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Why use component-based methods in sensory science?
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DOI：10.1016/j.foodqual.2023.105028.png)
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摘要
En 中文
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.
Keyword:
Component-based method
Principal component analysis (PCA)
Partial least squares regression (PLSR PLS)
Multiple factor analysis (MFA)
Parallel factor analysis (PARAFAC)
Temporal check-all-that-apply (TCATA)
Projective mapping (PM)
Quantitative descriptive analysis (QDA)
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期刊
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4.9
论文数:
4.4K
被引数:
1.9W
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