Measurement uncertainty arising from sampling: a guide to methods and approaches, 1st Edition.

This Guide aims to describe various methods that can be used to estimate the uncertainty of measurement, particularly that arising from the processes of sampling and the physical preparation of samples. It takes a holistic view of the measurement process to include all of these steps as well as the analytical process, in the case where the measurand is defined in term of the value of the analyte concentration in the sampling target, rather than in just the sample delivered to the laboratory. The Guide begins by explaining the importance of knowing the total uncertainty in a measurement for making reliable interpretation of measurements, and judging their fitness for purpose. It covers the whole measurement process, defining each of the component steps, and describing the effects and errors that cause uncertainty in the final measurement. Two main approaches to the estimation of uncertainty from sampling are described. The empirical approach uses repeated sampling and analysis, under various conditions, to quantify the effects caused by factors such as the heterogeneity of the analyte in the sampling target and variations in the application of one or more sampling protocols, to quantify uncertainty (and usually some of its component parts). The modelling approach uses a predefined model that identifies each of the component parts of the uncertainty, making estimates of each component, and sums them in order to make an overall estimate. Models from sampling theory can sometimes be used in this approach to estimate some of the components from a knowledge of the characteristics of particulate constituents. Worked examples are given of each of these approaches, across a range of different application areas. These include investigations of the environment (of soil and water), of food (at growing and processing) and of animal feed. The estimates of the total uncertainty of measurement range from a few per cent up to 84% relative to the measurand. The contribution of the sampling is occasionally small but is often dominant (>90% of the total measurement variance). This suggests an increased proportion of the expenditure needs to be aimed at the sampling, rather than the chemical analysis, if the total uncertainty needs to be reduced in order to achieve fitness for purpose. Management issues addressed include the responsibility of the quality of the whole measurement process, which needs to include the sampling procedure. Guidance is given on the selection of the most appropriate approach for any application, and whether one initial validation of the system is sufficient, or whether there is a need for ongoing monitoring of the uncertainty from sampling using quality control of sampling. The extra cost of estimating uncertainty is also considered in relation to the cost savings that can be made by knowing the uncertainty of measurement more reliably. Such a Guide can never be fully comprehensive, and although there are appendices with details of some of the statistical techniques employed and sources of more detailed advice, there will often be a need for expert advice in more complex situations. This Guide aims to be a useful introduction to this subject, but we hope it will also stimulate further research into improved methods of uncertainty estimation