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SPC Analysis of Optimal Strategies for Restandardization of X-ray Fluorescence Analyses

Published online by Cambridge University Press:  06 March 2019

P. B. DeGroot*
Affiliation:
Hoechst Celanese Corp. P. O. Box 9077 Corpus Christi, TX 78469
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Abstract

Quantitative XRF analyses almost always depend on measurements of x-ray emission intensities of standards of known composition. The usual approach is a careful initial standardization with multiple standards lhat are checked for accuracy, stability, homogeneity, and internal consistency. This is followed by limited periodic restandardization or recalibration with one or two of the initial standards or a reference sample. The frequency and protocol of these periodic restandardizations tend to be rather arbitrary; for example adjustment on the basis of the average of a triplicate measurement of a standard at the beginning of the work day. There are certainly a large number of other possible restandardization strategies. They range all they way from no restandardization at all, through single or n replicate restandardization measurements before analysis of each group of M unknowns, to cumulative or moving averages, again with variable replicate size, frequency, and now averaging range. This paper examines the outcomes of some of these restandardization strategies applied to synthesized, realistic XRF analysis data which incorporate both abrupt and gradual changes in instrument response. SPC (Statistical Process Control) control charts are shown to be a very useful tool for determining optimum restandardization strategies. Optimal strategies (least scatter in calculated concentrations with fewest standards) which emerged from this study included not restandardizing at all except when indicated by an SPC chart of a reference standard intensity run concurrently with each unknown, and a moving range of 3, with standards run every 5 unknowns, coupled with SPC monitoring of the standards used for this restandardization.

Type
Research Article
Copyright
Copyright © International Centre for Diffraction Data 1989

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References

1. Wheeler, D.J. and Chambers, D. S., Understanding Statistical Process Control, Statistical Process Controls, Inc., Knoxville, TNT, (1986)Google Scholar