Measurement uncertainty from sampling and its role in validation of measurement procedures

Abstract

Abstract It is now widely accepted that the measurement process usually begins when the primary sample is taken. The uncertainty of measurement (MU) must therefore include contributions that arise from the primary sampling, and also from any physical preparation of the sample which often occurs before the sample reaches the laboratory. Guidance on how to estimate MU that includes that arising from sampling (UfS) has been widely applied to a wide range of application sectors (e.g. food, feed, water, sediment, soil, gases). Recent revision of ISO/IEC 17025:2017 ( https://www.iso.org/standard/66912.html ) has also recognised the inclusion of sampling within the measurement process. This recognition has implications for the validation of measurement procedures that include sampling (VaMPIS). The scope of method (or procedure) validation has therefore to be expanded and reassessed, in order to include all of these components. The uncertainty of the measurement value (MU) is a key parameter that encompasses the effects of all the other operating characteristics of the analytical procedure that is traditionally considered during its validation. It has the further advantage that it can also incorporate the uncertainty due to sampling and physical sample preparation, thus providing a single value of uncertainty that derives from the entire measurement procedure. The fitness for purpose (FnFP) of the whole measurement procedure, which is required for validation, can be judged by comparing the estimated MU (including UfS), against a Target MU, however that is set. A case study for the determination of nitrate in glasshouse lettuce shows how this VaMPIS approach can be applied to a whole measurement procedure. The experimental MU is estimated using the Duplicate Method and compared against a Target MU set using the Optimised Uncertainty (OU) method. The measurement procedure published in EU guidance is shown not to be fit for purpose (FFP). However, this approach identifies how that sampling procedure can be modified to achieve FnFP for the whole procedure, by increasing the number of sample increments per batch from 10 to 40.

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