Analysis of TCATA Fading data imputation of gaps in temporal profiles

Temporal Check-All-That-Apply (TCATA) Fading is a variant of TCATA where selected terms gradually and automatically become unselected over a predefined period of time and assessors are asked to re-select the terms if they still apply. Gaps in the temporal profile for a TCATA term may arise if ssess...

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Otros Autores: Vidal, Leticia, Castura, John C., Coste, Elena Beatriz, Picallo, Alejandra Beatriz, Jaeger, Sara R., Ares, Gastón
Formato: Artículo
Lenguaje:Inglés
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Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2017vidal.pdf
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024 |a 10.1016/j.foodqual.2017.02.008 
040 |a AR-BaUFA 
245 |a Analysis of TCATA Fading data  |b imputation of gaps in temporal profiles 
520 |a Temporal Check-All-That-Apply (TCATA) Fading is a variant of TCATA where selected terms gradually and automatically become unselected over a predefined period of time and assessors are asked to re-select the terms if they still apply. Gaps in the temporal profile for a TCATA term may arise if ssessors do not immediately re-select a fully faded term, making it difficult to ascertain whether a term did apply continuously over a broken interval or if it did not apply for a period of time. In this context, the aim of the present work was to evaluate the influence of using data imputation to fill the gaps on results from TCATA fading. Eight studies were conducted with consumers or trained assessors using different product categories. Gaps were identified in all of the studies, lasting 0.1–72.2 s and representing 1–9% of the raw data. Imputation of gaps from an unselected to a selected state occurred in two ways: imputation of 75% of the gaps with the shortest duration, or imputation of all gaps. Compared to the analysis of the raw data, data imputation provided smoother TCATA curves and slightly increased the duration of the significant differences among samples. Furthermore, the results were more coherent from a sensory point of view, as the evolution of the sensory characteristics during the evaluation period fitted expectations. The present work suggests that data imputation should be considered in the analysis of TCATA Fading data, but further research is required to optimise imputation approaches. 
653 |a TEMPORAL METHODS 
653 |a SENSORY CHARACTERIZATION 
653 |a CHECK-ALL-THAT-APPLY QUESTIONS 
653 |a CATA 
653 |a TCATA 
653 |a TCATA FADING 
700 1 |9 67345  |a Vidal, Leticia  |u Sensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, By Pass de Rutas 8 y 101 s/n, CP. 91000 Pando, Canelones, Uruguay - E-mail : lvidal@fq.edu.uy 
700 1 |9 67346  |a Castura, John C.  |u Compusense Inc., 255 Speedvale Ave. W., Guelph, Ontario N1H 1C5, Canada 
700 1 |9 47345  |a Coste, Elena Beatriz  |u Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Avda San Martín 4453, CP 1417 Buenos Aires, Argentina 
700 1 |9 34561  |a Picallo, Alejandra Beatriz  |u Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Avda San Martín 4453, CP 1417 Buenos Aires, Argentina 
700 1 |9 67347  |a Jaeger, Sara R.  |u The New Zealand Institute for Plant and Food Research Ltd., 120 Mt Albert Road, Private Bag 92169, Auckland, New Zealand 
700 1 |9 32412  |a Ares, Gastón  |u Sensometrics and Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, By Pass de Rutas 8 y 101 s/n, CP. 91000 Pando, Canelones, Uruguay 
773 |t Food quality and preference  |g Vol.59 (2017), p.114-122, grafs., tbls. 
856 |f 2017vidal  |i en intranet  |q application/pdf  |u http://ri.agro.uba.ar/files/intranet/articulo/2017vidal.pdf  |x ARTI201806 
856 |u https://www.elsevier.com  |z LINK AL EDITOR 
942 |c ENLINEA 
942 |c ARTICULO 
976 |a AAG