Cosmetic Raw Material Identification Testing Through Transparent and Opaque Containers

Significance of the topic

The adoption of stringent GMP requirements in cosmetics manufacturing has increased the demand for rapid, accurate raw material identification at receipt. Traditional QC processes involve lengthy handling, sampling, and laboratory analysis steps, which can delay production and elevate costs. The implementation of handheld Raman spectroscopy directly through containers offers a streamlined solution that enhances operational efficiency and ensures compliance with evolving regulations.

Objectives and study overview

This application note evaluates the performance of the Agilent Vaya handheld spatially offset Raman spectrometer for identifying a range of cosmetic raw materials through both transparent and opaque containers. It compares conventional QC workflows to the simplified at-point-of-need approach enabled by Vaya, and demonstrates its impact on process time, handling requirements, and regulatory compliance.

Methodology and Used Instrumentation

All materials were tested in their original shipping containers without prior sampling. The Vaya system employs spatially offset Raman spectroscopy (SORS) with a CCD detector and built-in algorithms to subtract container signals and extract pure raw material spectra. Users simply select the container type (e.g., glass, HDPE, PP, FIBC, PC) in the software wizard, and the instrument automatically configures acquisition parameters and performs container subtraction.

Main results and discussion

• Emollients and occlusives (e.g., almond, olive, flaxseed, castor oils) yielded distinct SORS spectra with high discrimination power, confirmed by challenge matrices showing perfect pass/fail separation.
• Humectants and preservatives (e.g., glycerol, urea, ethanol, citric acid, benzyl alcohol) were accurately identified through semitransparent HDPE, white HDPE, and amber glass containers.
• Surfactants (e.g., SDS, polysorbates 20 and 80, Triton X-100) and essential oils (e.g., tea tree, lavender, orange oils) exhibited clear spectral features, with effective fluorescence mitigation in amber glass.
• Vitamins (e.g., vitamin C, B3, A palmitate) produced high signal-to-noise spectra through glass bottles, demonstrating sensitivity to minor Raman signals despite low analyte concentrations or loose packing.
• Use of the Vaya system eliminated sample transfer, container opening, and cleaning validation steps, reducing warehouse handling time by 50% and associated costs by 50%.

Benefits and practical applications

• Rapid on-site ID in quarantine areas (transparent containers: 10–15 s per sample; opaque containers: 35–40 s per sample).
• Minimized container handling and consumables use (sampling booth, glassware, PPE), reducing cross-contamination risks.
• Compliance support for ISO 22716, FDA draft cosmetics GMP, EU Regulation 1223/2009, pharmacopeia requirements (USP <858>, EP 2.2.48, JP, Chinese pharmacopeia).
• Suitable for non-spectroscopist operators due to wizard-based workflow and PASS/FAIL reporting.

Future trends and possibilities

Integration of expanded spectral libraries and machine-learning classification could further enhance specificity and throughput. Integration with laboratory information management systems (LIMS) would automate data logging and traceability. Emerging portable Raman technologies and advanced SORS algorithms may extend applicability to complex formulations and multi-layer packaging, driving broader adoption in pharmaceutical and food industries.

Conclusion

The Agilent Vaya handheld Raman spectrometer leveraging SORS provides an efficient, accurate at-point-of-need raw material identification solution for cosmetics manufacturing. By enabling through-container analysis, it streamlines QC workflows, reduces time and costs, and ensures regulatory compliance in GMP environments.

References

• Prulliere F, Welsby C. Cosmetic Raw Material Identification Testing Through Transparent and Opaque Containers. Agilent Technologies Application Note; 2024.