“One of the nice things offered by Stat-Ease is a consulting service in which they assist with designing the experiment and interpreting the results,” Bhatia said. “Since this was one of my first DOE projects, I decided to take advantage of this service. The design space looked unusual to me because the enzymes are used at very low concentrations, in the parts-per-million range. They looked over the design and confirmed that although the design space looked narrow, everything was correct.”
Bhatia asked the lab to produce all of the samples in a single day in order to reduce variability. Each sample was evaluated based on 10 different responses. Four of the responses were objective and included volume, length, the height of the bread after baking, and the width of the bread after baking.
A panel of taste testers subjectively evaluated the other responses. They considered overall likability, crust flavor, crumb flavor, softness, cell size, and crumb density. Bhatia entered the responses into Design-Expert, and the software generated a series of statistical analyses.
Bhatia used the analysis of variance results to verify the significance and accuracy of the model. The normal plot of residuals showed a high level of correlation between the data points, indicating that the results were internally consistent. Bhatia then developed a desirability function that provided weights and constraints for the responses, which he used to optimize the factors. The predicted response values for a most desirable formulation are shown in Figure 1 (above). It depicts settings and resulting predictions in the form of number lines superimposed on desirability scales, the higher the better. A 3-D rendering of the desirable region is shown in Figure 2 (left).
“I examined other desirable formulations to evaluate their distinctness and robustness as well as their overall likeability, which is the most important response,” Bhatia said. “I eliminated several because they were very close in the recipe space to other formulations that had higher overall likability. I also evaluated the robustness of the recommended formulations in order to account for manufacturing variability.”
Bhatia ordered samples of four selected recipes and did a follow-up sensory testing. Two of these samples scored highly; he then tested both of these formulations in the manufacturing process. The result? Based on manufacturability and cost, one of the four recipes was selected, and that recipe forms the basis of the company’s new line of bread doughs.