{"id":10,"date":"2024-04-04T00:39:45","date_gmt":"2024-04-03T21:39:45","guid":{"rendered":"https:\/\/sisu.ut.ee\/lcms_method_validation\/103-different-ways-evaluate-robustness\/"},"modified":"2024-06-20T09:58:00","modified_gmt":"2024-06-20T06:58:00","slug":"103-different-ways-evaluate-robustness","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/lcms_method_validation\/103-different-ways-evaluate-robustness\/","title":{"rendered":"10.3 Different ways to evaluate robustness"},"content":{"rendered":"

In the lecture 10.1 Robustness and ruggedness relation to LC-MS method development<\/a> we saw different LC-MS parameters that influence robustness and ruggedness, as well as what the influence of these parameters. On the basis of this information it is possible to plan changes to the method. As a reminder, there was a list of LC parameters, sample and sample preparation parameters and mass spectrometry parameters.<\/p>\n

Because of the very large number of potentially variable parameters it is reasonable to divide assessment of ruggedness into separate parts. A very logical division would be to test ruggedness separately for the sample preparation and for the LC-MS analytical part.<\/p>\n

Before starting the investigation of robustness it is crucial to find out\u00a0what are the most important performance characteristics of the method.<\/p>\n

For example, if the method\u2019s\u00a0LoQ is very close to the LoQ required by legislation, then the changes in the LoQ value have to be monitored against small changes in the method parameters. The most influential method parameters impacting the LoQ could be MS parameters, mobile phase pH and sample preparation parameters.<\/p>\n

The main criteria for choosing parameters are (a) how much a given method parameter can influence the critical characteristic and (b) how likely it is that this parameter will change uncontrollably.<\/p>\n

If those parameters are chosen, then we should use one of two options to evaluate the method robustness and ruggedness \u2013 to use the experimental design or the One Factor At a Time approach.<\/p>\n

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