Sequential Factor Analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: an application to a bulk chemical characterization of fluvial deposits (Rhine-Meuse delta, The Netherlands)

Sequential Factor Analysis (seqFA) is presented here as an enhanced alternative to multivariate factorial techniques including robust and classical Factor Analysis (FA) or Principal Component Analysis (PCA). A geochemical data set of 145 sediment samples from very heterogeneous, mainly riverine, deposits of the Rhine-Meuse delta (The Netherlands) analyzed for 27 bulk parameters was used as a test case. The innovative approach explicitly addresses the priority issues when performing PCA or FA: heterogeneity and overall integrity of the data, the number of factors to be extracted, and which optimum minimal set of key variables to be included in the model. The stepwise decision process is based on quantitative and objectively derived statistical criteria, yet also permitting arguments based on geochemical expertise. The results show that seqFA, preferably in combination with robust methods, yields a highly consistent factor model, and is favorable over classical methods