Cluster Computing

, Volume 19, Issue 3, pp 1219–1225 | Cite as

Detection of abnormal processes of wine fermentation by support vector machines

  • Gonzalo Hernández
  • Roberto León
  • Alejandra Urtubia
Article

Abstract

The early detection of problematic fermentations is one of the main problems that appear in winemaking processes, due to the significant impacts in wine quality and utility. This situation is specially important in Chile because is one of the top ten wine production countries. In last years, different methods coming from Multivariate Statistics and Computational Intelligence have been applied to solve this problem. In this work we detect normal and problematic (sluggish and stuck) wine fermentations applying the support vector machine method with three different kernels: linear, polynomial and radial basis function. For the training algorithm, we use the same database of 22 wine fermentation studied in [1, 2] that contains approximately 22,000 points, considering the main chemical variables measured in this kind of processes: total sugar, alcoholic degree and density. Our main result establishes that the SVM method with third degree polynomial and radial basis kernels predict correctly 88 and 85 % respectively. The fermentation behavior results have been obtained for a 80–20 % training/testing percentage configuration and a time cutoff of 48 h.

Keywords

Abnormal Wine fermentations Support vector Machines 

References

  1. 1.
    Urtubia, A., Hernández, G., Román, C.: Prediction of problematic wine fermentations using artificial neural networks. Bioprocess Biosyst. Eng. 34, 1057–1065 (2011)CrossRefGoogle Scholar
  2. 2.
    Urtubia, A., Hernández, G., Roger, J.M.: Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques. J. Biotechnol. 159, 336–341 (2012)CrossRefGoogle Scholar
  3. 3.
    Executive Report Chilean Wine Production, Servicio Agrí cola y Ganadero de Chile, 2011–2014Google Scholar
  4. 4.
    Informe del Sector, La industria del vino en Chile, Elena Fabiano, 2009Google Scholar
  5. 5.
    Bisson, L., Butzke, C.: Diagnosis and rectification of stuck and sluggish fermentations. Am. J. Enol. Vitic. 51(2), 168–177 (2000)Google Scholar
  6. 6.
    Blateyron, L., Sablayrolles, J.M.: Stuck and slow fermentations in enology: statical study of causes and effectiveness of combined additions of oxygen and diammonium phosphate. J. Biosci. Bioeng. 91(2), 184–189 (2001)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Pszczólkowski, P., Carriles, P., Cumsille, M., Maklouf, M.: Reflexiones sobre la madurez de cosecha y las condiciones de vinificaci ón, con relación a la Problemática de fermentaciones alcohó licas lentas y/o paralizante en Chile. Pontificia Universidad Católica de Chile, Facultad de Agronomía (2001)Google Scholar
  9. 9.
    Beltran, G., Novo, M., Guillamón, J., Mas, A., Rozés, N.: Effect of fermentation temperature and culture media on the yeast lipid composition and wine volatile compounds. Int. J. Food Microbiol. 121, 169–177 (2008)CrossRefGoogle Scholar
  10. 10.
    Varela, C., Pizarro, F., Agosin, E.: Biomass Content govern fermentation rate in nitrogen-deficient wine musts. Appl. Environ. Microbiol. 70(6), 3392–3400 (2004)CrossRefGoogle Scholar
  11. 11.
    D’Amatto, D., Corbo, M., Del Nobile, M., Sinigaglia, M.: Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system. Int. J. Food Sci. Technol. 41, 1152–1157 (2006)CrossRefGoogle Scholar
  12. 12.
    Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley, Chichester (2007)CrossRefGoogle Scholar
  13. 13.
    Bishop, C.M.: Pattern Rcognition and Machine Learning. Springer, New York (2006)Google Scholar
  14. 14.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)MATHGoogle Scholar
  15. 15.
    Abe, S.: Support Vector Machines for Pattern Classification, 2nd edn. Springer, London (2010)CrossRefMATHGoogle Scholar
  16. 16.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2008)MATHGoogle Scholar
  17. 17.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)Google Scholar
  18. 18.
    Sánchez, D.: Advanced support vector machines and kernel methods. Neurocomputing 55, 5–20 (2003)CrossRefGoogle Scholar
  19. 19.
    Yu, H.Y., Niu, X.Y., Lin, H.J., Ying, Y.B., Li, B.B., Pan, X.X.: A feasibility study on on-line determination of rice wine composition by Vis-NIR spectroscopy and least-squares support vector machines. Food Chem. 113(1), 291–296 (2009)CrossRefGoogle Scholar
  20. 20.
    Jurado, M.J., Alcázar, A., Palacios-Morillo, A., de Pablos, F.: Classification of Spanish DO white wines according to their elemental profile by means of support vector machines. Food Chem. 135(3), 898–903 (2012)CrossRefGoogle Scholar
  21. 21.
    Fagerlund, S.: Bird species recognition using support vector machines. EURASIP J. Adv. Signal Process. 1, 1–8 (2007)MATHGoogle Scholar
  22. 22.
    Pierna, Fernandez, Pierna, J.A., Baeten, V., Renier, A.M., Cogdill, R.P., Dardenne, P.: Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds. J. Chemom. 18, 341–349 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Gonzalo Hernández
    • 1
  • Roberto León
    • 2
  • Alejandra Urtubia
    • 3
  1. 1.Departamento de Ingeniería IndustrialUniversidad de Santiago de ChileSantiagoChile
  2. 2.Facultad de IngenieríaUniversidad Andres BelloViña del MarChile
  3. 3.Departamento de Ingeniería Química y AmbientalUniversidad Técnica Federico Santa MaríaValparaísoChile

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