Improving the sensitivity of SPR sensing using MAP information fusion algorithm

Surface Plasmon Resonance has been developed into a widely-used methodology for various biosensing applications. For the most popular angular-interrogation SPR system, a convergent light beam and a photo diode array are used, where the photo diode array with a large amount of pixels is necessary for high performance, and unavoidable trivial vibration between sensing unit and photo diode array will cause angular uncertainty and affect the sensing accuracy. In this paper, we improve the accuracy and sensitivity of SPR sensing by using the Maximum A Posterior (MAP) Information Fusion Algorithm instead of upgrading hardware in the SPR system. First, a number of SPR curves with random translations are acquired sequentially in a short time. Then, by estimating the translations of the acquired curves between each other, an expected angular vector is determined to overcome the angular uncertainty. On this expected angular vector, MAP information fusion algorithm is used to estimate a SPR curve with a higher sampling density, by fusing the information of the acquired low-sampling-density ones. Finally, we evaluate the accuracy of SPR sensing information drawn from the estimated high-sampling-density curve using classical polynomial fits.