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- W3041912456 abstract "The ionizable molecular composition of 36 lacustrine oil samples and 29 marine oil samples from different off-shore Brazilian basins have been screened by ultrahigh-resolution and accuracy Fourier transform ion cyclotron resonance mass spectrometry with electrospray ionization in the negative-ion mode [ESI(−)-FT-ICR MS]. The negative mode has been demonstrated successfully to characterize source rock because it favors the detection of heteroatomic compounds such as nitrogen-, sulfur-, and oxygen-containing (NSO), which reveal information about geochemical environmental. ESI(−)-FT-ICR MS data was processed with a new approach that unifies information from the chemometrics tool and petroleomics conventional data processing. Novel diagrams were proposed that use variable important to projection (VIP), such as molecular formula obtained, via partial least-square discriminant analysis (PLS-DA) to plot in diagrams, such as those that display double bond equivalence (DBE) versus number carbon atom (NCA) and H/C ratio versus molecular mass (MM) trends. This new approach is advantageous since it is able to verify and compare all samples at once, obtaining more selective variables, such as molecular formula, and with more confidence because the figures of merit are acquired by statistical tests from PLS-DA, whereas it also provides the usual information from petroleomics tools such DBE, NCA, H/C ratio, MM, and molecular classes. This new approach revealed significant differences in the molecular distribution of heteroatomic components for N-containing and O-containing molecules (N class and O class) allowing a clear separation of all samples in two groups of marine or lacustrine crude oils." @default.
- W3041912456 created "2020-07-16" @default.
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- W3041912456 date "2020-07-10" @default.
- W3041912456 modified "2023-10-15" @default.
- W3041912456 title "Lacustrine versus Marine Oils: Fast and Accurate Molecular Discrimination via Electrospray Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Multivariate Statistics" @default.
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- W3041912456 doi "https://doi.org/10.1021/acs.energyfuels.9b04404" @default.
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