Signal detection of an optical fiber surface plasmon resonance sensor

The optical fiber surface plasmon resonance (SPR) sensor is an advanced and high-precision sensor, used mainly in environmental pollution detection and detection of biopolymers. Firstly, based on the plasmon resonce sensing theory, the estimate of meaning linear model was obtained by a discussion of our experimental results and the system's data-processing issue. Then, based on the estimate of meaning linear model, a number of groups of a solutions were measured at different times and under the same circumstances and their spectral data was obtained, leading to the estimate of the effective resonance wavelength. Secondly, a wavelet analysis for the SPR reflected spectrum is carried out. The deviation of the resonant wavelength caused by noise was corrected, and the experimental data was filtered by a wavelet analysis, improving the system precision. Parameters impacting the fiber-optic SPR sensor performance were analyzed by theoretical calculation and simulation used Matlab, to optimize the sensing system design. Differences of the refractive index of test solutions such as distilled water, alcohol, etc, have been measured. Our design of the optical fiber SPR sensing systems was proved to be feasible and worked well. The result shows the relationship between the refractive index and SPR wavelength that is well linear within the measurable refractive index range.