Lighting the Path: Raman Spectroscopy’s Journey Through the Microbial Maze DOI Creative Commons
Markus Salbreiter, Sandra Baaba Frempong,

Shimon Even

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(24), P. 5956 - 5956

Published: Dec. 17, 2024

The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical structural information, has gained significant traction these fields, especially with the adoption various excitation wavelengths tailored optical setups. choice wavelength setup spectroscopy influenced by factors such as applicability, cost, whether bulk or single-cell analysis performed, each impacting sensitivity specificity bacterial detection. In this study, we investigate potential different identification, utilizing a mock culture composed six species: three Gram-positive (S. warneri, S. cohnii, E. malodoratus) Gram-negative (P. stutzeri, K. terrigena, coli). To improve classification, applied machine learning models analyze extract unique spectral features from data. results indicate that significantly influences spectra obtained, thereby accuracy effectiveness subsequent classification results.

Language: Английский

Lighting the Path: Raman Spectroscopy’s Journey Through the Microbial Maze DOI Creative Commons
Markus Salbreiter, Sandra Baaba Frempong,

Shimon Even

et al.

Molecules, Journal Year: 2024, Volume and Issue: 29(24), P. 5956 - 5956

Published: Dec. 17, 2024

The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical structural information, has gained significant traction these fields, especially with the adoption various excitation wavelengths tailored optical setups. choice wavelength setup spectroscopy influenced by factors such as applicability, cost, whether bulk or single-cell analysis performed, each impacting sensitivity specificity bacterial detection. In this study, we investigate potential different identification, utilizing a mock culture composed six species: three Gram-positive (S. warneri, S. cohnii, E. malodoratus) Gram-negative (P. stutzeri, K. terrigena, coli). To improve classification, applied machine learning models analyze extract unique spectral features from data. results indicate that significantly influences spectra obtained, thereby accuracy effectiveness subsequent classification results.

Language: Английский

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