TY - CONF A1 - Büchele, Dominique A1 - Rühlmann, Madlen A1 - Ostermann, Markus A1 - Schmid, Thomas T1 - Comparison between uni- and multivariate data Analysis for the determination of nutrients in soils using XRF N2 - As part of the BonaRes research initiative funded by the German Federal Ministry of Education and Research (BMBF), strategies are being developed to use soil as a sustainable resource in the bioeconomy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarized in models and decision-making algorithms will be used to control fertilization and accordingly improve soil functions. This would allow investigations in close meshed dynamic grid and fast analysis of large areas to generate higher yields. This is important given that the distribution of minor and trace elements varies widely. Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-analysis of arable land. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis. Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. Calibration models were obtained by partial least squares regression (PLSR) and the content of macro- and micronutrients in German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients. T2 - ESAS/CANAS CY - Berlin, Germany DA - 20.03.2018 KW - Soil KW - XRF KW - PCA KW - PLSR PY - 2018 AN - OPUS4-45010 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -