Subscribe to our newsletter and blog

Stay on top of the latest news and blogs by subscribing to our mailing list

An article about time-gated Raman in the Biotechnology Process

Biotechnology Process published an article about using Raman spectroscopy as a monitoring technique for bioprocesses with the title of "Comparison of time‐gated surface‐enhanced raman spectroscopy (TG‐SERS) and classical SERS based monitoring of Escherichia coli cultivation samples". The group of researchers compared different Raman variants to overcome the background signal caused by fluorescing compounds of the samples and time-gated Raman performed well in this study. Below is the abstract and the study can be accessed in Wiley Online Library.



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., :1–10, 2018