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Simultaneous Recognition and Detection of Adenosine Phosphates by Machine Learning Analysis for Surface-Enhanced

Ryosuke Nishitsuji1, Tomoharu Nakashima2, Hideaki Hisamoto3

  • 1Department of Information Networking, Graduate School of Information Science and Technology, Osaka University, 2-8 Yamadaoka, Suita 565-0871, Osaka, Japan.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using surface-enhanced Raman scattering (SERS) and machine learning to detect adenosine phosphates (AMP, ADP, ATP). The multilayer perceptron model achieved high accuracy, enabling precise analysis from limited spectral data.

Keywords:
adenosine phosphatesgold nanostructuremachine learningsmall data analyticssurface-enhanced Raman scattering

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Adenosine phosphates (AMP, ADP, ATP) are crucial for biological energy storage and signal transduction.
  • Simultaneous detection of adenosine phosphates is vital for understanding cellular energy dynamics.
  • Existing methods face challenges due to the structural similarities of adenosine phosphates.

Purpose of the Study:

  • To develop a novel measurement system for simultaneous recognition and detection of adenosine phosphates.
  • To overcome the limitations of current techniques requiring extensive training data.
  • To enhance the accuracy of adenosine phosphate detection using limited spectral data.

Main Methods:

  • Utilized surface-enhanced Raman scattering (SERS) with fabricated gold nanostructures for spectral measurements.
  • Employed feature selection and data augmentation for preprocessing limited adenosine phosphate spectral data.
  • Trained and compared various machine learning models, including multilayer perceptron.

Main Results:

  • A multilayer perceptron model was successfully trained on augmented spectral data.
  • The model achieved a high accuracy of 0.914 in detecting AMP, ADP, and ATP.
  • Demonstrated effective recognition and detection of adenosine phosphates from limited SERS data.

Conclusions:

  • Established a novel measurement system for accurate adenosine phosphate detection.
  • Showcased the potential of combining SERS with machine learning for complex molecular analysis.
  • Overcame the challenge of limited training data in spectroscopic analysis.