Utilization of data classification in the realization of a surface plasmon resonance readout system using an FPGA controlled RGB LED light source

This work presents the realization of a surface Plasmon resonance (SPR) sensor readout system using a tricolor red, green and blue (RGB) light emitting diode (LED) light source. Time domain intensity modulation of each color channel is applied to interrogate three bands of interest in the SPR spectrum using a single photodiode detector. A low computing resource classification approach is used through the combination of k-nearest neighbor (kNN) and adapted clustering using representative (CURE). An optimized number of representatives is chosen in the validation process to reduce the required amount of data for the kNN classification. This scheme was used to classify the concentrations of different glucose solutions. The sensor readout system hardware is based on the use of a field programmable gate array (FPGA) and the glucose solution classification is developed and undertaken on a personal computer (PC) using the Python open source programming language.