Robustness testing during

The analysis time is adjusted according to the worst case results during robustness testing. 

The ICH guidelines define robustness as The robusmess of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage.Thus, the robustness of a method is a measure of its capacity to be transferred to another laboratory without affecting the analytical result. A robustness test is the experimental setup applied to evaluate the robustness of the method. The ICH guidelines also state that One consequence... 

FIGURE 4 Factor level intervals examined during (a) method optimization and (b) robustness testing. 

A check of robustness includes preliminary experiments on precision. During robustness testing, a single standard is repetitively analyzed before starting the actual calibration. Without sufficient precision at a single concentration, it is fruitless to calibrate. However, the RSD obtained from repeated injections underestimates the overall error by a factor of up to 3. ... 

Repeatability is automatically tested during robustness testing. It should be better than 2% RSD for the area of a main peak without IS, less than 1 % using relative PAs or analyte/IS, and better than 10% RSD for a trace impurity. Repeatability in CE is generally lower than in HPLC because of the small injection volume (between 2 and 20 nl), but can be significantly improved by the use of internal standard. 

System suitability. During the robustness testing of method validation, critical method parameters such as mobile phase composition and column temperature are varied to mimic the day-to-day variability. Therefore, the system suitability results from these robustness experiments should reflect the expected range for the system suitability results. As a result, system suitability results in these method validation experiments are very useful in determining the system suitability... 

Robustness test for the impinging-jet mixer - fouling sensitivity during precipitations... 

The responses of main interest are different during both applications. In optimization, responses related to the separation of peaks (Section 6.2) are modelled. In robustness testing the quantitative aspect (the content determination) of the method is of most interest, since it is the one that should remain unaffected by small variations in the variables. Responses related to the separation (resolution, relative retention) or describing the general quality of the chromatogram (capacity factors, analysis times, asymmetry factors, and column efficacy) are often also studied. As recommended by the ICH guidelines the results of a robustness test can be used to define system suitability test limits for some of the responses [82]. 

In addition to validation of the automation, full validation of the chromatographic procedure, as described in Chapter 12, should be conducted for late-phase methods. This should include specification of system suitability parameters to ensure that the performance obtained during method development and validation is maintained during routine use. The system suitability parameters may include specification of acceptable injection repeatability, criteria for resolution between critical pairs, maximum allowable tailing factors, and a means of verifying that the requisite sensitivity is obtained. As recommended by Vander Heyden et al., system suitability limits are best set following robustness tests. 

The ability of a method to perform effectively in a typical lab environment and with acceptable variations is evaluated during robustness testing. 

To determine the robustness of a method, several approaches exist. Basically, the situation for robustness testing is similar to that for screening during optimization, except for the range within which the factors are examined. The influence of small but deliberate changes in parameters on the response (s) is evaluated using either an OVAT or an experimental design approach (12). 

In this chapter, the use of multivariate approaches during method optimization and robustness testing is elaborated, discussed, and illustrated with examples. 

The responses of main interest also are different in method development and robustness testing. In development, the considered responses are related to the quality of the separation (l),such as, for electrophoretic methods, migration times, peak shapes, and the resolutions between neighboring peaks. When the separation is optimized and the method is validated, thus also in robustness testing, the responses of main interest are related to the quantitative aspects of the method, such as contents, concentrations, or recoveries. The responses considered during development occasionally are considered in a second instance, for example, as system suitability test (SST) parameters. 

The factors and their levels examined during a screening phase in method development (27), an optimization phase in method development (28), and a robustness test (29) are presented in Tables 2.2,2.3, and 2.4, respectively. 

Reflected FF and PB designs were applied during robustness testing of CE methods in References 66-71. To screen the factor(s) at three levels, three-... 

During method optimization, initially qualitative responses, related to the quality of the separation, are considered. On the other hand, during robustness testing, first quantitative responses are studied. Nevertheless, all types of responses can be evaluated during both method optimization and robustness testing. 

During robustness testing, in a first instance, the considered responses usually represent quantitative aspects of the method (5,16). An analytical method is considered robust if no significant effects are found on the response(s) describing the quantitative aspect of the method. Although during method optimization usually quantitative responses are initially not considered, they can, however, be studied. 

In Reference 29, the response studied in the 8-experiment PB design (Table 2.9) during the robustness testing of a CE method was quantitative, that is, peak area/migration time ratio Alt (Table 2.19). 

In the second approach, (SE)e is obtained from Hn a priori declared negligible effects, E, such as two-factor interaction effects in robustness testing or higher-order interaction effects in screening during method... 

The robustness test is important to identify the operating factors which are not necessarily considered in the development phase of the method, but could influence the results, and therefore to anticipate problems that may occur during the application of the chosen method. A series of curves to the robust sensitivity function analyzed by using WinReg is shown in figure (10). The new polynomials of the controller and the reference model are given as follows ... 

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