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.

中文翻译：

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使用 GC×GC-TOFMS 通过溴化分析汽油中的烯烃，然后进行基于发现的比较分析

报告了一种分析汽油中烯烃的分析工作流程，该工作流程将选择性溴化和综合二维 (2D) 气相色谱飞行时间质谱 (GC×GC-TOFMS) 与基于发现的分析相结合。首先，一个包含n的标准混合物-烷烃、1-烯烃和芳香族物质被溴化和量化，使用反应百分比作为每个化合物类别的指标，定义为溴化样品和原始样品之间的总峰面积差异，标准化为原始样品。烷烃的平均反应百分比 (1 sd) 值为 -1.45% (2.8%)，烯烃为 99.5% (0.4%)，芳烃为 6.7% (11.6%)，证明了对烯烃的出色选择性少量芳香族溴化。然后将溴化化学应用于汽油，然后对原始汽油和溴化汽油进行 GC×GC-TOFMS 分析。然后将此 GC×GC-TOFMS 数据集提交给监督发现工具基于瓦片的F-比率分析（FRA），将大数据集减少到仅区分原始和溴化汽油样品的色谱区域。FRA 发现了 314 个命中，其中 56 个命中是使用组合空分布分析 (CNDA) 确定的具有统计学意义，这是一种基于排列的显着性检验。由于溴化烯烃在 2D 色谱图的非拥挤区域中洗脱，并且在原始样品中没有信号，因此相对于原始烯烃，它们的可发现性大大提高。通过结合从溴化烯烃标准品中获得的信息和 GC×GC 分离的结构化模式，将排名靠前的化合物确定为单烯烃的二溴烷烃产物，在物种水平上确定了五种 C5 单烯烃。