Why use component-based methods in sensory science?
Publikasjonsdetaljer
Tidsskrift : Food Quality and Preference , vol. 112 , p. 1–18 , 2023
Internasjonale standardnummer
:
Trykt
:
0950-3293
Elektronisk
:
1873-6343
Publikasjonstype : Vitenskapelig artikkel
Lenker
:
DOI
:
doi.org/10.1016/j.foodqual.202...
ARKIV
:
hdl.handle.net/11250/3103577
Forskningsområder
Sensorikk
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Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no
Sammendrag
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.
