Multi-resistant diarrheagenic<i>Escherichia coli</i>identified by FTIR and machine learning: a feasible strategy to improve the group classification-Reference-Cited by-同舟云学术

Multi-resistant diarrheagenicEscherichia coliidentified by FTIR and machine learning: a feasible strategy to improve the group classification

Published:2023 Issue:36 Volume:13 Page:24909-24917
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Marangoni-Ghoreyshi Yasmin Garcia¹,Franca Thiago²,Esteves José²,Maranni Ana²,Pereira Portes Karine Dorneles³,Cena Cicero²^ORCID,Leal Cassia R. B.¹

Affiliation:

1. UFMS – Universidade Federal de Mato Grosso do Sul, Graduate Program in Veterinary Science (CIVET), Campo Grande, MS, Brazil

2. UFMS – Universidade Federal de Mato Grosso do Sul, Optics and Photonic Lab (SISFOTON-UFMS), Campo Grande, MS, Brazil

3. UFMS – Universidade Federal de Mato Grosso do Sul, Animal Science Undergraduate, PIBIT/CNPq, Campo Grande, MS, Brazil

Abstract

A new method based on FTIR spectroscopy and machine learning is proposed to identify multidrug-resistant strains fromE. colispecies responsible for diarrhea in calves.

Funder

Universidade Federal de Mato Grosso do Sul

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RA/D3RA03518B

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