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Comparison between uni- and multivariate data analysis for the determination of nutrients in soils using XRF

  • Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S - “Intelligence for Soil” is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-application of arable land.Fast soil mapping for agricultural purpose allows the site-specific optimized introduction of plant essential nutrients like S, K, Ca, and Fe. This is important given that the distribution of minor and trace elements varies widely. 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 physicalAim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S - “Intelligence for Soil” is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-application of arable land.Fast soil mapping for agricultural purpose allows the site-specific optimized introduction of plant essential nutrients like S, K, Ca, and Fe. This is important given that the distribution of minor and trace elements varies widely. 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. Empirical 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. A calibration curve was obtained by partial least squares regression (PLSR) and the elemental content of 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.zeige mehrzeige weniger

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Metadaten
Autor*innen:Dominique Büchele
Koautor*innen:Madlen Rühlmann, Thomas Schmid, Markus Ostermann
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:PCA; PLSR; Soil; XRF
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Veranstaltung:BonaRes Conference
Veranstaltungsort:Berlin, Germany
Beginndatum der Veranstaltung:26.02.2018
Enddatum der Veranstaltung:28.02.2018
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:25.05.2018
Referierte Publikation:Nein
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