The experimental data formed a large data set including mineralogical
components of solids and soil samples. Analysis of a large
data set with multiple variables require analytical approaches
capable of clustering similar data together whilst identifying relationships
between variables. In these circumstances, the application
of multivariate analytical techniques has been found to be
the most appropriate (Herngren et al., 2005; Settle et al., 2007).
In this study, Principal Component Analysis (PCA), which is an analytical
technique frequently applied in the analysis of environmental
data was used.