#### Luigi Fabbrisa , Alfonso Piscitellib a Tolomeo studi e ricerche, Padua, Italy **Experience, sensorial skills and personality qualifying a wine consumer as an expert**

**Experience, sensorial skills and personality qualifying a wine consumer as an expert** 

 Department of Agricultural Sciences, Federico II University of Naples, Naples, Italy Luigi Fabbris, Alfonso Piscitelli

# **1. Introduction**

b

This paper highlights the characteristics of wine consumers that may qualify them as wine experts. In this work, the expertise of wine consumers was measured through various degrees of self-perceived ability. Participants ranged from limited-knowledge consumers to consumers with enough knowledge to perceive wine quality or recognise certain wines and, finally, to professional experts.

Wine is an 'experience good' in that its quality is unknown before consumption. Thus, a wine expert is not only knowledgeable about wine but also practises wine consumption as a continual consumer. In Italy, wine is a cultural product as well, as the consumption habitus depends on consumer taste, which is crucial in choosing products (Bourdieu, 2005). Wine culture is defined as the capacity to harmonise wine and food and conceive of wine as a nutritional, social and health-related means. In this work, the cultural roots of wine were measured through a 'semantic differential' (Osgood et al., 1957) of wine preferences, which was determined with a rating scale designed to measure the assessors' preferences for wine.

Our basic hypothesis is that wine expertise is causally dependent on cognitive and noncognitive characteristics of the wine experience, sensorial skills that are relevant to wine assessment and wine consumption culture. To test this hypothesis, we evaluated a convenience sample of consumers to examine the relationships between their self-assessment of wine expertise and qualification of their wine-related training and experience (consumption, production, purchase), their sensorial skills (visual, olfactory, gustative), their enogastronomic culture and their approach to evaluating a set of selected wines. The research data were obtained from an evaluation questionnaire completed by a sample of wine assessors at a tasting experiment which was held during a scientific meeting in Pescara, Italy in September 2018. The sample includes both meeting participants and external experts involved in AIS-Abruzzo, the regional association of chartered sommeliers.

The paper is organised as follows. After this introduction, Section 2 describes the methodological aspects of the tasting experience and introduces the model for data analysis. Then, Section 3 presents the main results of the statistical analysis of the collected data. Finally, Section 4 interprets the data with reference to the mainstream literature on wine expertise analysis.

## **2. Data and methods**

### **2.1 The tasting experience**

In September 2018, a sensory evaluation experiment was conducted on 12 white wines originating from six grape varieties (*Trebbiano d'Abruzzo, Pecorino d'Abruzzo, Passerina d'Abruzzo, Pagadebit di Romagna* and *Pignoletto di Romagna*) from two Italian regions, Abruzzo and Romagna. All wines were controlled designation of origin (DOC) products. The pool of tasters included 48 individuals, of whom 30 typically consumed mild amounts of wine

213 Luigi Fabbris, University of Padua, Italy, luigi.fabbris@unipd.it, 0000-0001-8657-8361

Alfonso Piscitelli, University of Naples Federico II, Italy, alfonso.piscitelli@unina.it, 0000-0001-6638-2759

FUP Best Practice in Scholarly Publishing (DOI 10.36253/fup\_best\_practice)

Luigi Fabbris, Alfonso Piscitelli, *Experience, sensorial skills and personality qualifying a wine consumer as an expert*, pp. 229-234, © 2021 Author(s), CC BY 4.0 International, DOI 10.36253/978-88-5518-461-8.43, in Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci (edited by), *ASA 2021 Statistics and Information Systems for Policy Evaluation. Book of short papers of the on-site conference*, © 2021 Author(s), content CC BY 4.0 International, metadata CC0 1.0 Universal, published by Firenze University Press (www.fupress.com), ISSN 2704-5846 (online), ISBN 978-88-5518-461-8 (PDF), DOI 10.36253/978-88-5518-461-8

(mild consumers), and 18 were professional sommeliers belonging to the AIS-Abruzzo association. Both mild consumers and sommeliers were selected on the basis of their interest in and availability for the experiment as well as their experience in wine consumption.

The wine characteristics considered in this evaluation experiment were selected through an anonymous paper questionnaire. This questionnaire asked participants to make judgements on 11 intrinsic attributes of appearance, nose and palate for four wines that were randomly selected from the 12 at hand. Subsequently, participants were instructed to provide an overall judgement of each wine. The questionnaire also gathered data on the tasters regarding their background characteristics, their drinking habits, and the relevance of wine in their diet and social life. In this work, we confine the analysis to the characteristics of tasters. The characteristics of the assessed wines enter the analysis only as distributional parameters (mean and variance) of the scores which single assessors assigned to the tasted wines.

## **2.2 The experiment**

The experiment involved a horizontal tasting, as it compared only white wines from the same terroir and of the same vintage. On this basis, it is possible to obtain comparative judgements between the selected wines. In accordance with a fractional factorial experiment, each taster was administered four randomly selected wines from different grapes. The sampling of the administered wines was carried out at the grape-variety level. Only four of the six possible varieties were administered to any taster, and one of the two potential cellars was randomly selected. In this case, the experiment sampled possible choices rather than choosers (Manski and Lerman, 1977).

The sampling design followed a systematic pattern such that each grape variety appeared 8 times every 12 trials. Thus, each wine variety had 32 repetitions once 48 tasters had performed their task; consequently, the number of repetitions of each variety by cellar was 16.

Each taster had five glasses: one for water and four for the wines. The wines were poured in a flight. In the tasting session, the judges received 6 centilitres of each of the four randomly selected wine varieties, which were served at the same cold temperature. The protocol envisaged that tasters could taste and re-taste before concluding preferential judgements, and they would evaluate the intrinsic attributes of each tasted wine.

## **2.3 Analytical model**

The model for data analysis includes the self-evaluation of wine expertise as a dependent variable, *Y*, a set of possible regressors, **X**, and a set of control variables, **Z**. The relationship may be written as

$$Y = f(X\_1, \, X\_2, \, X\_3, \, X\_4, \, X\_5 \mid Z),$$

where *X1* denotes wine expertise and learning experience, *X2* represents the descriptors of wine habits, *X3* refers to the sensorial skills, *X4* signifies the descriptors of wine-related attitudes and culture, and *X5* is the evaluation style of the tasted wines. The latter was measured through the mean and standard deviation of the scores for the four tasted wines. The underlying hypothesis was that the evaluations by experts would be more critical and uniform than those of nonexperts. The control variables, which were forced into the model, were gender, age and smoking experience. The *Y* (ordinal) variable was measured on four levels.

The ordinal logistic regression model is written as follows (Agresti, 2002; Bilder and Loughin, 2014):

$$\text{logit}\left[p(Y \le j)\right] = \beta\_{j0} + \beta\_I X\_I + \dots + \beta\_p X\_p \quad (j = I, \dots, J-I), \quad i$$

where *logit(p) = ln[(p/(1-p)]*, and *βi* measures the relation between *Y* and *Xi* when all other variables in the model remain fixed. We adopted the proportional odds model, which assumes that the logit of the cumulative probabilities changes linearly as the regressors change, and the slope of the relationship between *Y* and the *X*'s is the same regardless of the category *j* of variable *Y*.

A logistic regression model to an ordered response variable was performed with the *polr* function from MASS package (R Core Team, 2021). After that, the *stepAIC* function was utilised to perform stepwise model selection with criterion *AIC*.

## **3. Results**

Of the 48 assessors, five (10.4%) considered themselves to be wine experts, and eight (16.7%) stated that they were able to recognise some wines but did not consider themselves to be wine experts. The majority of the participating sommeliers classified themselves in the latter category. A larger group of assessors (47.9%) indicated that they possessed sufficient knowledge of wine to adequately understand its quality. Finally, 25% of the assessors admitted that they knew little or very little about wine.

Overall, our sample included a group of experts and a group of nonexperts (each accounting for approximately one-quarter of the tasters) as well as a larger, intermediate category of mildly informed amateurs (about one-half of the tasters). Only 3 of the 48 assessors produced or bottled their own wine; the others bought it occasionally or on a monthly basis either at vineries or in supermarkets or wine shops. A few (8.3%) purchased wine through the internet.

Regarding wine practice, about 56% of assessors had been consuming wine for decades, usually with dinner. The majority (54.2%) had attended a wine-tasting session coordinated by a sommelier. One-half of the tasting sample was female, in which the average age was 47. This group mostly had a college degree (66.7%), worked mainly at a university (81.3%) and did not smoke (41.7% had never smoked, and 29.2% had formerly smoked).

Table 1 summarises the results of the regression analysis and presents the estimates of the regression betas and their significance. We highlight the elevated significance of the statistical analysis: R2 =61.3%. To corroborate the regression results, selected covariates are crossed with the self-perceived expertise of assessors (Table 2).


**Table 1.** Beta estimates of the regression model with expertise level as criterion variable (forward stepwise selection of regressors, n=48; R2 =61.3%; AIC criterion=76.53;

*Note: Some regressors are not individually significant but are significant wrt AIC criterion.*

The analysis supports the following claims:



**Table 2.** Some covariates, by the self-perceived expertise of assessors

*Note: The levels "being wine experts" and "being able to recognise some wines" of the self-evaluation of wine expertise, have been merged into the "Expert" category.* 


## **4. Discussion and conclusion**

This work has aimed to define the characteristics of wine experts. An expert is a person who possesses in-depth knowledge, abundant experience, a proclivity for vivid imagery and a stronger descriptive capacity than that of other people (Parr et al., 2002; Ericsson et al., 2007; Croijmans and Majid, 2016; Croijmans et al., 2020). A wine expert also demonstrates an acute capacity to recognise, classify and evaluate wine characteristics. Notably, skill training can be particularly effective with practice.

The analysis illustrates that experts assumed a leading role in wine selection, which is a habit that they adopted decades ago and had improved over time. The attendance of specific courses and tasting events led by sommeliers or between-peer contests improved their expertise and self-confidence. An expert clearly seeks to train themself through both the exploration of new sensations and products and the intensification of their sensorial skills.

Furthermore, the data analysis indicates that experts perceive themselves as different from producers and bottlers. For both producers and experts, wine is a professional means as well as an important part of their life. However, producers (should) know how to make high-quality wines, whereas experts approach wine from a position which resembles that of an explorer who is constantly seeking out new land to discover. For experts, such exploration targets unfamiliar sources of sensation (e.g. aromas, bouquets, flavours, terroirs, ages, faults) for themselves and possibly for other initiates as well.

Several experiments have reported the tendency of experts to scout out new sensations. For example, in a study by Mariño-Sánchez et al. (2010), Spanish wine tasters perceived more odours as intense but fewer as irritating compared to the non-trained healthy population. The identification of wine peculiarities, in particular when practicing the olfactory-gustatory skill, involves cognitive skills. Croijmans et al. (2020) have suggested that expertise entails a heightened ability to imagine hidden structures, thus extending the plasticity of cognition which underlies the chemical senses. On this basis, a wine expert can recognise, discriminate and match the peculiarities of a wine much more effectively than a novice. According to Ericsson et al. (2007), a particular kind of practice – a deliberate practice – is imperative to develop expertise. The practice of wine experts essentially concerns revelation, as they strive to identify elements of wine that were previously unknown or difficult for most people to detect independently. Therefore, a wine expert resembles a member of an uncovered sect or, in more politically correct terms, a highly exclusive professional cluster.

In this study, the experts evaluated the tasted wines more highly than the other tasters, which may be considered an indirect compliment to the people who selected the tasted wines. Moreover, the category of experts displayed significantly less divergence in their evaluation scores compared to those of the other tasters. This finding supports the view of experts as a compact cluster and implies that their judgements of wine are generally more reliable than those of other assessors.

Finally, the findings highlight some differences amongst the evaluation styles of experts. In view of this, future research could consider an analysis of between-expert differences.

# **References**

Agresti, A. (2002). *Categorical Data Analysis*, 2nd edition. Wiley, Hoboken, NJ.

