Why use component-based methods in sensory science?
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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.
Originalsprog | Engelsk |
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Artikelnummer | 105028 |
Tidsskrift | Food Quality and Preference |
Vol/bind | 112 |
Antal sider | 18 |
ISSN | 0950-3293 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
The authors would also like to thank for the financial support received from Research council of Norway and the Norwegian Foundation for Research Levy on Agricultural Products FFL, through the research program “Food for future’ and ‘Precision’ (2021-2024).
Publisher Copyright:
© 2023 The Author(s)
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