Principal Component Analysis Approach for Modeling Sulfur K‐XANES Spectra of Humic Acids

• DOI: 10.2136/sssaj2002.8300
• Publication Date: 2016-05-20T02:52:57Z
• Authors: Suzanne Beauchemin; Dean Hesterberg; Mario Beauchemin
• Keywords: 0401 agriculture, forestry, and fisheries; 04 agricultural and veterinary sciences; 01 natural sciences; 0105 earth and related environmental sciences

Quantitative application of x‐ray absorption near edge structure (XANES) spectroscopy to soils and other geochemical systems requires a determination of the proportions of multiple chemical species that contribute to the measured spectrum. Two common approaches to fitting XANES spectra are spectral deconvolution and least‐squares linear combination fitting (LCF). The objective of this research was to evaluate principal component analysis (PCA) coupled with target transformation to model S K‐XANES spectra of humic acid samples, and to compare the results with least‐squares LCF. Principal component analysis provided a statistical basis for choosing the number of standard species to include in the fitting model. Target transformation identified which standards were statistically more likely to explain the spectra of the humic acid samples. The selected standards and the scaling coefficients obtained by the PCA approach deviated by ≤6 mol% from results obtained by performing LCF using a large number of binary, ternary, and quaternary combinations of seven S standards. Because no energy shift is allowed in the PCA approach, fitting may be refined, when appropriate, by using afterwards a least‐squares method that includes energy offset parameters. Statistical ranking of the most likely standard spectra contributing to the unknown spectra enhanced LCF by reducing the analysis to a smaller set of standard spectra. The PCA approach is a valuable complement to other spectral fitting techniques as it provides statistical criteria that improve insight to the data, and lead to a more objective approach to fitting.