Fit-for-purpose risks in conformity assessment of a substance or material – A case study of synthetic air. (January 2022)

• Record Type: Journal Article
• Authors: Pennecchi, Francesca R.; Kuselman, Ilya; Brynn Hibbert, D.; Sega, Michela; Rolle, Francesca; Altshul, Vladimir
• Is Part Of: Measurement. Volume 188(2022)
• Publication Date: 2022-01
• Subjects: Conformity assessment -- Synthetic air -- Measurement uncertainty -- Mass balance constraint -- Risk of false decisions -- Fitness for purpose; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110542
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: A fit-for-purpose Bayesian approach to conformity assessment is described. The developed technique is based on comparison of the risks of false decisions. The technique considers measurement uncertainty, correlation and mass balance. Fit-for purpose risks are calculated for industrial medicinal synthetic air. Such risks in conformity of air as a standard gas mixture of an NMI are evaluated. Abstract: A technique is described for evaluation of the fit-for-purpose risks in conformity assessment of the chemical composition of a substance or material, based on a multivariate Bayesian approach. The approach takes into account measurement uncertainty, correlation and the mass balance constraint. Two datasets related to synthetic air (provided as electronic supplementary material to this paper) were studied. The first dataset was from an industrial factory producing routinely medicinal synthetic air according to the European Pharmacopoeia. The second dataset was from the National Metrology Institutes which participated in key comparison CCQM-K120 "Carbon dioxide at background and urban level". The fitness for purpose of the preparation of synthetic air was interpreted as total risks of false decisions on the conformity of the air composition to the tolerance limits of the contents of its main components. Calculations of these risks were performed with code written in the R programming environment.