Harnessing Machine Learning to Revolutionize Electrochemical Detection of Vitamin E Acetate in E-Liquids

This study presents the development of a novel molecularly imprinted electrochemical sensor for the sensitive and selective detection of vitamin E acetate (VEA) in e-cigarette liquids, a critical step in addressing the rising public health concern of e-cigarette, or vaping, product use-associated lung injury. VEA-imprinted polymeric nanoparticles, intended to serve as the recognition element on the sensor surface, were synthesized using surfactant-free emulsion polymerization. The synthesized polymer was characterized using Fourier Transformed Infrared Spectroscopy, scanning electron microscope, and zeta potential analyses. The sensor, fabricated using VEA-imprinted poly-(HMA-<i>co</i>-PA)/Nafion on screen-printed carbon electrodes, demonstrated a limit of quantification (LoQ) of 112.3 μg/mL (3.3× S/N) with a wide linear range extending to 3.0 mg/mL (10× S/N). While the sensor exhibited limitations in detecting VEA at concentrations below the LoQ, the integration of machine learning algorithms effectively mitigated these challenges. Machine learning models successfully classified the presence of VEA, even at subdetection limit concentrations, significantly enhancing the sensor's analytical capabilities. Rigorous testing on real-world e-cigarette liquid samples yielded high recovery rates (96.83% ± 2.79-102.56% ± 3.84), validating the sensor's accuracy and selectivity in complex matrices. This research not only establishes a promising platform for the rapid and sensitive detection of VEA in e-cigarette liquids but also underscores the transformative potential of integrating artificial intelligence with sensor technologies for addressing critical public health challenges.