Exploring the common and unique variability in TDS and TCATA data: a comparison using canonical correlation and orthogonalization

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Exploring the common and unique variability in TDS and TCATA data : a comparison using canonical correlation and orthogonalization. / Berget, Ingunn; Castura, John C.; Ares, Gaston; Næs, Tormod; Varela, Paula.

I: Food Quality and Preference, Bind 79, 103790, 2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Berget, I, Castura, JC, Ares, G, Næs, T & Varela, P 2020, 'Exploring the common and unique variability in TDS and TCATA data: a comparison using canonical correlation and orthogonalization', Food Quality and Preference, bind 79, 103790. https://doi.org/10.1016/j.foodqual.2019.103790

APA

Berget, I., Castura, J. C., Ares, G., Næs, T., & Varela, P. (2020). Exploring the common and unique variability in TDS and TCATA data: a comparison using canonical correlation and orthogonalization. Food Quality and Preference, 79, [103790]. https://doi.org/10.1016/j.foodqual.2019.103790

Vancouver

Berget I, Castura JC, Ares G, Næs T, Varela P. Exploring the common and unique variability in TDS and TCATA data: a comparison using canonical correlation and orthogonalization. Food Quality and Preference. 2020;79. 103790. https://doi.org/10.1016/j.foodqual.2019.103790

Author

Berget, Ingunn ; Castura, John C. ; Ares, Gaston ; Næs, Tormod ; Varela, Paula. / Exploring the common and unique variability in TDS and TCATA data : a comparison using canonical correlation and orthogonalization. I: Food Quality and Preference. 2020 ; Bind 79.

Bibtex

@article{851699a013384f76941d956bb81504bf,
title = "Exploring the common and unique variability in TDS and TCATA data: a comparison using canonical correlation and orthogonalization",
abstract = "Temporal Dominance of Sensations (TDS) and Temporal Check-all-that-Apply (TCATA) from three different case studies are compared by means of canonical correlation analysis, orthogonalization and principal component analysis of the vertically unfolded data (which means that the matrices compared have samples*timepoints in the rows and attributes in the columns). The multivariate analyses decompose the datasets into common and distinct components. The results showed that the major part of the variation is common between the two methods for the cases investigated, but that there were subtle differences showing better discrimination for TCATA than TDS. TDS showed a more complex data structure and more unique variation. The unique variation in TDS is, however, difficult to interpret. The methods are more different towards the end of the mastication, this can be explained both by the difficulty of assessors to agree on the dominant attributes at the bolus stage for TDS, and that assessors may forget to unclick attributes in TCATA. This work builds on recent methodological studies on temporal methods that aim to better understand differences among methodologies and ultimately to identify what methods could be better for answering different objectives.",
author = "Ingunn Berget and Castura, {John C.} and Gaston Ares and Tormod N{\ae}s and Paula Varela",
year = "2020",
doi = "10.1016/j.foodqual.2019.103790",
language = "English",
volume = "79",
journal = "Food Quality and Preference",
issn = "0950-3293",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Exploring the common and unique variability in TDS and TCATA data

T2 - a comparison using canonical correlation and orthogonalization

AU - Berget, Ingunn

AU - Castura, John C.

AU - Ares, Gaston

AU - Næs, Tormod

AU - Varela, Paula

PY - 2020

Y1 - 2020

N2 - Temporal Dominance of Sensations (TDS) and Temporal Check-all-that-Apply (TCATA) from three different case studies are compared by means of canonical correlation analysis, orthogonalization and principal component analysis of the vertically unfolded data (which means that the matrices compared have samples*timepoints in the rows and attributes in the columns). The multivariate analyses decompose the datasets into common and distinct components. The results showed that the major part of the variation is common between the two methods for the cases investigated, but that there were subtle differences showing better discrimination for TCATA than TDS. TDS showed a more complex data structure and more unique variation. The unique variation in TDS is, however, difficult to interpret. The methods are more different towards the end of the mastication, this can be explained both by the difficulty of assessors to agree on the dominant attributes at the bolus stage for TDS, and that assessors may forget to unclick attributes in TCATA. This work builds on recent methodological studies on temporal methods that aim to better understand differences among methodologies and ultimately to identify what methods could be better for answering different objectives.

AB - Temporal Dominance of Sensations (TDS) and Temporal Check-all-that-Apply (TCATA) from three different case studies are compared by means of canonical correlation analysis, orthogonalization and principal component analysis of the vertically unfolded data (which means that the matrices compared have samples*timepoints in the rows and attributes in the columns). The multivariate analyses decompose the datasets into common and distinct components. The results showed that the major part of the variation is common between the two methods for the cases investigated, but that there were subtle differences showing better discrimination for TCATA than TDS. TDS showed a more complex data structure and more unique variation. The unique variation in TDS is, however, difficult to interpret. The methods are more different towards the end of the mastication, this can be explained both by the difficulty of assessors to agree on the dominant attributes at the bolus stage for TDS, and that assessors may forget to unclick attributes in TCATA. This work builds on recent methodological studies on temporal methods that aim to better understand differences among methodologies and ultimately to identify what methods could be better for answering different objectives.

U2 - 10.1016/j.foodqual.2019.103790

DO - 10.1016/j.foodqual.2019.103790

M3 - Journal article

AN - SCOPUS:85072261739

VL - 79

JO - Food Quality and Preference

JF - Food Quality and Preference

SN - 0950-3293

M1 - 103790

ER -

ID: 228251688