A Scalable and Generalizable Method to Minimize Solvent Interference in Identification of Chemical Reaction Networks...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Process Analytical Technology (PAT)

Background:

• Spectroscopic techniques are crucial for analyzing complex chemical reactions.
• Solvent interference often obscures spectral bands, limiting the applicability of these techniques.
• Existing methods require prior knowledge of system constituents, restricting their generalizability.

Purpose of the Study:

• To develop a generic and scalable method for minimizing solvent interference in spectroscopic analysis.
• To enable direct adoption of spectroscopic techniques for complex reacting systems without prior constituent information.
• To extract meaningful information from obscured spectral bands.

Main Methods:

• Framing solvent effect minimization as a tensorial factorization problem.
• Segregating solute and solvent contributions into latent factors.
• Employing direct and orthogonal approaches to distinguish latent factors.

Main Results:

• Demonstrated efficiency across four case studies using spectroscopic process data.
• Successfully minimized solvent interference and extracted useful information from obscured bands.
• Generated solvent-free spectroscopic data and applied latent factors to mixture characterization and reaction network generation.

Conclusions:

• The proposed method is generic, scalable, and adaptable to various solvents and large datasets.
• Enables robust spectroscopic analysis of complex reacting systems under varying conditions.
• Facilitates advanced data analysis tasks like impurity detection, predictive modeling, and data mining.