Classification of weathered crude oils using multimethod chemical analysis, statistical methods and SIMCA pattern recognition

Abstract As part of a continuous research programme on oil spill identification new methods in computerized statistical pattern recognition are being investigated. Twenty six artificially weathered crude oils have been analysed for NI,V,N-C 17 Alkane,Pristane,N-C 18 Alkane,Phytane,Sulphur, Triterpanes and Steranes. These parameters, 47 in all, have been screened to investigate the possibility of classification and distinction between the different sources. Four different statistical methods were considered: absolute ranking (‘peak-index-fit’), T -test, K nearest neighbour (KNN) and soft independent modelling of class analogy (SIMCA). The results show that all groups of oils may be separated by geographic origin using a ‘peak-index-fit’ ranking, a simple T -test and KNN. SIMCA is the only method capable of distinguishing between sources within the same geographical region. Based on the experience with these methods a statistical multimethod approach for identification of unknown oils from fingerprint data bases is suggested.