Abstract
This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they define linear combinations of the variables to achieve data compression, interpretation, and prediction. The common properties of the component-based methods are listed and their advantages illustrated by examples. The paper equips practitioners with a list of solid and concrete arguments for using this methodology.
Original language | English |
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Article number | 105028 |
Journal | Food Quality and Preference |
Volume | 112 |
Number of pages | 18 |
ISSN | 0950-3293 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Author(s)
Keywords
- Component-based method
- Multiple factor analysis (MFA)
- Parallel factor analysis (PARAFAC)
- Partial least squares regression (PLSR, PLS)
- Principal component analysis (PCA)
- Projective mapping (PM)
- Quantitative descriptive analysis (QDA)
- Temporal check-all-that-apply (TCATA)