Application of green analytical chemistry to a green chemistry process: Magnetic resonance and Raman spectroscopic process monitoring of continuous ethanolic fermentation
Autor: | Till Tetzlaff, Alexander Wirtz, Anna Nickisch-Hartfiel, Martin Jaeger, Tim Koza, Robin Legner |
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Rok vydání: | 2019 |
Předmět: |
0106 biological sciences
0301 basic medicine Green chemistry Magnetic Resonance Spectroscopy Materials science Analytical chemistry Bioengineering Ethanol fermentation Spectrum Analysis Raman 01 natural sciences Applied Microbiology and Biotechnology 03 medical and health sciences symbols.namesake 010608 biotechnology Ethanol Spectrometer Green Chemistry Technology Environmentally friendly 030104 developmental biology Scientific method Fermentation Proton NMR symbols Raman spectroscopy Biologie Biotechnology |
Zdroj: | Biotechnology and Bioengineering. 116:2874-2883 |
ISSN: | 1097-0290 0006-3592 |
DOI: | 10.1002/bit.27112 |
Popis: | Compact 1 H NMR and Raman spectrometers were used for real-time process monitoring of alcoholic fermentation in a continuous flow reactor. Yeast cells catalyzing the sucrose conversion were immobilized in alginate beads floating in the reactor. The spectrometers proved to be robust and could be easily attached to the reaction apparatus. As environmentally friendly analysis methods, 1 H NMR and Raman spectroscopy were selected to match the resource- and energy-saving process. Analyses took only a few seconds to minutes compared to chromatographic procedures and were, therefore, suitable for real-time control realized as a feedback loop. Both compact spectrometers were successfully implemented online. Raman spectroscopy allowed for faster spectral acquisition and higher quantitative precision, NMR yielded more resolved signals thus higher specificity. By using the software Matlab for automated data loading and processing, relevant parameters such as the ethanol, glycerol, and sugar content could be easily obtained. The subsequent multivariate data analysis using partial linear least-squares regression type 2 enabled the quantitative monitoring of all reactants within a single model in real time. |
Databáze: | OpenAIRE |
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