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Application of Computational Intelligence Techniques for Forecasting Problematic Wine Fermentations Using Data from Classical Chemical Measurements

  • Gonzalo Hernández
  • Roberto León
  • Alejandra Urtubia
Chapter
Part of the Food Microbiology and Food Safety book series (FMFS)

Abstract

The early forecasting of normal and problematic wine fermentations is one of the main problems of winemaking processes, due to its significant impacts in wine quality and utility. In Chile this is a critical problem because it is one of the top ten wine-producing countries. In this chapter, we review the computational intelligence methods that have been applied to solve this problem. Both methods studied, support vector machines and artificial neural networks, show excellent results with respect to the overall prediction error for different training/testing/validation percentages, different time cutoffs, and several parameter configurations. These results are of great importance for wine production because they are based only on measurement of classical chemical variables and they confirm that computational intelligence methods are a useful tool to the winemakers in order to correct in time a potential problem in the fermentation process.

Keywords

Support vector machines Neural networks Wine fermentations Classical chemical variables 

Notes

Acknowledgement

Research supported by grants: FONDECYT 1120679 and Conicyt PIA/Basal FB0821.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gonzalo Hernández
    • 1
  • Roberto León
    • 2
  • Alejandra Urtubia
    • 3
    • 4
  1. 1.Universidad de Santiago de Chile, Departamento de Ingeniería IndustrialSantiagoChile
  2. 2.Universidad Andrés Bello, Facultad de IngenieríaViña del MarChile
  3. 3.Universidad Técnica Federico Santa María, Departamento de Ingeniería Química y AmbientalValparaísoChile
  4. 4.Centro Regional de Estudios en Alimentación SaludableValparaísoChile

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