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Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates

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Enhancing surface enhanced Raman scattering (SERS) detection of propranolol with multiobjective evolutionary

Clare Levene¹, Elon Correa, Ewan W Blanch

¹Faculty of Life Sciences, Manchester Institute of Biotechnology, University of Manchester, UK.

Analytical Chemistry

|September 1, 2012

Summary

A new multiobjective evolutionary algorithm (MOEA) improves surface-enhanced Raman scattering (SERS) reproducibility and sensitivity. This approach significantly enhances detection of propranolol, a beta-blocker drug, using minimal experiments.

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Area of Science:

Analytical Chemistry
Spectroscopy
Nanotechnology

Background:

Surface-enhanced Raman scattering (SERS) is a powerful analytical technique limited by poor reproducibility due to complex parameter interactions.
Optimizing SERS requires understanding the interplay between colloid type, concentration, and aggregating agents, which is currently poorly understood.

Purpose of the Study:

To enhance reproducibility and sensitivity in SERS measurements.
To develop a systematic approach for optimizing SERS experimental conditions.

Main Methods:

A multiobjective evolutionary algorithm (MOEA) based on Pareto optimality was employed.
The study explored combinations of five colloids, six aggregating agents, varying concentrations, and three laser excitation wavelengths.
Propranolol was used as the target analyte, with enhancement and reproducibility as objective functions.

Main Results:

The MOEA successfully identified optimal SERS conditions using only 4% of the total possible experimental combinations.
Achieved a limit of detection for propranolol at 2.36 ng/mL (7.97 nM), a >25-fold improvement over previous SERS studies.
Demonstrated significantly improved enhancement and reproducibility compared to empirical optimization methods.

Conclusions:

The developed MOEA provides an efficient and effective strategy for optimizing SERS parameters.
This approach overcomes the limitations of traditional SERS optimization, leading to enhanced analytical performance.
The optimized SERS method shows great promise for sensitive and reproducible detection of analytes like propranolol.