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Comparison of time‐gated surface‐enhanced raman spectroscopy (TG‐SERS) and classical SERS based monitoring of Escherichia coli cultivation samples
Author(s) -
Kögler Martin,
Paul Andrea,
Anane Emmanuel,
Birkholz Mario,
Bunker Alex,
Viitala Tapani,
Maiwald Michael,
Junne Stefan,
Neubauer Peter
Publication year - 2018
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.2665
Subject(s) - raman spectroscopy , surface enhanced raman spectroscopy , analytical chemistry (journal) , metabolite , chemistry , substrate (aquarium) , detection limit , chromatography , raman scattering , materials science , biochemistry , optics , biology , ecology , physics
The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time‐gated Raman (TG‐Raman)‐, continuous wave NIR‐process Raman (NIR‐Raman), and continuous wave micro‐Raman (micro‐Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell‐free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off‐line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG‐Raman were comparable to those of micro‐Raman resulting in well‐discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR‐Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. © 2018 American Institute of Chemical Engineers Biotechnol. Prog ., 34:1533–1542, 2018