USING CHECK-ALL-THAT-APPLY (CATA) TO CATEGORIZE WINES: A DECISION-MAKING TOOL FOR WINE SELECTION
Abstract
Bordeaux is the largest appellation vineyard in France. This contrasting vineyard with varied terroirs offers all styles of wine, resulting from the blending of several grape varieties. If these different profiles make the renown of Bordeaux wines, it can appear as a constraint when the aim is to study Bordeaux wines in their diversity. The selection of a representative sample can be performed by a sensory analysis carried out by trained panelists or by wine professionals, which can take several forms: consensus among experts, conventional descriptive analysis, typicality or quality evaluation. However, because of time, economic, and logistical constraints, these methods have limited applications. As an alternative to classical descriptive analysis, more intuitive methods that do not require training have been proposed recently to describe wines using an expert panel such as Napping, Free Choice or Flash Profiling, CATA or RATA. However, in order to categorize a large number of wines, the CATA method seems to be the most appropriate, especially when working with wine professionals. CATA was used in order to define the distinct profile of 143 red Bordeaux wines sold at less than 8€ and to select the wines that best represent each profile. The wines were evaluated by 62 descriptors divided into 12 groups comprising 6 visual, 33 aroma, 5 flavors, 3 taste, and 15 mouthfeel attributes, as well as overall quality perception by 48 wine experts. The results were analyzed by Correspondence Analysis (CA) followed by Hierarchical Cluster Analysis (HCA) leading to the categorization of the wines into twelve groups. One to three representative wines of each group were selected to reach 20 wines in total. In order to validate the approach, trained panelists then analyzed the selected wines with a conventional descriptive analysis and these results were compared to those obtained with CATA questions by Multiple Factor Analysis (MFA). Both methods highlighted the same main sensory characteristics as well as a similar overall quality score. Color, woody character, vegetal notes, sweetness and pleasant mouthfeel were evaluated similarly for both panels. In contrast, fruity note evaluation seems to be more complicated and highlighted limitations for the two sensory analysis approaches. Nevertheless, CATA appears as a fast and reproducible technique for categorizing a large number of wines in order to select a representative sample of the products to be studied.
DOI:
Issue: OENO Macrowine 2023
Type: Poster
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Keywords
Check-All-That-Apply, Hierarchical Cluster Analysis (HCA), multiple factor analysis (MFA), sensory characterization