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DOI

  • T.A. Aliev
  • F.V. Lavrentev
  • A.V. Dyakonov
  • D.A. Diveev
  • V.V. Shilovskikh
  • E.V. Skorb
We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approach is based on a system which includes gallium-indium (eGaIn) alloy to provide conductivity and a hydrogel system to preserve bacteria and their metabolic species during the analysis. The work is dedicated to accurate and fast detection of Escherichia coli bacteria in different environments with the supply of machine learning methods. Electrochemical data obtained during the analysis is processed via multilayer perceptron model to identify i.e. predict bacterial concentration in the samples. The performed approach provides the effectiveness of bacteria identification in the range of 102–109 colony forming units per ml with the average accuracy of 97%. The proposed bioelectrochemical system combined with machine learning model is prospective for food analysis, agriculture, biomedicine. © 2024
Original languageEnglish
JournalBiosensors and Bioelectronics
Volume259
DOIs
StatePublished - 1 Sep 2024

    Research areas

  • Bacteria, eGaIn, Electrochemical platform, Hydrogels, Machine learning, Escherichia coli, Gallium alloys, Indium alloys, Bacteria detection, Egain, Electrochemical data, Electrochemical platforms, Escherichia coli bacteria, Fast detections, Hydrogel system, Machine learning methods, Machine-learning, Reduce time, alloy, cetirizine, gallium, hydrogel, indium, accuracy, agriculture, Article, bacterium, biomedicine, cell culture, colony forming unit, cyclic voltammetry, electrochemical analysis, electron transport, energy dispersive X ray spectroscopy, food analysis, hydrogen bond, impedance spectroscopy, machine learning, nonhuman, nutrition, perceptron, pluripotent stem cell, Raman spectrometry, receiver operating characteristic, scanning electron microscopy, sensitivity analysis, three dimensional printing, voltammetry, X ray, X ray emission spectroscopy, X ray spectroscopy, chemistry, devices, equipment design, genetic procedures, human, isolation and purification, procedures, Biosensing Techniques, Electrochemical Techniques, Equipment Design, Gallium, Humans, Machine Learning

ID: 126391132