Quality Assessment Statistic Evaluation of X-Ray Fluorescence via NIST and IAEA Standard Reference Materials

Abstract

The aim of this study is a quality assessment of X-ray fluorescence laboratory located at the University of Khartoum. The X-ray fluorescence spectrometer system consists, a set of three 109 Cd sources of an initial nominal activity of 10 µCi, and Si(Li) detector Energy Dispersive XRF(EDXRF) systems. It is important to carry out this work because it has an effective contribution for a wide range of research and services. The assessment was carried out by measuring 8 NIST-2709a (soil) and 13 IAEA-155 (milk powder) standard reference material samples for repeatability examinations to test the measurement precision. The total combined standards uncertainty values for XRF lab were estimated by an error from repeatability measurements adding 2.6% for error propagation related to the method. For accuracy assessment, three standard statistic approaches were applied, i.e. the Bias %, zeta-score, and E n -number. The bias of all elements for both standard materials was found to be within a deviation range from −28% to 7.8%. The results of all elements for both the zeta-score test and E n -number have satisfactory results except Th (Thorium) and Zr (Zirconium) which consider as questionable results for NIST SRM 2709a and unsatisfactory results for E n -number.

Machine learning and soil sciences: a review aided by machine learning tools

“…Especially for applications involving public funding, but generally as a consumer protection measure, these type of products should be certifiable in the same way many soil laboratories are. A usual approach is the use of reference materials (Dybczyński et al, 1979;Pueyo et al, 2001;Ahmed et al, 2017), which should be consistent with the model coverage reported. The properties measured in the reference materials should fall within the prediction interval produced by the model, with a confidence defined for each application.…”

Machine learning and soil sciences: A review aided by machine learning tools

“…Specially for applications involving public funding, but generally as a consumer protection measure, this type of products should be certifiable, in the same way many soil laboratories are. A usual approach is the use of reference materials (Dybczyński et al, 1979;Pueyo et al, 2001;Ahmed et al, 2017), which should be consistent with the model coverage reported.…”

Liquid–Liquid Equilibria of Green and Biobased Solvents 2-Methyltetrahydrofuran + (Ethanol or 1-Butanol) + Water and Cyclopentyl-methyl-ether + (Ethanol or 1-Butanol) + Water at 298.15 K

“…To determine the standard uncertainty of the phase compositions, the ISO Guide to the Expression of Uncertainty Measurements (GUM) and associated methods , were used with the following formulas x̅ = 1 n ∑ i = 1 n x i s false( x i false) = ∑ i = 1 n ( x i − x̅ ) 2 ( n − 1 ) u = s ( x i ) n where x i is the experimental mole fraction for the i -th experiment; x̅ is the average of the mole fractions x i ; n is the number of repetitions for each tie-line; s ( x i ) is the standard deviation, and u is the standard uncertainty.…”

“…To determine the standard uncertainty of the phase compositions, the ISO Guide to the Expression of Uncertainty Measurements (GUM) and associated methods 22,23 were used with the following formulas…”