Raman spectral unmixing for quantitative analysis of multicomponent amino acid mixtures

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Biotechnology

Background:

• Quantifying individual amino acids in mammalian cell cultures is difficult using traditional Raman spectroscopy due to spectral overlaps.
• Existing chemometric methods often rely on empirical regression, lacking mechanistic grounding.
• Accurate amino acid monitoring is crucial for understanding and controlling cell culture processes.

Purpose of the Study:

• To develop and validate a Raman spectral unmixing method for accurate amino acid quantification in aqueous mixtures.
• To establish a mechanistically grounded, scalable framework for analyzing complex amino acid samples.
• To assess the correlation between spectral characteristics and quantification performance.

Main Methods:

• Constructed a comprehensive Raman spectral library of all 20 proteogenic amino acids in aqueous solution.
• Applied linear spectral unmixing to binary, ternary, senary, and 20-component mixtures.
• Evaluated prediction performance using R², nRMSEP, and median %RSD, optimizing by pH titration.

Main Results:

• Achieved accurate and reproducible quantification for most amino acids, even in highly congested 20-component mixtures.
• Amino acids with chemically rich side chains showed superior accuracy (R² > 0.8) and precision (nRMSEP < 13%).
• Unmixing performance correlated with spectral concordance, improvable by adjusting pH of amino acids.

Conclusions:

• Raman spectral unmixing provides a mechanistic and scalable framework for quantitative amino acid analysis.
• The method overcomes limitations of empirical regression and spectral overlap in complex mixtures.
• This work paves the way for real-time amino acid monitoring and control in mammalian cell cultures.