Liking 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.
Liking studies are designed to ascertain consumers likes and dislikes on a variety of products. However, itcan be undesirable to construct liking studies where each panelist evaluates every target product. In suchcases, an incomplete-block design, where each panelist evaluates only a subset of the target products, canbe 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 theirnature, they cannot favor placing dissimilar products next to one another. A novel incomplete-blockdesign is introduced that utilizes the target product’s sensory profile to allocate products to each panelistso that they are, in general, as dissimilar as possible while also ensuring position balance. The resultingdesign is called a sensory informed design (SID). Herein, details on the formulation of SIDs are given. Dataarising from these SIDs are analyzed using a simultaneous clustering and imputation approach, and theresults are discussed.
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