The application of generalized S-transform in the denoising of surface plasmon resonance (SPR) spectrum

• Published in: Analytical methods Vol. 15; no. 45; pp. 6184 - 621
• Main Authors: Junfeng, Dai, Li-hui, Fu
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 23.11.2023
• Subjects: Algorithms; Noise levels; Noise reduction; Resonance; Root-mean-square errors; Search algorithms; Signal to noise ratio; Surface plasmon resonance; Transformations (mathematics)
• ISSN: 1759-9660; 1759-9679
• DOI: 10.1039/d3ay01462b

In order to obtain accurate resonance peaks from surface plasmon resonance (SPR) spectral curves, a reasonable denoising method is of great significance for SPR sensing systems. Therefore, the generalized S-transform is combined with the Bald Eagle Search algorithm (BES) in this study, and a denoising method based on the generalized S-transform optimized by BES is proposed and applied to the denoising processing of the SPR spectrum. First, a fiber SPR sensing system is used to obtain the original noised spectrum; then, the generalized S-transform is performed to obtain the corresponding S-domain spectrum. Next, the denoising threshold λ n is optimized by the BES algorithm, which is used to denoise and reconstruct the SPR reflection spectrum. Finally, two fitness functions are evaluated until the optimal denoising threshold λ n and denoising effect are obtained. The relevant validation experiments are completed, and the experimental results show that the proposed method has the best denoising performance when p is between 0.5 and 1, and λ is between 1.5 and 2.5. Meanwhile, compared to the other denoising methods, the BES-S method can maintain a relatively stable denoising effect on the SPR spectrum with high or low levels of noise; the average values of root mean square error (RMSE) and signal-to-noise ratio (SNR) are 0.27 and 23.61, respectively. Ranking first in terms of comprehensive denoising performance, it can also maintain the original shape of the SPR spectrum and better reflect its characteristic peak while filtering out noise. This method can overcome the problem of arbitrary selection of basic functions and thresholds in conventional denoising methods, and it can improve the detection accuracy of SPR sensors and provide a new idea for SPR spectrum denoising, which also lays the foundation for the application of substance composition detection based on SPR sensor. A method based on generalized S-transform optimized by BES algorithm is applied to the denoising of SPR spectrum. Two fitness functions are used to evaluate the denoising effect, the influence of time width and attenuation trend are studied.