Intelligent Surface Plasmon Resonance Sensor for Refractive Index Substance Identification With Convolutional Neural Network Image-Based Model

The surface plasmon resonance (SPR) sensors are technologically attractive for applications that demand quick and accurate biological substance monitoring. Through its typical SPR image response, the resonance condition indicated by the minimum reflectivity values works like an optical signature for changes in the refractive index (RI) of the substance under analysis. Recently, the incorporation of machine and deep learning methods (MDLMs) on SPR sensors to create intelligent tasks along the signal processing chain employed in SPR biosensing was witnessed. One possible intelligent application is substance identification based on the analysis of SPR responses. Occasionally, this problem is addressed with data from SPR curves, requiring a prior SPR image manipulation for the respective curve generation, leading to extra process steps and time consumption. This article presents the design of an intelligent SPR sensor with analyte identification capabilities directly from its SPR image, offering guidance on the precise moment for substance switching during injection routines. An image-based prediction model with convolutional neural networks (CNNs) was fine-tuned to directly identify individually aqueous solutions with different refractive indices. A new approach was described to generate SPR images (Fresnel images) from calculation with the Fresnel analysis (FA) framework. The proposed CNN architecture was evaluated and compared with seven state-of-the-art CNN architectures. The models were integrated into the experimental setup for real-time identification. The experimental tests demonstrate the viability of the overall pipeline for the model conception, being able to reach more than 96% accuracy in performing the identification task.