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Estimating measurement uncertainty in quantitative methods not based on chromatography for doping control purposes
Author(s) -
Van Eenoo P.,
Van Renterghem P.,
Dimopoulou C. H.,
Delbeke F.T.,
Georgakopoulos C. G.
Publication year - 2010
Publication title -
drug testing and analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 54
eISSN - 1942-7611
pISSN - 1942-7603
DOI - 10.1002/dta.94
Subject(s) - computer science , set (abstract data type) , term (time) , quality (philosophy) , control (management) , data mining , point (geometry) , data set , measurement uncertainty , raw data , statistics , mathematics , artificial intelligence , philosophy , physics , geometry , epistemology , quantum mechanics , programming language
Estimation of measurement uncertainty (MU) for quantitative results is a requirement of ISO/IEC17025. This concept is well established for chromatographic methods in doping control and forensic analysis. For non‐chromatographic methods, however, very few practical methodologies have been published. In this paper, the applicability of a top‐down model, established for estimating uncertainty in chromatography, was evaluated for two other methodologies with different sets of raw data as a starting point. The first case study involves the estimation of MU for the determination of haematological parameters. In this case, a large data set of quality control material and proficiency testing results was available to establish MU. The second case study involves the estimation of MU for the recently approved method for the determination of human growth hormone misuse. In this case the amount of data available to establish MU was limited to results from method validation and a basic set of analysis data. In both cases a methodology based upon long‐term bias, long‐term imprecision and—eventually—a correction for standard impurity is proposed. The proposed methodology can be regarded as a dynamic procedure, which allows re‐evaluation of MU on a regular basis. Finally, a concept for the verification and evaluation of MU estimations using proficiency testing results is proposed. Copyright © 2010 John Wiley & Sons, Ltd.