Robustness testing qualitative factors

Qualitative factors are also frequently considered in a robustness test. " For CE methods, factors such as the batch or manufacturer of the capillary, reagent or solvent can be selected. When evaluating the influence of such qualitative factor, the analyst should be aware that the estimated effect is only valid or representative for the examined discrete levels and not for any other level of that factor, and certainly not for the whole population. For example, when examining two capillaries X and Y, the estimated effect only allows drawing conclusions about these two capillaries and not about other capillaries available on the market. Such approach allows evaluating whether capillary Y is an alternative for capillary X, used, for instance, to develop the method. 

For qualitative factors, only discrete values are possible, e.g., capillaries X, Y, or Z. As already indicated, this means that only conclusions can be drawn about the examined capillaries and no extrapolation to other capillaries can be made. The most logical in a robustness test is to compare the nominal capillary with an alternative one. 

In reference 88, response surfaces from optimization were used to obtain an initial idea about the method robustness and about the interval of the factors to be examined in a later robustness test. In the latter, regression analysis was applied and a full quadratic model was fitted to the data for each response. The method was considered robust concerning its quantitative aspect, since no statistically significant coefficients occurred. However, for qualitative responses, e.g., resolution, significant factors were found and the results were further used to calculate system suitability values. In reference 89, first a second-order polynomial model was fitted to the data and validated. Then response surfaces were drawn for... 

In this chapter, the possibilities to set up and treat the results of a robustness test were reviewed (Sections I-VIII). Robusmess usually is verified using two-level screening designs, such as FF and PB designs. These designs allow examining the effects of several mixmre-related, quantitative, and qualitative factors, on one or several responses, describing either quantitative and/or qualitative aspects of the analytical method. 

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