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Partial least squares (PLS) regression-based methods have been proven to be a good alternative for quantification in X-ray fluorescence spectroscopy. These methods are fast and easy to use though giving satisfactory results under certain conditions. One of these conditions is the necessity of having a great number of spectra to build the model (training set). The choice of the constituent concentration range in the training set has a big influence on the accuracy of the model. Better accuracy is obtained if the model is built in relatively narrow regions containing (or close to) the real concentration value.
In the present work, Monte Carlo (MC) simulated spectra are used to form the training set. The advantage to use MC generated training spectra is the unlimited availability of perfect standards.
This paper aims to improve the accuracy of the method by introducing a multiple step procedure in order to build the PLS model using narrow concentration range close to (or containing) the real concentration values in the samples to be measured.
This approach consists of an initial guess of the constituents' concentrations and a preliminary PLS model before building the final model. The prediction of ten MC simulated alloy standard samples containing Ti, Mn, Fe, Co, Cu, Zn, Sr, Zr, and Mo using this method allowed to have average relative prediction errors less than 5% for elements with narrow concentration ranges.
Besides conventional scanning X-ray fluorescence imaging at synchrotron sources, full-field X-ray fluorescence (FF-XRF) imaging techniques that do not implicitly require spatial scanning of the sample have become available. FF-XRF has become achievable thanks to the development of a new type of energy dispersive CCD-based 2D detector, also referred to as a 'color X-ray camera (CXC)' or 'SLcam'. We report on different imaging schemes for biological samples using FF-XRF imaging: (a) 2D 'zoom' imaging with pinhole optics using the 'camera obscura' principle; (b) 2D 'fixed magnification' imaging using magnifying polycapillary optics; and (c) 3D-FF-XRF imaging using an X-ray sheet beam or computed tomography (CT). The different FF-XRF imaging modes are illustrated using the crustacean Daphnia magna, a model organism for investigating the effects of metals on organism/ecosystem health, and foraminifera, a class of amoeboid protist. Detailed analytical characterization of the set-up is performed through analyzing various reference materials in order to determine limits of detection (LODs) and sensitivities. Experiments were performed using the BAMline at the BESSY synchrotron (Berlin, Germany) and using the P06 Hard X-ray Microprobe at the PETRAIII synchrotron (Hamburg, Germany).