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Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()

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 applicabi...

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Autores principales: Koch, Cosima, Posch, Andreas E., Goicoechea, Héctor C., Herwig, Christoph, Lendl, Bernhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894717/
https://www.ncbi.nlm.nih.gov/pubmed/24356226
http://dx.doi.org/10.1016/j.aca.2013.10.042
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author Koch, Cosima
Posch, Andreas E.
Goicoechea, Héctor C.
Herwig, Christoph
Lendl, Bernhard
author_facet Koch, Cosima
Posch, Andreas E.
Goicoechea, Héctor C.
Herwig, Christoph
Lendl, Bernhard
author_sort Koch, Cosima
collection PubMed
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.
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spelling pubmed-38947172014-01-17 Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution() Koch, Cosima Posch, Andreas E. Goicoechea, Héctor C. Herwig, Christoph Lendl, Bernhard Anal Chim Acta Article 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. Elsevier 2014-01-07 /pmc/articles/PMC3894717/ /pubmed/24356226 http://dx.doi.org/10.1016/j.aca.2013.10.042 Text en © 2013 The Authors https://creativecommons.org/licenses/by-nc-nd/3.0/This is an open access article under the CC BY NC ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/).
spellingShingle Article
Koch, Cosima
Posch, Andreas E.
Goicoechea, Héctor C.
Herwig, Christoph
Lendl, Bernhard
Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title_full Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title_fullStr Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title_full_unstemmed Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title_short Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
title_sort multi-analyte quantification in bioprocesses by fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894717/
https://www.ncbi.nlm.nih.gov/pubmed/24356226
http://dx.doi.org/10.1016/j.aca.2013.10.042
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