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Additional resources for Relating Consumer, Descriptive, and Laboratory Data to Better Understand Consumer Responses: Manual 30 (Astm Manual Series)
At this stage of the analysis, a graphical technique can be used to gain an initial understanding of the relationship between the products and descriptive attributes. Figure 3 shows a biplot  of the first two principal components. These components together explain 71% of the system variability. In the biplot, rays extend from the center and are labeled with the sensory attribute names; the products (coded a through 1) are plotted as points. Attributes that have rays extending in the same general direction are positively correlated; those that have rays extending in opposite directions are negatively correlated; and those with rays that are near perpendicular are essentially uncorrelated.
Contrary to what one might expect, the correlation between consumers' ratings of smoothness and descriptive ratings of textural lumpiness is positive, although not very strong. " LU O •c O z < _i O iS o a. < o liJ Q > X YELLOW COLOR THICK APPEARANCE > I- < LU CO CO SALTY SWEET cc O -I U- o o I CO o I MUSTARD FLAVOR LUMPY TEXTURE FIG. 1—Plot of the relationship between consumer (vertical axis) and descriptive (horizontal axis) ratings for six attribute pairs. 42 CONSUMER DATA RELATIONSHIPS In what follows, three multivariate approaches to the study of data relationships are described, namely principal component regression, Generalized Procrustes Analysis, and partial least squares regression.
Relationships between consumer liking and laboratory data The researcher can confirm the face validity of the results by studying the direction of liking with changing levels of the variables. In most cases, a positive relation should exist between the "on" or desirable product attributes (chocolate flavor, fuzziness in paper, rinsability is soaps) and liking, while a negative relation should exist between "off or undesirable product attributes (oxidation, plastic, metallic, gritty) and liking.