Stat-Ease Blog

Blog

Perfecting pound cake via mixture design for optimal formulation

posted by Mark Anderson on Nov. 21, 2024

Thanksgiving is fast approaching—time to begin the meal planning. With this in mind, the NBC Today show’s October 22nd tips for "75 Thanksgiving desserts for the sweetest end to your feast" caught my eye, in particular the Donut Loaf pound cake. My 11 grandkids would love this “giant powdered sugar donut” (and their Poppa, too!).

I became a big fan of pound cake in the early 1990s while teaching DOE to food scientists at Sara Lee Corporation. Their ready-made pound cakes really hit the spot. However, it is hard to beat starting from scratch and baking your own pound cake. The recipe goes backs hundreds of years to a time when many people could not read, thus it simply called for a pound each of flour, butter, sugar and eggs. Not having a strong interest in baking and wanting to minimize ingredients and complexity (other than adding milk for moisture and baking powder for tenderness), I made this formulation my starting point for a mixture DOE, using the Sara Lee classic pound cake as the standard for comparison.

As I always advise Stat-Ease clients, before designing an experiment, begin with the first principles. I took advantage of my work with Sara Lee to gain insights on the food science of pound cake. Then I checked out Rose Levy Beranbaum’s The Cake Bible from my local library. I was a bit dismayed to learn from this research that the experts recommended cake flour, which costs about four times more than the all-purpose (AP) variety. Having worked in a flour mill during my time at General Mills as a process engineer, I was skeptical. Therefore, I developed a way to ‘have my cake and eat it too’: via a multicomponent constraint (MCC), my experiment design incorporated both varieties of flour. Figure 1 shows how to enter this in Stat-Ease software.


Setting up an optimal design with constraints in Stat-Ease software

Figure 1. Setting up the pound cake experiment with a multicomponent constraint on the flours

By the way, as you can see in the screen shot, I scaled back the total weight of each experimental cake to 1 pound (16 ounces by weight), keeping each of the four ingredients in a specified range with the MCC preventing the combined amount of flour from going out of bounds.

The trace plot shown in Figure 2 provides the ingredient directions for a pound cake that pleases kids (based on tastes of my young family of 5 at the time) are straight-forward: more sugar, less eggs and go with the cheap AP flour (its track not appreciably different than the cake flour.)


Screenshot of the trace plot in Stat-Ease software

Figure 2. Trace plot for pound cake experiment

For all the details on my pound cake experiment, refer to "Mixing it up with Computer-Aided Design"—the manuscript for a publication by Today's Chemist at Work in their November 1997 issue. This DOE is also featured in “MCCs Made as Easy as Making a Pound Cake” in Chapter 6 of Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures.

The only thing I would do different nowadays is pour a lot of powdered sugar over the top a la the Today show recipe. One thing that I will not do, despite it being so popular during the Halloween/Thanksgiving season, is add pumpkin spice. But go ahead if you like—do your own thing while experimenting on pound cake for your family’s feast. Happy holidays! Enjoy!

To learn more about MCCs and master DOE for food, chemical, pharmaceutical, cosmetic or any other recipe improvement projects, enroll in a Stat-Ease “Mixture Design for Optimal Formulations” public workshop or arrange for a private presentation to your R&D team.