The application of calibrated difference-from-control for sensory quality control of distilled beverages

Sara King/ May 30, 2017/ Oral Presentation, Poster/ 0 comments

Sensory quality control is an essential mechanism for ensuring the sensory integrity of a product is not compromised. By conducting sensory testing throughout the distilling process it becomes possible to detect and reject faulty raw ingredients and intermediate products before these advance to the next stages, resulting in further contamination.

Immediate feedback training for difference from control panels

Sara King/ August 22, 2015/ Poster/ 0 comments

Panelist training is essential for a successful analytical sensory analysis, like Difference from Control (DFC). Panelist performance requires feedback, calibration and motivation. Typically, panelists are recruited, screened, trained and qualified before becoming part of an ongoing DFC quality panel. This study compared the impact of training on two groups of vodka quality panelists. A pool of panelists, inexperienced in vodka

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Feedback calibration training improves whisky sensory profiling

Sara King/ September 8, 2014/ Oral Presentation, Poster/ 0 comments

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

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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.

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

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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

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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

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Generating, refining, and calibrating targets: comparing the performance of panellists on two white wine panels

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

Two panels, one composed of experienced red wine panellists (Panel T), the other of panellists without experience in sensory analysis (Panel U), were recruited and trained to evaluate white wine. Each used the Wine Aroma Wheel to develop white wine lexicons over five 2.5h training sessions. Panels T and U used 110 and 76 line scale attributes, respectively. Each panel

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