Optimizing the proficiency of wine panels trained using feedback calibration

Sara King/ July 28, 2004/ Oral Presentation/ 0 comments

The performance of descriptive panels is typically determined by post-hoc data analysis. Poor panel performance is determined after the fact and arrives too late to help the panel leader in training. The Feedback Calibration Method (FCM®) is an effective method for training descriptive panellists. FCM optimizes proficiency by ensuring efficient panel training.

Two panels were recruited and trained to evaluate white wine; one panel was composed of experienced red wine panellists (Panel T), the other of panellists with no experience in sensory analysis (Panel U). Each panel used the Wine Aroma Wheel to develop their own white wine lexicon over 5 days of training sessions of 2.5h each. Panels T and U used 110 and 76 line scale attributes, respectively. Four additional training sessions were used to apply best practices from conventional training and computerized feedback. Training targets were based on 90% confidence intervals around the mean values on line scales anchored at 0 and 100. The panels refined their own training targets iteratively. At the conclusion of training, each panel evaluated the same 20 white wines in triplicate.

Permutation tests of the RV coefficient were used to compare the panels in terms of the underlying sensory space. The results of the panels were similar, and both Panel T and U were superior to a proficient conventionally trained red wine panel (Panel D). Panel U performed similarly to Panel T on proportion of attributes discriminated and disagreement using a two-way mixed-model analysis of variance and on multivariate discrimination evaluated by a MANOVA with the same mixed model. Evaluation means for product*attribute fell within the training range targets in 59% of the cases for Panel T and 68% for and U, providing an indication of the panels’ abilities to hit the training targets. Panel U was shown to be proficient (p=0.05) after only 9 formal training sessions (22.5h), a reduction in training time of 48.75%.

Findlay, C.J., Castura, J.C., Schlich, P., & Lesschaeve, I. (2004). Optimizing the proficiency of wine panels trained using feedback calibration. In: 7th Sensometrics Meeting. July 28-30. Davis, CA, USA.


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