Denoising the Reflection Spectrum of an LSPR-Based Optical Fiber Sensor Using Fast Fourier Transform in Python

This study explores the use of Fast Fourier Transform (FFT) for denoising signals from a Localized Surface Plasmon Resonance-based Optical Fiber (LSPR-OF) sensor. By applying FFT, high-frequency noise was effectively suppressed, enhancing measurement precision. An optimal cut-off frequency of 0.01 was identified for balancing noise reduction and signal preservation. Results demonstrated shifts in resonance wavelengths, with varying sensitivity across metals. The findings highlight the potential of FFT filtering to improve the clarity of LSPR spectrum. Full Text: PDF References K. Kant et al., ""Plasmonic nanoparticle sensors: current progress, challenges, and future prospects", Nanoscale Horizons 9, 2085 (2024). CrossRef Y. Hong, J. Ha, "Enhanced refractive index sensitivity of localized surface plasmon resonance inflection points in single hollow gold nanospheres with inner cavity", Scientific Reports 12, 1 (2022). CrossRef K.H. Chen, J. Hobley, Y.L. Foo, X. Su, "Wide-field single metal nanoparticlespectroscopy for high throughput localized surface plasmon resonance sensing", Lab on a Chip 11, 11 (2011). CrossRef S. Palani et al., "Multispectral Localized Surface Plasmon Resonance (msLSPR) Reveals and Overcomes Spectral and Sensing Heterogeneities of Single Gold Nanoparticles", ACS Nano 17, 3 (2023). CrossRef R. Aviles-Espinosa, H. Dor, E. Rendon-Morales, "An Experimental Method for Bio-Signal Denoising Using Unconventional Sensors", Sensors 23, 7 (2023). CrossRef