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Increase Efficiency and Model Applicability Domain When Testing Options That Are at First Glance Multilevel Categorical Factors

Silvio Miccio, Procter & Gamble

Senior Scientist

Increase efficiency and model applicability domain when testing options that are at first glance multilevel categorical factors


When testing options of e.g. different raw materials or formulation ingredients, common practice is to vary them as multilevel categorical variable e.g. A, B, C…. in an experiment. Hence, for identifying the best option all of them have to be tested. A consequence of this is

  • time consuming physical testing is required and the
  • resulting model is only applicable to predict the tested options but cannot predict options that have not been tested


A much more efficient approach is to design the experiment based on the physical/chemical properties of each option. This,

  • significantly decreases the number of required experimental conditions and
  • results in a model that can predicted options not tested before.


The presentation will demonstrate how to:

  1. compress the available information describing the option properties via principal components
  2. select the “corners of the box” for testing representative raw materials based on Design of Experiments.
  3. model the data via PLS
  4. find the overall optimum solution
  5. identify physical available options closest to the calculated optimum solution
  6. Demonstrates the efficiency of this approach

Notice that what is shown here is based on a method commonly used in Chemometrics, called Quantitative Structure – Activity Relationship (QSAR).


Great presentation Silvio!


Steffen Brandenhoff


Silvio, I attended your talk which was just great !!! Emmanuel

Great work.
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