Data Analysis in the Optical Fiber Surface Plasmon Resonance Sensing Systems
光纤表面等离子体共振传感检测系统中的数据分析

The output signal model of optical fiber Surface Plasmon Resonance (SPR) sensing systems is presented in which the noise is proved as Gaussian distribution by adopting skewness and kurtosis test, so signal averaging method has been used and the stand deviation is decreased. Further, the estimated mean after signal averaging processing still includes some noise which has also been proved as Gaussian noise by using the same test method and linear estimate model is used to determine the resonant wavelength and the light intensity at the wavelength. The results show that both the wavelength and the normalized light intensity detection precisions are approximate to the ones of the used spectrometer. Finally, the optimization method on the data processing is discussed and the data processing work is decreased as 2% of the initial one by used the method, which is particularly adaptable to measure the sensing medium whose refractive index varies with time quickly.