Design optimization of high-sensitivity PCF-SPR biosensor using machine learning and explainable AI.

ABSTRACT: Photonic crystal fiber based surface plasmon resonance (PCF-SPR) biosensors are sophisticated optical sensing platforms that enable precise detection of minute refractive index (RI) variations for various applications. This study introduces a highly sensitive, low-loss, and simply designed PCF-SPR biosensor for label-free analyte detection, operating across a broad RI range of 1.31 to 1.42. In addition to conventional methods, machine learning (ML) regression techniques were integrated to predict key optical properties, while explainable AI (XAI) methods, particularly Shapley Additive exPlanations (SHAP), were used to analyze model outputs and identify the most influential design parameters. This hybrid approach significantly accelerates sensor optimization, reduces computational costs, an

SUBMITTER: Khatun MR 

PROVIDER: S-EPMC12435684 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature