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There is a high demand of monitoring in the era of QbD in industrial scale require new approaches to gain data rapidly and of sufficient quality in real time. Raman spectroscopy technology has great potential but not yet shown it fully in process on-line monitoring due to limitations such as i) uncomplete separation between cells and growth media alone, ii) general weak Raman signals of analytes in complex solutions and iii) strong background signals such as the auto-fluorescence, cosmic rays and surrounding lights overlapping the weak Raman signals. Here we demonstrate a Proof-of-Concept on an the example lactic acid bacteria process using a Streptococcus thermophiles fermentation. Results from three different Raman approaches are presented: 1) Time-Gated Raman Spectroscopy (TG-Raman), 2) Surface Enhanced Raman Spectroscopy (SERS) and 3) Raman process spectroscopy with NIR excitation combined with multivariate data analysis (MVDA) using Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR).
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 NIRprocess 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.
Raman spectroscopy is becoming a powerful process analytical technology (PAT) tool. Until now Raman technology has not shown its full potential in bioprocess on-line monitoring due to several technical challenges. Because of only small? interference from water molecules, Raman-spectroscopy is in contrast to IR spectroscopy able to follow changes of metabolite concentrations in dilute aqueous solutions. Results from common CCD-based process Raman-spectrometers reveal only barely identifiable peaks with a dominating (fluorescence) background. To solve this, Raman spectroscopy needs: (A) an enhancement to increase the limit of detection (LOD), and (B) a reliable method to distinguish the Raman signal from the sample-related auto-fluorescence.
SERS (surface enhanced Raman spectroscopy) acts as an optical "nano-antenna"-effect. It causes a dipolar localized surface Plasmon resonance effect due to noble metallic nanoparticles or roughened metal and improves the limit of detection (LOD) significantly. Another new process-monitoring technique, which removes the fluorescence background in Raman-measurements is called time-gated Raman spectroscopy. It uses a picosecond pulsed Nd:YVO4-laser as emission source (exc= 532 nm) and a gated SPAD-array (Single Photon Avalanche Detector) detector instead of commonly used CCD (Charged Coupled Device)-detectors and CW (Continues Wave) laser emission. Time-Gate can measure the Raman-signal before the stronger fluorescence signal reaches the detector.
In this study we utilized both SERS (Surface Enhanced Raman Spectroscopy) and time-gated Raman spectroscopy (TG-Raman) in combination on cell-free supernatant samples of an Escherichia coli cultivation with mineral salt media and a lactic acid bacteria fermentation with complex media. As a reference method for the estimation of amino acids and other metabolites, HPLC-RID and HPLC-FLD were used to evaluate the Raman-based detection. The quantitative evaluation of Raman data was performed by multivariate data analysis such as principal component analysis (PCA) and partial least squares regression (PLSR). For the first time, we can show that both qualitative and quantitative measurements are conducted successfully with both, SERS and time-gated Raman methods in industrially relevant media, so that a fast and reliable in situ or bypassed concentration measurement becomes feasible.