Guidance Document on Measurement Uncertainty for GMO Testing Laboratories

Guidance Document on Measurement Uncertainty for GMO Testing Laboratories
Author :
Publisher :
Total Pages : 41
Release :
ISBN-10 : 9279112287
ISBN-13 : 9789279112287
Rating : 4/5 (87 Downloads)

Book Synopsis Guidance Document on Measurement Uncertainty for GMO Testing Laboratories by :

Download or read book Guidance Document on Measurement Uncertainty for GMO Testing Laboratories written by and published by . This book was released on 2009 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This technical report outlines the technical issues related to the estimation of measurement uncertainty (MU) involved in the GMO sector. In particular it gives guidance to GMO testing laboratories how to estimate the analytical variability of quantitative analytical results obtained by real-time PCR. This guidance document has been written on request of the European Network of GMO Laboratories (ENGL) as a follow-up of a workshop on Measurement Uncertainty in the GMO sector organised by the Institute for Reference Materials and Measurements (IRMM), Geel, Belgium and held on 05.07.2005. It is recognised that in order to be able to judge if an analytical results exceeds a threshold; the MU must be estimated and reported together with the measurement result. Enforcement Authorities shall therefore estimate the MU associated with an analytical result and use it to decide whether an analytical result falls within the specification of food and feed control. The value obtained by subtracting the expanded uncertainty from the reported concentration is used to assess compliance. Only if this value is greater than the legal threshold, it is sure 'beyond reasonable doubt' that the sample concentration of the analyte is beyond what is permissible. Two selected approaches for the estimation of MU are presented in detail; references to alternative approaches are given. The first approach presented in detail is using data from collaborative trial in combination with in-house quality control data for the estimation of MU. Prerequisites for the use of such collaborative trial data are outlined. In case no suitable collaborative trial data are available, an alternative approach using data from within-laboratory samples for the estimation of MU is presented.


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