Temporal Drivers of Liking

Thomas, Arnaud; Visalli, Michel; Cordelle, Sylvie; Schlich, Pascal

doi:10.1016/j.foodqual.2014.03.003

Cited by 97 publications

(65 citation statements)

References 15 publications

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“…The assessor can re-endorse the same attribute if it remains applicable. This strategy was recently introduced for TDS ( Thomas et al, 2015), where the dominant attribute selection fades to a deselected state within 3 s. A similar modification to the TCATA method could be investigated. One of the characteristics of TCATA is that it permits concurrent attribute selection, which may permit assessors to indicate sensations that are perceived simultaneously (cf.…”

Section: Discussionmentioning

confidence: 99%

“…Pineau et al (2012, p. 164) propose a variant of TDS in which the dominant attribute is selected, without scaling its intensity, an approach often followed by other researchers (e.g., Thomas, Visalli, Cordelle, & Schlich, 2015;Zorn, Alcaire, Vidal, Giménez, & Ares, 2014). Time-Quality Tracking (Halpern, 1991;Zwillinger & Halpern, 1991) is an earlier method that also captures a sequence of attributes without intensities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Check-All-That-Apply (TCATA): A novel dynamic method for characterizing products

Castura¹,

Antúnez

Giménez

et al. 2016

Food Quality and Preference

241

191

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a b s t r a c tTemporal Check-All-That-Apply (TCATA) is introduced as a new dynamic method for describing multidimensional sensory properties of products as they evolve over time. TCATA extends the Check-All-That-Apply (CATA) method. Selection and deselection of attributes are tracked continuously over time, permitting assessors to characterize the evolution of sensory changes in products. TCATA is presented using results from trained panel evaluations of yogurt products. Data are also used to illustrate approaches for exploratory data analysis. Raw data from each assessor are represented using indicator charts. Panel data are aggregated into TCATA product plots. Reference lines are added to provide additional guidance. Product pairwise comparisons are made in TCATA difference plots, emphasizing differences that are less likely to have arisen from chance. Correspondence analysis (CA) is used to visualize product trajectories over time in a sensory space, providing a summary multivariate understanding of the dynamic sensory properties. CA conducted on the TCATA yogurt data highlight the importance of the dynamic profile, and suggest that understanding the complexity of products requires investigation of temporal changes. Results indicate that the TCATA method has potential for evaluating temporal aspects of sensory perception but further research is required to identify methodological issues and to refine the methodology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Check-All-That-Apply (TCATA): A novel dynamic method for characterizing products

Castura¹,

Antúnez

Giménez

et al. 2016

Food Quality and Preference

241

191

View full text Add to dashboard Cite

show abstract

“…The relationship between the given liking score and a cited descriptor was obtained by calculating the Temporal Drivers of Liking (TDL), based on the concept of "liking while dominant" according to Thomas et al [16]. Liking while dominant is the average rating given by a consumer to a product while a certain attribute was chosen as dominant.…”

Section: Temporal Appreciation Of Products and Their Combinationmentioning

confidence: 99%

Wine and Cheese: Two Products or One Association? A New Method for Assessing Wine-Cheese Pairing

et al. 2018

Self Cite

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Abstract:The aim of this study was to identify which attributes impacted the dynamic liking of cheese and wine individually, as well as when consumed together. Three wines (one white, Pouilly Loché; and two red, Maranges and Beaujolais) and three cheeses (Comté, Époisses, Chaource) were individually evaluated by a group of 60 consumers using mono-intake Temporal Dominance of Sensations (TDS) with simultaneous hedonic ratings. The same data acquisition screen was used for all products showing a unique list of 14 descriptors (covering cheese and wine perception) and a hedonic scale for dynamical rating of liking. The dynamic hedonic data were associated with the TDS profiles obtaining Temporal Drivers of Liking (TDL). Furthermore, the nine associations that resulted from combining each wine with each cheese were evaluated by multi-bite and multi-sip TDS. Individually, Chaource had practically no TDL; for Comté, mushroom flavor was a positive TDL, and in Époisses, salty was a negative TDL. As for wines, negative TDL were only found in the red wines: bitter, sour and astringent. Positive TDL for wines were: fruity, spicy and woody. Changes in the dynamic perception had a bigger impact on liking of wine compared to cheese. For the associations, the negative TDL were only three and mostly wine related: sour (for seven out of nine combinations), bitter (six out of nine) and astringent (five out of nine). Positive TDL were more varied (a total of 10 descriptors) and were related either to wine or cheese. As opposed to what was found in cheese alone, salty was a positive TDL in two of the combinations. It was observed that the dynamic sensory perception had a more important impact on liking in wine-cheese combinations than when consumed separately. TDS and TDL have a big potential in the study of food pairing, which should be further exploited.

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“…The data set was provided for a workshop at the 2014 Sensometrics conference and is described in more detail in Thomas et al (2015) (the data corresponds to sessions 1-3 described in that paper). In brief with a focus on the aspects necessary for this paper, data on 6 commercial flavored fresh cheeses were collected from 68 consumers and 11 trained panelists.…”

Section: Datamentioning

confidence: 99%

“…Details and pertinent references can be found in Thomas, Visalli, Cordelle, and Schlich (2015) and are omitted here for brevity. A workshop organized by Pascal Schlich and Arnaud Thomas for the 12th Sensometrics meeting in Chicago IL, USA, in 2014 addressed this method and aimed at generating additional ideas of how to analyze TDS data, with particular focus on the addition of temporal liking evaluated by consumers on 6 flavored fresh cheeses.…”

Section: Introductionmentioning

confidence: 99%

Temporal liking and CATA analysis of TDS data on flavored fresh cheese

Meyners

2016

Food Quality and Preference

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Temporal Drivers of Liking

Cited by 97 publications

References 15 publications

Temporal Check-All-That-Apply (TCATA): A novel dynamic method for characterizing products

Temporal Check-All-That-Apply (TCATA): A novel dynamic method for characterizing products

Wine and Cheese: Two Products or One Association? A New Method for Assessing Wine-Cheese Pairing

Temporal liking and CATA analysis of TDS data on flavored fresh cheese

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