Journal of Bacteriology & Parasitology

Biomedical applications of laser induced breakdown spectroscopy in bacterial identification

3^rd International Congress on Bacteriology and Infectious Diseases

August 04-06, 2015 Valencia, Spain

J O Caceres1, S Manzoor1, S Moncayo1, J A Ayala2 and R Izquierdo-Hornillos1

Posters-Accepted Abstracts: J Bacteriol Parasitol

Abstract:

Antibiotic resistant bacterial strains belonging to same species were identified and discriminated using laser induced
breakdown spectroscopy (LIBS) and neural networks (NN) algorithm. The method has been applied to identify 40
bacterial strains i.e. Escherichia coli (Ec), Pseudomonas aeruginosa (Pa), Klebsiella pneumoniae (Kp), Salmonella typhimurium
(St), Salmonella pullorum (Sp) and Salmonella salamae (Ss). The bacterial samples analyzed included strains isolated from
clinical samples and constructed in laboratory. The strains differed from each other in mutations as a result of their resistance
to one or more antibiotics. Kp, Ec and Pa strains showed multidrug antibiotic resistance and multiple genes mutations whereas
St, Sp and Ss were resistant to kanamycin and differed in only one gene. LIBS are a non-microbiological technique which has
been used in various studies to deal with rapid bacterial identification based on the elemental composition of bacterial cells. In
a previous study by our group, LIBS/NN has shown to be a promising methodology to classify and predict bacterial samples at
genus level. This work is an extension of the previous study in order to investigate the application of LIBS/NN to discriminate
different antibiotic resistant strains of same bacterial species and address its use as a rapid potential diagnostic methodology.
The objective was to determine if genetic variations between bacterial strains of the same bacterial species even when there is
a difference in only one gene, generate sufficient or significant changes in their atomic composition which can be detected by
LIBS/NN method in order to achieve their identification and discrimination. Single shot LIBS measurements combined with
supervised neural network method were sufficient for a clear identification and classification of bacterial strains differing in
multiple and even single mutation. The results demonstrate the potential of this method to be used for continuous monitoring
of the bacterial infections and identify pathogenic bacteria at an early stage of infection which can be significant towards an
early treatment of the infections.