Identification of petroleum hydrocarbons using a reduced number of PAHs selected by Procrustes rotation.
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.
Fernández-Varela
R
R
Department of Analytical Chemistry, University of A Coruña, Campus da Zapateira s/n, E-15071 A Coruña, Spain.
Andrade
J M
JM
Muniategui
S
S
Prada
D
D
Ramírez-Villalobos
F
F
D016000:Cluster Analysis; D016571:Neural Networks, Computer; D010578:Petroleum / Q000032:analysis*; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry*; D025341:Principal Component Analysis
D010578:Petroleum; D011084:Polycyclic Aromatic Hydrocarbons