Why use component-based methods in sensory science?

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Why use component-based methods in sensory science? / Næs, Tormod; Varela, Paula; Castura, John C.; Bro, Rasmus; Tomic, Oliver.

I: Food Quality and Preference, Bind 112, 105028, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Næs, T, Varela, P, Castura, JC, Bro, R & Tomic, O 2023, 'Why use component-based methods in sensory science?', Food Quality and Preference, bind 112, 105028. https://doi.org/10.1016/j.foodqual.2023.105028

APA

Næs, T., Varela, P., Castura, J. C., Bro, R., & Tomic, O. (2023). Why use component-based methods in sensory science? Food Quality and Preference, 112, [105028]. https://doi.org/10.1016/j.foodqual.2023.105028

Vancouver

Næs T, Varela P, Castura JC, Bro R, Tomic O. Why use component-based methods in sensory science? Food Quality and Preference. 2023;112. 105028. https://doi.org/10.1016/j.foodqual.2023.105028

Author

Næs, Tormod ; Varela, Paula ; Castura, John C. ; Bro, Rasmus ; Tomic, Oliver. / Why use component-based methods in sensory science?. I: Food Quality and Preference. 2023 ; Bind 112.

Bibtex

@article{c70f7979b5e04507b1b753fdc3e4958e,
title = "Why use component-based methods in sensory science?",
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.",
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)",
author = "Tormod N{\ae}s and Paula Varela and Castura, {John C.} and Rasmus Bro and Oliver Tomic",
note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",
year = "2023",
doi = "10.1016/j.foodqual.2023.105028",
language = "English",
volume = "112",
journal = "Food Quality and Preference",
issn = "0950-3293",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Why use component-based methods in sensory science?

AU - Næs, Tormod

AU - Varela, Paula

AU - Castura, John C.

AU - Bro, Rasmus

AU - Tomic, Oliver

N1 - Publisher Copyright: © 2023 The Author(s)

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Component-based method

KW - Multiple factor analysis (MFA)

KW - Parallel factor analysis (PARAFAC)

KW - Partial least squares regression (PLSR, PLS)

KW - Principal component analysis (PCA)

KW - Projective mapping (PM)

KW - Quantitative descriptive analysis (QDA)

KW - Temporal check-all-that-apply (TCATA)

U2 - 10.1016/j.foodqual.2023.105028

DO - 10.1016/j.foodqual.2023.105028

M3 - Journal article

AN - SCOPUS:85175244461

VL - 112

JO - Food Quality and Preference

JF - Food Quality and Preference

SN - 0950-3293

M1 - 105028

ER -

ID: 376294977