Strategy of Experiments for Formulations: Try Screening First!

Consider Screening Down Components to a Vital Few Before Studying Them In-Depth

At the outset of my chemical engineering career, I spent 2 years working with various R&D groups for a petroleum company in Southern California. One of my rotations brought me to their tertiary oil-recovery lab, which featured a wall of shelves filled to the brim with hundreds of surfactants. It amazed me how the chemist would seemingly know just the right combination of anionic, nonionic, cationic and amphoteric varieties to blend for the desired performance. I often wondered, though, whether empirical screening might have paid off by revealing a few surprisingly better ingredients. Then after settling in on the vital few components doing an in-depth experiment may very well have led to discovery of previously unknown synergisms. However, this was before the advent of personal computers and software for mixture design of experiments (DOE), and, thus, extremely daunting for non-statisticians.

Nowadays I help many formulators make the most from mixture DOE via Stat-Ease softwares’ easy-to-use statistical tools. I was very encouraged to see this 2021 meta-analysis that found 200 or so recent publications (2016-2020) demonstrating the successful application of mixture DOE for food, beverage and pharmaceutical formulation development. I believe that this number can be multiplied many-fold to extrapolate these findings to other process industries—chemicals, coatings, cosmetics, plastics, and so forth. Also, keep in mind that most successes never get published—kept confidential until patented.

However, though I am very heartened by the widespread adoption of mixture DOE, screening remains underutilized based on my experience and a very meager yield of publications from 2016 to present from a Google-Scholar search. I believe the main reasons to be:

• Formulators prefer to rely on their profound knowledge of the chemistry for selection of ingredients (refer to my story about surfactants for tertiary oil recovery)
• The number of possibilities get overwhelming; for example, this 2016 Nature publication reports that experimenters on a pear cell suspension culture got thrown off by the 65 blends they believed were required for simplex screening of 20 components (too bad, as shown in the Stat-Ease software screenshot below, by cutting out the optional check blends and constraint-plane-centroids, this could be cut back to substantially.)

• Misapplying factorial screening to mixtures, which, unfortunately happens a lot due to these process-focused experiments being simpler and more commonly used. This is really a shame as pointed out in this Stat-Ease blog post

I feel sure that it pays to screen down many components to a vital few before doing an in-depth optimization study. Stat-Ease software provides some great options for doing so. Give screening a try!!

For more details on mixture screening designs and a solid strategy of experiments for optimizing formulations, see my webinar on Strategy of Experiments for Optimal Formulation. If you would like to speak with our team about putting mixture DOE to good use for your R&D, please contact us.