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Quartz is one of the most common minerals in soils, mostly present in sand and silt fractions. Although quartz is basically formed of SiO2, other elements can be easily detected and assessed nowadays using a portable X-ray fluorescence (pXRF) spectrometer. Our study aims to evaluate the chemical composition of different quartz varieties, identifying their main elements, and relating them to soil attributes. Six quartz varieties (hyaline, amethyst, milky, rose, smoky, and ferriferous) were analyzed via pXRF and 13 oxides/trace elements were identified and used for quartz discrimination (Al2O3, CaO, P2O5, SiO2, Cl, Cr, Fe, K2O, Mn, Rb, S, Ti, and V). Hyaline quartz was characterized by the highest SiO2 and the lowest contents of other elements. Al2O3 was the second-highest compound present in all varieties of quartz, reaching 21,547 mg kg−1 in the smoky variety. S, P2O5, Cl, SiO2, and K2O were the main components determined by Random Forest algorithm for discriminating quartz varieties. Some elements detected may serve as a reserve of nutrients to plants to be released in soils along weathering, depending on quartz particle size, soil texture, leaching, and associated attributes. pXRF enhanced the information on chemical characterization of quartz varieties, without the generation of chemical pollutants.

ABSTRACT Portable X-ray fluorescence (pXRF) spectrometry has been useful worldwide for determining soil elemental content under both field and laboratory conditions. However, the field results are influenced by several factors, including soil moisture (M), soil texture (T) and soil organic matter (SOM). Thus, the objective of this work was to create linear mathematical models for conversion of Al2O3, CaO, Fe, K2O, SiO2, V, Ti and Zr contents obtained by pXRF directly in field to those obtained under laboratory conditions, i.e., in air-dried fine earth (ADFE), using M, T and SOM as auxiliary variables, since they influence pXRF results. pXRF analyses in field were performed on 12 soil profiles with different parent materials. From them, 59 samples were collected and also analyzed in the laboratory in ADFE. pXRF field data were used alone or combined to M, T and SOM data as auxiliary variables to create linear regression models to predict pXRF ADFE results. The models accuracy was assessed by the leave-one-out cross-validation method. Except for light-weight elements, field results underestimated the total elemental contents compared with ADFE. Prediction models including T presented higher accuracy to predict Al2O3, SiO2, V, Ti and Zr, while the prediction of Fe and K2O contents was insensitive to the addition of the auxiliary variables. The relative improvement (RI) in the prediction models were greater in predictions of SiO2 (T+SOM: RI=22.29%), V (M+T: RI=18.90%) and Ti (T+SOM: RI=11.18%). This study demonstrates it is possible to correct field pXRF data through linear regression models.