Improvement of sensing precision for surface plasmon resonance sensor based on optimization of centroid algorithm

Surface plasmon resonance (SPR) sensor is a fast, label-free and real-time optical sensing technology. In this paper, an improved data-processing method of centroid algorithm was employed and the Fast Fourier Transform Algorithm (FFT) was also adopted to improve the standard deviation (SD) of resonance angle, a representative of sensing precision. The significant parameters in this study are the height ratio and selected spectrum width of centroid algorithm and the FFT cut-off frequency. To generate a RI change of sensing medium, pure water and 3 g/L NaCl solution were prepared for experiments. The SD was optimized in case of pure water, and then the RI sensitivity was also calculated for comparison. As results, two optimized regions were obtained, those are upper and under regions. The upper region is related with the selected spectrum width, and a low FFT cut-off frequency was suitable. The under region has a little change with the spectrum width and the FFT cut-off frequency. In experiments, the resolution of 2.11 × 10^− 6 RIU was obtained with samples of ultrapure water and 3 g/L NaCl solution. Furthermore, the limit of detection for BSA molecule as low as 8.36 pM was achieved without any other noise reduction method. This study aims to provide a new design idea and theoretical support for the optimal design of SPR sensor, and has an important research significance and application prospect in the fields of environmental monitoring and biochemical sensing.