Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Biometrical Letters

The Journal of Polish Biometric Society

2 Issues per year

Open Access
Online
ISSN
1896-3811
See all formats and pricing
More options …

A Bayesian model to compare vinification procedures

Federico Mattia Stefanini
  • Dipartimento di Statistica, Informatica, Applicazioni `G.Parenti', Università degli Studi di Firenze, viale Morgagni 59, I-50134 Firenze, Italia
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ottorino-Luca Pantani
  • Corresponding author
  • Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente, Università degli Studi di Firenze, Piazzale Cascine 28, I-50144 Firenze, Italia
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-12-10 | DOI: https://doi.org/10.2478/bile-2013-0018

Summary

The effects of three pre-fermentative techniques (standard procedure, cold soak pre-fermentation and cryomaceration), temperature (20 or 30°C) and saignée (with/without) on the extraction of total anthocyanins were investigated during maceration of must obtained from Sangiovese grapes. A Bayesian hierarchical model was developed to estimate time-dependent contrasts while addressing the peculiar features displayed by the experimental units (wine tanks): substantial heterogeneity among replicates, departure from low-order `textbook' kinetics and the occasional presence of very low observations. Prior distributions of critical model parameters were elicited with the help of wine{making experts and by considering the results of previous experiments. The posterior distribution of model parameters was approximated by Markov Chain Monte Carlo simulation using JAGS software. Among the main findings, it is to be highlighted that temperature and saignée increased the total anthocyanin concentration in all the techniques, although at different times during maceration. In all the procedures the total anthocyanin gain decreased as the maceration came to an end.

Keywords: semiparametric regression; outliers; MCMC; wine making; pre-fermentation treatments

  • Amendola D., De Faveri D., Spigno G. (2010): Grape marc phenolics: extrac­tion kinetics, quality and stability of extracts. Journal of Food Engineering 97(3): 384-392.Web of ScienceCrossrefGoogle Scholar

  • Amrani Joutei K., Glories Y. (1995): Tanins at anthocyanes: localisation dans la baie de raisin et mode d’extraction. Revue Francaise d’Oenologie 153: 28-31.Google Scholar

  • Andrich G., Zinnai A., Venturi F., Fiorentini F. (2005): A tentative mathematical model to describe the evolution of phenolic compounds during the maceration of Sangiovese and Merlot grapes. Italian journal of food science 17: 45-58.Google Scholar

  • Brooks S., Gelman A. (1997): General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7: 434-455.Google Scholar

  • Bucic-Kojic A., Planinic M., Tomas S., Bilic M., Velic D. (2007): Study of solid- liquid extraction kinetics of total polyphenols from grape seeds. Journal of Food Engineering 81(1): 236-242.CrossrefGoogle Scholar

  • Buratti S., Ballabio D., Benedetti S., Cosio M. (2007): Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Chemistry 100(1): 211-218.Web of ScienceCrossrefGoogle Scholar

  • Couasnon M.B. (1999): Une nouvelle technique: la maceration prefermentaire a froid - Extraction a la neige carbonique. Premier partie: Resultats oenologiques. Revue des Oenologues 92: 26-30.Google Scholar

  • Cowles M., Carlin B. (1996): Markov Chain Monte Carlo convergence diagnos­tics: a comparative study. Journal of the American Statistical Association 91: 883-904.CrossrefGoogle Scholar

  • Cuenat P., Lorenzini F., Bregy C., Zufferey E. (1996): La maceration prefer- mentataire a froid du Pinot noir. Aspects technologiques et microbiologiques. Revue suisse de viticulture arboriculture horticulture 28: 259-265. de Boor C. (1978): A Practical Guide to Splines, number 27 in Applied Mathe­matical Sciences Series. New York: Springer-Verlag.Google Scholar

  • Di Stefano R., Cravero M. C., Gentilini M. (1989): Metodi per lo studio dei polifenoli dei vini. L’enotecnico 5: 83-89.Google Scholar

  • Dierckx P. (1995): Curve and Surface Fitting with Splines. Oxford: Oxford University Press. ISBN13: 9780198534402 ISBN10: 019853440X.Google Scholar

  • Eilers P.H.C., Marx B.D. (1996): Flexible smoothing with B-splines and penalties. Statistical Science 11: 89-121.CrossrefGoogle Scholar

  • Feuillat M. (1997): Vinification du Pinot noir en Bourgogne par maceration prefermentaire a froid. Revue des Oenologues 82: 29-31.Google Scholar

  • Gao L., Girard B., Mazza G., Reynolds A.G. (1997): Changes in anthocyanins and color characteristics of Pinot Noir wines during different vinification pro­cesses. Journal of Agricultural and Food Chemistry 45(6): 2003-2008.CrossrefGoogle Scholar

  • Garthwaite P.H., Kadane J.B., O’Hagan A. (2005): Statistical methods for elicit­ing probability distributions. Journal of the American Statistical Association 100: 680-701.CrossrefGoogle Scholar

  • Gerbaux V. (1993): Etude de quelques conditions de cuvaison susceptibles d’augmenter la composition polyphenolyque des vins de pinot noir. Revue des Oenologues 69: 15-18.Google Scholar

  • Gerbaux V., Vuittenez B., Vincent B., L’Heveder A. (2002): Macerazione prefer- mentativa a freddo e macerazione finale a caldo su Pinot nero in Bourgogne, <http://www.infowine.com4>: 1-5. ISSN 1826-1590.Google Scholar

  • Glories Y. (1988): Anthocyanins and tannins from wine: organoleptic properties. Progress in clinical and biological research 280: 123-34.Google Scholar

  • Gómez-Míguez M., Gonzalez-Miret M. L., Heredia F. J. (2007): Evolution of colour and anthocyanin composition of Syrah wines elaborated with pre- fermentative cold maceration. Journal of Food Engineering 79(1): 271-278.CrossrefWeb of ScienceGoogle Scholar

  • Gordillo B., Lopez-Infante M.I., Ramirez-Perez P., Gonzalez-Miret M.L., Heredia F.J. (2010): Influence of prefermentative cold maceration on the color and an- thocyanic copigmentation of organic Tempranillo wines elaborated in a warm climate. Journal of Agricultural and Food Chemistry 58(11): 6797-6803. PMID: 20455543.Web of ScienceCrossrefGoogle Scholar

  • Harbertson J.F., Hodgins R.E., Thurston L.N., Schaffer L.J., Reid M.S., Landon J.L., Ross C.F., Adams D.O. (2008): Variability of tannin concentration in red wines. American Journal of Enology and Viticulture 59(2): 210-214.Google Scholar

  • Hedeker D., Gibbons R. (2006): Longitudinal Data Analisys, Wiley Series in Probability and Statistics. Hoboken, New Jersey: John Wiley and Sons.Google Scholar

  • Lang S., Brezger A. (2004): Bayesian P-Splines. Journal of Computational and Graphical Statistics 13(1): 183-212.CrossrefWeb of ScienceGoogle Scholar

  • Parenti A., Spugnoli P., Calamai L., Ferrari S., Gori C. (2004): Effects of cold maceration on red wine quality from Tuscan Sangiovese grape. European Food Research and Technology 218: 360-366. 10.1007/s00217-003-0866-1.Google Scholar

  • Plummer M. (2003): JAGS: A program for analysis of Bayesian graphi­cal models using Gibbs sampling, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). http://www-ice.iarc.fr/martyn/software/jags/ Google Scholar

  • Reynolds A., Cliff M., Girard B., Kopp T.G. (2001): Influence of fermentation temperature on composition and sensory properties of Semillon and Shiraz wines. American Journal of Enology and Viticulture 52(3): 235-240.Google Scholar

  • Ribereau-Gayon P., Glories Y. (1986): Phenolics in grapes and wines, in 6th Australian Wine Industry Technical Conference, ed. T. Lee, Australian Wine Industry Technical Conference Inc., Adelaide, Australia: 247-256.Google Scholar

  • Robert C., Casella G. (2010): Introducing Monte Carlo Methods with R, num­ber 27 in Use R!. Heidelberg: Springer-Verlag.Google Scholar

  • Ruppert D., Wand M., Carroll R. (2003): Semiparametric Regression. Cambridge: Cambridge University Press.Google Scholar

  • Sacchi K.L., Bisson L.F., Adams D.O. (2005): A review of the effect of winemaking techniques on phenolic extraction in red wines. American Journal of Enology and Viticulture 56(3): 197-206.Google Scholar

  • Sarkar D. (2008): Lattice: multivariate data visualization with R. New York: Springer. ISBN 978-0-387-75968-5. <http://lmdvr.r-forge.r-project.org>Google Scholar

  • Soleas G.J., Tomlinson G., Goldberg D.M. (1998): Kinetics of polyphenol release into wine must during fermentation of different cultivars. Journal of Wine Research 9: 27-41.CrossrefGoogle Scholar

  • Team R.D.C. (2010): R: a language and environment for statistical computing. ISBN 3-900051-07-0. <http://www.R-project.org/>Google Scholar

  • Zamora J.B., Luengo G., Margalef P., Magrina M., Arola L. (1994): Nota: Efecto del sangrado sobre el color y la composicion en compuestos fenolicos del vino tinto. Revista Espanola de Ciencia y Tecnologia de Alimentos 34: 663-671. Google Scholar

About the article

Published Online: 2013-12-10

Published in Print: 2013-12-01


Citation Information: Biometrical Letters, Volume 50, Issue 2, Pages 61–80, ISSN (Print) 1896-3811, DOI: https://doi.org/10.2478/bile-2013-0018.

Export Citation

This content is open access.

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
L. Federico Casassa, Esteban A. Bolcato, Santiago E. Sari, Martín L. Fanzone, and Viviana P. Jofré
LWT - Food Science and Technology, 2016, Volume 66, Page 134

Comments (0)

Please log in or register to comment.
Log in