Enlightened Experimental Design
The traditional approach of designing experiments by changing one factor at a time is outmoded.
Phil Kay |
I hate to see people wasting their time – and money – which is why I am such a passionate advocate of statistical Design of Experiments (DoE).
By definition, DoE is the most efficient way to experiment to understand a process or system and it is extremely relevant for analytical science. Just enter “HPLC design of experiments” into your favorite search engine; you will find plenty of examples where scientists have optimized the resolution of HPLC methods using DoE. The authors will likely have listed the benefits of the approach: the reduced time, cost and risk; the increased understanding of the complex behavior of the system; the structure and rigor that it brings to the development process. They may also mention that they identified that some of the factors have important interactions. For example, they discovered that the best temperature was dependent on the flow rate. They will acknowledge that they would not have found the optimum using a conventional one-factor-at-a-time (OFAT) approach.
Those of you who have developed chromatography methods will probably have seen such examples and, if so, will be aware of the benefits. And yet many of you will not use DoE. And I would be willing to wager that a large number of the DoE case study authors still use OFAT as their default approach. Published examples, by their nature, are special cases. Nevertheless, HPLC development lends itself very well to DoE because experimentation is relatively cheap. A large experiment can be run with few human, equipment and material resources, for instance. In a setting where each run or trial is more expensive, my experience tells me that the scientist will be less likely to use DoE – despite the fact that the imperative for efficiency is even greater!
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