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Chemical Fingerprinting of Petroleum Biomarkers Using Time Warping and PCA

机译:使用时间扭曲和PCA的石油生物标志物化学指纹图谱

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摘要

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows: boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.
机译:描述了一种石油生物制造者化学指纹图谱的新方法。该方法包括GC-MS分析,GC-MS色谱图的预处理以及所选区域的主成分分析(PCA)。预处理包括通过衍生化,归一化和使用相关优化的翘曲对齐来去除基线。该方法适用于溢油样品和来源油的m / z 217(三环和四环甾烷)的色谱图。在波罗的海运输船漏油事件发生后的几周内,从沿海环境收集的漏油样本与主要波罗的海油箱中的油样本(源油)聚集在主要成分1至4中。通过使用加权最小二乘标准取消选择最不确定的变量或根据不确定性对它们进行缩放,可以增强PCA的判别能力。四个主要成分的解释如下:沸点范围(PC1),粘土含量(PC2),烃源岩中固醇的碳数分布(PC3)和油的热成熟度(PC4)。总之,与其他指纹方法相比,该方法可以使用快速,客观的程序进行色谱图分析,并且可以更全面地使用数据。

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