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Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial

Sakib Mahmud1, Faizul Rakib Sayem2, Manal Hassan3

  • 1Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|September 24, 2025
PubMed
Summary

This study introduces a deep learning framework to improve pathogen detection using surface-enhanced Raman spectroscopy (SERS). The technology enhances spectral quality from portable devices for reliable, rapid identification at the point of care.

Keywords:
Analyte preparationBacterial pathogen classificationDomain transformationInstrument standardizationSuper-ONNsSurface-enhanced Raman spectroscopy

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Biotechnology

Background:

  • Surface-enhanced Raman spectroscopy (SERS) offers label-free pathogen detection but faces challenges in clinical diagnostics.
  • Inconsistent spectral quality, poor reproducibility, and limited machine learning generalizability hinder SERS adoption.
  • These limitations impede reliable, rapid pathogen identification at the point of care.

Purpose of the Study:

  • To develop a deep learning framework to enhance SERS spectral quality from portable devices.
  • To improve the accuracy and generalizability of machine learning models for pathogen classification.
  • To enable reliable, real-time pathogen identification at the point of care.

Main Methods:

  • Collected SERS spectra from 20 analyte classes using portable and laboratory-grade Raman systems.
  • Developed SERS-D2DNet, a sequence-to-sequence network, to transform portable SERS spectra into laboratory-grade equivalents.
  • Implemented SuperRaman, a lightweight super-operational neural network, for multiclass bacterial classification.

Main Results:

  • SERS-D2DNet significantly improved spectral quality, reducing mean absolute error to 0.01 and increasing R² to over 98%.
  • SuperRaman achieved up to 100% classification accuracy after spectral transformation.
  • The combined framework demonstrated superior performance over existing methods with a compact footprint and fast inference time.

Conclusions:

  • The proposed deep learning framework bridges the performance gap between portable and laboratory-grade SERS systems.
  • This scalable, real-time solution facilitates rapid sepsis detection and pathogen identification.
  • The technology is well-suited for portable deployment, advancing point-of-care diagnostics.