Hybrid intelligence-driven nanopolymeric sensor for precise electrochemical vitamin C analysis, free from LoD: application in real lemon juice

Official URL: https://dx.doi.org/10.1021/acsaelm.5c00822

This study presents the design, synthesis, and systematic evaluation of an electrochemical nanosensor platform tailored for the precise and selective quantification of vitamin C. The sensor architecture integrates His-functionalized poly(2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) (His-pHEG) polymeric nanoparticles onto Nafion-modified screen-printed carbon electrodes (SPCE), thereby providing a bioactive interface with enhanced analyte affinity and stability. The His-pHEG nanoparticles were synthesized via emulsion polymerization and covalently grafted with l-histidine, as confirmed by FTIR, SEM, and zeta potential analyses. This functionalization endowed the nanoparticles with enhanced affinity and high selectivity toward vitamin C molecules, while ensuring colloidal stability and uniform morphology. Sensor fabrication parameters, including Nafion film thickness and polymer concentration, were systematically optimized to maximize electrochemical performance. The resulting His-pHEG/Nafion-modified SPCE demonstrated superior analytical characteristics, achieving a low limit of detection and a broad linear dynamic range, as determined by cyclic voltammetry and differential pulse voltammetry measurements. To overcome the fundamental limitations of conventional calibration-based electrochemical methods, such as nonlinearity and variability, a two-stage hybrid machine learning framework, specifically tailored to the inherent nature of the sensor data, was developed and integrated into the sensing workflow. The two-stage model utilized CatBoost classification to distinguish analyte presence, followed by CatBoost regression to estimate vitamin C concentration, with hyperparameter optimization ensuring robustness and predictive accuracy. Real-sample validation using lemon juice confirmed the sensor’s high recovery rates and practical applicability, demonstrating reliable performance in complex matrices. This multidisciplinary approach bridges polymer chemistry, nanotechnology, electrochemical sensing, and artificial intelligence to deliver a portable, cost-effective, and highly sensitive vitamin C detection system. Future efforts will focus on translating this platform into mobile-based, real-time analytical devices, enabling on-site applications in food quality control, healthcare, and pharmaceutical industries.