The role of expert judgments in wine quality assessment: the mismatch between chemical, sensorial and extrinsic cues

Purpose The complexity in determining the quality of a credence good like wine increases due to the lack of mandatory ingredient labeling. This has generated a significant information asymmetry in the wine market, leading consumers to delegate their purchase decisions to expert rankings and wine guides. This paper explores whether expert assessments reduce the information asymmetry caused by the absence of ingredient labeling in the wine market.Design/methodology/approach By employing analysis of variance (ANOVA) in a sample of 304 wines included in the Wine Guide of the Spanish Consumers Organization (OCU), this paper assesses the extent to which expert assessments based on sensory evaluations converge with the objective cues provided by laboratory analysis in wine quality evaluations.Findings Results reveal a mismatch between expert assessments and laboratory analyses. Chemical aspects such as SO2 levels or volatile acidity, sensorial factors such as intensity and persistence, and extrinsic variables such as the region of origin or wine type play an important role in the quality ranking of wines.Originality/value These findings call for the inclusion of objective intrinsic cues in expert sensory assessments to provide consumers reliable information about wines and to resolve the apparent dissonances in wine quality assessments.

Machine Learning Techniques Disclose the Combined Effect of Fermentation Conditions on Yeast Mixed-Culture Dynamics and Wine Quality

The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers’ demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding the factors that determine the balance between the yeast-yeast interactions and their responses triggered in complex environments. Our previous studies demonstrated that the strain Hanseniaspora guilliermondii UTAD222 has potential to be used as an adjunct of S. cerevisiae in the wine industry due to its positive impact on the fruity and floral character of wines. To rationalize the use of this yeast consortium, this study aims to understand the influence of production factors such as sugar and nitrogen levels, fermentation temperature, and the level of co-inoculation of H. guilliermondii UTAD222 in shaping fermentation and wine composition. For that purpose, a Central Composite experimental Design was applied to investigate the combined effects of the four factors on fermentation parameters and metabolites produced. The patterns of variation of the response variables were analyzed using machine learning methods, to describe their clustered behavior and model the evolution of each cluster depending on the experimental conditions. The innovative data analysis methodology adopted goes beyond the traditional univariate approach, being able to incorporate the modularity, heterogeneity, and hierarchy inherent to metabolic systems. In this line, this study provides preliminary data and insights, enabling the development of innovative strategies to increase the aromatic and fermentative potential of H. guilliermondii UTAD222 by modulating temperature and the availability of nitrogen and/or sugars in the medium. Furthermore, the strategy followed gathered knowledge to guide the rational development of mixed blends that can be used to obtain a particular wine style, as a function of fermentation conditions.