simultaneously the impact of the three ionization modes in order to explain the differences between the samples and compare them.

中文翻译：

傅里叶变换离子回旋共振质谱数据集的低级融合，用于表征真空瓦斯油中的氮和硫化合物。

考虑了三种不同电离模式（电喷雾电离（ESI）（+），ESI（-）和大气压光电离（APPI）（+ ））表征这些样品中所含的氮和硫化合物。首先使用双键当量（DBE）对碳原子数（#C）图对生成的数据集进行经典数据分析，以便观察不同工业生产的样品中含氮和含硫分子类别之间的相似性和差异流程。在第二步中，考虑到三个维度，为每种电离模式生成了三向阵列：与芳香性有关的DBE，与烷基化有关的碳原子数以及样品。然后使用低级数据融合策略将这些三向数组连接起来，以获得具有三种新模式的新张量：芳香性，烷基化和样品。然后，首次将PARAFAC方法应用于此三向数据结构。两种成分的分解使我们能够在加氢处理过程中突出显示具有意外反应行为的独特样品。获得的载荷导致确定了负责此特定字符的变量。这种原始策略提供了一种快速可视化工具，能够同时突出显示三种电离模式的影响，以便解释样品之间的差异并进行比较。烷基化，并取样。然后，首次将PARAFAC方法应用于此三向数据结构。两种成分的分解使我们能够在加氢处理过程中突出显示具有意外反应行为的独特样品。获得的载荷导致确定了负责此特定字符的变量。这种原始策略提供了一种快速的可视化工具，能够同时突出显示三种电离模式的影响，以便解释样品之间的差异并进行比较。烷基化，并取样。然后，首次将PARAFAC方法应用于此三向数据结构。两种成分的分解使我们能够突出显示在整个加氢处理过程中具有意外反应行为的独特样品。获得的载荷导致确定了负责此特定字符的变量。这种原始策略提供了一种快速可视化工具，能够同时突出显示三种电离模式的影响，以便解释样品之间的差异并进行比较。获得的载荷导致确定了负责此特定字符的变量。这种原始策略提供了一种快速可视化工具，能够同时突出显示三种电离模式的影响，以便解释样品之间的差异并进行比较。获得的载荷导致确定了负责此特定字符的变量。这种原始策略提供了一种快速可视化工具，能够同时突出显示三种电离模式的影响，以便解释样品之间的差异并进行比较。

更新日期：2020-01-24

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