Portable electrochemical impedance biosensing with DRT-enabled machine learning for detecting E. coli O157:H7 in...

Ensuring food safety requires rapid and accurate detection of pathogens such as Escherichia coli O157:H7. Here, we report a portable electrochemical immunosensor coupled with machine learning (ML) that enables quantitative prediction even when the impedance response is not strictly linear with concentration. The sensor employs protein A-mediated oriented antibody immobilization on a gold electrode and measures target binding using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. To move beyond single-parameter equivalent-circuit fitting, we apply distribution of relaxation times (DRT) deconvolution to resolve the impedance spectrum into mechanistic contributions associated with charge-transfer kinetics, double-layer charging, and transport-limited (diffusion/Warburg-type) processes, and use these DRT-derived features for concentration inference. Multiple ML models (partial least squares (PLS), Random Forest, histogram-based gradient boosting, support vector regression, ridge regression, and Gaussian process regression) were evaluated using leave-one-concentration-out cross-validation and independent hold-out testing, demonstrating accurate prediction on unseen concentration levels. Validation in poultry meat samples artificially inoculated with E. coli O157:H7 confirmed applicability in a food-relevant matrix, along with high selectivity against non-target bacteria and stable performance during storage. This work is novel in combining DRT-based mechanistic feature extraction with ML-based inference to deliver a field-deployable immunosensing platform for robust pathogen quantification in complex food samples.