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Quality Assurance and Quality Control in the Analytical Chemical Laboratory, 2018, Vol.1, p.73-92
Auflage
2
Ort / Verlag
United Kingdom: Routledge
Erscheinungsjahr
2018
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Each analytical result is a consequence of the measurement. The overriding aim of the analyst is to obtain the analysis as closely as possible to the expected value. Certainty of the analysis depends on the uncertainty at all stages of the proceedings the analysis using which it is derived. Therefore, it is necessary to determine the sources and types of uncertainty for each individual steps of the analytical procedure, and more specifically for each size measured.
The following issues are described in this chapter: Evaluating the uncertainty of measurement, the impact of measurement uncertainty on the quality of the result, methods of estimating measurement uncertainty, and budgeting uncertainty accompanied with examples of estimating uncertainty.
This chapter evaluates the uncertainty of measurement, the impact of measurement uncertainty on the quality of the result, methods of estimating measurement uncertainty, and budgeting uncertainty accompanied with examples of estimating uncertainty. There is a difference between measurement error and uncertainty. The error is a difference between the determined and expected values, and uncertainty is a range into which the expected value may fall within a certain probability. There are several approaches for uncertainty estimation: bottom-up, fitness-for-purpose, top-down, validation-based, and robustness-based. Correct estimation of uncertainty needs an understanding of whole analytical procedure by analyst. The most helpful tools used for that are: flow diagram, and ishikawa, or cause-and-effect, or fishbone diagram. In some cases, the value of uncertainty can be estimated as a confidence interval. The basic principle of the uncertainty propagation is underlining the influence of the quantity with the highest value.