Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-20T10:45:32.247Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating Wine-Tasting Results and Randomness with a Mixture of Rank Preference Models*

Published online by Cambridge University Press:  25 February 2015

Jeffrey C. Bodington
Jeffrey C. Bodington*
Affiliation:
Bodington & Company, 50 California St. #630, San Francisco, CA 94111; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Evaluating observed wine-tasting results as a mixture distribution, using linear regression on a transformation of observed results, has been described in the wine-tasting literature. This article advances the use of mixture models by considering that existing work, examining five analyses of ranking and mixture model applications to non-wine food tastings and then deriving a mixture model with specific application to observed wine-tasting results. The mixture model is specified with Plackett-Luce probability mass functions, solved with the expectation maximization algorithm that is standard in the literature, tested on a hypothetical set of wine ranks, tested with a random-ranking Monte Carlo simulation, and then employed to evaluate the results of a blind tasting of Pinot Gris by experienced tasters. The test on a hypothetical set of wine ranks shows that a mixture model is an accurate predictor of observed rank densities. The Monte Carlo simulation yields confirmatory results and an estimate of potential Type I errors (the probability that tasters appear to agree although ranks are actually random). Application of the mixture model to the tasting of Pinot Gris, with over a 95% level of confidence based on the likelihood ratio and t statistics, shows that agreement among tasters exceeds the random expectation of illusory agreement. (JEL Classifications: A10, C10, C00, C12, D12)

Keywords

mixture modelpreference rankstatisticswine tasting
Type
Articles
Information
Journal of Wine Economics , Volume 10 , Issue 1 , May 2015 , pp. 31 - 46
Copyright
Copyright © American Association of Wine Economists 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

The author thanks an anonymous reviewer and Professor Thomas Brendan Murphy, School of Mathematical Sciences at University College Dublin for their helpful comments. All remaining errors and omissions are the responsibility of the author alone.

References

Ashton, R.H. (2012). Reliability and consensus of experienced wine judges. Journal of Wine Economics, 7(1), 70–87.Google Scholar
Bockenholt, U. (1992). Thurstonian representation for partial ranking data. British Journal of Mathematical and Statistical Psychology, 45, 31–49.Google Scholar
Bodington, J. (2012). 804 tastes: Evidence on randomness, preferences and value from blind tastings. Journal of Wine Economics, 7(2), 181–191.Google Scholar
Cao, J. (2014). Quantifying randomness versus consensus in wine quality ratings. Journal of Wine Economics, 9(2), 202–213.Google Scholar
Cleaver, G., and Wedel, M. (2001). Identifying random-scoring respondent in sensory research using finite mixture regression results. Food Quality and Preference, 12 (2001), 373–384.Google Scholar
Critchlow, D.E. (1985). Metric Methods for Analyzing Partially Ranked Data. New York: Springer.Google Scholar
Critchlow, D.E., and Fligner, M.A. (1991). Paired comparison, triple comparison, and ranking experiments as generalized linear models, and their implementation on glim. Psychometrika, 56(3), 517–533.CrossRefGoogle Scholar
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.Google Scholar
Gormley, I.C., and Murphy, T.B. (2008). A mixture of experts model for rank data with applications in election studies. Annals of Applied Statistics, 2(4), 1452–1477.Google Scholar
Hanson, D.J. (2013). Historic Paris Wine Tasting of 1976 and Other Significant Competitions. Accessed May 10, 2014, at http://www2.potsdam.edu/alcohol/Controversies/20060517115643.html#.U3KmVy8x_5U.Google Scholar
Linacre, J.M. (1992). Rank order and paired comparisons as the basis for measurement. Paper presented at American Educational Research Association Annual Meeting, April.Google Scholar
Mallows, C.L. (1957). Non-null ranking models. Biometrika, 44 (1–2), 114–130.Google Scholar
Maltman, A. (2013). Minerality in wine: A geological perspective. Journal of Wine Research, 24(3), 169–181.Google Scholar
Marden, J.I. (1995). Analyzing and Modeling Rank Data. London: Chapman & Hall.Google Scholar
McLachlan, G., and Peel, D. (2000). Finite Mixture Models. New York: John Wiley & Sons.Google Scholar
Mengersen, K.L., Robert, C.P., and Titterington, D.M. (2011). Mixtures: Estimation and Application. New York: John Wiley & Sons.Google Scholar
Nombekla, S.W., Murphy, M.R., Gonyou, H.W., and Marden, J.I. (1993). Dietary preferences in early lactation cows affected by primary tastes and some common feed flavors. Journal of Dairy Science, 77, 2393–2399.Google Scholar
Pendergrass, P.N., and Bradley, R.A. (1960). Ranking in triple comparisons. In Olkin, I. et al. (Ed.), Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Palo Alto: Stanford University Press, 331–351.Google Scholar
Plackett, R.L. (1975). The analysis of permutations. Applied Statistics, 24, 193–202.CrossRefGoogle Scholar
Quandt, R.E. (2012). Comments on the Judgment of Princeton. Journal of Wine Economics, 2(7), 152–154.Google Scholar
Soares, S., Sousa, A., Mateus, N., and de Freitas, V. (2012). Effect of condensed tannins addition on the astringency of red wines. Chemical Senses, 32(2), 191–198.Google Scholar
Taber, G.M. (2004). Judgment of Paris: California vs. France and the Historic 1976 Paris Tasting That Revolutionized Wine. New York: Scribner.Google Scholar
Thuesen, K.F. (2007). Analysis of ranked preference data. Master's thesis, Technical University of Denmark, Kongens Lyngby, Denmark.Google Scholar
Vigneau, E., Courcoux, P., and Semenou, M. (1999). Analysis of ranked preference data using latent class models. Food Quality and Preference, 10(1999), 201–207.Google Scholar