Transition RMID Operations Between Handheld Raman Devices

Importance of the Topic

This application note addresses the seamless transfer of validated raw material identification (RMID) libraries and verification models from the NanoRam 785 handheld Raman spectrometer to the Metrohm Instant Raman Analyzer (MIRA) P platform. Ensuring continuity of RMID workflows across instruments enables laboratories and manufacturing sites to maintain high standards of quality control while leveraging new hardware advances without the need to rebuild extensive spectral libraries or retrain models.

Objectives and Study Overview

The primary aim is to guide existing NanoRam 785 users in migrating both identification libraries and verification models into MIRA P’s software environment (MIRA Cal P). The note outlines a four-step process—data export, format conversion, software configuration, and model validation—and highlights key parameter settings and validation outcomes using a lactose example. This establishes a template for other substances and strengthens method reproducibility across devices.

Instrumentation

• Metrohm NanoRam 785 handheld Raman spectrometer with NanoRam ID software
• Metrohm Instant Raman Analyzer (MIRA) P with MIRA Cal P software
• Metrohm file conversion tool (CSV to BRMS)

Methodology and Instrumentation

The migration workflow comprises:

1. Data Export: Extract identification library or verification method data from NanoRam ID as CSV files.
2. Format Conversion: Use Metrohm’s conversion utility to transform CSV files into MIRA P’s binary BRMS format.
3. Software Configuration: Import converted files into MIRA Cal P. For identification, build and synchronize a new library. For verification, create an operating procedure (OP) in MIRA Cal P and collect validation spectra.
4. Model Creation and ROC Optimization: Assemble training and validation sets in MIRA Cal P, generate ROC curves, select optimal threshold, and save the model into the OP. Synchronize the model to the MIRA P instrument.

Main Findings and Discussion

After transfer, ROC-optimized settings for a lactose verification model included principal component scaling (PCS 3), mean-centering pretreatment, min/max normalization, polynomial smoothing (order 3), and derivative processing. Validation with diverse lactose types yielded high specificity and robustness; p-values confirmed correct pass/fail classification even for challenging fluorescent samples. These results demonstrate that transferred models perform comparably on MIRA P without further recalibration.

Benefits and Practical Applications

• Rapid transition between Raman platforms, preserving existing libraries and models
• Reduced downtime and resource savings by avoiding full model redevelopment
• Consistent RMID performance at point-of-inspection or in the laboratory
• Support for regulated environments through FDA 21 CFR Part 11 compliance on MIRA P

Future Trends and Applications

Integration of cloud-based library sharing and centralized model management will further streamline cross-site instrument deployments. Advances in chemometric algorithms, machine learning-driven model optimization, and expanded spectral databases promise enhanced discrimination of complex mixtures. Emerging portable Raman platforms may adopt standardized transfer protocols, enabling seamless collaboration among research, QA/QC, and manufacturing teams.

Conclusion

The documented workflow allows existing NanoRam 785 users to migrate identification libraries and verification models to MIRA P efficiently and reliably. By following a structured export, conversion, configuration, and validation process, laboratories can maintain continuity of RMID operations and capitalize on MIRA P’s advanced hardware and software features.

References

1. Gelwicks, M. J. Real World Raman: Simplifying Incoming Raw Material Inspection. Analyze This – The Metrohm Blog, 2021.