Raman-Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell-free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real-time, on-line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae-based biopharmaceutical production.