Partial least squares regression (PLSR) associated with spectral response to predict soil attributes in transitional lithologies

New techniques and improvements are required to quantify soil’s chemical and physical properties on production environment, reducing environmental impacts and minimizing soil analysis time. The aim of this study is to evaluate the possibility to estimate the content of silt, sand, clay, total iron and organic matter in soils formed by different lithologies in Parana State, Brazil, using VIS-NIR spectrum associated with Partial Least Square Regression (PLSR). 200 soil samples were collected in an area formed by Lixisols, Cambisols, Ferralsols, Arenosols and Nitisols in a depths of 0–0.2 and 0.2–0.8 m. Spectral readings were obtained in laboratory by FieldSpec 3 JR sensor. The spectral curves of the samples were correlated to the attributes through PLSR. The results obtained for sand in prediction were better when compared to the other attributes, presenting R² = 0.90, r = 0.95 and RPD = 2.3. Clay and total iron presented satisfactory results, mainly for RPD values, which were above 2.4. Based on the results, it can be concluded that the PLSR technique associated with the spectral response of the soils, was able to estimate sand, clay and total iron with accuracy in a region formed by reworked materials, derived from several lithologies.