Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods.

Autor: Aliev TA; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia., Lavrentev FV; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia., Dyakonov AV; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia., Diveev DA; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia., Shilovskikh VV; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia; Saint Petersburg State University, Universitetskaya Embankment 7-9, Saint-Petersburg, 199034, Russia., Skorb EV; Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg, 191002, Russia. Electronic address: skorb@itmo.ru.
Jazyk: angličtina
Zdroj: Biosensors & bioelectronics [Biosens Bioelectron] 2024 Sep 01; Vol. 259, pp. 116377. Date of Electronic Publication: 2024 May 19.
DOI: 10.1016/j.bios.2024.116377
Abstrakt: 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 10 2 -10 9 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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ekaterina V. Skorb reports financial support was provided by Russian Science Foundation.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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