Abstract
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 language | English |
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Pages (from-to) | 455-461 |
Journal | Food Quality and Preference |
Volume | 14 |
Issue number | 5-6 |
DOIs | |
Publication status | Published - 2003 |
Keywords
- sensory profile
- attributes
- cheeses