Towards a New Pseudo-quantitative Approach to Evaluate the Ionization Response of Nitrogen Compounds in Complex Matrices

Abstract

Ultra high-resolution mass spectrometry (FT-ICR MS) coupled to electrospray ionization (ESI) provides unprecedented molecular characterization of complex matrices such as petroleum products. However, ESI faces major ionization competition phenomena that prevent the absolute quantification of the compounds of interest. On the other hand, comprehensive two-dimensional gas chromatography (GC × GC) coupled to specific detectors (HRMS or NCD) is able to quantify the main families identified in these complex matrices. In this paper, this innovative dual approach has been used to evaluate the ionization response of nitrogen compounds in gas oils as a case study. To this extent, a large gas oil dataset has been analyzed by GC × GC/HRMS, GC × GC-NCD and ESI(+/-)-FT-ICR MS. Then, the concentrations obtained from GC × GC-NCD have been compared to those obtained from FT-ICR MS hence proving that strong ionization competitions are taking place and also depending on the origin of the sample. Finally, multilinear regressions (MLR) have been used to quantitatively predict nitrogen families from FT-ICR MS measurements as well as start rationalizing the ionization competition phenomena taking place between them in different types of gas oils.

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

“…Nitrogen and sulfur families have been attributed according to DBE values. 15,21 Chemometric Approach. Here, the value of 100 stands for the range of the number of carbon atoms (from 1 to 100), 25 for the range of aromaticity values (from 1 to 25), and 108 for the number of acquired spectra on the whole data set (6 replicates for each of the 18 vacuum gas oil samples).…”

“…18−20 In a previous study, a data formatting strategy has been introduced in order to simultaneously manage DBE values, number of carbon atoms, and sample for the exploration of a single ionization mode (APPI(+)) focusing only on sulfur compounds. 21 In order to simultaneously explore the data obtained for basic/neutral nitrogen and sulfur compounds from several ionization modes, a low-level data fusion strategy will be applied to generate a new tensor from the three-way arrays obtained for each ionization mode. Multiway approaches will be then required for this tensor exploration such as Parallel Factor Analysis (PARAFAC).…”

Evaluating the Benefits of Data Fusion and PARAFAC for the Chemometric Analysis of FT-ICR MS Data Sets from Gas Oil Samples

“…Energy & Fuels Component Analysis (PCA) has been applied on each array. 17 (2) A low-level data fusion method has been applied to concatenate the previously obtained 3D cubes along the DBE axis obtaining one hypercube of size 50 × 75 × 138 containing the information on the three ionization modes. In this specific case, the PARAFAC method has been applied using the alternating least squares algorithm (ALS).…”

“…The results obtained considering APPI(+)-FT-ICR/MS data have been exhaustively discussed in a previous work. 17 Six principal components have been considered as significant and explain 96.7% of the total variance. The score plot obtained over PC1−PC2 is available in Figure S5 in the Supporting Information.…”

“…14−16 Generally, principal component analysis (PCA) is applied on single data sets obtained for a given ionization mode or on several data sets with variables being the molecular formula of the compounds and their corresponding abundances or relative intensities. 17,18 This methodology does not allow to directly assess the contribution from the aromaticity and the alkylation degrees throughout the loadings analysis and thus the direct identification of the sulfur and basic/neutral nitrogen compounds explaining the observed variance between samples. Recently, a new approach has been introduced by merging several data sets obtained from different ionization modes and applying the PARAFAC method on this multidimensional hypercube.…”