Profiling Olefins in Gasoline by Bromination Using GC×GC-TOFMS Followed by Discovery-Based Comparative Analysis

Abstract

An analytical workflow for the analysis of olefins in gasoline that combines selective bromination and comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) with discovery-based analysis is reported. First, a standard mix containing n-alkanes, 1-alkenes, and aromatic species was brominated and quantified using % reacted as a metric for each compound class, defined as the difference in the total peak area between the brominated and original samples normalized to the original sample. The average % reacted (1 s.d.) values were −1.45% (2.8%) for the alkanes, 99.5% (0.4%) for the alkenes, and 6.7% (11.6%) for the aromatics, demonstrating excellent selectivity toward the alkenes with only minor aromatic bromination. The bromination chemistry was then applied to gasoline, followed by GC×GC-TOFMS analysis of the original and brominated gasoline. This GC×GC-TOFMS data set was then submitted to the supervised discovery tool tile-based F-ratio analysis (FRA), which reduced the large data set to only the chromatographic regions which distinguish between the original and brominated gasoline samples. FRA discovered 314 hits, 56 of which were determined statistically significant using combinatorial null distribution analysis (CNDA), a permutation-based significance test. Since the brominated olefins elute in an uncrowded region of the 2D chromatogram and have no signal in the original sample, their discoverability was greatly increased relative to the original olefins. By combining the information gained from brominated olefin standards and the structured patterns of the GC×GC separations, the top hits were identified as the dibromoalkane products of mono-olefins, with five C5 mono-olefins identified on a species level. The analytical workflow has broad implications for using selective reaction chemistries to facilitate supervised discovery by targeting desired compound classes.

Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC–HRMS

“…While this was visualized tuning only one parameter, it is advised to optimize both parameters simultaneously with spiked standards as in Figure to ensure that the data are accurately represented for the intended analysis, especially for low-concentration analytes of interest. Furthermore, we expect this workflow to be readily adapted to comprehensive two-dimensional LC (LC × LC) with HRMS data as both the ROI procedure and tile-based F -ratio analysis have been demonstrated for various comprehensive two-dimensional chromatography data applications. ,,,− …”

Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

“…Ochoa et al developed an automated approach to quantify the signal ratio (equivalent to the concentration ratio for a pure m / z ) between two classes using statistical inference . Trinklein et al used selective bromination in conjunction with tile-based FRA to profile olefins in gasoline …”

Molecular Characterization of Heavy Olefins in Slurry-Phase Hydrocracking Products Using High-Resolution Mass Spectrometry

“…Liu et al 20 used bromine addition coupled with a gas chromatograph with an atomic emission detector to characterize the olefin composition in gasoline samples. Recently, Trinklein et al 21 combined selective bromine addition of olefins with GC × GC−MS to measure the molecular composition of the mono-olefins and diene compounds in gasoline. Another approach by Li et al 22 converted olefins into halogenated hydrocarbons using iodine chloride as a reaction reagent, followed by characterization of the derivatization product using atmospheric pressure chemical ionization Orbitrap mass spectrometry.…”