Quantitative X-ray fluorescence analysis of geological materials using partial least-squares regression†

Abstract

A computerised algorithm to select automatically appropriate wavelength (or 2θ) variables for subsequent multivariate calibration modelling was applied to the determination of iron, manganese, potassium, calcium, titanium, silicon, aluminium, magnesium and sodium in a range of certified geological materials by XRF spectrometry. The application of partial least-squares (PLS) regression is shown to be superior in terms of predictive performance to univariate linear regression modelling and multiple linear regression analysis. The combined process of automated variable selection and PLS modelling is amenable to providing an automated XRF quantitative analysis software system.