In sensory evaluation, principal component analysis (PCA) is often used to explore differences between products. In some studies, there is one control product (e.g. a reference or benchmark) and many test products, where test-control paired differences are of primary interest. We discovered there are two equivalent ways to investigate these results using PCA. The first approach is a centred PCA
A unified framework is provided for analysing check-all-that-apply (CATA) product data following the “one citation, one vote” principle. CATA data arise from studies where A consumers evaluate P products by describing samples by checking all of the T terms that apply. Giving every citation the same weight, regardless of the assessor, product, or term, leads to analyses based on the
Supervised principal component regression (supervised PCR; SPCR) of sensory and instrumental results is conducted in new ways focusing on paired comparisons. Conventionally, SPCR is conducted on all objects. SPCR of all objects is shown to be equivalent to SPCR of all paired comparisons but different from SPCR of a subset of paired comparisons. SPCR of test-control paired comparisons is useful when the