Product selection for liking studies: The sensory informed design

Abstract

a b s t r a c tLiking studies are designed to ascertain consumers likes and dislikes on a variety of products. However, it can be undesirable to construct liking studies where each panelist evaluates every target product. In such cases, an incomplete-block design, where each panelist evaluates only a subset of the target products, can be used. These incomplete blocks are often balanced, so that all pairs occur the same number of times. While desirable in many situations, balanced incomplete blocks have the disadvantage that, by their nature, they cannot favor placing dissimilar products next to one another. A novel incomplete-block design is introduced that utilizes the target product's sensory profile to allocate products to each panelist so that they are, in general, as dissimilar as possible while also ensuring position balance. The resulting design is called a sensory informed design (SID). Herein, details on the formulation of SIDs are given. Data arising from these SIDs are analyzed using a simultaneous clustering and imputation approach, and the results are discussed.

Consumer’s research for new functional bakery product development

“…Researches regarding consumers and attitudes towards functional food, similar to ours, showed different willingness to use it (Chen, 2011;Hess et al, 2011), considering nutrition information when choose food products with the functional ingredient (Wills et al, 2012) or with benefit to health and fitness (Sonnenberg et al, 2013;Stewart-Knox et al, 2013). Consumers prefer special food quality to stay healthy and the food industry, dieticians and educators should help them to select products suitable for nutrient-or food-based diets (Sanders, 2009;Franczak et al, 2015;Lassale et al, 2013).…”

Handling missing data in consumer hedonic tests arising from direct scaling

“…The white bread data set contains missing values by design; therefore, we cannot compare the estimates of the missing values to true values like we could in a simulation study or if we created artificial missing values. To evaluate the results obtained by these imputation procedures, we can fit all of the models to designs which are nested within the structure of the design (see Franczak et al, , for details).…”

“…The SID is an incomplete block design that allocates the products to preserve position balance. That is, every product appears in each position the same number of times such that no consumer evaluates a single product more than once (see Franczak et al, , for details). For illustrative purposes, we display the first six rows of the results from the brown bread study in Table .…”

“…Josse and Husson (2012), who introduced and assessed an interactive PCA procedure using an orange juice data set that arose from a descriptive sensory panel. And, in Franczak, Browne, McNicholas, and Findlay (2015), where the authors introduced a sensory informed design (SID) that allocates products to assessors for consumer liking studies.…”

A partial EM algorithm for model‐based clustering with highly diverse missing data patterns

“…When tasting wine, for example, a consumer can only evaluate three or four samples before “fatigue” sets in, compromising the quality of the resulting data. The fact that consumers tend to behave like experts puts into doubt the value of data obtained over multiple tasting sessions (Franczak et al, 2015). Therefore, a balanced‐incomplete‐block (BIB) design is typically used for high‐fatigue products.…”