The Top 10 Most Influential Applications of Near-Infrared Spectroscopy in Biopharmaceutical Analysis (2025)

Abstract

Near-infrared spectroscopy has become a critical enabler of modern biopharmaceutical analysis through its non-destructive, rapid, and information-rich measurement capabilities. In 2025, a series of influential publications has reconfigured the application landscape of NIR spectroscopy by imaginative integration of advanced chemometrics, deep learning, hybrid spectroscopic sensing, and closed-loop process control. This review summarizes ten of the most impactful contributions during this period, highlighting their technical innovations, application domains, and influence on quality by design (QbD), process analytical technology (PAT), and real-time (RT) release testing. Collectively, these works demonstrate how NIR spectroscopy is advancing into a centralized sensor technology for intelligent, adaptive, and data-driven biopharmaceutical manufacturing.

Introduction

Biopharmaceutical manufacturing presents a uniquely complex analytical environment characterized by highly variable raw materials, complex multicomponent biological systems, and stringent regulatory expectations for quality and consistency. Near-infrared (NIR) spectroscopy has long been valued in this space for its speed, minimal sample preparation, and compatibility with solids, liquids, and heterogeneous samples. Historically, its greatest impact has been realized through multivariate calibration chemometrics for compositional analysis and process monitoring.

As reported in 2025, however, the role of NIR spectroscopy has expanded significantly. The convergence of real-time sensing, advanced chemometrics approaches, machine learning, and automation has repositioned NIR spectroscopy as a core digital sensor within advanced PAT frameworks. The ten papers reviewed here were selected based on citation growth, methodological novelty, industrial relevance, and their influence on subsequent research and implementation. Together, they illustrate how NIR spectroscopy is redefining biopharmaceutical analysis across upstream, downstream, and integrated manufacturing environments.

Narrative

1. Transformative Trends in Biomedical and Pharmaceutical NIR Spectroscopy

Kos et al. provided one of the most comprehensive and forward-looking reviews of NIR spectroscopy applications in biomedical and pharmaceutical analysis published in the past decade (1). The paper systematically connects instrumentation advances—particularly miniaturized and portable spectrometers—with expanding applications in pharmaceutical quality assurance, counterfeit detection, and biomedical diagnostics.

This work has been widely cited because it consolidates fragmented advances across biomedicine and pharmaceutical science into a unified vision of NIR spectroscopy as a democratized, low-cost, and globally deployable analytical platform. Its emphasis on accessibility, regulatory relevance, and hybrid analytical workflows positioned it as a foundational reference for both academic and industrial researchers.

2. Hybrid NIR–Raman Spectroscopy with AI for Bioprocess Control

Xu et al. demonstrated a landmark integration of NIR and Raman spectroscopy with artificial intelligence for real-time bioprocess monitoring and control during gentamicin fermentation (2). By fusing orthogonal spectral information and machine learning, the authors achieved unprecedented predictive accuracy and automated feeding control.

This study represents a decisive step beyond monitoring toward closed-loop control, showing how NIR spectroscopy can directly drive process optimization. Its demonstration of sustained glucose control and significant titer improvement has made it a benchmark for intelligent biomanufacturing platforms.

3. Deep Learning and Synthetic Spectral Augmentation for Fermentation Monitoring

Tian et al. introduced a Transformer-based deep learning framework enhanced with generative spectral augmentation for monitoring hyaluronic acid fermentation using NIR spectroscopy (3). The combination of Wasserstein generative adversarial network with gradient penalty (WGAN-GP) synthetic spectra and advanced neural architectures addressed longstanding calibration data limitations.

The work is frequently cited as a breakthrough in overcoming sparse and noisy bioprocess data. By showing that synthetic spectra can materially improve calibration robustness, this paper accelerated adoption of deep learning in NIR-based bioprocess analytics.

4. Pharmaceutical Process Monitoring and Quality Control Using NIR

Zhang et al. delivered an authoritative review of NIR spectroscopy applications in pharmaceutical process monitoring, quality control, and PAT-enabled manufacturing (4). The article spans qualitative and quantitative analysis, continuous manufacturing, and regulatory perspectives.

Its influence stems from its clarity and breadth. The paper has become a go-to reference for regulatory scientists and process engineers seeking a comprehensive yet practical overview of NIR implementation in modern pharmaceutical manufacturing.

5. Spectroscopy as PAT Tools for Biopharmaceutical Development

Massei’s doctoral thesis work provided one of the most detailed experimental evaluations of NIR and Raman spectroscopy as PAT tools in biopharmaceutical manufacturing, particularly for monoclonal antibody products (5).

Although technically a thesis, this work has been heavily cited because it bridges academic rigor and industrial relevance. Its demonstration of NIR spectroscopy for freeze-drying monitoring and residual moisture determination has directly influenced PAT deployment strategies across biopharmaceutical applications.

6. Calibration-Free NIR Modeling for Continuous Powder Streams

Velez-Silva et al. introduced external variable augmented iterative optimization technology (EVA-IOT) to enhance the robustness of “calibration-light” NIR models in continuous pharmaceutical powder streams (6).

The reduction of the calibration burden by up to 97% directly addresses one of the most persistent barriers to industrial process NIR adoption. EVA-IOT is now frequently referenced in discussions of scalable PAT deployment in continuous manufacturing.

7. NIR Spectroscopy and CNNs for Cell Culture Media Characterization

Gangwar et al. applied NIR spectroscopy coupled with one-dimensional convolutional neural networks (CNNs) to characterize complex cell culture media and predict glycosylation outcomes (7).

This study connected raw material variability directly to product quality attributes using NIR spectroscopy, reinforcing its role in QbD frameworks and elevating its importance in upstream bioprocess control.

8. Inline NIR Spectroscopy in Microreactor Process Monitoring

Mahler et al. demonstrated inline NIR spectroscopy combined with chemometrics for real-time reaction monitoring within microreactor systems (8).

By showing that NIR spectroscopy can safely monitor fast, exothermic reactions, this work expanded the perceived applicability of NIR into micro-PAT and intensified process development environments.

9. PAT in Downstream Biopharmaceutical Processing

Sathiyapriyan et al. reviewed the evolving PAT landscape in downstream biopharmaceutical processing, with spectroscopy highlighted as a core enabling technology (9).

This review synthesized analytical, regulatory, and digital perspectives, helping to align NIR spectroscopy with real-time release testing and digital twin strategies in downstream operations.

10. Model Maintenance and Control Using In-Situ NIR Spectroscopy

Chandrasekaran et al. presented a robust framework for NIR model maintenance, monitoring, and control during Lactococcus lactis fermentation (10).

The paper addressed a formerly critical gap—model drift and lifecycle management—demonstrating how NIR spectroscopy can remain reliable in long-term industrial deployment.

Final Summary

The ten papers reviewed here collectively illustrate the rapid advancement of NIR spectroscopy from a mature analytical technique into a digitally integrated, AI-enabled sensor platform. Across upstream, downstream, and continuous manufacturing environments, NIR spectroscopy now supports real-time monitoring, predictive analytics, and automated control with unprecedented sophistication.

Conclusion

During 2025, NIR spectroscopy has redefined its role in biopharmaceutical analysis. No longer limited to off-line or at-line measurements, NIR spectroscopy is now embedded within intelligent manufacturing systems that emphasize adaptability, robustness, and regulatory compliance. The influence of the ten papers discussed here extends beyond citation metrics; together, they define best practices and emerging paradigms for the next generation of biopharmaceutical PAT and quality assurance.

References

(1) Kos, J.; Pavelek, D.; Kaykhaii, M.; Olsen, M.; Jampilek, J.; Halko, R. Unveiling the Transformative Power of Near-Infrared Spectroscopy in Biomedical and Pharmaceutical Analysis: Trends, Advancements, and Applications. Eur. J. Pharm. Sci. 2025, 196, 107175. DOI: 10.1016/j.ejps.2025.107175

(2) Xu, F.; Su, L.; Gao, H.; Wang, Y.; Ben, R.; Hu, K.; Mohsin, A.; Li, C.; Chu, J.; Tian, X. Harnessing Near-Infrared and Raman Spectral Sensing and Artificial Intelligence for Real-Time Monitoring and Precision Control of Bioprocesses. Bioresour. Technol. 2025, 421, 132204. DOI: 10.1016/j.biortech.2025.132204

(3) Tian, W.; Zang, L.; Li, Y.; Peng, C.; Zhang, J.; Shao, H.; Xian, R.; Sun, R.; Liu, F.; Tan, H.; Ling, P. A Transformer-Based Framework with Generative Spectral Augmentation for Online Monitoring of Hyaluronic Acid Fermentation. Carbohydr. Polym. 2025, 369, 124278. DOI: 10.1016/j.carbpol.2025.124278

(4) Zhang, C.; Liu, Y.; Tang, Z.; Zhang, Y.; Wang, H.; Chen, X. Recent Applications of Near-Infrared Spectroscopy in Pharmaceutical Process Monitoring and Quality Control. Anal. Sci. 2025, 41, 923–944. DOI: 10.1007/s44211-025-00794-w

(5) Massei, A. Spectroscopic Techniques as Process Analytical Technology Monitoring Tools for the Development of Biopharmaceutical Product Processes. Ph.D. Thesis, University of Bologna, Bologna, Italy, 2025. Pp. 26–52. https://tesidottorato.depositolegale.it/bitstream/20.500.14242/202045/1/conv_tesi_final_reviewed.pdf (accessed 2026-02-10).

(6) Velez-Silva, N. L.; Rish, A. J.; Drennen, J. K., III; Anderson, C. A. Robust Near-Infrared Modeling for Pharmaceutical Powder Streams: External Variable Augmented Iterative Optimization Technology (EVA-IOT). Eur. J. Pharm. Biopharm. 2025, 207, 114626. DOI: 10.1016/j.ejpb.2025.114626

(7) Gangwar, N.; Balraj, K.; Rathore, A. S. Near-Infrared Spectroscopy Coupled with Convolutional Neural Networks as a Checkpoint Tool for Cell Culture Bioprocess Media Characterization. Biotechnol. Prog. 2025, 41 (6), e70056. DOI: 10.1002/btpr.70056

(8) Mahler, L.; Desel, P.; Sladkov, M.; Hempelmann, R.; Kockmann, N. A Process Monitoring Microreactor Assembly for Real-Time Reaction Analysis Using Inline Near-Infrared Spectroscopy and Chemometrics. Anal. Bioanal. Chem. 2025, 417, 1431–1439. DOI: 10.1007/s00216-025-05779-2

(9) Sathiyapriyan, P.; Mukherjee, S.; Vogel, T.; Essen, L.-O.; Boerema, D.; Vey, M.; Kalina, U. Current Process Analytical Technology Landscape in the Downstream Processing of Biopharmaceuticals. Anal. Sci. Adv. 2025, 6 (1), e70013. DOI: 10.1002/ansa.70013

(10) Chandrasekaran, K.; Chandrasekaran, K.; Jayaraman, G.; Bhatt, N. Model Maintenance, Monitoring, and Control Framework Using In Situ Near-Infrared Spectroscopy and Offline Process Data for Lactococcus lactis Fermentation. Chem. Eng. J. 2025, 475, 165116. DOI: 10.1016/j.cej.2025.165116