Analysis and Experimental Design
Is a poor analytical technique preventing you from moving forward with your chemistry? We often see cases where the analysis of a reaction intermediate is highly problematic and implies inconsistent results, when it is in fact the analytical method which is giving rise to the problems.
It is extremely important to have robust and accurate analytical methods for several reasons. Experimental design is based around the creation of mathematical models to explain the interactions and dependencies in the reaction, the accuracy of these models depends on reliable results. Any discrepancies in the measurement of the response could affect the models and therefore the analysis and conclusions of the design. Additionally, the robustness of an analytical method is also critical in quality by design (QbD) when the data obtained is used to make important decisions for official approval of the material. Therefore, the methods used need to provide consistently precise data.
What you may not know is that experimental design can be used to help with the development of analytical methods in the same way as it works for process development or reaction optimisation. All the important factors need to be assessed then either controlled or varied against a measured outcome.
The response to be measured can align with the requirement of the method, for example, accuracy, precision, selectivity, sensitivity, linearity and/or robustness. Other options for responses for further information include sample time, throughput capacity, total cost for analysis, and ease of operation.
Sample preparation can be complicated by various features of the reaction such as sampling from suspensions, maintaining temperatures or pressures, air or moisture sensitivity, and more. However, it is preferable to analyse a sample as unaltered as possible from collection so other factors, such as the work up, do not affect the result or add experimental error and lead to misinterpretation of the result (e.g. no model due to uncontrolled variation). Where complicated sample preparation is necessary, DoE can be used to both identify and ensure a robust sample preparation protocol.
The importance of an accurate and robust analytical method cannot be understated. The data generated during analysis are used for making important decisions in the development process and for final approval of the target material.
DoE can be used for method development, method validation, and sample preparation as well as proving the robustness of the final method. Using DoE in this way will provide better methods more quickly with significantly less experimentation. Using DoE to develop and test the method for robustness will give confidence in your results.