How AI and Raman spectroscopy convergence is driving a new era for biopharma quality monitoring

The NIR and Raman spectroscopy market is forecast to grow from $2.05 billion in 2025 to $2.35 billion this year, representing a compound annual growth rate of 15.1 percent. Much of this expansion is being driven by demand from the pharmaceutical sector, where Raman spectroscopy is transitioning from a supporting analytical tool to what industry observers describe as a central enabling technology for process monitoring and quality assurance.

The momentum has been fuelled by a series of recent studies demonstrating how machine learning (ML) can address longstanding barriers to Raman’s use as a process analytical technology (PAT) tool — notably the challenges of spectral interpretation and transferring calibration models between instruments and production environments. A 2025 study published in Frontiers in Bioengineering and Biotechnology showed that soft sensors combining Raman spectroscopy with partial least squares (PLS) regression could be successfully transferred to filtration-based recovery steps for virus-like particles (VLPs), enabling simultaneous monitoring of multiple quality attributes within a single measurement.

That work builds on a collaborative study between Purdue University and Merck & Co., published in October 2025, which reported what the researchers described as the first Raman-based PAT tool for detecting human cytomegalovirus (CMV) particles during continuous vaccine manufacturing. The tool delivered results in 30 seconds or less and, because Raman is non-destructive, could operate with water-based samples — making it, the team noted, “ideal for biological samples such as vaccines.”

Meanwhile, efforts to improve the accessibility of Raman data for pharmaceutical researchers are also gathering pace. A dataset published in Nature Scientific Data has made 3,510 open-source Raman spectra covering 32 chemical compounds freely available to the scientific community, aiming to support the development of more accurate calibration models for active pharmaceutical ingredient (API) analysis.

The convergence of these developments aligns with the broader regulatory push for PAT adoption under ICH Q8–Q12 guidelines and the principles of Quality by Design (QbD). By providing continuous, in-line data rather than relying on end-point testing, Raman PAT tools offer manufacturers the potential to detect deviations earlier, reduce batch failures and support the transition to continuous manufacturing — a priority that extends across biologics, cell and gene therapies and mRNA-based products.

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