Multivariate Curve Resolution and Carbon Balance Constraint to Unravel FTIR Spectra from Fed-Batch Fermentation Samples

Bioengineering

卷:4

Volker Schünemann¹  Dennis Vier¹  Klaus-Uwe Gollmer²  Stefan Wambach³ 

[1] AG Biophysik und Medizinische Physik, Technische Universität Kaiserslautern, Kaiserslautern 67663, Germany;

[2] Fachbereich Angewandte Informatik, Hochschule Trier, Umwelt-Campus Birkenfeld, Birkenfeld 55761, Germany;

[3] Fachbereich Bioverfahrenstechnik, Hochschule Trier, Umwelt-Campus Birkenfeld, Birkenfeld 55761, Germany;

关键词: multivariate curve resolution;    E. coli;    fed-batch;    fermentation;    carbon mass balance constraint;    soft constraints;    alternating least squares;    hybrid modelling;   

DOI  :  10.3390/bioengineering4010009

来源: DOAJ

【 摘 要 】

The current work investigates the capability of a tailored multivariate curve resolution–alternating least squares (MCR-ALS) algorithm to analyse glucose, phosphate, ammonium and acetate dynamics simultaneously in an E. coli BL21 fed-batch fermentation. The high-cell-density (HCDC) process is monitored by ex situ online attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy and several in situ online process sensors. This approach efficiently utilises automatically generated process data to reduce the time and cost consuming reference measurement effort for multivariate calibration. To determine metabolite concentrations with accuracies between ±0.19 and ±0.96·gL−l, the presented utilisation needs primarily—besides online sensor measurements—single FTIR measurements for each of the components of interest. The ambiguities in alternating least squares solutions for concentration estimation are reduced by the insertion of analytical process knowledge primarily in the form of elementary carbon mass balances. Thus, in this way, the established idea of mass balance constraints in MCR combines with the consistency check of measured data by carbon balances, as commonly applied in bioprocess engineering. The constraints are calculated based on online process data and theoretical assumptions. This increased calculation effort is able to replace, to a large extent, the need for manually conducted quantitative chemical analysis, leads to good estimations of concentration profiles and a better process understanding.

【 授权许可】

Unknown