Raman spectra-based structural classification analysis of quinoidal and derived molecular systems

Area of Science:

• Spectroscopy
• Chemometrics
• Molecular Modeling

Background:

• Quinones and related structures are vital in chemical and biological processes.
• Applications span pharmacology, toxicology, and medicine.
• Accurate classification of these molecules is crucial for research and development.

Purpose of the Study:

• To develop and validate a classification analysis method for quinones using vibrational Raman spectra.
• To spectrally order and classify 38 quinones and related structures.
• To establish spectral characteristics linked to chemical structures and properties.

Main Methods:

• Utilized vibrational Raman spectroscopy for spectral data acquisition.
• Employed principal component analysis (PCA) for dimensionality reduction.
• Applied K-means clustering for compound classification.
• Analyzed both theoretical simulations and experimental data.

Main Results:

• Successfully classified 38 quinones and related structures based on their Raman spectra.
• Established clear spectral ordering and distinct clusters for different compounds.
• Correlated spectral features with specific chemical structures and properties.

Conclusions:

• The developed Raman spectra-based classification method is effective for quinones.
• The combination of PCA and K-means clustering provides a robust analytical strategy.
• This protocol has broad applicability for classifying molecular and solid-state systems.