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

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Innovations in Technologies for Fermented Food and Beverage Industries

Part of the book series: Food Microbiology and Food Safety ((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.

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Acknowledgement

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

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Authors and Affiliations

  1. Universidad de Santiago de Chile, Departamento de Ingeniería Industrial, Santiago, Chile

    Gonzalo Hernández

  2. Universidad Andrés Bello, Facultad de Ingeniería, Viña del Mar, Chile

    Roberto León

  3. Universidad Técnica Federico Santa María, Departamento de Ingeniería Química y Ambiental, Valparaíso, Chile

    Alejandra Urtubia

  4. Centro Regional de Estudios en Alimentación Saludable, Valparaíso, Chile

    Alejandra Urtubia

Authors
  1. Gonzalo Hernández
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  2. Roberto León
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  3. Alejandra Urtubia
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Corresponding author

Correspondence to Gonzalo Hernández .

Editor information

Editors and Affiliations

  1. School of Basic Sciences, Indian Institute of Technology, Bhubaneswar, India

    Sandeep Kumar Panda

  2. Department of Food Science and Technology, Pondicherry University, Kalapet, Pondicherry, India

    Prathapkumar Halady Shetty

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Hernández, G., León, R., Urtubia, A. (2018). Application of Computational Intelligence Techniques for Forecasting Problematic Wine Fermentations Using Data from Classical Chemical Measurements. In: Panda, S., Shetty, P. (eds) Innovations in Technologies for Fermented Food and Beverage Industries. Food Microbiology and Food Safety. Springer, Cham. https://doi.org/10.1007/978-3-319-74820-7_16

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