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Prediction of problematic wine fermentations using artificial neural networks

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Abstract

Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

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Acknowledgments

A. Urtubia thanks the financial support from FONDECYT Project (Grant Number 11070251).

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Authors and Affiliations

  1. Escuela de Ingeniería Industrial, Universidad de Valparaíso, Av. Brasil 1762, Valparaiso, Chile

    R. César Román & O. Gonzalo Hernández

  2. Centro de Modelamiento Matemático, Universidad de Chile, Blanco Encalada 2120, Seventh Floor, Santiago, Chile

    O. Gonzalo Hernández

  3. Departamento de Ingeniería Química y Ambiental, Universidad Técnica Federico Santa María, Av. España 1680, Casilla 110-V, Valparaiso, Chile

    U. Alejandra Urtubia

  4. Centro Regional de Estudios de Alimentos Saludables, Blanco 1623, of. 1402, Valparaiso, Chile

    U. Alejandra Urtubia

Authors
  1. R. César Román
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  2. O. Gonzalo Hernández
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  3. U. Alejandra Urtubia
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Correspondence to U. Alejandra Urtubia.

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Román, R.C., Hernández, O.G. & Urtubia, U.A. Prediction of problematic wine fermentations using artificial neural networks. Bioprocess Biosyst Eng 34, 1057–1065 (2011). https://doi.org/10.1007/s00449-011-0557-4

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  • DOI: https://doi.org/10.1007/s00449-011-0557-4

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