A new review by researchers from the University of Waterloo, Sanofi, and McGill University highlights how vibrational and fluorescence spectroscopy are reshaping real-time monitoring of pharmaceutical bioprocesses. The authors detail recent advances in UV-Vis, NIR-MIR, Raman, and fluorescence sensing, supported by modern chemometrics and AI tools.
Introduction
Real-time monitoring of pharmaceutical bioprocesses, central to vaccines, monoclonal antibodies, and biologics, has rapidly evolved in recent years as demand for speed, quality, and regulatory compliance intensifies. In a comprehensive new review published in Spectroscopy Journal, Abhishek Mishra, Mohammad Aghaee, Ibrahim M. Tamer, and Hector Budman survey the expanding landscape of vibrational and fluorescence spectroscopic techniques for real-time monitoring in fermentation and cell-culture systems (1). Their analysis highlights how Process Analytical Technology (PAT) initiatives have accelerated the shift toward non-invasive, inline sensing of key biochemical variables (1–2). This work involves researchers from the University of Waterloo and Sanofi in Ontario, with the corresponding author now based in the Bioengineering Department at McGill University in Montreal.
The work expands on earlier fluorescence-based monitoring studies led by the team in Bordetella pertussis fermentations and now assesses how techniques such as infrared (NIR-MIR), Raman, UV-Visible, and fluorescence can jointly transform industrial biotechnology.
Spectroscopic Tools Move Toward Real-Time Precision
As the review notes, PAT frameworks promote real-time measurement, rapid data interpretation, and automated control, placing spectroscopy at the forefront of modern bioprocess analytics (1). UV-Vis, vibrational (NIR-MIR), Raman, and fluorescence spectroscopy are among the most widely adopted tools. The authors emphasize that while spectroscopic signals often correlate with biomass, metabolites, and coenzymes, these relationships are experiment-specific—requiring new calibrations for each application (1).
In complex fermentation media, many critical molecules appear in low concentrations yet strongly influence outcomes. As a result, sensors must be non-invasive, sterile-compatible, and capable of resolving multicomponent mixtures without disturbing the culture environment (1). Optical probes and flow-cell configurations enable in-line and on-line deployment even in high-pressure or high-temperature operation.
Vibrational Spectroscopy: From NIR to MIR to Raman
Vibrational spectroscopy continues to be a powerful route to molecular fingerprinting. The review highlights how MIR spectroscopy accesses fundamental vibrational transitions, providing strong, chemically specific absorption bands useful for metabolite identification (1). NIR, while less sensitive, offers superior robustness against scattering and is generally more cost-effective.
Raman spectroscopy receives significant attention due to its suitability in aqueous systems. Unlike infrared methods, Raman signals are minimally affected by water, allowing clearer detection of nutrients and metabolites directly in fermentation broth (1). Its non-invasive nature and highly characteristic Raman shifts make it an increasingly attractive PAT sensor, despite higher instrumentation costs (1).
Fluorescence Spectroscopy: Cost-Effective and Highly Sensitive
Fluorescence spectroscopy, the authors noted, remains one of the most cost-effective and sensitive approaches for in-line monitoring (1). Capable of detecting intrinsic fluorophores such as NADPH or aromatic amino acids, fluorescence measurements can be sustained for weeks with little recalibration. However, challenges such as background fluorescence, pH sensitivity, turbidity, and photobleaching still limit broader industrial adoption (1).
Chemometrics and AI Transform Spectral Interpretation
A major theme in the review is the importance of data preprocessing and advanced chemometric analysis. Raw spectra are multidimensional and noisy, requiring smoothing, normalization, baseline correction, and filtering (1). Due to spectral collinearity, dimensionality reduction methods such as PCA and PLS remain indispensable for extracting relevant features (1). More recently, MCR, ANN-based tools (1), and hybrid modeling strategies (1) have enabled improved quantification.
Artificial intelligence (AI) is now reshaping bioprocess monitoring, with deep learning–based “soft sensors” showing exceptional performance in complex nonlinear systems (1). These tools promise robust prediction of biomass, substrate consumption, and product formation from continuously acquired spectra.
Looking Ahead
The authors conclude that spectroscopic PAT technologies are poised to play an even greater role in emerging fields such as mRNA therapeutics and cell-free protein synthesis, where real-time control of nucleotide stability and enzyme activity is essential. Integrated with AI-driven models and improved sensor hardware, real-time in-line spectroscopy may soon enable fully automated, continuous biomanufacturing.
References
(1) Mishra, A.; Aghaee, M.; Tamer, I. M.; Budman, H. Spectroscopic Advances in Real Time Monitoring of Pharmaceutical Bioprocesses: A Review of Vibrational and Fluorescence Techniques. Spectrosc. J. 2025, 3 (2), 12. DOI:
(2) Hinz, D. C. Process Analytical Technologies in the Pharmaceutical Industry: The FDA’s PAT Initiative. Anal. Bioanal. Chem. 2006, 384, 1036–1042. DOI:
