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Accreditation and Quality Assurance

, Volume 17, Issue 6, pp 603–612 | Cite as

Software support for the Nordtest method of measurement uncertainty evaluation

  • Teemu Näykki
  • Atte Virtanen
  • Ivo Leito
Practitioner's Report

Abstract

Software support for the Nordtest method of measurement uncertainty evaluation is described. According to the Nordtest approach, the combined measurement uncertainty is broken down into two main components—the within-laboratory reproducibility (intermediate precision) s Rw and the uncertainty due to possible laboratory bias u(bias). Both of these can be conveniently estimated from validation and quality control data, thus significantly reducing the need for performing dedicated experiments for estimating detailed uncertainty contributions and thereby making uncertainty estimation easier for routine laboratories. An additional merit of this uncertainty estimation approach is that it reduces the danger of underestimating the uncertainty, which continues to be a problem at routine laboratories. The described software tool—MUkit (measurement uncertainty kit)—fully reflects the versatility of the Nordtest approach: it enables estimating the uncertainty components from different types of data, and the data can be imported using a variety of means such as different laboratory data systems and a dedicated web service as well as manual input. Prior to the development of the MUkit software, a laboratory survey was carried out to identify the needs of laboratories related to uncertainty estimation and other quality assurance procedures, as well as their needs for a practical tool for the calculation of measurement uncertainty.

Keywords

Measurement uncertainty Validation Within-laboratory reproducibility Bias Software Testing laboratories Quality control Proficiency testing Nordtest 

Notes

Acknowledgments

This work has received funding from the Strategic Centre for Science, Technology and Innovation of the Finnish energy and environment cluster’s (Cleen Ltd) programme for Measurement, Monitoring, and Environmental Efficiency Assessment (MMEA), which in turn receives funding from the Finnish Funding Agency for Technology and Innovation (TEKES). The work has also been partially supported by Joint graduate school ‘Functional materials and technologies’ of Tartu University and Tallinn Technical University receiving funding from the European Social Fund under project 1.2.0401.09-0079 in Estonia. The authors also acknowledge registered association ‘Maa- ja vesitekniikan tuki ry’ for financial support. Personnel of SYKE Laboratory (Mirja Leivuori, Markku Ilmakunnas, Timo Vänni, Mika Sarkkinen, and Marketta Turunen), Information Centre (Esa Hirvonen and Sami Korhonen) and Graphic Services (Erika Varkonyi and Satu Turtiainen) are acknowledged for their invaluable contribution during the development of the software. The pilot laboratories (Nablabs laboratories, Mervi Tabell; University of Helsinki, Department of Environmental Sciences, Jukka Pellinen; UPM, Pietarsaari, Tomi Heikkinen, Stora Enso Oyj Research Centre, Jouni Kumpulainen; Environmental Laboratory of Haapavesi, Tuula Savolainen; and Labtium Ltd, Juha Virtasalo), are also acknowledged for their important contribution for testing the software.

Supplementary material

769_2012_932_MOESM1_ESM.pdf (724 kb)
Supplementary material 1 (PDF 723 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Environmental Measurement and Testing LaboratoryFinnish Environment InstituteHelsinkiFinland
  2. 2.Data and Information CentreFinnish Environment InstituteHelsinkiFinland
  3. 3.Institute of ChemistryUniversity of TartuTartuEstonia

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