Advanced characterization of the products of the hydrotreatment of gas oils is of high interest for refiners and can be achieved using ultrahigh resolution mass spectrometry (FT-ICR MS). However, the analysis of gas oil samples by FT-ICR MS generates complex data sets with numerous variables whose exhaustive analysis requires the use of multivariate methods. Relevant information about nitrogen and sulfur compounds contained in several industrial gas oils are obtained by using three different ionization modes that are electrospray ionization (ESI) used in positive and negative polarities and atmospheric pressure photoionization (APPI) used in positive polarity. For data sets generated for a single ionization mode, classical multivariate methods such as Principal Component Analysis (PCA) are commonly used. When the key information is spread into several ionization modes and thus into several data sets, a data fusion approach is highly interesting to simultaneously explore these data sets and can be followed by Parallel Factor analysis (PARAFAC). Nevertheless, many more variables are simultaneously considered when data fusion is performed and the sensitivity of PARAFAC and its ability to extract the most relevant variables compared to classical multivariate methods has not been assessed yet in the framework of FT-ICR MS. In this paper, a comparison of the classical data analysis (PCA) approach and the data fusion combined with the PARAFAC analysis approach is presented. The results have shown that applying PARAFAC on fused data sets is highly sensitive and able to put forward features and variables that are individually identified through classical data analysis with greater ease of implementation and interpretation of results. As an example, dibenzothiophenes and carbazole families (DBE 9) have explained most of the variance between samples and remain the most refractory compounds in hydrotreated samples. A significant difference in alkylation between the different types of gas oils has also been spotted. This paper validates the power and efficiency of this approach to explore complex data sets simultaneously without any loss of significant information.

中文翻译：

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评估数据融合和PARAFAC对来自粗柴油样品的FT-ICR MS数据集进行化学计量分析的好处

粗油加氢处理产品的高级表征对于精炼商来说非常重要，可以使用超高分辨率质谱（FT-ICR MS）实现。但是，通过FT-ICR MS对粗柴油样品进行分析会生成具有众多变量的复杂数据集，其详尽的分析需要使用多元方法。通过使用三种不同的电离模式可获得有关几种工业粗柴油中所含氮和硫化合物的相关信息，这些模式分别是用于正负极性的电喷雾电离（ESI）和用于正极性的大气压光电离（APPI）。对于为单个电离模式生成的数据集，通常使用经典的多变量方法，例如主成分分析（PCA）。当关键信息传播到多个电离模式并因此传播到多个数据集时，一种非常有趣的数据融合方法是同时探索这些数据集，随后可以进行并行因子分析（PARAFAC）。但是，在进行数据融合时，同时要考虑更多变量，并且与FT-ICR MS相比，PARAFAC的敏感性及其与经典多变量方法相比提取最相关变量的能力尚未得到评估。本文对经典数据分析（PCA）方法与结合PARAFAC分析方法的数据融合方法进行了比较。结果表明，将PARAFAC应用于融合数据集非常敏感，并且能够提出特征和变量，这些特征和变量可以通过经典数据分析进行单独识别，从而更易于实现和解释结果。例如，二苯并噻吩和咔唑家族（DBE 9）解释了样品之间的大部分差异，并在加氢处理的样品中仍然是最难熔的化合物。人们还发现了不同类型的粗柴油之间烷基化的显着差异。本文验证了这种方法的能力和效率，可以同时探索复杂的数据集，而不会丢失任何重要信息。二苯并噻吩和咔唑家族（DBE 9）解释了样品之间的大部分差异，并且在加氢处理的样品中仍然是最难熔的化合物。人们还发现了不同类型的粗柴油之间烷基化的显着差异。本文验证了这种方法的能力和效率，可以同时探索复杂的数据集，而不会丢失任何重要信息。二苯并噻吩和咔唑家族（DBE 9）解释了样品之间的大部分差异，并且在加氢处理的样品中仍然是最难熔的化合物。人们还发现了不同类型的粗柴油之间烷基化的显着差异。本文验证了这种方法的能力和效率，可以同时探索复杂的数据集，而不会丢失任何重要信息。