X-ray fluorescence spectroscopy (XRF) is a powerful tool of elemental analysis; however in most of the experimental set-ups it does not allow quantification of the light elements (with atomic number below 11). The use of scattering X-ray radiation as a source of useful analytical information is getting more and more popular in X-ray studies. The common trend in this field is in using the standard XRF instrumentation, where polychromatic incident X-ray radiation and fixed geometry are employed. In this study we explore the opportunity of obtaining valuable physical and chemical information on plastic samples using monochromatic radiation and varying incident radiation angles. The use of machine learning techniques for multivariate regression modeling of scattering radiation spectra allows quantification of certain physical and chemical properties in commercial plastic samples which are normally not available from standard XRF measurements.
|Journal||Measurement: Journal of the International Measurement Confederation|
|State||Published - Feb 2021|
Scopus subject areas
- Electrical and Electronic Engineering
- Multivariate regression
- X-ray fluorescence spectroscopy
- X-ray scattering