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Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration

A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies....

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Autores principales: Siegl, Manuel, Kämpf, Manuel, Geier, Dominik, Andreeßen, Björn, Max, Sebastian, Zavrel, Michael, Becker, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959347/
https://www.ncbi.nlm.nih.gov/pubmed/36850777
http://dx.doi.org/10.3390/s23042178
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author Siegl, Manuel
Kämpf, Manuel
Geier, Dominik
Andreeßen, Björn
Max, Sebastian
Zavrel, Michael
Becker, Thomas
author_facet Siegl, Manuel
Kämpf, Manuel
Geier, Dominik
Andreeßen, Björn
Max, Sebastian
Zavrel, Michael
Becker, Thomas
author_sort Siegl, Manuel
collection PubMed
description A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%).
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spelling pubmed-99593472023-02-26 Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration Siegl, Manuel Kämpf, Manuel Geier, Dominik Andreeßen, Björn Max, Sebastian Zavrel, Michael Becker, Thomas Sensors (Basel) Article A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%). MDPI 2023-02-15 /pmc/articles/PMC9959347/ /pubmed/36850777 http://dx.doi.org/10.3390/s23042178 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Siegl, Manuel
Kämpf, Manuel
Geier, Dominik
Andreeßen, Björn
Max, Sebastian
Zavrel, Michael
Becker, Thomas
Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title_full Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title_fullStr Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title_full_unstemmed Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title_short Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
title_sort generalizability of soft sensors for bioprocesses through similarity analysis and phase-dependent recalibration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959347/
https://www.ncbi.nlm.nih.gov/pubmed/36850777
http://dx.doi.org/10.3390/s23042178
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