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Using historical data for bioprocess optimization: modeling wine characteristics using artificial neural networks and

S Vlassides1, J G Ferrier, D E Block

  • 1Department of Viticulture and Enology, University of California, One Shields Avenue, Davis, CA 95616, USA.

Biotechnology and Bioengineering
|March 20, 2001
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs) can optimize fermentation processes by learning from historical data. This approach predicts fermentation kinetics and wine quality, improving productivity and product characteristics.

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Area of Science:

  • Biotechnology
  • Machine Learning
  • Food Science

Background:

  • Fermentation process optimization is complex and challenging for traditional methods, especially at production scales.
  • Existing manufacturing data is often underutilized for process improvement.
  • Biological systems' complexity limits the effectiveness of mathematical models and designed experiments.

Purpose of the Study:

  • To develop a novel optimization method using historical process data and artificial neural networks (ANNs).
  • To correlate processing inputs with productivity and quality outputs in fermentation.
  • To identify optimal processing conditions for desired product characteristics and constraints.

Main Methods:

  • Training ANNs with historical process data to model input-output relationships.
  • Employing an optimization routine with trained ANNs to determine optimal processing conditions.
  • Utilizing a hybrid neural network training method, Stop Training with Validation (STV), for optimal architecture and training.
  • Investigating the interpolation and extrapolation capabilities of trained ANNs with sparse industrial data.

Main Results:

  • Demonstrated successful prediction of yeast-fermentation kinetics, chemical, and sensory properties of wine using trained ANNs.
  • Validated the ability of ANNs to predict wine characteristics based on grape properties and processing parameters.
  • Showcased the effectiveness of the STV method for neural network training.
  • Confirmed the capability of ANNs to interpolate and extrapolate using non-evenly spaced industrial data.

Conclusions:

  • Historical process data combined with ANNs offers a powerful approach for fermentation optimization.
  • This method can be generalized for various fermentation processes to enhance quality and productivity.
  • The developed technique leverages existing industrial data for cost-effective process improvement.