Machine Learning-Guided Electrochemical Fingerprinting for Rapid Polyethylene Microplastic Detection in Seawater and Seafood Matrices

Polyethylene (PE) microplastics are increasingly recognized as a critical environmental and food-safety concern; however, routine monitoring remains limited by conventional methods that are labor-intensive, time-consuming, and difficult to translate into rapid, on-site screening. Here, we report a machine learning-guided electrochemical fingerprinting platform for rapid PE microplastic detection using a chitosan–PE interfacial film coupled with electrochemical impedance spectroscopy (EIS) and coulometry. The platform generated concentration-dependent electrical fingerprints in artificial ocean water, captured through Bode, Nyquist, and charge–time responses. Quantification was achieved across 1–256 ng/mL with strong linearity (R2 = 0.976) and an ultralow LoD of 0.1 ng/mL, demonstrating high analytical sensitivity. Practical applicability was validated through spike–recovery in ocean water (R2 = 0.967) and shrimp-derived matrices with matrix-matched normalization, yielding recoveries of 90–105% across low, mid, and high spike levels. Under the tested particle set, PE produced stronger responses than non-target polypropylene (PP) and polystyrene (PS), supporting empirical polymer discrimination. Machine learning classification using impedance-derived features achieved an AUC = 0.98, with 100% correct identification of Low and 95.24% correct identification of High samples. Overall, this electrochemical–ML framework enables rapid, sensitive, and matrix-tolerant PE microplastic screening in environmental water and seafood-related matrices, offering a promising pathway toward portable microplastic monitoring.