Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of Neurological Biomarkers

Abstract

This study presents an attractive single-component ratiometric fluorescent sensor that utilizes the oxidation of BSA-protected Au nanoclusters (BSA-Au NCs) by N-Bromosuccinimide (NBS) to detect catecholamine neurotransmitters and their metabolites, which are critical biomarkers for neurological diseases like neuroblastoma, pheochromocytomas, and paragangliomas. In this detailed sensing platform, NBS induces a noticeable fluorescence change in the emission of BSA- Au NCs, including the extinction of the emission peak at 650 nm and the simultaneous appearance of an emission peak at 450 nm. This shift represents a clear transition in the emission color of the probe from red to blue. The oxidation of Au NCs offers a promising approach for developing a ratiometric probe using a single fluorophore, eliminating the need to combine two individual fluorophores. The presence of neurogenic biomarkers inhibits the oxidation of BSA-Au NCs, varying with the concentration and identity of each analyte, making distinct changes in the spectral profiles along with vivid color variations. Spectral changes and RGB data derived from emission colors were analyzed using machine learning techniques, specifically linear discriminant analysis (LDA) for classification and partial least-squares regression (PLS-R) for multivariate calibration. Results from LDA and PLS-R highlighted the strong potential of the designed sensor for differentiating and quantifying these biomarkers. Furthermore, the successful application of this sensor in detecting and distinguishing these analytes in human urine provides valuable insights for clinical analysis in screening and diagnosing neurological disorders.