3.2 Spectral preprocessing influence
Spectra obtained from Vis-NIR and MIR spectrometers were subject to preprocessing, such as absorbance, first order derivative, second order derivative, multiple scatter correction and standard normal variate. Spectra pre-treatment is a mathematical manipulation that enhances the spectral information and eliminates the physical effect of light scattering, which can be due to particles of different sizes and shapes of samples (Minasny and McBratney, 2008) and is thus the most important step before any chemometric modeling. Different pre-processing transformations have been applied in numerous studies to transform soil spectral data, remove noise, accentuate features, and prepare them for chemometric modelling. However, the first derivative, second derivative, SNV and MSC manipulation did not greatly enhances some of the spectral features compared to reflectance. Moreover reflectance (unprocessed spectra) presented the best performance as compared to other preprocessing methods, irrespective of the models used (PLSR, RF, SVR or MARS) (Table 2 & 3) and was thus considered to be the most robust spectral preprocessing method based on its predictive performance for EC. Some earlier results (Moros et al. 2009) also suggest that calibration models in which spectra were not preprocessed are more sensitive to changes compared to models for which preprocessing was applied and Nawar et al. (2016) re-confirmed it, and used no preprocessing for prediction. Reflectance has also been successfully used in other studies, to estimate soil properties (Viscarra Rossel et al. 2006, Nawar et al. 2016). Vibhute et al., (2018) reported electrical conductivity to be better calibrated (R2 = 0.80 and RMSE = 2.07) before pre-treatments than after pretreatment of spectra and Nocita et al. (2014) applied continuous removal reflectance to predict the soil properties by diffuse reflectance spectroscopy from soil samples throughout the European Union. The present study demonstrates that reflectance (unprocessed spectra) (Fig 3 a & b) is better than any preprocessing tool for prediction of EC regardless of the method applied and demonstrates its suitability for prediction of EC, both in the Vis-NIR and MIR spectral regions.