Top 10 Most Influential Articles on Raman Spectroscopy in Biopharmaceutical Applications during 2023–2025

Abstract

Raman spectroscopy has undergone a rapid evolution in biopharmaceutical applications over the past three years, driven by advances in AI-driven spectral interpretation, inline deployment strategies, and high-throughput experimental platforms. This article summarizes ten of the most influential papers published between 2023 and 2026 that collectively define the current state of Raman spectroscopy in biopharmaceutical analysis. These studies span AI-guided spectral interpretation, inline monitoring of protein quality attributes, real-time chromatography and bioreactor control, low-dose API quantification, and emerging applications in mRNA and viral vector manufacturing. Together, they demonstrate how Raman spectroscopy has matured into a robust, predictive, and increasingly autonomous analytical platform supporting real-time release testing and digital biomanufacturing.

Introduction

The biopharmaceutical industry has long sought analytical technologies capable of providing real-time, molecularly specific insight into complex biological processes. Raman spectroscopy, with its minimal sample preparation requirements and compatibility with aqueous systems, has been a promising candidate for decades. However, practical limitations—low signal-to-noise ratios, spectral complexity, and challenges in model robustness—historically constrained its broader adoption.

From 2023 onward, these barriers began to fall. Improvements in chemometrics, deep learning, automation, and sensor integration transformed Raman spectroscopy from a primarily exploratory tool into a cornerstone technology for process understanding, control, and quality assurance. The ten papers reviewed here represent pivotal contributions that accelerated this transition and collectively influenced regulatory thinking, industrial implementation, and future research directions.

Influential Advances in Raman Spectroscopy for Biopharmaceutical Applications

1. AI-Guided Raman Spectroscopy: A Conceptual and Practical Turning Point

Liu et al. provided one of the most comprehensive syntheses of AI-guided Raman spectroscopy to date, bridging theoretical developments with practical biopharmaceutical applications (1). By systematically reviewing convolutional neural networks (CNNs), transformer architectures, and hybrid deep learning models, the authors demonstrated how AI overcomes classical Raman limitations such as baseline drift, fluorescence interference, and low signal intensity.

This work became a conceptual anchor for the field, legitimizing deep learning as not merely an enhancement but a necessity for next-generation Raman analysis. It unified disparate AI efforts into a coherent framework and accelerated industrial acceptance of AI-driven Raman systems by explicitly addressing interpretability, robustness, and regulatory considerations.

2. Raman Spectroscopy as a PAT Tool for Protein A Chromatography

Chen et al. reported the first successful deployment of Raman spectroscopy for real-time monitoring of Protein A chromatography during downstream processing (2). Using partial least squares (PLS) regression, the authors achieved continuous prediction of Critical Quality Attributes (CQAs) at 28-second intervals, directly within a production-relevant chromatographic workflow.

This study marked a decisive shift from offline analytics toward true real-time downstream Process Analytical Technology (PAT). It demonstrated that Raman spectroscopy could operate reliably in high-pressure, fast-flow chromatographic environments—previously considered impractical for vibrational spectroscopy.

3. Inline Product Quality Monitoring During Biomanufacturing

Wang et al. (2023) show how process analytical technologies, specifically in-line Raman spectroscopy combined with hardware automation and machine-learning analysis, can overcome major bottlenecks in biopharmaceutical manufacturing by enabling real-time monitoring of critical quality attributes such as product aggregation and fragmentation (3). By integrating calibration, validation, and data collection into a single robotic workflow, the study dramatically reduces the labor and time required to build reliable models, while achieving accurate quality measurements every 38 seconds. The resulting high-throughput, in-process analytics improve immediate process understanding and pave the way for more robust, controlled, and cost-effective clinical manufacturing.

This work is influential because it demonstrates a practical, scalable path to real-time quality assurance in bioprocessing, helping move the field closer to truly autonomous and regulatory-ready manufacturing systems.

4. Transmission Raman for Low-Concentration API Quantification

Guo et al. demonstrate the use of transmission Raman spectroscopy (TRS) combined with partial least squares (PLS) regression as a fast, non-destructive method to quantify low levels of the highly toxic drug colchicine (0.83% w/w) in commercial tablets (4). A robust calibration model was built using tablets spanning 70–130% of label claim and validated against high performance liquid chromatography (HPLC), showing excellent accuracy, repeatability, and sensitivity, with low calibration and cross-validation errors (~0.04%), detection and quantification limits of 0.13% and 0.40% w/w, and relative errors within 3.8%. Notably, TRS achieved superior precision (1.2% RSD) compared to HPLC (2.9% RSD), while eliminating the need for destructive sample preparation and exposure to toxic materials.

This work is influential because it validates TRS as a practical, safer alternative to conventional analytical methods for content uniformity testing of low-dose, narrow-therapeutic-index drugs, supporting its adoption in modern pharmaceutical quality control and process analytical technology (PAT).

5. Automatic Raman Measurements in High‐Throughput Experimentation

This study by Lange et al. demonstrates an integrated hardware–software system that automates and accelerates Raman spectroscopy for high-throughput biotechnology workflows, addressing a key bottleneck in analyzing rapidly generated samples (5). The platform processes eight parallel microliter-scale samples delivered by a pipetting robot and completes measurement, handling, cleaning, and concentration prediction in about 45 seconds per sample. By combining automated Raman acquisition with machine-learning models to predict metabolite concentrations such as glucose and acetate during E. coli fermentations, the system enables reliable, rapid, and scalable spectral data collection suitable for monitoring, calibration, and offline analysis. The resulting high-quality, high-volume datasets support the development of more robust and generalizable predictive models.

This work is influential because it shows how automation and machine learning can make Raman spectroscopy truly compatible with high-throughput experimentation, accelerating bioprocess development and data-driven biotechnology.

6. Raman Insights into mRNA and Viral Vector Manufacturing

Matuszczyk et al. show that Raman spectroscopy (RS) has emerged as a leading technology for real-time monitoring and control of critical process parameters (CPPs) in modern bioprocessing, addressing key challenges in process economics and deep process understanding (6). As a noninvasive, information-rich analytical tool, RS enables online access to process dynamics and supports quality-by-design (QbD) principles by measuring a broad range of analytes, including cell type and differentiation as well as quality attributes such as protein structure and viral titer. Recent advances demonstrate its versatility across established protein production systems and its growing potential in virus, cell therapy, and mRNA manufacturing, where enhanced process control strategies are increasingly essential.

This is influential in the field because Raman spectroscopy fundamentally expands real-time process insight, enabling more robust, data-driven control and accelerating the adoption of QbD-compliant biomanufacturing.

7. Surface-Enhanced Raman Spectroscopy Overview of Recent Advances and Future Challenges.

Marking the 50th anniversary of its discovery, this review by Lin et al. surveys how surface-enhanced Raman spectroscopy (SERS) has evolved into a powerful biomedical tool, driven by advances in substrates, nanotags, instrumentation, and spectral analysis (7). It highlights progress in colloidal, solid, and 3D hydrogel plasmonic substrates, innovations in SERS nanotags with interior gaps, orthogonal reporters, and NIR-II responsiveness, and emerging platforms such as optical tweezers, plasmonic nanopores, and wearable sensors for single-cell and single-molecule studies. The review also covers modern spectral analysis approaches, including denoising, signal digitalization, and deep learning, and showcases applications ranging from nucleic acids and proteins to metabolites, single cells, liquid biopsy, and in vivo deep Raman spectroscopy, concluding with a discussion of clinical translation and commercialization challenges.

This work is influential because it integrates materials science, analytical innovation, and biomedical application into a unified roadmap that clarifies how SERS can realistically transition from the lab to clinical and process practice.

8. Real-Time Bioreactor Control of CO₂ and pH

This study by Wallocha et al. demonstrates that Raman off-gas spectroscopy can provide accurate, time-resolved measurements of CO₂ in fermentation exhaust streams, enabling effective real-time monitoring of mammalian cell culture processes across different media and production lots (8). The work shows that off-gas CO₂ levels measured by Raman spectroscopy strongly correlate with fermentation pH, allowing reliable pH prediction comparable to conventional indirect methods. While direct in-situ prediction of CO₂ and pH using a Raman submersible probe was less accurate, likely due to the lack of a pretrained CO₂ model, the off-gas approach proved robust, precise, and well suited for process control applications. Overall, the results highlight Raman off-gas spectroscopy as a powerful and efficient tool for real-time bioprocess monitoring.

This work is influential because it validates Raman off-gas spectroscopy as a practical, noninvasive PAT approach for linking gas-phase measurements directly to critical liquid-phase process variables such as pH, advancing real-time control strategies in bioprocessing.

9. Machine Learning for Inline Detection of Protein Size Variants

Research by Heyer-Müller et al. demonstrates a Raman spectroscopy–based process analytical technology for real-time detection and quantification of protein monomers and aggregates in biopharmaceutical manufacturing, addressing the limitations of slow offline methods like size-exclusion chromatography (9). Using bovine serum albumin as a model system, controlled aggregation, Latin Hypercube sampling, and comparison to chromatographic references ensured that spectral changes were specifically linked to aggregation rather than concentration effects. By combining identified Raman spectral markers with advanced chemometric machine learning—particularly convolutional neural networks—the study achieved robust, selective, and accurate quantification of protein size variants under realistic process conditions, outperforming traditional chemometric models.

This study is influential because it shows, for the first time in a practical manufacturing context, how Raman spectroscopy coupled with modern machine learning can enable reliable, inline, real-time control of protein aggregation, a long-standing challenge in biopharmaceutical process monitoring.

10. Predicting Drug Release Kinetics Using Raman and Machine Learning

This study by Mahdi et al. evaluates advanced nonlinear regression models for predicting drug release from polysaccharide-coated solid dosage forms using a high-dimensional Raman spectroscopy dataset with over 1500 spectral and categorical variables. Using 5-aminosalicylic acid as a model drug, release at 2, 8, and 24 hours was accurately modeled by integrating Kernel Ridge Regression, Kernel-based Extreme Learning Machines, and Quantile Regression with rigorous preprocessing, including PCA for dimensionality reduction, Leave-One-Out encoding for categorical features, and Isolation Forest for outlier detection, alongside hyperparameter optimization via the Sailfish Optimizer. Among the approaches, Kernel Ridge Regression showed outstanding performance, achieving near-perfect predictive accuracy and substantially outperforming alternative models.

This work is influential because it demonstrates a scalable, data-driven framework that successfully couples Raman spectroscopy with modern machine-learning optimization to predict complex drug-release behavior from highly multivariate pharmaceutical datasets, advancing predictive formulation design.

Final Summary

Collectively, these ten papers document Raman spectroscopy’s transformation into a predictive, AI-enabled analytical platform central to modern biopharmaceutical manufacturing. They demonstrate a consistent trend toward inline deployment, automation, and direct measurement of quality attributes rather than surrogate variables.

Conclusion

From AI-guided interpretation and automated calibration to real-time bioreactor control and release kinetics prediction, Raman spectroscopy has matured into a cornerstone technology for biopharmaceutical analysis. The studies reviewed here not only advanced technical capabilities but also reshaped industrial and regulatory expectations. As digital twins, autonomous manufacturing, and real-time release testing become standard practice, Raman spectroscopy—empowered by AI and machine learning—will play an increasingly decisive role in ensuring product quality, process robustness, and patient safety.

References

(1) Liu, Y.; Chen, S.; Xiong, X.; Wen, Z.; Zhao, L.; Xu, B.; Guo, Q.; Xia, J.; Pei, J. Artificial Intelligence Guided Raman Spectroscopy in Biomedicine: Applications and Prospects. J. Pharm. Anal. 2025, 15 (11), 101271. DOI: 10.1016/j.jpha.2025.101271.

(2) Chen, J.; Wang, J.; Hess, R.; Wang, G.; Studts, J.; Franzreb, M. Application of Raman Spectroscopy during Pharmaceutical Process Development for Determination of Critical Quality Attributes in Protein A Chromatography. J. Chromatogr. A 2024, 1718, 464721. DOI: 10.1016/j.chroma.2024.464721.

(3) Wang, J.; Chen, J.; Studts, J.; Wang, G. In-Line Product Quality Monitoring during Biopharmaceutical Manufacturing Using Computational Raman Spectroscopy. mAbs 2023, 15 (1), e2220149. DOI: 10.1080/19420862.2023.2220149.

(4) Guo, N.; Niu, S.; Geng, Y.; Shan, G.; Wei, N.; Chen, H. Non-Destructive Quantification of Low Colchicine Concentrations in Commercially Available Tablets Using Transmission Raman Spectroscopy with Partial Least Squares. Int. J. Pharm. X 2025, 9, 100321. DOI: 10.1016/j.ijpx.2025.100321.

(5) Lange, C.; Seidel, S.; Altmann, M.; Stors, D.; Kemmer, A.; Cai, L.; Born, S.; Neubauer, P.; Bournazou, M. N. C. A Setup for Automatic Raman Measurements in High-Throughput Experimentation. Biotechnol. Bioeng. 2025, 122 (10), 2751–2769. DOI: 10.1002/bit.70006.

(6) Matuszczyk, J. C.; Zijlstra, G.; Ede, D.; Ghaffari, N.; Yuh, J.; Brivio, V. Raman Spectroscopy Provides Valuable Process Insights for Cell-Derived and Cellular Products. Curr. Opin. Biotechnol. 2023, 81, 102937. DOI: 10.1016/j.copbio.2023.102937.

(7) Lin, L. L.; Alvarez-Puebla, R.; Liz-Marzán, L. M.; Trau, M.; Wang, J.; Fabris, L.; Wang, X.; Liu, G.; Xu, S.; Han, X. X.; Yang, L. Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges. ACS Appl. Mater. Interfaces 2025, 17 (11), 16287–16379. DOI: 10.1021/acsami.4c17502.

(8) Wallocha, T.; Poth, M. Harnessing Raman Spectroscopy for Enhanced Bioprocess Monitoring: Predictive CO₂ Analysis and Robust pH Determination in Bioreactor Off-Gas Stream. Fermentation 2025, 11 (6), 317. DOI: 10.3390/fermentation11060317.

(9) Heyer-Müller, J.; Schiemer, R.; Robbel, L.; Schmitt, M.; Hubbuch, J. Development of Raman Spectroscopy and Machine Learning Methods for Protein Aggregate Quantification: Application to BSA in Chromatographic Processes. Biotechnol. Bioeng. 2026, ASAP. DOI: 10.1002/bit.70163.

(10) Mahdi, W. A.; Alhowyan, A.; Obaidullah, A. J. Combination of Machine Learning and Raman Spectroscopy for Prediction of Drug Release in Targeted Drug Delivery Formulations. Sci. Rep. 2025, 15, 25139. DOI: 10.1038/s41598-025-10417-z.