Principal component analysis to evaluate the spatial variation of major elements in kaolin deposit
This paper explores the variations in major elements concentrations in kaolins from four different deposits in Botswana. The data were obtained from four different kaolin deposits with an additional four-class label based on particle sizes of the rock – providing a natural comparative basis between detected structural features with those of the original data attributes. Using principal component analysis (PCA), the paper reduces the data dimensionality and establishes inherent distinctive attributes of major elements accounting for the highest variation in chemical compositions of the kaolins. The principal components extracted are validated using graphical data visualization tools applied on a 28x11- dimensional data matrix of the oxides of Na, Mg, Al, Si, P, K, Ti, Mn and Fe, and loss on ignition (LOI). The validated results show that structures based on three retained components exhibit clearly discernible variations within the samples. Discretisation of the particle sizes is highlighted as both a challenge and an opportunity and it is recommended that it be used as a tuning parameter in gauging kaolin variations across samples and in validating new predictive modeling applications. Successful applications will depend on how clay and data scientists keep track, synchronise and share information relating to potentially dynamic data such as the impact of discretisation of kaolin particle sizes.
KEY WORDS: Graphical data visualization, Kaolin, Kaolinite, Particle size, X-Ray fluorescence spectrophotometry, Multi-collinearity
Bull. Chem. Soc. Ethiop. 2015, 29(1), 41-51