Most information regarding the suitability of wine and cheese pairs is anecdotal information. The objective of this research was to provide recommendations based on scientific research for the most desirable "wine & cheese pairs" using nine award-winning Canadian cheeses and 18 BC wines (six white, six red and six specialty wines). Twenty-seven wine and food professionals rated the wine and cheese pairs using a bipolar structured line scale (12 cm). The "ideal pair," scored at the midpoint of the scale, was defined as a wine and cheese combination where neither the wine nor the cheese dominated. For each cheese, mean deviation-from-ideal scores were determined and evaluated by analysis of variance. Scores closest to six were considered "ideal," while higher or lower scores represented pairs where the "wine" or the "cheese" dominated, respectively. In general, white wines had mean scores closer to six ("ideal") than either the red or specialty wines. The late harvest, ice and port-type wines were more difficult to pair. Judges varied considerably in their individual assessments reflecting a high degree of personal expectation and preference.

Cluster analysis, consonance analysis, principal component analysis (PCA) and the GRAPES program (Schlich 1994) were compared for the evaluation of panel performance. Ten judges evaluated 25 Merlot wines for 24 color, aroma and flavor attributes. Cluster analysis grouped similar judges. PCA identified judges according to their attribute use. Consonance analysis determined a numerical index for attribute agreement and the GRAPES program compared judges in their use of the scale, reliability, discrimination and disagreement. Three of the four techniques provided a graphical representation of similarities and differences between judges. Methodologies were best used in conjunction with one another. Ultimately the application of these tools will serve to improve the quality of sensory evaluations.

Three data collection procedures, sorting and two forms of projective mapping (PM), were compared for ease‐of‐use and the ability to produce meaningful spatial maps when analyzed using Multidimensional Scaling (MDS), Generalized Procrustes Analysis (GPA) or Coordinate Averaging (CA). Eighteen commercially available snack bars were evaluated for similarity‐of‐use by two panels of 24. MDS of the sorting data and Procrustes analyses of PM data collected on unlabeled axis grouped the bars according to function and provided a meaningful spatial relationship in one dimension. However, MDS analysis of these PM data grouped the bars by similarity‐of‐use and provided a meaningful spatial interpretation in two dimensions. The CA analysis was not effective in separating the bars by similarity‐of‐use but did provide an indication of liking. A comparison of spatial configurations using RV coefficients showed moderate correlations between the methods. A panelist survey showed no significant differences in the ease‐of‐use, task interest or level‐of‐satisfaction with the final arrangement between the sorting and the PM data collection methods, but panelists did find it easier to change their minds using the PM procedure.

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