Verification, p-values, and Training Sets for the Mira P

Significance of the Topic

Rapid and non-destructive material identification and verification are essential in pharmaceutical and chemical industries to ensure product quality, compliance and safety. Handheld Raman spectrometers like the Mira P allow on-site screening and verification of known materials, reducing analysis time and operational costs.

Objectives and Overview of the Study

This white paper examines two analytical approaches for the Metrohm Mira P Raman spectrometer:

• Identification of unknown samples using Pearson correlation and Hit Quality Index (HQI).
• Verification of known materials using Principal Component Analysis (PCA) with p-values and confidence intervals.

It illustrates the limitations of HQI in distinguishing similar compounds and demonstrates how PCA-based methods improve specificity.

Methodology and Instrumentation

This study leverages the Metrohm Instant Raman Analyzer Pharmaceutical (Mira P) and MiraCal software. Key methodological steps include:

• Spectral acquisition: minimum of 20 spectra per substance under defined parameters (laser power, integration time, temperature, scan number).
• Identification: calculating HQI values through Pearson correlation against a spectral library with a typical match threshold of 0.85.
• Verification: building PCA models with Hotelling T2 ellipsoids defining 90%–95% confidence intervals and deriving p-values to test sample membership in the training set.
• Training set design: inclusion of deterministic (instrument settings, sample source) and stochastic (ambient light, container effects, sample heterogeneity) variations to enhance robustness.

Instrument used:

• Metrohm Instant Raman Analyzer Pharmaceutical (Mira P)

Main Results and Discussion

Comparison of fatty acid isomers highlighted critical differences:

• HQI-based identification returned high match scores (>0.85) for all compounds, leading to false positives.
• PCA with p-value classification achieved clear separation: each substance passed only for its own model and failed for others.
• Hotelling T2-based confidence intervals (90% vs. 95%) illustrate the trade-off between acceptance strictness and variance coverage.

Robust training set editing by visual inspection and alignment checks further improves model accuracy.

Benefits and Practical Applications

• Improved specificity in verification reduces false positives when discriminating similar molecules.
• PCA-based p-values provide statistical confidence in pass/fail decisions for known materials.
• Incorporation of controlled variance ensures reliable performance under field conditions (temperature, light, container differences).
• Rapid, on-site screening supports QA/QC processes in pharmaceutical manufacturing and chemical handling.

Future Trends and Applications

Advancements are expected in several areas:

• Integration of machine learning algorithms for automated model optimization.
• Expansion of spectral libraries with higher-quality reference data and diverse sample origins.
• Real-time monitoring and cloud-based data management for remote verification workflows.
• Enhanced hardware stability and sensor miniaturization to broaden field deployment.

Conclusion

The white paper clarifies that while HQI-based Raman identification is useful for unknown screening, PCA with p-values offers robust verification of known materials. Building comprehensive training sets that capture both deterministic and stochastic variations is essential to achieve reliable model performance with the Mira P system.

References

1. Bakeev K. A.; Chimenti R. V. Pros and cons of using correlation versus multivariate algorithms for material identification via handheld spectroscopy. Eur Pharm Rev. 2013.
2. Dahiru T. p-value, a true test of statistical significance? A cautionary note. Ann Ibadan Postgrad Med. 2008;6(1):21–26.
3. Varmuza K.; Filzmoser P. Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press; 2009:321.
4. O’Connell M.-L. et al. Qualitative Analysis Using Raman Spectroscopy and Chemometrics: A Comprehensive Model System for Narcotics Analysis. Appl Spectrosc. 2010;64(10):1109–1121.
5. Papoulis A.; Pillai U. Probability, Random Variables and Stochastic Processes. 4th ed. McGraw-Hill; 2001.