Using principal components analysis (PCA) with cluster analysis to study the organic geochemistry of sinking particles in the ocean

Principal components analysis (PCA) is a multivariate data analysis tool that can be used to recombine the variables of a large multivariate dataset in such a way that the first few variables of the reconstructed dataset account for the majority of the variance in the data. Application of PCA in marine geochemistry has become quite common in recent years. In this study, we illustrate the use of PCA through examples that arose while investigating the geochemistry of sinking particles during the MedFlux project. The examples presented do not simply repeat the analyses of the original study, but instead extend them in the context of simultaneous application of PCA and cluster analysis. Our results show that constructing a one dimensional (1D) “degradation index” using only the first principal component (PC) is in most cases oversimplified, and that constructing 2D or 3D “degradation trajectories” with the first 2 or 3 PCs is more informative. Use of the first three PCs is indicated when the variance explained by the third PC is comparable in magnitude to that explained by the second PC in the reconstructed dataset. We also discuss the use of scree plots and cluster analysis in helping decide whether the third PC is needed to capture the essential information in the dataset.