Unsupervised component analysis: PCA, POA and ICA data exploring - connecting the dots.
Under controlled conditions, each compound presents a specific spectral activity. Based on this assumption, this article discusses Principal Component Analysis (PCA), Principal Object Analysis (POA) and Independent Component Analysis (ICA) algorithms and some decision criteria in order to obtain unequivocal information on the number of active spectral components present in a certain aquatic system. The POA algorithm was shown to be a very robust unsupervised object-oriented exploratory data analysis, proven to be successful in correctly determining the number of independent components present in a given spectral dataset. In this work we found that POA combined with ICA is a robust and accurate unsupervised method to retrieve maximal spectral information (the number of components, respective signal sources and their contributions).
Department of Chemistry, University of Coimbra, P-3004 535 Coimbra, Portugal. Electronic address: jcpereira@qui.uc.pt.
Jorge Costa
JC
Pereira
Department of Chemistry and Biology, Federal University of Technology - Parana, Rua Deputado Heitor de Alencar Furtado, 4900, 81280-340 Curitiba, PR, Brazil.
Julio Cesar R
JCR
Azevedo
Department of Hydraulic and Sanitation, Federal University of Parana, Centro Politecnico, Bl. 5, Av. Cel Francisco H. dos Santos, 81531-990 Curitiba, PR, Brazil.
Heloise G
HG
Knapik
Department of Chemistry, University of Coimbra, P-3004 535 Coimbra, Portugal.
Hugh Douglas
HD
Burrows
ICA; PCA; Principal object; Robust spectral analysis; Synchronous fluorescence; Unsupervised analysis