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Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides

Author

Listed:
  • Emily Xi Tan

    (Chemical Engineering and Biotechnology, Nanyang Technological University)

  • Shi Xuan Leong

    (Chemical Engineering and Biotechnology, Nanyang Technological University)

  • Wei An Liew

    (Chemical Engineering and Biotechnology, Nanyang Technological University)

  • In Yee Phang

    (Jiangnan University)

  • Jie Ying Ng

    (KK Research Centre, KKH)

  • Nguan Soon Tan

    (Nanyang Technological University
    Nanyang Technological University Singapore)

  • Yie Hou Lee

    (KK Research Centre, KKH
    Duke-NUS Medical School
    Singapore-MIT Alliance for Research and Technology)

  • Xing Yi Ling

    (Chemical Engineering and Biotechnology, Nanyang Technological University
    Jiangnan University
    Nanyang Technological University
    Nanyang Technological University)

Abstract

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with

Suggested Citation

  • Emily Xi Tan & Shi Xuan Leong & Wei An Liew & In Yee Phang & Jie Ying Ng & Nguan Soon Tan & Yie Hou Lee & Xing Yi Ling, 2024. "Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46838-z
    DOI: 10.1038/s41467-024-46838-z
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