Issue 36, 2023

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

Abstract

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

Graphical abstract: Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

Article information

Article type
Paper
Submitted
25 may 2023
Accepted
14 avq 2023
First published
21 avq 2023
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2023,13, 24909-24917

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

Y. G. Marangoni-Ghoreyshi, T. Franca, J. Esteves, A. Maranni, K. D. Pereira Portes, C. Cena and C. R. B. Leal, RSC Adv., 2023, 13, 24909 DOI: 10.1039/D3RA03518B

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