Denoising of Surface Plasmon Resonance (SPR) Spectra Using the Generalized S-transform and the Bald Eagle Search (BES) Algorithm

Abstract

To obtain accurate resonance peaks from surface plasmon resonance (SPR) spectra, a denoising approach based on generalized S-transform optimized by a new iterative algorithm of bald eagle search (BES) is reported and applied. First, a fiber SPR sensing system is used to collect the original noisy spectra, and the generalized S-transform is performed to obtain the corresponding S-domain spectrum. Next, the denoising threshold (${\lambda }_n$) is optimized by the BES algorithm to denoise and reconstruct the SPR reflection spectrum. Finally, the original SPR reflection and the denoised reflection spectra are used to evaluate the fitness function until the optimal denoising threshold (${\lambda }_n$) and denoising effect are obtained. The experimental results show that the developed method maintains a relatively stable denoising for SPR reflection spectra as the average values of root mean square error ($\mathit{\text{RMSE}}$) and signal to noise ratio ($\mathit{\text{SNR}}$) are 0.27 and 23.6, respectively. This method overcomes the problem of arbitrary selection of basic functions or thresholds in conventional denoising methods, improves the accuracy of SPR sensor, and provides a new approach for spectral denoising.

Keywords:

• Bald eagle search (BES) algorithm
• denoising
• generalized S-transform
• surface plasmon resonance (SPR) sensor
• threshold optimization

Disclosure statement

No potential conflict of interest was reported by the authors.