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 established their own training targets based on 90% confidence intervals. Panels were calibrated with CompusenseFCM®. Training targets were iteratively refined over 4 sessions. When training concluded each panel evaluated the same 20 white wines in triplicate. Permutation tests of the RV coefficient demonstrated strong similarity between the panels’ product configurations in sensory space.
There were 46 attributes (29 aroma, 6 taste/mouth feel, 11 flavour) with similar or identical descriptors and reference standards. For each common attribute, panellist mean scores across all products were calculated. Centroid cluster analysis formed panellist groups consistent with panel membership, reflecting the panel-specific manner in which line scales were used. For each panellist, the scale distance between the maximum and minimum wine mean scores was then obtained for each attribute, and Fisher’s LSD (p=0.05) calculated. Dividing range by LSD value reflected a panellist’s ability to discriminate wines using the attribute; higher scores indicated greater differences being detected. Groups formed when these quotients were submitted to centroid cluster analysis did not reflect panel membership. Quotients calculated on panel mean scores showed Panel T had higher quotients for 20 of 46 attributes, further supporting the similarity in ability to detect differences.
Regardless of previous descriptive sensory training and calibration that resulted from panel-independent generation and refinement of training targets using Compusense FCM®, individual panellists performed similarly in detecting differences among wines. Furthermore, both panels produced meaningful product profiles and displayed similar abilities to detect differences.
Castura, J.C., & Findlay, C.J. (2005). Generating, refining, and calibrating targets: comparing the performance of panellists on two white wine panels. In: 6th Pangborn Sensory Science Symposium. August 7-11. Harrogate, Yorkshire, UK.