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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning

Author

Listed:
  • Ridhanya Sree Balamurugan
  • Yusuf Asad
  • Tommy Gao
  • Dharmakeerthi Nawarathna
  • Umamaheswara Rao Tida
  • Dali Sun

Abstract

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm.

Suggested Citation

  • Ridhanya Sree Balamurugan & Yusuf Asad & Tommy Gao & Dharmakeerthi Nawarathna & Umamaheswara Rao Tida & Dali Sun, 2025. "Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0317130
    DOI: 10.1371/journal.pone.0317130
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