Sensory characterization is one of the most powerful, sophisticated, and extensively applied tools in sensory science. Descriptive analysis with trained assessors has been traditionally used for sensory characterization. Due to the cost of time and money required for its application, several novel methodologies, which do not require training, have been recently developed and are gaining popularity as quick and reliable
Temporal dominance of sensations (TDS) data consist of temporal sequences of dominant attributes. TDS has been conventionally performed with trained assessors, but recently TDS has been proposed as a rapid method using consumers.
Studies that investigate drivers of consumer liking involve both descriptive sensory and consumer data collection. These two components can be run in parallel to reduce project time, but at significant expense.
Several novel methodologies for sensory characterization have emerged in the last years, motivated by the need to gather product descriptions directly from consumers and to reduce the time and resources required for the implementation of descriptive analysis with trained assessors. These novel descriptive methodologies are simple, flexible and rapid alternatives for sensory characterization with trained and untrained assessors and have
Sensory descriptive analysis of whisky is a valuable tool for understanding the sensory properties of products, the impact of process, aging and blending. When descriptive analysis (DA) is calibrated it can be used to compare products over time and origin. Traditionally, calibration was achieved by lengthy training of panellists and the precision of their results was limited. This made DA
Temporal methods have become an increasingly important tool for understanding changes that occur over an eating experience.
TDS involves selection and continuous update of a dominant attribute, and provides sequence data to characterize products. We investigate the flavoured fresh cheese TDS data set as a series of attribute dyads; e.g, a TDS sequence Start > Cream > Salty > Stop would be tokenized into dyads: Start > Cream, Cream > Salty, Salty > Stop.
Thurstonian-derived models are used widely for interpretation of sensory discrimination test results. Estimates of d´ are a signal-to-noise ratios, for which measurement sensitivity provides important context. Sensitivity is often defined descriptively (e.g. employees) rather than quantitatively (e.g. employees with sensitivity 1.2±0.2 for the relevant product category). We sought to select discrimination panelists based on quantified sensitivity estimates, and to investigate
The development of methodologies that consider the multidimensionality of sensory perception over time can contribute to the development of successful food products. The aim of the present work was to introduce a novel method for dynamic sensory characterization: Temporal Check-All-That-Apply (TCATA). TCATA is basically an extension of check-all-that-apply questions, involving continuous selection/deselection of attributes to indicate the changing sample characteristics.
Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB)