Quantitative monitoring of yeast fermentation using Raman spectroscopy

Abstract

Compared to traditional IR methods, Raman spectroscopy has the advantage of only minimal interference from water when measuring aqueous samples, which makes this method potentially useful for in situ monitoring of important industrial bioprocesses. This study demonstrates real-time monitoring of a Saccharomyces cerevisiae fermentation process using a Raman spectroscopy instrument equipped with a robust sapphire ball probe. A method was developed to correct the Raman signal for the attenuation caused by light scattering cell particulate, hence enabling quantification of reaction components and possibly measurement of yeast cell concentrations. Extinction of Raman intensities to more than 50 % during fermentation was normalized with approximated extinction expressions using Raman signal of water around 1,627 cm(-1) as internal standard to correct for the effect of scattering. Complicated standard multi-variant chemometric techniques, such as PLS, were avoided in the quantification model, as an attempt to keep the monitoring method as simple as possible and still get satisfactory estimations. Instead, estimations were made with a two-step approach, where initial scattering correction of attenuated signals was followed by linear regression. In situ quantification measurements of the fermentation resulted in root mean square errors of prediction (RMSEP) of 2.357, 1.611, and 0.633 g/L for glucose, ethanol, and yeast concentrations, respectively.

Closed loop control of lactate concentration in mammalian cell culture by Raman spectroscopy leads to improved cell density, viability, and biopharmaceutical protein production

“…We chose Raman spectroscopy for its ability to perform direct, online measurements of solution analytes, including glucose, and lactate. Because of its many advantages, Raman spectroscopy has increasingly been used as a tool for the online monitoring of cell culture metabolites (Abu‐Absi et al, ; André et al, ; Ashton et al, ; Avila et al, ; Iversen et al, ; Mehdizadeh et al, ; Singh et al, ; Whelan et al, ), and even control a CHO cell culture glucose feeding (Berry et al, ; Craven et al, ).…”

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

“…As previously described and explained in the literature, cells have a significant influence on quality of PLSR models, which is a major challenge for bioreactor monitoring . In this work, PLSR models based on measurements from the cell free culture supernatant and the culture broth were compared for three process variables.…”

Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing

“…In situ Raman quantification of glucose, ethanol, and yeast concentrations during a S. cerevisiae fermentation was reported by Picard et al in 2007 and Iversen et al in 2014 [77, 78]. In the study by Iversen et al, a specially-designed probe delivered 785 nm light and collected Raman signal.…”

“…An explicit model was used in the earliest examples of Raman analysis of bioprocesses, using the weak water peak ~1640 cm –1 as an internal standard [72, 73]. This approach has been recently extended to in situ Raman monitoring of E. coli culture [90], confocal Raman microscopy of media [91], and yeast fermentation [78]. A 2007 report of yeast fermentation used the 980 cm –1 sulfate band as an internal standard [77].…”

“…Even when an explicit model is used, there are often limitations to the known model, which may affect its robustness. In the 2014 study by Iversen et al, the authors noted that the relationship of yeast cell concentration with light extinction could not be adequately described using Beer-Lambert law, that non-linear, wavelength-dependent attenuation necessitated describing the relationship for each individual component using quadratic equations, and that the modeling was valid because the yeast cells were the only Lorenz-Mie scattering particles in the bioprocess [78]. An explicit model for CHO cell culture fed-batch was developed to account for bioprocess nonlinearities, with demonstrated success for in-process control of glucose concentrations [85].…”