Rapid Identification of Bacterial Species by Fluorescence Spectroscopy and Classification Through Principal Components Analysis

This work presents the development of a method for rapid bacterial identification based on the autofluorescence spectrum. It was demonstrated differences in the autofluorescence spectrum in three bacterial species and the subsequent separation, through the Principal Components Analysis (PCA) technique, in groups with high likeness, that could identify the bacteria in less than 10 min. Fluorescence spectra of 60 samples of 3 different bacterial species (Escherichia coli, EC, Enterococcus faecalis, EF and Staphylococcus aureus, SA), previously identified by automated equipment Mini API, were collected in 10 excitation wavelengths from 330 to 510 nm. The PCA technique applied to the fluorescence spectra showed that bacteria species could be identified with sensitivity and specificity higher than 90% according to differences that occur within the spectra with excitation of 410 nm and 430 nm. This work presented a method of bacterial identification of three more frequent and more clinically significant species based on the autofluorescence spectra in the excitation wavelengths of 410 and 430 nm and the classification of the spectra in three groups using PCA. The results demonstrated that the bacterial identification is very efficient with such methodology. The proposed method is rapid, ease to perform and low cost compared to standard methods.