Synchronous detection of adenine/hypoxanthine using fractional-order voltammetry and K-means clustering

The abnormal accumulation of purine metabolites, such as adenine (A) and hypoxanthine (Hx), is closely associated with the pathogenesis of hyperuricemia and gout, underscoring the urgent need for precise methodologies enabling their simultaneous detection. To overcome the inherent limitations of conventional analytical techniques and resolve the challenge of overlapping voltammetric signatures in electrochemical sensing, this study presents an integrated analytical strategy encompassing an advanced sensing platform, sophisticated signal processing, and intelligent data analytics. A glassy carbon electrode modified with carboxymethyl cellulose-functionalized multi-walled carbon nanotubes (CMC-fMWCNTs/GCE) serves as the foundation for highly sensitive detection. Crucially, the application of half-order derivative voltammetry effectively resolves the overlapping oxidation peaks of A and Hx, thereby enhancing analytical selectivity. Complementing this, a K -means clustering algorithm is employed to enable automated, data-driven assessment of sensing stability. These components are integrated into a web-based intelligent system that automates the complete analytical workflow, from raw data ingestion to quantitative concentration readout. The proposed system exhibits a broad linear dynamic range (0.08–80 μM), low limits of detection (0.062 μM for A and 0.075 μM for Hx), and high analytical reproducibility (relative standard deviation < 5%), while streamlining the analytical process. Collectively, this work not only achieves the precise synchronous quantification of A and Hx but also establishes a transferable analytical framework for the intelligent analysis of small molecules with analogous structural features.