Dept. Analytical Chemistry, University of A Coruña, Campus da Zapateira s/n, E-15071 A Coruña, Spain.
J Chromatogr A. 2010 Dec 24;1217(52):8279-89. doi: 10.1016/j.chroma.2010.10.043.
A set of 34 crude oils was analysed by GC-MS (SIM mode) and a suite of 28 diagnostic ratios (DR) calculated. They involved 18 ratios between biomarker molecules (hopanes, steranes, diasteranes and triaromatic steroids) and 10 quotients between polycyclic aromatic hydrocarbons. Three unsupervised pattern recognition techniques (i.e., principal components analysis, heatmap hierarchical cluster analysis and Kohonen neural networks) were employed to evaluate the final dataset and, thus, ascertain whether the crude oils grouped as a function of their geographical origin. In addition, an objective variable selection procedure based on Procrustes Rotation was undertaken to select a reduced set of DR that comprised for most of the information in the original data without loosing relevant information. A reduced set of four DR (namely; TA21, D2/P2, D3/P3 and B(a)F/4-Mpy) demonstrated to be sufficient to characterize the crude oils and the groups they formed.
采用气相色谱-质谱联用仪（选择离子监测模式）对 34 组原油进行分析，并计算了 28 种诊断比值（DR）。这些比值涉及生物标志物分子（藿烷类、甾烷类、重排甾烷类和三芳甾族化合物）之间的 18 种比值以及多环芳烃之间的 10 种商数。利用三种无监督模式识别技术（即主成分分析、热图层次聚类分析和科恩神经网络）对最终数据集进行评估，从而确定原油是否可以根据其地理来源进行分组。此外，还采用基于普罗克鲁斯旋转的客观变量选择程序，选择了一组包含原始数据大部分信息的减少的 DR 集，同时不会丢失相关信息。四个减少的 DR（即 TA21、D2/P2、D3/P3 和 B(a)F/4-Mpy）足以表征原油及其形成的组。
