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Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution
Authors:Cosima Koch  Andreas E Posch  Héctor C Goicoechea  Christoph Herwig  Bernhard Lendl
Institution:1. Institute of Chemical Technologies and Analytics, Vienna University of Technology, Getreidemarkt 9/164 AC, 1060 Vienna, Austria;2. Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Research Area Biochemical Engineering, Institute of Chemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1a, 1060 Vienna, Austria;3. Laboratorio de Desarrollo Analítico y Quimiometría-LADAQ, Universidad Nacional del Litoral-CONICET, Facultad de Bioquímica y Ciencias Biológicas, Ciudad Universitaria, 3000 Santa Fe, Argentina
Abstract: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.
Keywords:Inline bioprocess monitoring  FTIR spectroscopy  P  chrysogenum  Partial least squares regression  Multivariate curve resolution  Chemometrics
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