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Real-Time Biomass Estimation in High-Density Yeast Fermentations Using Soft Sensor Modeling.

Ana G Del Hierro1,2,3, José Luis Checa-Barrera3, Juan A Moreno-Cid3,4

  • 1School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin, Ireland.

Biotechnology and Bioengineering
|June 11, 2026
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Summary

Accurate biomass monitoring in industrial fermentation is improved using soft sensor models. These models convert nonlinear optical density at 860 nm (OD860) signals into dry cell weight (DCW) and optical density at 600 nm (OD600) for real-time control.

Keywords:
biomass estimationbioprocess monitoringcross‐validationoptical densityregression‐modelingsoft sensoryeast fermentation

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Area of Science:

  • Biotechnology and Biochemical Engineering
  • Process Analytical Technology (PAT)
  • Industrial Microbiology

Background:

  • Accurate biomass monitoring is critical for industrial fermentation process control.
  • Conventional offline methods (DCW, OD600) are not suitable for real-time applications.
  • Online OD860 probes offer continuous monitoring but suffer from signal nonlinearity due to light scattering.

Purpose of the Study:

  • To develop and validate interpretable soft sensor regression models.
  • To convert nonlinear OD860 signals into reliable DCW and OD600 measurements.
  • To enable real-time biomass monitoring across different yeast species.

Main Methods:

  • Evaluation of 14 candidate regression models (nonlinear, spline-based) using fed-batch data from 19 independent batches.
  • Nested leave-one-batch-out cross-validation (LOBO-CV) for robust model performance assessment.
  • Parsimony criterion for model selection (simplest model within 5% of minimum RMSE).

Main Results:

  • Selected soft sensor models achieved high accuracy for dry cell weight (DCW) prediction (R²CV: 0.85-0.91) across Pichia pastoris, Kluyveromyces marxianus, and Yarrowia lipolytica.
  • Optical density at 600 nm (OD600) predictions showed variable accuracy (R²CV: 0.49-0.90), influenced by dilution-driven noise.
  • A dual-model strategy was proposed, including simplified piecewise-linear equations for industrial calculator deployment.

Conclusions:

  • Interpretable soft sensor models effectively translate nonlinear OD860 signals into accurate DCW estimations for industrial fermentation.
  • The developed models support real-time biomass monitoring, overcoming limitations of conventional offline methods.
  • The dual-model approach offers practical solutions for both software-optimized and calculator-based industrial applications.