Multi-resistant diarrheagenic<i>Escherichia coli</i>identified by FTIR and machine learning: a feasible strategy to improve the group classification
Yasmin Garcia Marangoni-Ghoreyshi, Thiago França, José Esteves, Ana Maranni, Karine Dorneles Pereira Portes, Cícero Cena, Cássia Rejane Brito Leal
Abstract
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.
Topics & Concepts
MicroorganismPrincipal component analysisFourier transform infrared spectroscopyIdentification (biology)Cluster analysisComputer scienceMicrobiologyArtificial intelligenceBacteriaBiologyEngineeringBotanyGeneticsChemical engineeringSpectroscopy Techniques in Biomedical and Chemical ResearchSpectroscopy and Chemometric AnalysesMineral Processing and Grinding