Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution

Autores
Koch, Cosima; Posch, Andreas E.; Goicoechea, Hector Casimiro; Herwig, Christoph; Lendl, Bernhard
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L−1 for Penicillin V and 0.32 g L−1 for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L−1 for Penicillin V and 0.15 g L−1 for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.
Fil: Koch, Cosima. Vienna University of Technology. Institute of Chemical Technologies and Analytics; Austria
Fil: Posch, Andreas E.. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; Austria
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina
Fil: Herwig, Christoph. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; Austria
Fil: Lendl, Bernhard. Vienna University of Technology. Institute of Chemical Technologies and Analytics; Austria
Materia
Inline Bioprocess Monitoring
Ftir Spectroscopy
Partial Least Squares Regression
Multivariate Curve Resolution
Chemometrics
P. Chrysogenum
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/15436
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/15436
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve ResolutionKoch, CosimaPosch, Andreas E.Goicoechea, Hector CasimiroHerwig, ChristophLendl, BernhardInline Bioprocess MonitoringFtir SpectroscopyPartial Least Squares RegressionMultivariate Curve ResolutionChemometricsP. Chrysogenumhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L−1 for Penicillin V and 0.32 g L−1 for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L−1 for Penicillin V and 0.15 g L−1 for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.Fil: Koch, Cosima. Vienna University of Technology. Institute of Chemical Technologies and Analytics; AustriaFil: Posch, Andreas E.. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; AustriaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; ArgentinaFil: Herwig, Christoph. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; AustriaFil: Lendl, Bernhard. Vienna University of Technology. Institute of Chemical Technologies and Analytics; AustriaElsevier Science2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15436Koch, Cosima; Posch, Andreas E.; Goicoechea, Hector Casimiro; Herwig, Christoph; Lendl, Bernhard; Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution; Elsevier Science; Analytica Chimica Acta; 807; 1-2014; 103-1100003-2670enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2013.10.042info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267013013858info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:19:25Zoai:ri.conicet.gov.ar:11336/15436instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:19:26.209CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
title Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
spellingShingle Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
Koch, Cosima
Inline Bioprocess Monitoring
Ftir Spectroscopy
Partial Least Squares Regression
Multivariate Curve Resolution
Chemometrics
P. Chrysogenum
title_short Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
title_full Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
title_fullStr Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
title_full_unstemmed Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
title_sort Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution
dc.creator.none.fl_str_mv Koch, Cosima
Posch, Andreas E.
Goicoechea, Hector Casimiro
Herwig, Christoph
Lendl, Bernhard
author Koch, Cosima
author_facet Koch, Cosima
Posch, Andreas E.
Goicoechea, Hector Casimiro
Herwig, Christoph
Lendl, Bernhard
author_role author
author2 Posch, Andreas E.
Goicoechea, Hector Casimiro
Herwig, Christoph
Lendl, Bernhard
author2_role author
author
author
author
dc.subject.none.fl_str_mv Inline Bioprocess Monitoring
Ftir Spectroscopy
Partial Least Squares Regression
Multivariate Curve Resolution
Chemometrics
P. Chrysogenum
topic Inline Bioprocess Monitoring
Ftir Spectroscopy
Partial Least Squares Regression
Multivariate Curve Resolution
Chemometrics
P. Chrysogenum
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L−1 for Penicillin V and 0.32 g L−1 for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L−1 for Penicillin V and 0.15 g L−1 for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.
Fil: Koch, Cosima. Vienna University of Technology. Institute of Chemical Technologies and Analytics; Austria
Fil: Posch, Andreas E.. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; Austria
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina
Fil: Herwig, Christoph. Vienna University of Technology. Institute of Chemical Engineering. Research Area Biochemical Engineering. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; Austria
Fil: Lendl, Bernhard. Vienna University of Technology. Institute of Chemical Technologies and Analytics; Austria
description This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L−1 for Penicillin V and 0.32 g L−1 for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L−1 for Penicillin V and 0.15 g L−1 for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/15436
Koch, Cosima; Posch, Andreas E.; Goicoechea, Hector Casimiro; Herwig, Christoph; Lendl, Bernhard; Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution; Elsevier Science; Analytica Chimica Acta; 807; 1-2014; 103-110
0003-2670
url http://hdl.handle.net/11336/15436
identifier_str_mv Koch, Cosima; Posch, Andreas E.; Goicoechea, Hector Casimiro; Herwig, Christoph; Lendl, Bernhard; Multi-analyte quantification in bioprocesses by FTIR spectroscopy using Partial Least Squares Regression and Multivariate Curve Resolution; Elsevier Science; Analytica Chimica Acta; 807; 1-2014; 103-110
0003-2670
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2013.10.042
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267013013858
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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