MULTIWAY PRINCIPAL COMPONENTS-ANALYSIS OF A COMPLEX DATA ARRAY RESULTING FROM PHYSICOCHEMICAL CHARACTERIZATION OF NATURAL-WATERS

Multi-way principal components analysis (MPCA) is an efficient tool for reducing higher dimensional data arrays. Using the Kroonenberg algorithm - which originally was developed for three-dimensional data arrays but may be generalized to arbitrary dimensions in a straightforward manner - MPCA is applied to a complex example from the chemistry of waters. The data originated from the measurements of fifteen physicochemical parameters (variables) at ten different locations (objects) within some specific area of the Niger delta. These measurements were consistently recorded 22 times (occasions) in the course of a year. MPCA allows the detection of spatial and temporal factors of influence and the classification of the parameters considered according to these factors.