Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet-visible, near- and middle-infrared spectroscopy

In recent years, several sediment fingerprinting studies have used ultraviolet-visible (UV-Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the information contained in the UV-Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions obtained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km(2)) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative SavitzkyGolay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV-Vis + NIR + MIR. The lowest errors were observed when the UV-Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV-Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions.

Automated summary

The study evaluated the accuracy of PLSR and SVM in estimating sediment source contributions, the impact of different spectral preprocessing techniques, and the influence of UV-Vis, NIR, and MIR regions on the results. The combination of UV-Vis, NIR, MIR bands with spectral preprocessing techniques showed significant improvements in model accuracy, highlighting the potential of spectroscopy as an alternative to conventional geochemical approaches in discerning sediment sources in agricultural catchments in subtropical regions.