TY - CONF A1 - Büchele, Dominique A1 - Chao, Madlen A1 - Ostermann, Markus T1 - Development of a robust calibration model for determination of nutrients in soils using EDXRF N2 - As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. 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 summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil. First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one. Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online Analysis. In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained. Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils. Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient. T2 - AK Prozessanalytik CY - Hannover, Germany DA - 03.12.2018 KW - EDXRF KW - Soil KW - Chemometrics PY - 2018 AN - OPUS4-48280 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Büchele, Dominique A1 - Chao, Madlen A1 - Ostermann, Markus T1 - Influence of moisture and grain sizes on the determination of nutriens in german agricultural soils using EDXRF N2 - As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. 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 summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil. First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one. Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis. In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained. Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils. Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient. T2 - BonaRes Statusseminar CY - Leipzig, Germany DA - 19.02.2019 KW - XRF KW - Mmoisture KW - Chemometrics PY - 2019 AN - OPUS4-48281 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Büchele, Dominique A1 - Chao, Madlen A1 - Ostermann, Markus A1 - Leenen, M. A1 - Bald, Ilko T1 - Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF JF - Scientic Reports N2 - Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred. KW - XRF KW - Chemometrics KW - Soil KW - Agriculture KW - Multivariate PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498671 DO - https://doi.org/10.1038/s41598-019-53426-5 VL - 9 SP - 17588 PB - Nature AN - OPUS4-49867 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -