Chemometrics for Raman Spectroscopy Harmonization

Area of Science:

• Chemistry
• Spectroscopy
• Data Science

Background:

• Raman spectroscopy is versatile but generates complex data (1D, 2D, 3D) requiring advanced analysis.
• Univariate methods suffice for simple spectra, but multivariate data analysis (MVDA) is crucial for complex datasets.
• Existing standards for MVDA methods in Raman spectroscopy need continuous improvement through research and development.

Purpose of the Study:

• To review existing and emerging MVDA techniques for Raman spectroscopic data.
• To highlight the role of instrumentation and data calibration in MVDA.
• To discuss data fusion and model transfer for enhanced discrimination, prediction, and harmonization.

Main Methods:

• Review of current literature on MVDA techniques for Raman spectroscopy.
• Exploration of data processing, calibration, and manipulation strategies.
• Discussion of data fusion and model transfer principles and applications.

Main Results:

• MVDA is a requirement for analyzing complex Raman data (maps, volumes).
• Data fusion from multiple sources enhances chemometric model performance.
• Spectra manipulation, calibration, and validation are critical for reliable models.
• Model transfer and harmonization across instruments are essential for consistency.

Conclusions:

• Continuous research and development are needed to improve MVDA methods for Raman spectroscopy.
• Standardized protocols for data processing and model transfer are vital for harmonization.
• An open-access database could facilitate ongoing harmonization of new Raman instruments.