Department of Analytical Chemistry, University of A Coruña, Campus da Zapateira s/n, E-15071 A Coruña, Spain.
Mar Pollut Bull. 2010 Apr;60(4):526-35. doi: 10.1016/j.marpolbul.2009.11.012. Epub 2009 Dec 14.
Identifying petroleum-related products released into the environment is a complex and difficult task. To achieve this, polycyclic aromatic hydrocarbons (PAHs) are of outstanding importance nowadays. Despite traditional quantitative fingerprinting uses straightforward univariate statistical analyses to differentiate among oils and to assess their sources, a multivariate strategy based on Procrustes rotation (PR) was applied in this paper. The aim of PR is to select a reduced subset of PAHs still capable of performing a satisfactory identification of petroleum-related hydrocarbons. PR selected two subsets of three (C(2)-naphthalene, C(2)-dibenzothiophene and C(2)-phenanthrene) and five (C(1)-decahidronaphthalene, naphthalene, C(2)-phenanthrene, C(3)-phenanthrene and C(2)-fluoranthene) PAHs for each of the two datasets studied here. The classification abilities of each subset of PAHs were tested using principal components analysis, hierarchical cluster analysis and Kohonen neural networks and it was demonstrated that they unraveled the same patterns as the overall set of PAHs.
鉴定释放到环境中的石油相关产品是一项复杂而困难的任务。如今，多环芳烃（PAHs）具有重要意义。尽管传统的定量指纹分析使用简单的单变量统计分析来区分油类并评估其来源，但本文应用了基于普罗克鲁斯旋转（PR）的多变量策略。PR 的目的是选择仍然能够对石油相关烃类进行令人满意识别的 PAHs 的减少子集。PR 为两个数据集选择了三个 PAHs（C2-萘、C2-二苯并噻吩和 C2-菲）和五个 PAHs（C1-十氢化萘、萘、C2-菲、C3-菲和 C2-荧蒽）的子集。使用主成分分析、层次聚类分析和科恩神经网络测试了每个 PAHs 子集的分类能力，结果表明它们揭示了与整个 PAHs 集相同的模式。
