Examples of Other Approaches
4. Uncertainty Components
All the features that are used to describe the performance of a method of analysis do not directly impact on the evaluation of the uncertainty of the analytical result. Table 11 summarizes the impact of different characteristics on the evaluation of uncertainty.
Table 11 Impacts of characteristics on uncertainty.
| Characteristics | Impacts on uncertainty |
|---|---|
| Specificity/selectivity | Effects related to the selectivity and specificity are overall measured when the evaluation of the uncertainty component associated with accuracy. |
| Precision | These variances are among the most significant elements for the assessment of uncertainty. |
| Linearity, sensitivity | 1st case: If the statistical test shows that the model is linear, no effect on the uncertainty. 2 nd case: If the test shows that the model is not linear, then we may either reduce the measurement field or add a component (of non-linearity) in the evaluation of uncertainty. This component can be the maximum deviation to the model. |
| Limit of detection | This feature does no effect on the uncertainty assessment; it serves to define the field of use of the method. |
| Robustness | For a factor whose change scope is limited to ∓a, then the corresponding uncertainty is: \( \upsilon _a = C ( a / \sqrt3)\), where c is the sensitivity coefficient of the response due to variations of the factor a. |
| Accuracy | In chemical analysis, it is not applied corrections of accuracy. The goal during the development of the method is to check that there is no significant systematic error. If: \(E_n \leq 2 ; \upsilon_{justesse} = \upsilon_{Ref}\) or if: \(| \overline X_i - X_{Ref}| \leq L; \upsilon_{justesse} = \frac{L}{\sqrt3}\) |