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A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

Enhanced plasmonic biosensors with machine learning for ultra-sensitive detection.

M Sahaya Sheela¹, A Ponraj², S Kumarganesh³

¹Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.

|January 4, 2026

Summary

This study introduces SERA, an AI framework using machine learning and Surface-Enhanced Raman Spectroscopy (SERS) data to optimize plasmonic biosensor performance. SERA accelerates development and enables adaptive sensing for improved biochemical detection.

Keywords:

Bayesian optimization Bio photonics Biosensor Machine learning Plasmonic biosensors Random forest Sensitivity Surface enhanced raman spectroscopy Surface plasmon resonance Ultra-sensitive detection

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Area of Science:

Plasmonics
Biosensing
Artificial Intelligence

Background:

Plasmonic biosensors like SPR and SERS offer real-time, label-free detection but face optimization challenges.
Complex plasmonic interactions with biomolecules hinder sensitivity and selectivity.
Current methods require extensive trial-and-error for biosensor design optimization.

Purpose of the Study:

To propose SERA, an AI-driven framework for predictive modeling and optimization of plasmonic biosensor performance.
To integrate machine learning with experimental SERS data for enhanced biosensing.
To accelerate the development of sensitive and selective plasmonic biosensors.

Main Methods:

Developed SERA, an AI framework utilizing supervised learning on SERS spectral data (SERS-DB).
Trained ML models on plasmonic nanostructure data to predict resonance shift, intensity variations, and binding efficiency.
Evaluated SERA on a dataset including pesticides and folate derivatives for performance assessment.

Main Results:

Achieved 92% accuracy, 90% precision/recall, and 92% F1-score on the SERS-DB dataset.
SERA demonstrated superior performance with an overall score of ~0.90 across 6 classes.
Outperformed conventional methods with 92% accuracy, 1000 nm/RIU sensitivity, and 95% optimization efficiency.

Conclusions:

SERA offers a scalable and cost-effective strategy for advancing plasmonic biosensor technology.
The AI-driven approach accelerates biosensor development and enables real-time adaptive sensing.
This framework holds significant potential for applications in medical diagnostics, environmental monitoring, and biophotonics.