Mid-infrared (MIR) spectroscopy is a powerful, eco-friendly, and cost-effective technique for predicting soil property. However, its predictive accuracy can be affected by factors such as moisture content, particle size, sensor variability, and the baseline noise. To address these limitations, this study investigated the impact of combining various preprocessing techniques with variable selection methods on the performance of partial least squares regression (PLSR) models. Soil samples from the Rhamna region of Morocco were analyzed to estimate key properties, including total nitrogen (TN), total carbon (TC), total organic carbon (TOC), clay, silt, sand, moisture content (MC), pH, phosphorus (P 2 O 5 ), and cation exchange capacity (CEC). Spectral data were preprocessed using methods such as standard normal variate (SNV), Savitzky–Golay smoothing (SG smoothing), first and second derivatives (SG1D and SG2D), and their combinations (e.g., SNV + SG2D). The best-performing preprocessing combinations were then used with variable selection approaches, interval PLS (iPLS), variable importance in projection (VIP), and selectivity ratio (SR). The results indicated that Savitzky–Golay (SG) derivatives combined with SNV generally improved model performance across most soil properties. In particular, total nitrogen (TN) prediction improved primarily with the first SG derivative, with R 2 cv increasing from 0.82 (raw spectra) to 0.88 (SG1D), while RMSEcv decreased from 0.03% to 0.01%. Further improvements were achieved through variable selection, with iPLS providing the most consistent enhancement across properties with a very low number of features compared to the other methods. Overall, the integration of optimal preprocessing and iPLS variable selection significantly improved the predictive accuracy and robustness of partial least squares regression (PLSR) models for soil property estimation compared with the full spectrum.
Mokere et al. (Wed,) studied this question.