TDS data can be characterized by a series of TDS dyads that represent the selection of a single dominant attribute, which is updated over time. As such, TDS data describes the presence of different dominant attributes in food products.
It is common practice to incorporate chance and significance lines in Temporal Dominance of Sensations (TDS) curves. These lines draw attention to time intervals at which an attribute is cited (significantly) more often than would be expected by chance.
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.
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.