Discriminating two bacteria via laser-induced breakdown spectroscopy and artificial neural network.

Rapid and successful clinical diagnosis and bacterial infection treatment depend on accurate identification and differentiation between different pathogenic bacterial species. A lot of efforts have been made to utilize modern techniques which avoid the laborious work and time-consuming of conventional methods to fulfill this task. Among such techniques, laser-induced breakdown spectroscopy (LIBS) can tell much about bacterial identity and functionality. In the present study, a sensitivity-improved version of LIBS, i.e. nano-enhanced LIBS (NELIBS), has been used to discriminate between two different bacteria (Pseudomonas aeruginosa and Proteus mirabilis) belonging to different taxonomic orders. Biogenic silver nanoparticles (AgNPs) are sprinkled onto the samples' surface to have better discrimination capability of the technique. The obtained spectroscopic results of the NELIBS approach revealed superior differentiation between the two bacterial species compared to the results of the conventional LIBS. Identification of each bacterial species has been achieved in light of the presence of spectral lines of certain elements. On the other hand, the discrimination was successful by comparing the intensity of the spectral lines in the spectra of the two bacteria. In addition, an artificial neural network (ANN) model has been created to assess the variation between the two data sets, affecting the differentiation process. The results revealed that NELIBS provides higher sensitivity and more intense spectral lines with increased detectable elements. The ANN results showed that the accuracy rates are 88% and 92% for LIBS and NELIBS, respectively. In the present work, it has been demonstrated that NELIBS combined with ANN successfully differentiated between both bacteria rapidly with high precision compared to conventional microbiological discrimination techniques and with minimum sample preparation.