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<p>For accurate detection of microbial indicators in spiked food matrices, a dual-mode electrochemical biosensor based on a magnetic nanoparticle (MNPs)-aptamer (Apt) complex was developed for highly sensitive quantification of <i>Escherichia coli</i> (<i>E. coli</i>). Fe<sub>3</sub>O<sub>4</sub>@Au nanoparticles were synthesized and functionalized with Apt via Au-S bonds to form the Fe<sub>3</sub>O<sub>4</sub>@Au@Apt complex. In the “signal-on” mode, the charge transfer resistance of the [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> probe increased as measured by electrochemical impedance spectroscopy (EIS); in the “signal-off” mode, the oxidation peak current of methylene blue (MB) decreased using differential pulse voltammetry (DPV). By integrating features from both EIS and DPV responses, 11 concentration-related features were extracted. A genetic algorithm (GA) was employed to optimize the hyperparameters of an XGBoost model for accurate prediction of <i>E. coli</i> concentrations in real samples. This dual-mode strategy integrates the complementary strengths of EIS and DPV, achieving a linear detection range from 10<sup>1</sup> to 10<sup>7</sup>&#xa0;CFU/mL with high recovery rates in real samples. The approach offers a robust and reliable tool for food safety and environmental monitoring.</p> Graphical Abstract <p></p>

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Dual-mode integrated electrochemical sensing of E. coli in real matrices enabled by machine learning

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Ying Xu,
  • Shijuan Cao,
  • Wei Yu,
  • Xiaobin Zhang,
  • Peiyan Dai,
  • Shenghui Chen,
  • Siqi Dong,
  • Hui Yu

摘要

For accurate detection of microbial indicators in spiked food matrices, a dual-mode electrochemical biosensor based on a magnetic nanoparticle (MNPs)-aptamer (Apt) complex was developed for highly sensitive quantification of Escherichia coli (E. coli). Fe3O4@Au nanoparticles were synthesized and functionalized with Apt via Au-S bonds to form the Fe3O4@Au@Apt complex. In the “signal-on” mode, the charge transfer resistance of the [Fe(CN)6]3−/4− probe increased as measured by electrochemical impedance spectroscopy (EIS); in the “signal-off” mode, the oxidation peak current of methylene blue (MB) decreased using differential pulse voltammetry (DPV). By integrating features from both EIS and DPV responses, 11 concentration-related features were extracted. A genetic algorithm (GA) was employed to optimize the hyperparameters of an XGBoost model for accurate prediction of E. coli concentrations in real samples. This dual-mode strategy integrates the complementary strengths of EIS and DPV, achieving a linear detection range from 101 to 107 CFU/mL with high recovery rates in real samples. The approach offers a robust and reliable tool for food safety and environmental monitoring.

Graphical Abstract