2008, 2007

Multivariate and probabilistic analyses of sensory science problems

Sara King/ August 20, 2007/ Book/ 0 comments

Sensory scientists are often faced with making business decisions based on the results of complex sensory tests involving a multitude of variables. Multivariate and Probabilistic Analyses of Sensory Science Problems explains the multivariate and probabilistic methods available to sensory scientists involved in product development or maintenance. The techniques discussed address sensory problems such as panel performance, product profiling, and exploration

1208, 2007

Applying enhanced descriptive sensory analysis training: A case study

Sara King/ August 12, 2007/ Poster/ 0 comments

The cost and time required for training descriptive analysis panels is often cited as a major barrier to the routine application of descriptive sensory analysis. Compusense FCM® was developed as a method to accelerate the training of descriptive panels and to provide a mechanism for calibration that would stabilize descriptive analysis data over time and across panels.

1208, 2007

Visualizing micro and macro structures in descriptive sensory training data

Sara King/ August 12, 2007/ Poster/ 0 comments

Training sessions often yield a limited dataset, which in turn restricts available analyses. Gathering ideal data sets for analysis might be at odds with imperatives of training regimen. Raw data is too voluminous to consider in numerical form. Humans have excellent ability for pattern recognition. Multifunctional graphs can reveal both macro and micro structures in the data.

2203, 2007

Feedback calibration: A training method for descriptive panels

Sara King/ March 22, 2007/ Peer-reviewed Paper/ 0 comments

Training targets were established using descriptive analysis profiles of 20 commercial red wines produced by a well-trained, experienced determination panel. After recruitment, screening and a basic sensory orientation of ten 2 h common training sessions, 16 inexperienced panelists were divided by lottery into two panels. The control panel received a more conventional performance debriefing at the end of each training

2609, 2006

A system for classifying sensory attributes

Sara King/ September 26, 2006/ Poster/ 0 comments

Descriptive analysis is applied to a diverse range of complex, real-world food and consumer products because the information it provides about those products is unrivalled in its richness. A common lexicon allows the descriptive sensory panel to reference sensory attributes of products undergoing study in a highly specific and consistent manner. When combined with best practices it eliminates ambiguity of

0208, 2006

Enriching sensory and consumer datasets with temporal metadata

Sara King/ August 2, 2006/ Oral Presentation/ 0 comments

Descriptive analysis provides valuable information about the sensory properties of consumer products, but this information lacks the temporal dimensionality of real-world sensory experiences. Type II error occurs when the descriptive sensory panel fails to differentiate between products known to be discriminable. Findlay (2000) reported no meaningful reduction in beta risk when descriptive analysis on manipulated salad dressings was augmented by

0208, 2006

Enriching sensory and consumer datasets with temporal metadata

Sara King/ August 2, 2006/ Oral Presentation/ 0 comments

2204, 2006

Use of feedback calibration to reduce the training time for wine panels

Sara King/ April 22, 2006/ Peer-reviewed Paper/ 0 comments

The performance of descriptive panels is typically determined by post-hoc data analysis. Poor panel performance is measured after the fact and often arrives too late to help the panel leader during training sessions. The feedback calibration method (FCM) optimizes proficiency by ensuring efficient panel training. A previously trained panel (Panel T) and an untrained panel (Panel U) developed and refined

2104, 2006

Monitoring calibration of descriptive sensory panels using distance from target measurements

Sara King/ April 21, 2006/ Peer-reviewed Paper/ 0 comments

Training targets can be established from product profiles that provide an objective representation of the underlying sensory characteristics of a group of products. If available to a panel leader, these training targets can be used to calibrate a panel and measure the accuracy of their responses.

2008, 2005

Setting meaningful attribute targets for feedback training of descriptive panellists

Sara King/ August 20, 2005/ Poster/ 0 comments

Compusense FCM® (feedback calibration method) has been shown to be an effective tool to train descriptive analysis panels. The key to making this method work is providing “true” information, feedback, to panellists at the time they evaluate the attribute. This permits immediate calibration of the response. If the feedback is either trivial or incorrect, the panellist may be confused and the