4.2 Article

PERCEPTUAL MAPPING OF APPLES AND CHEESES USING PROJECTIVE MAPPING AND SORTING

Journal

JOURNAL OF SENSORY STUDIES
Volume 25, Issue 3, Pages 390-405

Publisher

WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1111/j.1745-459X.2009.00266.x

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Projective mapping, also called napping, was introduced to the chemosensory community as a multidimensional data-collection method in which panelists place products directly into a two-dimensional space based on their perceived similarity. Sorting is a form of nominal level measurement in that a pair of products is either placed in the same category or not for each subject. Analysis of projective mapping data is typically performed with multiple factor analysis, and sorting is typically performed with multidimensional scaling (MDS). This study took an exploratory empirical look at apple and cheese product systems separately in a direct comparison of sorting (analyzed by MDS) with projective mapping (analyzed with MFA). Product maps were similar for both the sorting and projective mapping procedures. Subjects had more difficulty with the apples than the cheeses. Cluster analysis was easier to interpret for the napping configurations. PRACTICAL APPLICATIONS Sorting has already gained wide use in such areas of applied sensory science as competitive evaluation, flavor exploration and category appraisals, among others. Projective mapping, also known as the napping method, was introduced relatively recently to the sensory community and has already gained much interest. It may have certain advantages or disadvantages of sorting and, at least in theory, can be applied in the same situations. This paper provides a comparison of projective mapping and sorting, and illustrates some of the differences and advantages/disadvantages of each method. It provides examples of the types of data analysis that can be used for each method. Based on this study, we conclude that projective mapping may have advantages over sorting where the product space contains relatively similar products, such as different apples or cheddar cheeses. Cluster analysis enriches the projective mapping interpretation by helping differentiate product groupings.

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