Light elements are hard to quantify by X-ray fluorescence (XRF) spectrometry because, after a photoelectric excitation, they predominantly relax emitting Auger electrons, greatly reducing the fluorescence count thus limiting the signal-to-noise ratios (SNR) observed. Low SNR values have deleterious outcomes in model building. Notable in ordinary least squares (OLS) regression based on peak height, they also affect more robust regression methods, such as partial least squares regression. While low SNR can also be observed with low concentrations of heavier elements, this paper focuses on boron.To overcome the low SNR hurdle, curve-fitting regression (CFR), a novel method elaborated in this paper, seeks to fit full scans with summed Gaussian curves. The methodology was illustrated with pressed microcrystalline cellulose spiked with sodium tetraborate decahydrate (borax) powder samples. The calibration set ranged from 0% to 21.5% m / m boron, and a PANalytical Axios wavelength dispersive X-ray fluorescence system with rhodium source was used to perform the tests. A calibration curve with determination coefficient (R
It is well-accepted that multivariate methods for the analysis of spectrum image (SI) datasets present several advantages compared to peak integration or top-hat filtering methods, especially in presence of peak overlap or a low signal-to-noise ratio [1]. Multivariate curve resolution alternating least squares (MCR-ALS) have emerged as a blind method to segment SI. Compared to principal component analysis, MCR-ALS yields spectra and maps that are physically sound (positive pixel counts and components sum to unity). Another method, which considers log-likelihood maximization (MCR-LLM) based on the noise characteristics, has shown superior sensitivity and precision compared to MCR-ALS, especially for lowcount data [2]. Here, using Monte Carlo simulations, we demonstrate that MCR-LLM is also able to segment SEM-EDS SI exhibiting strongly overlapping peaks much more reliably than MCR-ALS.
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