Temporal drivers of textural complexity: A pilot study and analysis of simultaneous temporal dominance of sensations and temporal textural complexity data

Abstract

AbstractThe aim of this study was to assess the applicability of simultaneous methodologies to describe dynamic textural complexity. In a simultaneous task, temporal dominance of sensations (TDS) and temporal textural complexity (TTC) were performed in one session (n = 17). A further 18 participants performed TDS and TTC on separate occasions; data from the simultaneous and separate session were then compared. A further aim was to investigate the temporal nature of textural complexity—temporal drivers of textural complexity were examined, as well as ratings of textural complexity across time. Regression modeling and multivariate analyses indicated little difference in how the participants in the simultaneous and separate tasks performed; only minor differences in the use of TDS attributes were noted. Positive drivers included texture contrast and crumbly/particles, while smooth was a negative driver. Significant differences in TTC data separated into three time intervals were found; a trend of increasing textural complexity during mastication until a midway point followed by a decline emerged. Further research with larger samples is needed to support these findings. However, for the first time this work supports the use of simultaneous tasks in sensory evaluation and indicates the importance of temporality in direct ratings of complexity.Practical applicationsSimultaneous tasks are not commonly used in sensory evaluation, this research suggests their applicability and value; ultimately they may save time and money in research and product development. Direct ratings of sensory complexity are often reported prior to or after descriptive temporal evaluation; this may underestimate true perceived complexity, indicating the importance of considering temporal measures. Sensory complexity is considered an important sensory construct given its arousal potential; thus, identification of drivers of complexity may inform product development.