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<p>Real-time quantification of individual amino acids in mammalian cell culture using Raman spectroscopy remains challenging due to significant spectral overlaps and reliance on empirical regression chemometrics. Presented herein is Raman spectral unmixing which offers a mechanistically grounded alternative based on linear spectral additivity for quantitative analysis of amino acids in aqueous mixtures at millimolar concentrations, thus systematically linking intrinsic molecular spectral features to prediction performance. Initially, a comprehensive Raman spectral library comprising all 20 proteogenic amino acids was constructed under aqueous conditions. The single-component Raman spectra were analyzed to characterize spectra with distinctive patterns and spectral features such as peak intensity, and to provide a basis for interpreting subsequent spectral unmixing results. Spectral unmixing was then applied to binary, ternary, senary, and full 20-component mixtures to assess scalability under increasing spectral overlap. Prediction performance was evaluated by <i>R</i><sup>2</sup>, nRMSEP, and median %RSD. Accurate and reproducible quantification was achieved for most amino acids, including fully congested 20-component combinations. Interestingly, amino acids with chemically rich side chains, particularly aromatic and aliphatic residues with abundant C–H vibrations, exhibited superior accuracy and precision (<i>R</i><sup>2</sup> up to &gt; 0.8, nRMSEP &lt; 13%, median %RSD &lt; 6%), whereas highly polar amino acids showed relatively reduced performance. Thus, our systematic analysis showed that unmixing performance correlates with intrinsic spectral characteristics and, more fundamentally, depends on spectral concordance between the reference and mixture spectra. This concordance can be improved by adjusting the ionization state of pH-responsive amino acids, via pH titration, to match the mixture conditions. Collectively, the current work establishes a mechanistic, scalable framework for applying Raman spectral unmixing to quantitative amino acid monitoring, with a clear path toward real-time analysis and control in complex mammalian cell culture systems.</p> Graphical abstract <p></p>

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Raman spectral unmixing for quantitative analysis of multicomponent amino acid mixtures

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Cheol-Hwan Park,
  • Seo-Young Park,
  • Jinsung Song,
  • Yunjoo Jeon,
  • Yung Doug Suh,
  • Dong-Yup Lee

摘要

Real-time quantification of individual amino acids in mammalian cell culture using Raman spectroscopy remains challenging due to significant spectral overlaps and reliance on empirical regression chemometrics. Presented herein is Raman spectral unmixing which offers a mechanistically grounded alternative based on linear spectral additivity for quantitative analysis of amino acids in aqueous mixtures at millimolar concentrations, thus systematically linking intrinsic molecular spectral features to prediction performance. Initially, a comprehensive Raman spectral library comprising all 20 proteogenic amino acids was constructed under aqueous conditions. The single-component Raman spectra were analyzed to characterize spectra with distinctive patterns and spectral features such as peak intensity, and to provide a basis for interpreting subsequent spectral unmixing results. Spectral unmixing was then applied to binary, ternary, senary, and full 20-component mixtures to assess scalability under increasing spectral overlap. Prediction performance was evaluated by R2, nRMSEP, and median %RSD. Accurate and reproducible quantification was achieved for most amino acids, including fully congested 20-component combinations. Interestingly, amino acids with chemically rich side chains, particularly aromatic and aliphatic residues with abundant C–H vibrations, exhibited superior accuracy and precision (R2 up to > 0.8, nRMSEP < 13%, median %RSD < 6%), whereas highly polar amino acids showed relatively reduced performance. Thus, our systematic analysis showed that unmixing performance correlates with intrinsic spectral characteristics and, more fundamentally, depends on spectral concordance between the reference and mixture spectra. This concordance can be improved by adjusting the ionization state of pH-responsive amino acids, via pH titration, to match the mixture conditions. Collectively, the current work establishes a mechanistic, scalable framework for applying Raman spectral unmixing to quantitative amino acid monitoring, with a clear path toward real-time analysis and control in complex mammalian cell culture systems.

Graphical abstract