Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy

This paper reports a first attempt to develop a simple, quantitative, non-destructive and inexpensive methodology to characterise the mineral composition of soil using diffuse reflectance spectroscopy (DRS). Although there are studies that qualitatively characterise soil minerals using DRS, few studies quantify their composition in soil. Our aims were to: (i) design an experiment that may be used to model mineral-organic mixes as a function of their ultra violet (UV), visible (vis) and near infrared (NIR) diffuse reflectance spectra and (ii) use these models to predict the mineral-organic composition of independent test mixes. Our experiments used a three-factor simplex lattice design with three levels, corresponding to kaolinite (K), illite (I) and smectite (S). To this simplex we added two levels of goethite (G) and two levels of a 50/50 mix of humic and fulvic acids (H–F). Finally we added 3 levels of quartz (Q) to the mixes. We modelled the data using the partial least squares regression 1 algorithm (PLSR1) and made predictions using bootstrap aggregation-PLSR (or bagging-PLSR). The numbers of factors in the PLSR models were selected by leave-one-out cross-validation. These models were independently tested using mixes that we made up to roughly represent the mineral-organic composition of common Australian soils. Predictions of the amount of K, I and S in the test mixes were accurate (RMSE = 3.6, 3.4 and 3.4%, respectively). Predictions of goethite and the H–F mix were biased. Predictions of Q were very poor as quartz does not have a spectral response in the UV–vis–NIR. A principal components analysis (PCA) was used to compare the spectra of these test mixes with those of corresponding Australian soils. In future work we plan to expand our spectral library and improve the experimental design and our models.