Publisert 2018

Les på engelsk


Tidsskrift : Food Quality and Preference , vol. 66 , p. 95–106 , 2018

Utgiver : Elsevier

Internasjonale standardnummer :
Trykt : 0950-3293
Elektronisk : 1873-6343

Publikasjonstype : Vitenskapelig artikkel

Bidragsytere : Nguyen, Quoc Cuong; Næs, Tormod; Varela-Tomasco, Paula

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Kjetil Aune


For describing the evolution of sensory properties during eating, dynamic sensory methods are still being developed and optimised. Temporal Dominance of Sensations (TDS) and Temporal Check All That Apply (TCATA) are currently the most used and discussed. The aim of this study was to compare TDS, TCATA and a variant of TDS, performed by modality (M-TDS) in the outcome of the dynamic sensory description. These methods were applied with the same trained panel (n = 10) for the evaluation of the dynamic properties of yoghurt samples, with identical composition, only varying in textural properties. Based on a design of experiment, the yoghurts varied in viscosity (thin/thick), size of cereal particle added (flour/flakes) and flavour intensity (low dose/optimised dose, by adding artificial sweetener and vanilla). The TDS curves revealed that the variation in viscosity and particle size led to differences in perception mainly at the beginning of the eating process (Thin/Thick and Gritty/Sandy). Additionally, all samples were also perceived as Bitter at the end of the eating process. TCATA and TDS by modality results were, generally, in agreement with TDS, but they unveiled more details of the samples’ dynamic profiles in all stages of the eating process, showing the effect of Vanilla and Sweet for the samples with optimised flavour, and the masked perception of Bitter. The duration of the eating process was standardized and split into three time intervals (T0-T40, T41-T80, T81-T100). Panelists’ responses were summarized as frequency values in each time interval. Principal Component Analysis was used to visualize sample trajectories over time in the sensory space, with the need to study up to the third dimension to better understand the trajectories. ANOVA models were used to find the attributes which were significantly differences among products. Panel performance was assessed based on MANOVA models for the three methods. The results indicated that TCATA was more discriminative and panelists were more in agreement. TCATA also described samples in more detail in terms of number of discriminating attributes as compared with TDS. The discussion also centers in the different aspects of perception that could respond to different research questions for the three compared methods.