2022
DOI: 10.1177/00037028221079661
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Detection of Bioactive Metabolites in Escherichia Coli Cultures Using Surface-Enhanced Raman Spectroscopy

Abstract: Detection of bioactive metabolites produced by bacteria is important for identifying biomarkers for infectious diseases. In this study, a surface-enhanced Raman spectroscopy-based technique was developed for the detection of bioactive metabolite indole produced by Escherichia coli in biological media. The use of highly sensitive Au@Ag core-shell nanoparticles resulted in the detection of indole concentration as low as 0.0886 mM in standard solution. The supplementation of growth media with 5 mM of exogenous tr… Show more

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Cited by 33 publications
(2 citation statements)
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References 69 publications
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“…The resulting turbidity is evidence of the ability of E. coli to move on a semisolid substrate [ 34 ]. Tryptophanase, an enzyme produced by E. coli , hydrolyzes the amino acid tryptophan into indole and pyruvic acid, causing the formation of the red ring [ 35 ].…”
Section: Discussionmentioning
confidence: 99%
“…The resulting turbidity is evidence of the ability of E. coli to move on a semisolid substrate [ 34 ]. Tryptophanase, an enzyme produced by E. coli , hydrolyzes the amino acid tryptophan into indole and pyruvic acid, causing the formation of the red ring [ 35 ].…”
Section: Discussionmentioning
confidence: 99%
“…In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, field-deployable detection methods that meet diverse requirements are among the imperative measures for food safety assurance [18]. With their advantages of being non-destructive, rapid and comparatively low-cost, spectroscopic techniques, such as ultraviolet-visible (UV-VIS), near infrared spectroscopy (NIR) [19,20], mid-infrared (MIR) spectroscopy and raman spectroscopy [21], are widely applied in meat detection on the basis of spectral analysis [22]. Thyholt and Isaksson [23] utilized NIR diffuse reflectance spectroscopy in combination with K-nearest neighbor (KNN) to classify frozen-thawed beef.…”
Section: Introductionmentioning
confidence: 99%