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Chemometric Determination of Target Compounds Used to Fingerprint Unweathered Diesel Fuels

机译:化学计量测定用于指纹化未风化柴油燃料的目标化合物

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摘要

Existing oil fingerprinting standard methods target mostly high-molecular-weight biomarkers for discrimination between samples. Fingerprinting of light petroleum distillates like diesel fuel is problematic with these methods because many of the target analytes are not present. To address this problem, 14 diesel fuel samples from refineries throughout the United States and Canada were analyzed by GC/MS. For each sample, peaks were integrated in 62 different extracted ion chromatograms that represented 22 classes of petroleum constituents. Over 300 individual peak ratios were calculated per sample. Principle components analysis (PCA) was used to reduce the number of ratios needed to differentiate the samples. Nine significant peak ratios were identified by PCA. The ratios included compounds in alkylbenzene, alkylphenanthrene, and sesquiterpane petroleum classes. The PCA model removed peak ratios that contributed to instrument replicate variability, which improved differentiation among samples. Real-world spill samples not subjected to evaporative or other weathering processes were used to test the robustness of the model with excellent results. The spill samples were readily seen to be significantly different. Additional studies such as the effects of weathering on these peak ratios are needed before this approach can be evaluated as a useful spill fingerprinting method.
机译:现有的油指纹图谱标准方法主要针对高分子量生物标记物,以区分样品。对于这些方法,轻质石油馏出物(如柴油燃料)的指纹分析存在问题,因为其中不存在许多目标分析物。为了解决此问题,通过GC / MS分析了来自美国和加拿大的炼油厂的14个柴油样品。对于每个样品,将峰积分到代表22类石油成分的62个不同的提取离子色谱图中。每个样品计算出300多个单独的峰比率。主成分分析(PCA)用于减少区分样品所需的比率数。 PCA鉴定出九个显着的峰比率。这些比率包括烷基苯,烷基菲和倍半萜石油类中的化合物。 PCA模型消除了导致仪器重复变异性的峰比率,从而改善了样品之间的差异。未经过蒸发或其他风化过程的真实泄漏样品用于测试模型的鲁棒性,并具有出色的结果。容易发现泄漏样品有明显不同。在将这种方法评估为有用的泄漏指纹分析方法之前,需要进行其他研究,例如风化对这些峰值比率的影响。

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