X-ray powder diffraction (XRPD) is widely applied for the qualitative and quantitative analysis of soil mineralogy. In recent years, high-throughput XRPD has resulted in soil XRPD datasets containing thousands of samples. The efforts required for conventional approaches of soil XRPD data analysis are currently restrictive for such large data sets, resulting in a need for computational methods that can aid in defining soil property – soil mineralogy relationships. Cluster analysis of soil XRPD data represents a rapid method for grouping data into discrete classes based on mineralogical similarities, and thus allows for sets of mineralogically distinct soils to be defined and investigated in greater detail. Effective cluster analysis requires minimisation of sample-independent variation and maximisation of sample-dependent variation, which entails pre-treatment of XRPD data in order to correct for common aberrations associated with data collection.
DOI:
https://doi.org/10.1016/j.geoderma.2018.09.044Altmetric score:
Dimensions Citation Count:
Publication year
2019
Authors
Butler, B.M.; Sila, A.M.; Shepherd, K.D.; Nyambura, M.; Gilmore, C.J.; Kourkoumelis, N.; Hillier, S.
Language
English
Keywords
soil organic matter, soil properties, soil analysis, soil
