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Plasmonically Calibrated Label-Free Surface-Enhanced Raman Spectroscopy for Improved Multivariate Analysis of Living

Wonil Nam1, Xiang Ren1, Inyoung Kim2

  • 1Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.

Analytical Chemistry
|March 5, 2021
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Summary

Electronic Raman scattering (ERS) from metal nanostructures acts as an internal standard for surface-enhanced Raman spectroscopy (SERS). This calibration improves the analysis of complex biological samples, enhancing cancer cell subtyping and drug response assessment.

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Area of Science:

  • Biophotonics
  • Spectroscopy
  • Nanotechnology

Background:

  • Label-free surface-enhanced Raman spectroscopy (SERS) offers rapid, nondestructive molecular fingerprinting for biological samples.
  • SERS analysis faces reliability challenges due to signal variations from plasmonic hotspots, impacting concentration correlations.

Purpose of the Study:

  • To introduce plasmonically enhanced electronic Raman scattering (ERS) as an internal calibration standard for SERS.
  • To enhance the multivariate analysis of living biological systems using label-free SERS.

Main Methods:

  • Utilized plasmonic nanostructures to generate ERS signals from metal nanostructures.
  • Implemented ERS as an internal standard for SERS calibration in multivariate analysis.
  • Compared ERS-calibrated SERS data with non-calibrated data for classification tasks.

Main Results:

  • ERS-based SERS calibration significantly improved supervised learning classification of living cell SERS spectra.
  • Demonstrated enhanced subtyping of breast cancer cells based on malignancy.
  • Showed improved assessment of cancer cell drug responses at varying dosages.

Conclusions:

  • ERS-based SERS calibration offers a robust method to overcome signal variability in complex biological matrices.
  • ERS calibration provides advantages including photostability, no spectral interference, and no hotspot competition.
  • This approach promises to significantly enhance multivariate analysis for label-free SERS applications.