EVALUATION OF WINE COMPETITION JUDGE PERFORMANCE USING PRINCIPAL COMPONENT SIMILARITY ANALYSIS

Principal component similarity (PCS) analysis was used to evaluate judge performance from a wine competition. Data were analyzed for five international judges and seven wine makers, for 42 white, 30 red and 25 specialty wines, using a 20-point quality scoring system. Principal similarity plots were used to group judges according to judging 'style' and to identify outliers, for each wine category. Judge groupings were consistent when three different references were used; however, the most interpretable PCS plot was obtained when the overall mean-judge-score was used as the reference. Results from PCS were compared to principal component analysis (PCA). PCS analysis allowed the information from all significant principal components to be graphically represented in two dimensions and was more successful in classifying judges than plots based on the first three principal components. The technique of PCS is an important complement to existing methodologies, and can provide wine competition coordinators with an objective technique for judge evaluation and selection.