Smart Noise Reduction in SPR Sensors Response Using Multiple-ANN Design

Abstract

Surface plasmon resonance (SPR) based sensors are commonly applied to detect molecular interactions from features extracted of the so-called SPR curve. The SPR curve represents the position of the resonance occurrence, and reflects the quality of the sensor measurements. The instrumentation used in the SPR sensors require very specific coupling conditions for optical-electronic, mechanical, and fluidic components to properly excitate the surface plasmons. Noise sources inherent to the sensor instrumentation are reflected in the SPR curve and affect the quality of its extracted features. A smart-filter based on multilayer artificial neural networks is designed to eliminate the main instrumental noise sources and noise due to sensing proceedings at the SPR sensor usage, which are perceived as distortions in the SPR curves. The filter, called NNSF, is embedded to SPR sensors operating in both spectral (WIM) and angular (AIM) interrogation modes, and achieved responses with a mean square error of 10−5, and tested model correlation indexes over 90%. The filter redressed noises, including artifacts such as metal-layer roughness, light source input angle/wavelength disagreements, prism birefringence and general oscillations in amplitude and phase of the curve due to no-rigorous control process in the environment and experimental routines. Thus, the NNSF delivers curves with enhanced features and consequently increases the sensor performances in the monitoring of the resonance features (Width, Energy, Phase, Asymmetry, and minimum Position). Furthermore, the proposed filter avoids replacement or incorporation of new components that cause considerable instrumental maintenance cost.