Wine Identification Experiment

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Table of Contents

Introduction
Method
Results
Discussion
References

Introduction

The expertise in identifying wines is stated to be based on the high perceptual skills of the experts. The question that might be asked in that matter is whether such skills can be trained. One of the important processes of evaluation is olfaction, considering that the aroma of the wine is an important part of its flavor. Accordingly, the importance of such a process is related to the fact that humans are stated to have impoverished language for describing odors. In that regard, considering that accurate identification of odors can occur following brief training, there is a rationale for investigating olfaction as a training option. With the data important for such assessment being based on whether the identification was successful or not, such data is quantifiable, for which a quantitative research method might be seen appropriate.

A quantitative experiment was conducted to investigate the ability of novices to differentiate different wines based on smell. Accordingly, the experiments aimed at identifying whether different factors can impact the ability of the novices to differentiate wine samples. The present report describes the methodology of the experiment and provides a discussion of the results.

Method

Participants

A total of 27 participants were selected for the experiment, ten males and 17 females all of which were of the same age group, with a mean of 25. The participants were divided into two groups, natural group and enhanced group, with 14 and 13 participants respectively. The natural group consisted of 3 males and 11 females, while the enhanced group consisted of 7 males and 6 females. There were no significant differences between the groups in terms of such aspects as the number of smokers, nasal issues, or concussions. According to the Australian Wine Knowledge Questionnaire, there were no significant differences between the groups in terms of wine knowledge.

Materials

Odor samples of three wines were prepared which are Shiraz, Cabernet Sauvignon, and Pinot Noir, along with three relevant additives, Pepper, Blueberry, and Cherry. For each of the wines, a combination of wine samples was presented along with two identical wine foils.

Procedure

The experiment consisted of two blocks of testing, 1 and 2, each with 18 trials. In each of the trials in all blocks, the participants had to identify the target wine among three samples of two foils. In the first block, the natural groups were exposed to wines without additives, while the enhanced group was exposed to the samples with the relevant additives. In the second block of testing, both groups were under the same conditions, where the target sample had a relevant additive, and the foils did not. The dependent variable in the study is the number of correctly identified odors. The independent variables in the study are the addition of relevant additive and their pre-exposure.

Results

The results of the experiment can be seen through the following table:

Group	Block One		Block Two
Group	Average of correct Items Identified	SD	Average of correct Items Identified	SD
Enhanced group	10.9	0.90	11.2	0.77
Natural group	6.5	0.87	13.5	0.75

Discussion

The probability of correctly identifying the odor by chance in this experiment is 6 out of 18, i.e. 33.3%. The results of each block of testing for both groups show results better than chance. The results of the natural group in the first block are not significantly better than chance, which might imply that such a task is difficult for novices. Nevertheless, the wines were discriminable, which supports Cains classic study, in which the participants were able to identify correctly 50 out 80 odors in an initial session.

The percentage of Cains study, 0.62 is essentially close to the results of this study, 0.6 for the enhanced group in the first block and 0.62 in the second. For the natural group, the result was less conforming to the study, having a result a little more than a chance probability in the first block, and an impressive 75 % in the second. The poor performance in the first block can be seen as a support of the notion that in the absence of cues most people will perform poorly in the identification of odors in the absence of a context. Accordingly, the absence of any of the factors on which successful odor identification might depend, which are (i) commonly encountered substances, (ii) a long-standing connection between an odor and its name, and (iii) aid in recalling the name.

The difference between the groups is statistically significant, which indicates that the inclusion of additives raises the chance of correctly identifying an odor, providing a context for the comparison. The differences between the groups in block 2, which constituted of trials in similar conditions, can be explained by such notion as the CS-pre-exposure effect. It is stated that the pre-exposure to to-be-conditioned stimuli results in a retarded development of inhibitory conditioning and conditioned taste aversion. In that regard, the natural group was not pre-exposed to an additive, in block 1, leading to a more significant increase in odor identification. Thus, it can be concluded that the reliance on smell alone might not provide sufficient information for identification, while the addition of relevant additives might raise the probability of correctly identifying wines.

References

Cain, W. S. (1979). To Know with the Nose: Keys to Odor Identification. Science, 203(4379), 467-470.

Nakajima, S., Takahashi, K., & Blaisdell, A. P. (2006). An assessment of context-specificity of the CS-preexposure effect in Pavlovian excitatory and inhibitory conditioning. Behavioural Processes, 73(1), 84-91. Web.

Parr, W. V., Heatherbell, D., & White, K. G. (2002). Demystifying Wine Expertise: Olfactory Threshold, Perceptual Skill and Semantic Memory in Expert and Novice Wine Judges. Chemical Senses, 27(8), 747-755. Web.

Russell, A. M. T., & Boakes, R. A. (2011). Identification of confusable odours including wines: Appropriate labels enhance performance. Food Quality and Preference, 22(3), 296-303. Web.

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