%0 Journal Article
	%A Mauro Giacomini and  Stefania Bertone and  Federico Caneva Soumetz and  Carmelina Ruggiero
	%D 2007
	%J International Journal of Bioengineering and Life Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 5, 2007
	%T An Advanced Approach Based on Artificial Neural Networks to Identify Environmental Bacteria
	%U https://publications.waset.org/pdf/11573
	%V 5
	%X Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.

	%P 66 - 73