Visualization of temporal differences between dominant perceptions in temporal dominance of sensations (TDS) and temporal check-all-that-apply (TCATA) perceptions using dominance-highlighted TCATA (dTCATA) curves

Abstract

The temporal dominance of sensations (TDS) method measures dynamic changes of panelists’ attention to the sensory attributes of products. The temporal check-all-that-apply (TCATA) method measures all sensory attributes perceived at each moment of an evaluation. However, unlike in TDS, significant levels cannot be calculated in TCATA. This study proposes the use of dominance-highlighted TCATA (dTCATA) curves, which are highlighted TCATA curves that show significant time periods for the TDS data of different panels. Twelve R&D panelists evaluated five commercial corn soups using the TCATA method. Then, 125 consumer panelists evaluated the same products using the TDS method. The dTCATA curves showed TCATA curves for all attributes for each product evaluated by the R&D panel highlighted with the dominance rates identified by the consumer panel in the TDS evaluation. For example, for product 1, some attributes (sweet, viscosity) showed relatively high citation proportions in the TCATA evaluations of the R&D panel and significant dominance rates in the TDS evaluations of the consumer panel. In contrast, consommé flavor showed relatively low citation proportions in TCATA but significant dominance rates in TDS. By merging TDS and TCATA data, we could compare consumers’ dominant sensations with the evaluations of R&D panelists. This comparison could provide useful insights to product developers. In some cases, we observed attributes with significant dominance rates that were under-identified by the R&D panel in TCATA. This could suggest that most of the R&D panel may not have perceived these attributes; therefore, during product development, these attributes should be carefully considered.

Introduction

To achieve consumer-driven product development, consumer perceptions of products must be understood (van Kleef, van Trijp, and Luning, 2005). During product development, product developers design the taste, flavor, and texture of the products by referring to sensory evaluation data. The sensory evaluation data collected from both the R&D and the consumer panels are usually used for product development. However, R&D panel members may have general knowledge regarding ingredients and food processing; hence, they have expectations based on this knowledge that consumers would not have. Therefore, they tend to perceive products differently than consumers. In practice, product developers choose the ingredients based on their own perceptions. Thus, understanding the differences in perception between consumers and product developers is important so that the products are not developed solely according to the product developer’s perceptions.

The temporal dominance of sensations (TDS, Pineau et al., 2009) method describes the evolution of “dominant” sensory attributes during consumption. A “dominant sensation” is defined as a sensation that catches a panelist’s attention at a given time (Pineau et al., 2009). Varela et al. (2018) mentioned that the dominant sensations in TDS are usually attribute intensity or changes in the sensory profiles of products. Additionally, sensory and hedonic expectations affect dominance. However, the TDS method does not capture the whole picture of the dynamic changes in a product during consumption (Ares et al., 2015). It can be considered suitable for measuring dynamic changes in the attention-grabbing perceptions of consumers.

More recently, the temporal check-all-that-apply (TCATA) method has been proposed for measuring the temporal profiles of a panel’s perceptions. The TCATA method is an extension of check-all-that-apply (CATA) questions that are used to measure all sensory attributes that are perceived at each moment of the evaluation (Castura, Antúnez, Giménez, & Ares, 2016). In the TCATA method, assessors are provided a list of sensory attributes and are instructed to check all applicable (and simultaneous) attributes describing the sensations they perceive at each time point. Attributes that are no longer perceived are unchecked as soon as possible. To visualize TCATA data, curves are presented for each attribute (similar to TDS). However, unlike in TDS, significant levels cannot be calculated. TCATA curves have been highlighted in different ways. First, Castura, Antúnez, et al. (2016) highlighted the periods of TCATA curves for a product of interest and contrasted these curves with “reference lines” (one per attribute), based on the average citation proportions of other products. Meyners and Castura (2018) highlighted TCATA curves by comparing per-attribute “chance lines” from the average TCATA curves of all products and proposed related hypothesis tests.

As descriptive analyses, TDS and TCATA have some advantages and disadvantages. With respect to assessors, both trained panels and consumer panels have been used in TDS (Laguna et al., 2013, Galmarini et al., 2016) and TCATA methods (Baker et al., 2016, Ares et al., 2016). Ares et al. (2015) compared TCATA and TDS methods using trained assessors and consumers and concluded that these methods complemented one another, because TCATA provides a more detailed characterization of dynamics than TDS.

TCATA panelists are instructed to select all attributes that they detected, whereas TDS panelists are instructed to select attributes which they perceived as being dominant from among the ones they detected. These different methods of evaluating products between TCATA and TDS should be considered in interpreting the results. Therefore, using a suitable panel to evaluate products using TCATA and TDS can provide useful information for consumer-driven product development. TCATA and TDS could be conducted with trained or consumer panels depending on the research purposes (Ares et al., 2015, Varela et al., 2018). R&D panelists have experience and knowledge about food products; hence, the attributes they select using TDS could possibly be different from those selected by the consumer panel. In addition, product developers need to know the attributes that catch consumers’ attention. Therefore, from a practitioner’s standpoint, this study conducted TCATA methods with a R&D panel to understand the dynamic profiles of products and TDS methods with a consumer panel to understand the dynamic changes in sensory attributes that catch consumers’ attention. Consumer-centric product development can be performed using the perceptions of both product developers and consumers. In this study, we propose dominance-highlighted TCATA (dTCATA) curves. dTCATA curves provide visual information regarding temporal differences between the consumer panel TDS curves and the R&D panel TCATA curves.