Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling.

Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty.

The duplicate method of uncertainty estimation: are eight targets enough?

This paper presents methods for calculating confidence intervals for estimates of sampling uncertainty (s(samp)) and analytical uncertainty (s(anal)) using the chi-squared distribution. These uncertainty estimates are derived from application of the duplicate method, which recommends a minimum of eight duplicate samples. The methods are applied to two case studies--moisture in butter and nitrate in lettuce.

Optimising uncertainty in physical sample preparation.

Uncertainty associated with the result of a measurement can be dominated by the physical sample preparation stage of the measurement process. In view of this, the Optimised Uncertainty (OU) methodology has been further developed to allow the optimisation of the uncertainty from this source, in addition to that from the primary sampling and the subsequent chemical analysis. This new methodology for the optimisation of physical sample preparation uncertainty (u(prep), estimated as s(prep)) is applied for the first time, to a case study of myclobutanil in retail strawberries.

Two-stage application of the optimised uncertainty method: a practical assessment.

Uncertainty estimates from routine sampling and analytical procedures can be assessed as being fit for purpose using the optimised uncertainty (OU) method. The OU method recommends an optimal level of uncertainty that should be reached in order to minimise the expected financial loss, given a misclassification of a batch as a result of the uncertainty. Sampling theory can used as a predictive tool when a change in sampling uncertainty is recommended by the OU method.

Measurement uncertainty from physical sample preparation: estimation including systematic error.

A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates.

Multi-analyte optimisation of uncertainty in infant food analysis.

The Optimised Uncertainty (OU) methodology has been developed to optimise multi-analyte situations. It has then been applied to a retail survey of infant food for trace elements, classifying the food as compliant or non-compliant with the regulatory thresholds or specification limits that are appropriate for each element. The large-scale survey of infant foods was successfully adapted to allow the estimation of uncertainties, from both primary sampling and chemical analysis, for elemental concentrations in infant formula (milk) and wet meals.

Optimised uncertainty in food analysis: application and comparison between four contrasting 'analyte-commodity' combinations.

The optimised uncertainty (OU) methodology is applied across a range of analyte-commodity combinations. The commodities and respective analytes under investigation were chosen to encompass a range of input factors: measurement costs (sampling and analytical), sampling uncertainties, analytical uncertainties and potential consequence costs which may be incurred as a result of misclassification. Two types of misclassification are identified-false compliance and false non-compliance.