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Curve-fitting regression: improving light element quantification with XRF

机译:曲线拟合回归:用XRF改善光元素量化

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摘要

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.
机译:光元素很难通过X射线荧光(XRF)光谱法进行量化,因为在光电激发后,它们主要释放俄歇电子,大大减少了荧光计数,从而限制了观察到的信噪比(SNR)。低信噪比值在模型构建中具有有害的结果。值得注意的是,在基于峰高的普通最小二乘(OLS)回归中,它们也会影响更稳健的回归方法,例如偏最小二乘回归。虽然较重元素的低浓度也可以观察到低信噪比,但本文主要关注硼。

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