Maintaining consistent product quality, yield, and efficiency is a common challenge in continuous pharmaceutical and fine chemical production.
Traditional offline sampling methods are slow and can interrupt the process. In-line spectroscopic techniques—especially Raman and near-infrared (NIR) spectroscopy—combined with chemometric models, offer a way to monitor reactions in real time.1
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Why Real-Time Monitoring Matters
Offline methods such as high-performance liquid chromatography (HPLC) are typically used to verify product quality or confirm reaction completion after processing is finished. This can delay decision-making and increase the risk of waste or rework.2
In-line, real-time monitoring with Raman and NIR spectroscopy provides immediate data on reaction progress and product characteristics. This allows adjustments to be made during the process, reducing the chance of producing out-of-spec material.2
This approach supports the goal of the FDA’s Process Analytical Technology (PAT) framework, which emphasizes building quality into the manufacturing process rather than relying on end-point testing.2
Raman vs. NIR: Complementary Techniques
Raman and NIR probes are typically installed directly within the flow path of reactors, mixers, or purification units using flow cells or windowed interfaces. These probes are often constructed with sapphire or diamond windows to withstand harsh chemical environments, high pressures, and temperatures.
Coupled to a spectrometer by fiber-optic cables, they collect molecular vibrational spectra in real time.1,3
- NIR spectroscopy detects overtones and combinations of molecular vibrations. It is well-suited for measuring concentrations, moisture levels, and impurities. The method is non-invasive and fast, but its broad spectral features can reduce specificity.1
- Raman spectroscopy is based on inelastic light scattering and produces narrow, molecule-specific spectra. It is effective for detecting low-concentration analytes (below 1 % w/w) and for identifying different crystal forms, including during melt extrusion.1
In some applications, Raman and NIR are used together in a sensor fusion setup. This approach extends the range of chemical and physical properties that can be monitored and quantified.
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Chemometrics and Spectral Fingerprinting
Building Models from Spectral Data
Chemometric modeling involves building predictive relationships between spectral features and known process parameters, such as concentration, purity, or composition.
This is especially useful for complex chemical systems, including actinide separation in nuclear fuel recycling or blending low-dose active pharmaceutical ingredients (APIs). In these cases, models are created using structured training datasets.
These datasets usually include spectral data—such as UV–vis, NIR, and Raman spectra—for samples tested under different process conditions.4,5
For example, training sets may include:
- Spectra of individual target components (e.g., U(IV), Pu(VI)) measured in different nitric acid concentrations
- Multicomponent mixtures that represent realistic process scenarios
- Data from multiple spectroscopic techniques, which may be analyzed separately to verify results across Raman, NIR, and UV–vis methods
After data collection, spectra are preprocessed using techniques like Standard Normal Variate (SNV) normalization. This helps remove baseline variation and differences in signal scaling. The cleaned data is then used to develop predictive models, often using Partial Least Squares (PLS) regression. PLS is a common method in chemometrics that can handle collinear and noisy data.
Software tools such as the Eigenvector PLS_Toolbox in MATLAB are frequently used to build and apply these models in both research and industrial environments.3,4
Transferability and Real-Time Deployment
In most cases, chemometric models are developed using a dedicated training instrument and then transferred to instruments installed on the production line. This process often requires adjustments, such as slope and intercept corrections, to account for small differences in hardware or optical components.
In more advanced setups, additional data are collected using the actual process-line instruments. This allows the creation of localized models that better reflect real operating conditions and improve reliability during deployment.5
This method has been applied in complex settings such as actinide separation and tablet manufacturing. For example, during the semi-continuous production of a low-dose drug (1 % API), Raman spectra from the tablet feed frame were used to monitor API concentration in real time.5
Spectral Fingerprinting for Process Verification
Spectral fingerprinting provides qualitative verification in addition to quantification. Each compound or material phase has a distinct spectral signature that can be used to confirm identity, detect contaminants, or verify polymorphic form.1, 3
In more complex processes, using multiple spectroscopic techniques for fingerprinting adds redundancy and improves reliability. For instance, confirming U(VI) detection with both Raman and NIR data helps validate the result.3
Fingerprinting also helps with troubleshooting. Operators can use small changes in spectral patterns to identify issues related to raw material variability, process disturbances, or equipment wear.3
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Industrial Case Studies
Continuous Crystallization
Jong et al. (2025) evaluated calibration strategies for spectroscopic analysis using paracetamol as a model compound. The study compared several calibration methods, including univariate, multivariate (PLS), and hybrid models. These were tested across different spectroscopic techniques (Raman, NIR, and MIR).
Multivariate methods, particularly PLS, showed better accuracy and robustness than univariate models.6 The study also emphasized the need for representative calibration datasets and proper validation to address sample variability. These results offer practical guidance for optimizing calibration strategies in pharmaceutical process monitoring.6
Polymer Synthesis and Extrusion
Dadou et al. investigated inline NIR spectroscopy for monitoring hot-melt extrusion (HME) of amorphous solid dispersions (ASDs). The study compared inline NIR data with results from traditional offline methods.
NIR spectroscopy, supported by chemometric modeling, successfully tracked key process variables in real time. These included drug concentration, polymer miscibility, and thermal degradation. The ability to detect formulation or process changes during HME allowed timely adjustments to maintain consistent product quality.7
Continuous Tablet Manufacturing (Low-dose API)
A study by Harms et al. compared Raman and NIR spectroscopy in a tablet compression feed frame. Raman spectroscopy detected API concentrations as low as 1 % w/w and showed less variability compared to NIR. It was selected for ongoing PAT support due to its reliability in monitoring low-dose formulations.8
Implementation Challenges
Deploying Raman and NIR spectroscopy for continuous real-time monitoring presents several practical challenges. One common issue is probe fouling. This occurs when solids or particulates build up on the probe window, which reduces signal quality.
To address this, systems may use heated or self-cleaning probe tips, carefully chosen probe locations, and predictive cleaning triggered by changes in the spectral signal.1
Calibration and model transferability also pose challenges. Chemometric models rely on representative data, but changes in materials, solvents, temperature, or probe setup can impact accuracy. Robust calibration using diverse datasets and periodic recalibration is essential to maintain model performance.2
Probe durability is critical in harsh environments with high heat, pressure, or corrosive media. While fiber-optic designs offer resilience, they are still vulnerable to background interference and wear. Regular checks, protective housings, and durable materials help ensure reliable long-term use.1,2
Looking Ahead: Real-Time, Mobile, and Smart
The future of process monitoring is shifting toward modular, robust spectroscopic tools that enable dynamic, decentralized, and intelligent manufacturing.
This includes mobile, plug-and-play probes integrated into flexible flow systems; real-time feedback control loops that adjust parameters like flow rate, temperature, or feed ratios based on quality attributes; and the incorporation of machine learning to detect anomalies, predict fouling, and optimize spectral analysis in real time.6
Additionally, digital twins that combine live spectral data with process simulations are emerging to support adaptive, automated control strategies.
For a practical demonstration of how Raman spectroscopy supports real-time process monitoring, watch:
Raman spectroscopy for real-time analysis & process monitoring control
References and Further Reading
1. De Beer, T.; Burggraeve, A.; Fonteyne, M.; Saerens, L.; Remon, J. P.; Vervaet, C., Near Infrared and Raman Spectroscopy for the in-Process Monitoring of Pharmaceutical Production Processes. International journal of pharmaceutics 2011, 417, 32-47. https://www.sciencedirect.com/science/article/pii/S0378517310009324
2. Miyai, Y.; Formosa, A.; Armstrong, C.; Marquardt, B.; Rogers, L.; Roper, T., Pat Implementation on a Mobile Continuous Pharmaceutical Manufacturing System: Real-Time Process Monitoring with in-Line Ftir and Raman Spectroscopy. Organic Process Research & Development 2021, 25, 2707-2717. https://pubs.acs.org/doi/full/10.1021/acs.oprd.1c00299
3. Lines, A. M.; Hall, G. B.; Asmussen, S.; Allred, J.; Sinkov, S.; Heller, F.; Gallagher, N.; Lumetta, G. J.; Bryan, S. A., Sensor Fusion: Comprehensive Real-Time, on-Line Monitoring for Process Control Via Visible, near-Infrared, and Raman Spectroscopy. Acs Sensors 2020, 5, 2467-2475. https://pubs.acs.org/doi/full/10.1021/acssensors.0c00659
4. Nelson, G. L.; Lines, A. M.; Casella, A. J.; Bello, J. M.; Bryan, S. A., Development and Testing of a Novel Micro-Raman Probe and Application of Calibration Method for the Quantitative Analysis of Microfluidic Nitric Acid Streams. Analyst 2018, 143, 1188-1196. https://pubs.rsc.org/en/content/articlelanding/2018/an/c7an01761h
5. Bro, R.; Eldén, L., Pls Works. Journal of Chemometrics 2009, 23, 69-71 DOI: https://doi.org/10.1002/cem.1177.
6. Jong, C. Y.; Tristan, G.; Felix, L. J. J.; Yeap, E. W. Q.; Dubbaka, S. R.; Rao, H. N.; Wong, S. Y., Systematic Assessment of Calibration Strategies in Spectroscopic Analysis: A Case Study of Paracetamol Crystallization. Organic Process Research & Development 2025. https://pubs.acs.org/doi/full/10.1021/acs.oprd.4c00496
7. Dadou, S. M.; Senta-Loys, Z.; Almajaan, A.; Li, S.; Jones, D. S.; Healy, A. M.; Tian, Y.; Andrews, G. P., The Development and Validation of a Quality by Design Based Process Analytical Tool for the Inline Quantification of Ramipril During Hot-Melt Extrusion. International Journal of Pharmaceutics 2020, 584, 119382. https://www.sciencedirect.com/science/article/pii/S0378517320303665
8. Harms, Z. D.; Shi, Z.; Kulkarni, R. A.; Myers, D. P., Characterization of near-Infrared and Raman Spectroscopy for in-Line Monitoring of a Low-Drug Load Formulation in a Continuous Manufacturing Process. Analytical chemistry 2019, 91, 8045-8053. https://pubs.acs.org/doi/full/10.1021/acs.analchem.8b05002
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