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.].
CITATION STYLE
Yogesha, M., Chawla, K., Bankapur, A., Acharya, M., D’Souza, J. S., & Chidangil, S. (2019). A micro-Raman and chemometric study of urinary tract infection-causing bacterial pathogens in mixed cultures. Analytical and Bioanalytical Chemistry, 411(14), 3165–3177. https://doi.org/10.1007/s00216-019-01784-4
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