Penalty analysis based on CATA questions to identify drivers of liking and directions for product reformulation

• Consumers scored their liking and used a CATA question to describe the products.
• Consumers also indicated the same CATA question on their ideal product.
• A new form of penalty analysis for CATA questions was applied.
• Drivers of liking and directions for product reformulation were identified.
• PLS modeling indicated the potential for improvement for each product.

One of the most important steps of new product development process is product optimization, which aims at identifying consumers’ ideal products and directions for product reformulation. The present work proposes the application of a penalty analysis based on consumer responses to CATA questions to identify drivers of liking and directions for product reformulation. Two studies were conducted in which 74 and 119 consumers evaluated a set of samples (5 apples and 8 yogurts) using a check-all-that-apply question related to sensory characteristics and were also asked to check all the terms they considered appropriate to describe their ideal product. Data were analyzed by counting the number of consumers who did not check an attribute as they did for their ideal product, and its associated mean drop. A dummy variable transformation approach was proposed to make linear regression models between CATA terms and overall liking scores using Partial Least Squares (PLS). Juiciness, sweetness, apple flavor, firmness and crispiness were the most relevant attributes for consumers in the apple study. Meanwhile, in the yogurt study smoothness, homogeneity and creaminess were the main drivers of liking and were responsible for the highest penalization on overall liking (more than 1 in the 9-point hedonic scale). PLS regression enabled the identification of the attributes which deviation from the ideal caused a significant decrease in overall liking. Penalty analysis on CATA questions proved to be a simple and useful approach to identify drivers of liking and directions for improving the products in both studies. Advantages and disadvantages of this approach are discussed, as well as directions for further research.