首页|Studies from Department of Research and Development Provide New Data on Escherichia coli (Classification of Water By Bacterial Presence Using Chemometrics Associated With Excitation-emission Matrix Fluorescence Spectroscopy)
Studies from Department of Research and Development Provide New Data on Escherichia coli (Classification of Water By Bacterial Presence Using Chemometrics Associated With Excitation-emission Matrix Fluorescence Spectroscopy)
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NETL
NSTL
Elsevier
Investigators publish new report on Gram-Negative Bacteria - Escherichia coli. According to news reporting originating from Serra, Brazil, by NewsRx correspondents, research stated, “Bacterial presence in water is an important indicator of water quality and, when found in high concentrations, may risk human health. The detection of total coliforms, thermotolerant coliforms, and Escherichia coli (E. coli) in water through standard methods involves time-consuming and expensive laboratory tests, which may not always provide timely and accurate results.” Funders for this research include FAPES (Fundacao de Amparo a Pesquisa e Inovacao do Espirito Santo), Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ). Our news editors obtained a quote from the research from the Department of Research and Development, “An alternative approach is excitation-emission matrix fluorescence spectroscopy (EEMFS), which offers fast detection of bacteria in water by analyzing fluorescent compounds. Chemometrics methods can be used to process EEMF spectrum, extract the relevant information, and differentiate water samples based on the presence of bacteria using classification models. In this study, various classification algorithms were applied to EEMFS datasets, including k-nearest neighbors (k-NN), partial least squares discriminant analysis (PLS-DA), multiway-PLS (NPLS-DA), principal component analysis with discriminant analysis (PCA-DA), support vector machines (SVM), and random forest (RF). Models were developed after the unfold multiway and parallel factor analysis (PARAFAC) to classify groundwater, freshwater, saltwater, and treated water samples according to the presence of E. coli, thermotolerant coliforms, and total coliforms. Among these models, PLS-DA, SVM, and RF demonstrated superior performance in discriminating the samples in most cases. In the test sets, the accuracy of the best models for total coliforms varied from 85.2% to 100% for groundwater, 71.4% to 98.2% for freshwater, 64.6% to 81.3% for treated water, and 65.8% to 71.1% for saltwater. Accuracy for E. coli and thermotolerant coliforms ranged from 89.3% to 100% in groundwater and from 64.7% to 87.5% for treated water.”
SerraBrazilSouth AmericaChemometricEmerging TechnologiesEnterobacteriaceaeEscherichia coliGram-Negative BacteriaMachine LearningProteobacteriaDepartment of Research and Development
NETL
NSTL
Elsevier
