Simple improvement of consumer fit in external preference mapping

N.M. Faber, J. Mojet, A.A.M. Poelman

    Research output: Contribution to journalArticleAcademicpeer-review

    30 Citations (Scopus)


    In the common implementation of external preference mapping consumer preferences are fitted as polynomial functions of the first two principal components (PCs) of the sensory data. A major weakness of the method is the relatively small number of consumers that can be significantly fitted. Several researchers have proposed to improve the consumer fit by including higher-numbered PCs in the analysis. We have explored the possibility of including higher-numbered PCs while restricting the model choice to the simplest polynomial function, i.e. the (linear) vector model. In addition, we have developed a heuristic decision rule for determining the number of PCs to keep in the fit. A practical example is discussed where the consumer fit improved from 51øtwo PCs, polynomial) to 80øfive PCs, vector).
    Original languageEnglish
    Pages (from-to)455-461
    JournalFood Quality and Preference
    Issue number5-6
    Publication statusPublished - 2003


    • sensory profile
    • attributes
    • cheeses


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