Monitoramento do corante pararosanilina em amostras biológicas

Silva, Humberto da; Freitas, Gutto Raffyson Silva de; Néri, Diogo Roberto Ferreira; Pereira, Francisco Régis da Silva; Farias, Robson Fernandes de; Pereira, Francisco Claudece

doi:10.1590/s0100-46702010000300015

Cited by 4 publications

(1 citation statement)

References 33 publications

(34 reference statements)

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“…Fuchsin presents risks regarding tissue neoplasia due to intoxication by occupational activities and/or environmental contamination, being considered carcinogenic to humans, in addition to being one of the rare dyes that have the property of becoming flammable under certain conditions. Several researches have been developed to degrade this pollutant due to the high risk of deliberate disposal of this type of dye in bodies of water, aquifers and supply sources [23].…”

Section: Introductionmentioning

confidence: 99%

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Farias,

Panero,

Riss

et al. 2023

Sensors

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Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

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Section: Introductionmentioning

confidence: 99%