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
Generating, refining, and calibrating targets: comparing the performance of panellists on two white wine panels
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
Typically, in multivariate analysis, the relationship between products is dependent upon the samples chosen. Once the sensory space is properly defined, products may be removed and inserted without disturbing the underlying sensory dimensions. This powerful approach allows sensory scientists to predict and model product behavior.