SENSORY PROFILING WITH PROBABILISTIC MULTIDIMENSIONAL SCALING

Variability is a fundamental characteristic of sensory profile data. Ignoring the variability may result in biased solutions that cannot be improved by the collection of additional data. Probabilistic multidimensional scaling (PMDS) models provide a means of accounting for the variability inherent in sensory data by using distributions, instead of points, to portray sensory objects. For profile data with high levels of variability, the probabilistic model recovers latent structure parameters very well — traditional deterministic MDS models and principal components analyses (PCA) do not. Advantages of the PMDS models include their parsimony, testability and extensibility. Two particularly attractive PMDS attributes are their ability to relate consumers' expressions of liking to product profiles and their ability to estimate a product's " perceptual share" from liking and profile data. Used as a criterion with what-if modeling, perceptual share estimates enable the evaluation of alternative product development strategies.