NIR and Raman: Complementary Techniques for Raw Material Identification

NIR and Raman Complementarity for Rapid Raw Material Identification (RMID)

Importance of the topic

Raw material identification (RMID) is a critical quality control activity across pharmaceutical, food and chemical manufacturing. Rapid, reliable and non‑destructive identification at receipt and at multiple process stages reduces the risk of out‑of‑specification product, prevents costly rework or waste, and supports Process Analytical Technology (PAT) goals. Vibrational spectroscopies — near‑infrared (NIR) and Raman — meet these operational needs by providing fast, reagent‑free analysis that can often be performed through containers and by non‑specialist operators when appropriate software features are used.

Objectives and study overview

This technical study evaluated the complementary use of FT‑NIR (Thermo Scientific Antaris II) and Raman (Thermo Scientific DXR SmartRaman) spectroscopy for RMID of a representative set of 27 common pharmaceutical raw materials (excipients, APIs, lubricants and salts). The goal was to determine an efficient workflow to identify most materials rapidly with NIR and to apply Raman as a follow‑up for materials that are challenging for NIR, thereby maximizing correct identifications under realistic process conditions (sampling through polyethylene bags where appropriate).

Methodology

• Sample set: 27 materials chosen to represent common pharmaceutical raw materials including sugars, polysaccharides, hydrated and anhydrous inorganic salts, lubricants and polymers.
• NIR acquisition: Antaris II FT‑NIR with integrating sphere; spectra recorded 10,000–4,000 cm⁻¹ using 16 scans at 4 cm⁻¹ resolution; samples were scanned through polyethylene bags when applicable.
• NIR data processing and classification: chemometric model built in Thermo Scientific TQ Analyst using Discriminant Analysis; preprocessing included multiplicative signal correction (MSC) for pathlength, first derivative to reduce baseline effects, and Norris smoothing (segment 9, gap 7).
• Quality metric: Mahalanobis distance used to quantify how far a sample spectrum lies from the class mean; distance to nearest incorrect class used to estimate misidentification risk.
• Raman follow‑up: DXR SmartRaman using 780 nm excitation and the Universal Platform sampling accessory; autoexposure aimed for S/N ≥ 100 (acquisition up to ~2 minutes); library searches used for identification with spectral match scores (0–100 scale).

Instrumentation used

• Thermo Scientific Antaris II FT‑NIR analyzer with integrating sphere.
• RESULT software for NIR operation (user‑oriented RMID workflows).
• TQ Analyst for chemometric model building (Discriminant Analysis, MSC, derivative, Norris smoothing).
• Thermo Scientific DXR SmartRaman spectrometer with 780 nm laser, autoexposure, Smart backgrounds, automated alignment/calibration and Universal Platform sampling accessory.

Main results and discussion

• NIR performance: The FT‑NIR chemometric model correctly and decisively classified most of the 27 materials. Mahalanobis distances to the nearest incorrect class were generally large, indicating clear class separation and low misidentification risk for the majority of materials.
• Challenging cases for NIR: Four materials showed Mahalanobis distances to the nearest incorrect class below ~3 units, indicating higher misidentification risk. Calcium carbonate (CaCO3) was particularly problematic because it exhibits negligible NIR absorbance in the measured region; spectra were dominated by the polyethylene bag, leading to misclassification. Other salts and materials with weak NIR-active bands or spectral overlap with container materials (e.g., magnesium stearate vs. polyethylene) also proved difficult.
• Raman follow‑up success: Raman library searches correctly identified the four problematic materials. Reported spectral match scores were high (CaCO3 95.5; Mg stearate 88.2; MgSO4 96.3; MnSO4 99.4). Raman’s sharper, well‑resolved bands and sensitivity to non‑polar bonds (e.g., C–C backbone and ionic lattice modes) enabled confident identification where NIR lacked diagnostic features.
• Complementary strengths: NIR offers deeper penetration, larger sampled volume and rapid acquisition (FT‑NIR can provide results in seconds), allowing non‑destructive analysis even through glass or plastic containers with minimal operator training. Raman provides higher spectral resolution and chemical specificity, enabling library matching and identification of substances that lack NIR features. Thus, an operational workflow using NIR as a first screen with Raman confirmatory analysis maximizes throughput and identification confidence.

Benefits and practical applications

• Operational efficiency: NIR screening reduces time and resource burden compared with chromatographic or wet chemical methods and preserves samples for archiving.
• Flexibility of deployment: NIR can be used by non‑specialist staff at multiple process points (receiving, in‑process), because of simple result reporting and turnkey software. Raman, with automated features, can be deployed for confirmatory testing by trained personnel or as an intermediate step in the workflow.
• Robust RMID: Combining chemometrics (for NIR) and spectral library search (for Raman) produces a practical identification system that balances speed and chemical specificity.
• Container testing: Both techniques can analyze materials through common packaging (plastic bags, glass), reducing handling and exposure risks; however, container spectral contributions must be considered during method development.

Future trends and potential applications

• Advanced chemometrics and machine learning: More sophisticated classification algorithms, anomaly detection and transfer learning to make NIR models more robust to batch variability and container effects.
• Multimodal data fusion: Integrating NIR and Raman spectral features into a unified decision engine could further reduce ambiguous classifications and automate the selection of confirmatory tests.
• Expanded, curated spectral libraries: Broader, quality‑controlled Raman/NIR libraries will improve identifications for less common materials and reduce false positives from incomplete libraries.
• Portable and at‑line integration: Continued miniaturization and ruggedization of spectrometers combined with PAT integration will enable continuous or near‑real‑time RMID at multiple process points.
• Automation and robotics: Automated sample handling, container scanning protocols and LIMS integration will streamline RMID workflows in high‑throughput manufacturing environments.

Conclusion

The study demonstrates that FT‑NIR and Raman spectroscopy are complementary and together provide an efficient, non‑destructive RMID strategy for pharmaceutical manufacturing. NIR is well suited for rapid screening of many materials and for sampling through containers, while Raman excels at identifying materials with poor NIR signatures or when higher chemical specificity is required. Implementing a two‑step workflow — NIR first, Raman confirmation for challenging cases — yields fast, reliable identification with minimal sample handling, supporting quality control and PAT initiatives.

References

• Strother T. NIR and Raman: Complementary Techniques for Raw Material Identification. Thermo Fisher Scientific Technical Note 51768; 2009.