Temporal Data

Sara King/ July 10, 2012/ Tutorial/ 0 comments

Many sensory experiences have a temporal dimension, and several approaches have been proposed to capture changes in sensations with time. The workshop will review a few of those methods, including Time intensity, Temporal Attribute Discrimination, Progressive Profiling, Sequential Profiling, Temporal Dominance of Sensations, and Temporal Order of Sensations.

You know what you like, but what about everyone else? A case study on incomplete block segmentation of white-bread consumers

Sara King/ June 20, 2012/ Oral Presentation/ 0 comments

“One man’s meat is another man’s poison.” There will always be a wide range of consumer liking response across any product category. Cluster analysis can provide consumer segments based upon common liking that reflect underlying sensory preferences. To determine valid population segments requires a large sample of consumers. As the number of products tested by each consumer increases, experimental bias

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A new method for analyzing time intensity curves based on the gamma distribution

Sara King/ May 27, 2012/ Poster/ 0 comments

Single-attribute time-intensity (TI) analysis is used to determine how a sensation changes with time for a given product or stimulus. Intensity data from a single TI panellist can be represented as a curve, which is roughly concave down but may be irregular or skewed. Depending on the system, a TI curve might display multiple peaks, but in some cases TI

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Shortlisting before ranking: perception of wine region quality by Ontario consumers

Sara King/ October 27, 2010/ Poster/ 0 comments

A choose-all-that-apply (CATA) question allows respondents to select multiple answers from a list. A technique called answer piping displays the respondent’s selections as possible responses in a subsequent question. Answer piping was used to allow consumers to shortlist wine-producing regions before ranking those regions for quality in a Chilean red wine consumer study conducted in fall 2007.