Rapid Identification of Raw Materials Inside Packaging

Significance of the Topic

The ability to identify raw pharmaceutical ingredients without removing them from their original packaging addresses critical quality and safety concerns in cGMP environments. Incidents such as diethylene glycol contamination in glycerin highlight the need for reliable, in-situ verification methods. Through-container identification accelerates material release, reduces contamination risk, and strengthens supply chain integrity.

Study Objectives and Overview

This application note evaluates the performance of the Agilent Vaya handheld Raman spectrometer employing Spatially Offset Raman Spectroscopy (SORS) for raw material identification inside various container types. The study aimed to develop and validate analytical methods in accordance with ICH Q2(R1) and USP<1225> guidelines, focusing on specificity tests against closely related analogs. Four compound classes were targeted: sugars, glycols/diols, long-chain hydrocarbons, and coating agents.

Methodology

Reagent-grade standards were used to build 39 identification methods across five packaging formats: multilayer paper sacks, transparent PE liners, white HDPE bottles, amber glass, and flexible intermediate bulk containers. Each method incorporated 10 scans for model development. Specificity was assessed through positive and negative challenge tests, generating pass/fail matrices to quantify correct identification (true positives) and rejection of analogs (true negatives). Spectral acquisition parameters were fully automated to minimize user variability.

Instrumentation Used

• Agilent Vaya handheld Raman spectrometer
• Spatially Offset Raman Spectroscopy (SORS) capability for through-container analysis
• Automated spectral acquisition and decision-engine combining R2 correlation and linear model coefficients for identity verification

Main Results and Discussion

Through-container SORS measurement successfully isolated analyte spectra by subtracting container contributions, even for opaque and colored packages. Overlay spectra for anhydrous dextrose, dextrose monohydrate, galactose, glycerin, diethylene glycol, and long-chain acids demonstrated retention of diagnostic Raman features without container interference. Challenge matrices for each class achieved near-perfect diagonal performance (pass rates >0.95) and minimal off-diagonal false positives (<0.10) once analogous spectra were included in method models. Coating agent differentiation required the addition of other colored agents into models to refine selectivity, yielding error-free identification across all variants.

Benefits and Practical Applications

• Rapid material release: single operator can screen large batches in hours rather than days
• Non-destructive testing: no need to open or dilute samples, preserving material integrity
• Reduced contamination and consumable costs: eliminates sampling booths, PPE, and waste generation
• Enhanced supply chain security: independent verification of raw materials without reliance on external certificates

Future Trends and Potential Applications

Advancements may include expanded libraries covering more complex formulations, integration of machine learning for automated anomaly detection, remote data management for global warehouse networks, and miniaturization for field deployment. Further application of through-container SORS could extend to finished dosage forms, multi-layer packaging, and real-time process monitoring.

Conclusion

The Agilent Vaya SORS Raman spectrometer provides a robust, selective, and efficient solution for raw material identification within sealed containers. Compliance with pharmacopeial and ICH validation criteria was demonstrated across multiple compound classes and packaging types, offering significant operational advantages for pharmaceutical QA/QC workflows.

References

• U.S. FDA, Testing of Glycerin for Diethylene Glycol Guidance, CDER, May 2007
• United States Pharmacopeia <858>, <1858>; European Pharmacopeia 2.2.48; Chinese Pharmacopeia 0421; Japanese Pharmacopeia chapter 2.26
• ICH Q2(R1): Validation of Analytical Procedures: Text and Methodology
• Gryniewicz-Ruzicka C. et al., Applied Spectroscopy 2011, 65(3):334-341
• Sagitova E.A. et al., Journal of Physics: Conference Series, 2018, 999:012002