A micro-Raman and chemometric study of urinary tract infection-causing bacterial pathogens in mixed cultures

M. Yogesha, Kiran Chawla, Aseefhali Bankapur, Mahendra Acharya, Jacinta S. D’Souza, Santhosh Chidangil

Research output: Contribution to journalArticle

Abstract

Detection of urinary tract infection (UTI)-causing bacteria uses conventional time-consuming microbiological techniques. The current need is to use a fast and reliable method of bacterial identification. In order to unambiguously distinguish the UTI-causing five bacterial species used in the current study, micro-Raman spectra were obtained from a home-assembled micro-Raman system and analyzed by multivariate statistical techniques such as principal component analysis (PCA), partial least square-discriminate analysis (PLS-DA), and support vector machine (SVM). Also, the micro-Raman spectra recorded from samples containing two and three bacterial species were tested and validated against the aforementioned calibration models using PLS-DA and SVM. The prediction accuracies of up to 73 and 89% were achieved with PLS-DA and SVM, respectively. Taken together, the present study depicts the capturing of unique micro-Raman spectral features manifesting from the biochemical content of each bacterium. Also, micro-Raman spectroscopy combined with multivariate data analysis can therefore be a reliable and faster technique for the diagnosis of UTI-causing bacteria. [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)3165-3177
Number of pages13
JournalAnalytical and Bioanalytical Chemistry
Volume411
Issue number14
DOIs
Publication statusPublished - 19-05-2019

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Pathogens
Least-Squares Analysis
Urinary Tract Infections
Support vector machines
Bacteria
Raman scattering
Microbiological Techniques
Raman Spectrum Analysis
Principal Component Analysis
Principal component analysis
Calibration
Raman spectroscopy
Multivariate Analysis
Support Vector Machine

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry

Cite this

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abstract = "Detection of urinary tract infection (UTI)-causing bacteria uses conventional time-consuming microbiological techniques. The current need is to use a fast and reliable method of bacterial identification. In order to unambiguously distinguish the UTI-causing five bacterial species used in the current study, micro-Raman spectra were obtained from a home-assembled micro-Raman system and analyzed by multivariate statistical techniques such as principal component analysis (PCA), partial least square-discriminate analysis (PLS-DA), and support vector machine (SVM). Also, the micro-Raman spectra recorded from samples containing two and three bacterial species were tested and validated against the aforementioned calibration models using PLS-DA and SVM. The prediction accuracies of up to 73 and 89{\%} were achieved with PLS-DA and SVM, respectively. Taken together, the present study depicts the capturing of unique micro-Raman spectral features manifesting from the biochemical content of each bacterium. Also, micro-Raman spectroscopy combined with multivariate data analysis can therefore be a reliable and faster technique for the diagnosis of UTI-causing bacteria. [Figure not available: see fulltext.].",
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A micro-Raman and chemometric study of urinary tract infection-causing bacterial pathogens in mixed cultures. / Yogesha, M.; Chawla, Kiran; Bankapur, Aseefhali; Acharya, Mahendra; D’Souza, Jacinta S.; Chidangil, Santhosh.

In: Analytical and Bioanalytical Chemistry, Vol. 411, No. 14, 19.05.2019, p. 3165-3177.

Research output: Contribution to journalArticle

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AU - D’Souza, Jacinta S.

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