Machine learning for direct quantification of XRF measurements

  • In X-ray fluorescence (XRF), a sample is excited with X-rays, and the resulting characteristic radiation is detected to detect elements quantitatively and qualitatively. Quantification is traditionally done in several steps: 1. Normalization of the data 2. Determination of the existing elements 3. Fit of the measured spectrum 4. Calculation of concentrations with fundamental parameters / MC simulations / standard based The problem with standard based procedures is the availability of corresponding standards. The problem with the calculations is that the measured intensities for XRF measurements are matrix-dependent. Calculations must, therefore, be performed iteratively (= time consuming) in order to determine the chemical composition. First experiments with gold samples have shown the feasibility of machine learning based quantification in principle. A large number of compositions were simulated (> 10000) and analyzed with a deep learning network. For first experiments, an ANNIn X-ray fluorescence (XRF), a sample is excited with X-rays, and the resulting characteristic radiation is detected to detect elements quantitatively and qualitatively. Quantification is traditionally done in several steps: 1. Normalization of the data 2. Determination of the existing elements 3. Fit of the measured spectrum 4. Calculation of concentrations with fundamental parameters / MC simulations / standard based The problem with standard based procedures is the availability of corresponding standards. The problem with the calculations is that the measured intensities for XRF measurements are matrix-dependent. Calculations must, therefore, be performed iteratively (= time consuming) in order to determine the chemical composition. First experiments with gold samples have shown the feasibility of machine learning based quantification in principle. A large number of compositions were simulated (> 10000) and analyzed with a deep learning network. For first experiments, an ANN (Artificial Neural Network) with 3 hidden layers and 33x33x33 neurons was used. This network learned the mapping of spectra to concentrations using supervised learning by multidimensional regression. The input layer was formed by the normalized spectrum, and the output layer directly yielded the searched values. The applicability for real samples was shown by measurements on certified reference materials.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Martin Radtke
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2019
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.3 Strukturanalytik
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Artificial intelligence; Machine learning; Neural network; Synchrotron; XRF
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Spurenanalytik und chemische Zusammensetzung
Veranstaltung:Denver X-ray Conference
Veranstaltungsort:Lombard, IL, USA
Beginndatum der Veranstaltung:05.08.2019
Enddatum der Veranstaltung:09.08.2019
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:10.09.2019
Referierte Publikation:Nein