Fast bacterial strain identification by laser induced breakdown spectroscopy and neural networks

S. Manzoor; S. Moncayo; F. Navarro-Villoslada; J.A. Ayala; R. Izquierdo-Hornillos; F.J. Manuel de Villena; J.O. Caceres

Fast bacterial strain identification by laser induced breakdown spectroscopy and neural networks

In: Industrial, medical and environmental applications of microorganisms

Authors:

S. Manzoor

S. Manzoor ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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S. Moncayo

S. Moncayo ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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F. Navarro-Villoslada

F. Navarro-Villoslada ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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J.A. Ayala

J.A. Ayala ²Centro de Biología Molecular ‘Severo Ochoa’, CSIC, C/Nicolás Cabrera 1, Cantoblanco 28049 Madrid, Spain

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R. Izquierdo-Hornillos

R. Izquierdo-Hornillos ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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F.J. Manuel de Villena

F.J. Manuel de Villena ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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, and

J.O. Caceres

J.O. Caceres ¹Departamento de Química Analítica, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain; jcaceres@ucm.es

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Type:: Chapter

Pages:: 647–653

DOI:: https://doi.org/10.3920/9789086867950_116

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A method for rapid bacterial strain identification based on Laser Induced Breakdown Spectroscopy (LIBS) and Neural Networks (NN) is reported. The study includes bacterial strains of the most relevant bacteria causing Hospital Acquired Infections (HAI), i.e. Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Salmonella typhimurium and Staphylococcus aureus. LIBS/NN methodology was evaluated for its capacity to discriminate different bacterial strains using their characteristic LIBS spectra from changes in their elemental composition as a result of genetic variations. The samples were measured for two different days to evaluate the time-dependent classification capacity of the methodology. A successful classification of bacterial strains by the proposed LIBS/NN method, with accuracy above 95%, shows its potential to address the safety and social-cost HAI-related issue.

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Industrial, medical and environmental applications of microorganisms

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E-Book ISBN:: 9789086867950

Publisher:: Wageningen Academic

Print Publication Date:: 01 Nov 2014

Subjects
- Life Sciences

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Fast bacterial strain identification by laser induced breakdown spectroscopy and neural networks

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Industrial, medical and environmental applications of microorganisms

Current status and trends

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