Literature DB >> 16710834

Near-infrared spectroscopic monitoring of biomass, glucose, ethanol and protein content in a high cell density baker's yeast fed-batch bioprocess.

Beverley Finn1, Linda M Harvey, Brian McNeil.   

Abstract

The use of at-line NIRS to monitor a high cell density fed-batch baker's yeast bioprocess was investigated. Quantification of the key analytes (biomass, ethanol and glucose) and the product quality indicator (percentage protein content) was studied. Biomass was quantitatively modelled using whole matrix samples (as was percentage protein content). The dominance of the whole matrix spectrum by biomass, and its associated light scattering effects, were overcome by use of filtrate samples and adapted (semi-synthetic) filtrate samples, which allowed successful ethanol and glucose modelling, respectively. Calibrations were rigorously challenged via external validation with large sample sets relative to the calibration sample size, ensuring model robustness and potential practical utility. The standard errors of calibration for biomass, glucose, ethanol and total intracellular protein were (g/l) 1.79, 0.19, 0.79 and 0.91, respectively, comparable to those of the primary assays. The calibration strategies necessary to generate quantitative models for this range of analytes in such a complex high cell density bioprocess fluid are discussed.

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Year:  2006        PMID: 16710834     DOI: 10.1002/yea.1371

Source DB:  PubMed          Journal:  Yeast        ISSN: 0749-503X            Impact factor:   3.239


  5 in total

1.  Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data.

Authors:  José Luis P Calle; Marta Barea-Sepúlveda; Ana Ruiz-Rodríguez; José Ángel Álvarez; Marta Ferreiro-González; Miguel Palma
Journal:  Sensors (Basel)       Date:  2022-05-19       Impact factor: 3.847

2.  In situ monitoring by quantitative Raman spectroscopy of alcoholic fermentation by Saccharomyces cerevisiae under high pressure.

Authors:  A Picard; I Daniel; G Montagnac; P Oger
Journal:  Extremophiles       Date:  2006-12-22       Impact factor: 3.035

3.  A robust flow cytometry-based biomass monitoring tool enables rapid at-line characterization of S. cerevisiae physiology during continuous bioprocessing of spent sulfite liquor.

Authors:  Charlotte Anne Vees; Lukas Veiter; Fritz Sax; Christoph Herwig; Stefan Pflügl
Journal:  Anal Bioanal Chem       Date:  2020-02-07       Impact factor: 4.142

4.  Ensemble-based adaptive soft sensor for fault-tolerant biomass monitoring.

Authors:  Manuel Siegl; Vincent Brunner; Dominik Geier; Thomas Becker
Journal:  Eng Life Sci       Date:  2022-01-08       Impact factor: 2.678

5.  Switching the mode of sucrose utilization by Saccharomyces cerevisiae.

Authors:  Fernanda Badotti; Marcelo G Dário; Sergio L Alves; Maria Luiza A Cordioli; Luiz C Miletti; Pedro S de Araujo; Boris U Stambuk
Journal:  Microb Cell Fact       Date:  2008-02-27       Impact factor: 5.328

  5 in total

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