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Inter and intra class discrimination based on multivariate analyses applied on bacterial SERS fingerprints

Ana Maria Raluca Gherman1, Nicoleta Elena Dina1

  1. Department of Molecular and Biomolecular Physics, National Institute for R&D of Isotopic and Molecular Technologies, Donat 67-103, 400293 Cluj-Napoca, Romania


Overusing and misusing of bactericidal medication in the past years led to the rapid emergence of antibiotic resistance in bacteria. As a result, designing new antibiotics is a constant need for the medical sector in order to be able to control human infectious diseases caused by different pathogens which become more and more resistant to the classical medication. In order to overcome these needs, besides designing new medicine, one should be able to detect and identify the pathogens correctly before prescribing a treatment.
A first step that we took several years ago into the neverending marathon of antibiotic resistance was to develop a fast method for detection and identification of pathogens involved in human infectious diseases with the aid of Surface-Enhanced Raman Scattering (SERS) [1-4].
Most recently, part of our research is focused on designing statistical models able to discriminate between different classes and species of pathogens and further identify unknown samples by using these models. Here we present several multivariate analyses applied on database containing SERS fingerprints of both Gram-positive (Staphylococcus aureus, Enterococcus faecalis) and Gram-negative (Pseudomonas aeruginosa) bacteria by employing different chemometric methods such as principal component analysis (PCA), linear discriminant analysis (LDA) and PCA-LDA.

Acknowledgements: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P1-1.1-TE-2019-0910, within PNCDI III.


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