Standard

Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods. / Aliev, T.A.; Lavrentev, F.V.; Dyakonov, A.V.; Diveev, D.A.; Shilovskikh, V.V.; Skorb, E.V.

в: Biosensors and Bioelectronics, Том 259, 01.09.2024.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Aliev, TA, Lavrentev, FV, Dyakonov, AV, Diveev, DA, Shilovskikh, VV & Skorb, EV 2024, 'Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods', Biosensors and Bioelectronics, Том. 259. https://doi.org/10.1016/j.bios.2024.116377

APA

Aliev, T. A., Lavrentev, F. V., Dyakonov, A. V., Diveev, D. A., Shilovskikh, V. V., & Skorb, E. V. (2024). Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods. Biosensors and Bioelectronics, 259. https://doi.org/10.1016/j.bios.2024.116377

Vancouver

Aliev TA, Lavrentev FV, Dyakonov AV, Diveev DA, Shilovskikh VV, Skorb EV. Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods. Biosensors and Bioelectronics. 2024 Сент. 1;259. https://doi.org/10.1016/j.bios.2024.116377

Author

Aliev, T.A. ; Lavrentev, F.V. ; Dyakonov, A.V. ; Diveev, D.A. ; Shilovskikh, V.V. ; Skorb, E.V. / Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods. в: Biosensors and Bioelectronics. 2024 ; Том 259.

BibTeX

@article{e99bd69cbc784233af0b08a1dcb20c24,
title = "Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods",
abstract = "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. {\textcopyright} 2024",
keywords = "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",
author = "T.A. Aliev and F.V. Lavrentev and A.V. Dyakonov and D.A. Diveev and V.V. Shilovskikh and E.V. Skorb",
note = "Export Date: 19 October 2024 CODEN: BBIOE Адрес для корреспонденции: Skorb, E.V.; Infochemistry Scientific Center, 9 Lomonosova Street, Russian Federation; эл. почта: skorb@itmo.ru Химические вещества/CAS: cetirizine, 83881-51-0, 83881-52-1, 163837-48-7; gallium, 7440-55-3, 14391-02-7; indium, 7440-74-6; Gallium Фирменные наименования: version 3.11 Производители: Megaclassic company Сведения о финансировании: Russian Science Foundation, RSF, 24-13-00355 Сведения о финансировании: Russian Science Foundation, RSF Текст о финансировании 1: This research was funded by RSF grant no. 24-13-00355. Priority 2030 Program is acknowledged for infrastructural support.",
year = "2024",
month = sep,
day = "1",
doi = "10.1016/j.bios.2024.116377",
language = "Английский",
volume = "259",
journal = "Biosensors and Bioelectronics",
issn = "0956-5663",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods

AU - Aliev, T.A.

AU - Lavrentev, F.V.

AU - Dyakonov, A.V.

AU - Diveev, D.A.

AU - Shilovskikh, V.V.

AU - Skorb, E.V.

N1 - Export Date: 19 October 2024 CODEN: BBIOE Адрес для корреспонденции: Skorb, E.V.; Infochemistry Scientific Center, 9 Lomonosova Street, Russian Federation; эл. почта: skorb@itmo.ru Химические вещества/CAS: cetirizine, 83881-51-0, 83881-52-1, 163837-48-7; gallium, 7440-55-3, 14391-02-7; indium, 7440-74-6; Gallium Фирменные наименования: version 3.11 Производители: Megaclassic company Сведения о финансировании: Russian Science Foundation, RSF, 24-13-00355 Сведения о финансировании: Russian Science Foundation, RSF Текст о финансировании 1: This research was funded by RSF grant no. 24-13-00355. Priority 2030 Program is acknowledged for infrastructural support.

PY - 2024/9/1

Y1 - 2024/9/1

N2 - 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

AB - 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

KW - Bacteria

KW - eGaIn

KW - Electrochemical platform

KW - Hydrogels

KW - Machine learning

KW - Escherichia coli

KW - Gallium alloys

KW - Indium alloys

KW - Bacteria detection

KW - Egain

KW - Electrochemical data

KW - Electrochemical platforms

KW - Escherichia coli bacteria

KW - Fast detections

KW - Hydrogel system

KW - Machine learning methods

KW - Machine-learning

KW - Reduce time

KW - alloy

KW - cetirizine

KW - gallium

KW - hydrogel

KW - indium

KW - accuracy

KW - agriculture

KW - Article

KW - bacterium

KW - biomedicine

KW - cell culture

KW - colony forming unit

KW - cyclic voltammetry

KW - electrochemical analysis

KW - electron transport

KW - energy dispersive X ray spectroscopy

KW - food analysis

KW - hydrogen bond

KW - impedance spectroscopy

KW - machine learning

KW - nonhuman

KW - nutrition

KW - perceptron

KW - pluripotent stem cell

KW - Raman spectrometry

KW - receiver operating characteristic

KW - scanning electron microscopy

KW - sensitivity analysis

KW - three dimensional printing

KW - voltammetry

KW - X ray

KW - X ray emission spectroscopy

KW - X ray spectroscopy

KW - chemistry

KW - devices

KW - equipment design

KW - genetic procedures

KW - human

KW - isolation and purification

KW - procedures

KW - Biosensing Techniques

KW - Electrochemical Techniques

KW - Equipment Design

KW - Gallium

KW - Humans

KW - Machine Learning

UR - https://www.mendeley.com/catalogue/a67403f1-5607-362d-a5d2-9a99add1d65a/

U2 - 10.1016/j.bios.2024.116377

DO - 10.1016/j.bios.2024.116377

M3 - статья

VL - 259

JO - Biosensors and Bioelectronics

JF - Biosensors and Bioelectronics

SN - 0956-5663

ER -

ID: 126391132