Twenty-three gas oil samples from different origins were analyzed in positive and negative ion modes by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(±)-FT-ICR MS). Sample ionization and ion transfer conditions were first optimized using Design of Experiment approach. Advanced characterization of basic and neutral nitrogen compounds in these samples was then performed through ESI(±)-FT-ICR MS analysis. A good repeatability was observed from the analysis of six replicates for each gas oil sample. Significant differences in molecular composition were spotted between the gas oils, either considering identified heteroatomic classes or within nitrogen families and were later correlated to samples macroscopic properties. The evolution of nitrogen relative intensities for one feed and two corresponding effluents has also been studied to monitor hydrotreatment reaction pathways toward aromaticity and alkylation levels evolutions.

Sulfur content in gas oils is strictly regulated by legal specifications for environmental reasons. Gas oils are composed of various aromatic sulfur compounds, and some of them are known to be very refractory for sulfur removal reactions. Thus, an accurate analysis of sulfur compounds is important to find the appropriate operating conditions of the gas oil hydrotreating processes. Aromatic sulfur compounds contained in 23 gas oils samples were analyzed using APPI(+)-FT-ICR MS considering six replicates. Significant differences were spotted within several processed gas oils. A comparison of one feed and its corresponding effluents also confirmed the well-known refractory character of sulfur compounds such as polyalkylated dibenzothiophenes. To go deeper in the molecular exploration, chemometric tools were applied on this spectral data set including principal component analysis (PCA) and hierarchical cluster analysis (HCA). A unique data rearrangement was performed directly inspired on DBE vs carbon number plots that are systematically used in petroleomics studies. Then, these chemometric tools provided a successful classification of each type of gas oils. The PCA model has also been validated on mixed blends allowing us to conclude that it could be applied to unknown samples in order to identify the process used to produce them. Moreover, the exploration of the generated loadings revealed key types of molecules driving the classification such as C3-DBT which is a dibenzothiophene core with three additional carbon atoms. Indeed, it is known to remain mainly in deeply hydrotreated samples, validating previous observations regarding its potential refractory character. The ability of chemometric tools to extract specific molecular information from ultra-high resolution MS spectra reveals its huge potential for an exhaustive study of highly complex mixtures such as crude oils.

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 threeway 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.

No 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.

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.

Here, we provide the dataset associated with our research article on the potential effects of ocean acidification on antimicrobial peptide (AMP) activity in the gills of Mytilus edulis, “Impact of ocean acidification on antimicrobial activity in gills of the blue mussel (Mytilus edulis)” [1]. Blue mussels were stimulated with lipopolysaccharides and samples were collected at different time points post injection. Protein extracts were prepared from the gills, digested using trypsin and a full in-depth proteome investigation was performed using liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS). Protein identification and quantification was performed using the MaxQuant 1.5.1.2 software, “MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification” [2].

Advanced characterization of the products of gas oils hydrotreatment is of high interest for the refiners and can be achieved by using ultra-high resolution mass spectrometry (FT-ICR MS). However, the analysis of gas oil samples by FT-ICR MS generates complex datasets 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 photo-ionization (APPI) used in positive polarity. For datasets 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 datasets, a data fusion approach is highly interesting to simultaneously explore these datasets 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, the 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 datasets is highly sensitive and able to put forward features and variables that individually identified through the 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 datasets simultaneously without any loss of significant information.