An Advanced Approach Based on Artificial Neural Networks to Identify Environmental Bacteria
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.
[1] Environmental Protection Agency (1989). National primary drinking
water rules and regulations. U.S. EPA surface water rule. Filtration,
disinfection, turbidity, Giardia lamblia, viruses, Legionella,
heterotrophic bacteria, Federal Register, 54, pp.27486-27541.
[2] Environmental Protection Agency (1994). National primary drinking
water regulation: Enhanced Surface Water Treatment Requirements:
U.S. EPA Proposed Rule, Federal Register, 59, 159, pp.38832-38858.
[3] M. Handfield, P. Simard, M. Couillard, and R. Letarte, "Aeromonas
hydrophila isolated from food and drinking water: hemagglutination,
hemolysis and cytotoxicity for a human intestinal cell line (HT-29)",
Applied Environmental Microbiology, vol. 62, pp. 3459-3461, 1996.
[4] P. Payment, E. Coffin, and G. Paquette, "Blood agar to detect virulence
factors in tap water heterotrophic bacteria", Applied Environmental
Microbiology, vol. 60, no. 4, pp. 1179-1183, 1994.
[5] P. Vandamme, B. Pot, M. Gillis, P. De Vos, K. Kersters, and J. Swings,
"Polyphasic taxonomy, a consensus approach to bacterial systematics",
Microbiology Review, vol. 60, pp. 407-438, 1996.
[6] K. Suzuki, M. Goodfellow, and A. G. O-Donnel, Handbook of new
bacterial systematics. London: Academic Press Ltd, 1993.
[7] M. Giacomini, C. Ruggiero, M. Maillard, F. B. Lillo, and O. E. Varnier
"Objective evaluation of two markers of HIV-1 infection (p24 antigen
concentration and CD4+ cell counts) by a self organising neural
network", Medical Informatics, vol. 21, pp. 215-228, 1996.
[8] D. T. Pham, and X. Liu, Neural Networks for identification, prediction
and control. London: Springer-Verlag, 1997.
[9] S. Bertone, M. Giacomini, C. Ruggiero, C. Piccarolo, and L. Calegari,
"Automated systems for identification of heterotrophic marine bacteria
on the basis of their fatty acid composition", Applied Environmental
Microbiology, vol. 62, pp.2122-2132, 1996.
[10] M. Giacomini, C. Ruggiero, S. Bertone, and L. Calegari, "Artificial
neural network identification of heterotrophic marine bacteria based on
their fatty acid composition", IEEE Transactions of Biomedical
Engineering, vol. 44, pp. 1185-1191, 1997.
[11] M. Giacomini, C. Ruggiero, L. Calegari, and S. Bertone, "Artificial
Neural Network based identification of environmental bacteria by gaschromatographic
and electrophoretic data", Journal of Microbioly
Methods, vol. 1, pp. 45-54, 2000.
[12] T. Kohonen, "The self -organizing map", IEEE Proceedings, vol. 78, no.
9, pp. 1464-1480, 1990.
[13] M. P. Lechevalier, "Lipids in bacterial taxonomy: a taxonomist-s view",
Clinical Microbiology Reviews, vol. 5, pp. 109-210, 1977.
[14] L. T. Miller, "Single derivatization method for routine analysis of
bacterial whole-cell fatty acid methyl esters, including hydroxy acids",
Journal of Clinical Microbiology, vol. 16, pp. 584-586, 1982.
[15] L. A. Briganti, and S. C. Wacker, "Fatty acid profiling and the
identification of environmental bacteria for drinking water utilities",
American Water Works Association Research Foundation and American
Water Works Association, Denver, Colorado, 1995.
[16] C. D. Norton, and M. W. LeChevallier, "A pilot study of bacteriological
population changes through potable water treatment and distribution",
Applied Environmental Microbiology, vol. 66, no.1, pp.268-276, 2000
[17] D. L. Hudson and M. E. Cohen, Neural networks and artificial
intelligence for biomedical engineering. New York: IEEE Press, 2000.
[1] Environmental Protection Agency (1989). National primary drinking
water rules and regulations. U.S. EPA surface water rule. Filtration,
disinfection, turbidity, Giardia lamblia, viruses, Legionella,
heterotrophic bacteria, Federal Register, 54, pp.27486-27541.
[2] Environmental Protection Agency (1994). National primary drinking
water regulation: Enhanced Surface Water Treatment Requirements:
U.S. EPA Proposed Rule, Federal Register, 59, 159, pp.38832-38858.
[3] M. Handfield, P. Simard, M. Couillard, and R. Letarte, "Aeromonas
hydrophila isolated from food and drinking water: hemagglutination,
hemolysis and cytotoxicity for a human intestinal cell line (HT-29)",
Applied Environmental Microbiology, vol. 62, pp. 3459-3461, 1996.
[4] P. Payment, E. Coffin, and G. Paquette, "Blood agar to detect virulence
factors in tap water heterotrophic bacteria", Applied Environmental
Microbiology, vol. 60, no. 4, pp. 1179-1183, 1994.
[5] P. Vandamme, B. Pot, M. Gillis, P. De Vos, K. Kersters, and J. Swings,
"Polyphasic taxonomy, a consensus approach to bacterial systematics",
Microbiology Review, vol. 60, pp. 407-438, 1996.
[6] K. Suzuki, M. Goodfellow, and A. G. O-Donnel, Handbook of new
bacterial systematics. London: Academic Press Ltd, 1993.
[7] M. Giacomini, C. Ruggiero, M. Maillard, F. B. Lillo, and O. E. Varnier
"Objective evaluation of two markers of HIV-1 infection (p24 antigen
concentration and CD4+ cell counts) by a self organising neural
network", Medical Informatics, vol. 21, pp. 215-228, 1996.
[8] D. T. Pham, and X. Liu, Neural Networks for identification, prediction
and control. London: Springer-Verlag, 1997.
[9] S. Bertone, M. Giacomini, C. Ruggiero, C. Piccarolo, and L. Calegari,
"Automated systems for identification of heterotrophic marine bacteria
on the basis of their fatty acid composition", Applied Environmental
Microbiology, vol. 62, pp.2122-2132, 1996.
[10] M. Giacomini, C. Ruggiero, S. Bertone, and L. Calegari, "Artificial
neural network identification of heterotrophic marine bacteria based on
their fatty acid composition", IEEE Transactions of Biomedical
Engineering, vol. 44, pp. 1185-1191, 1997.
[11] M. Giacomini, C. Ruggiero, L. Calegari, and S. Bertone, "Artificial
Neural Network based identification of environmental bacteria by gaschromatographic
and electrophoretic data", Journal of Microbioly
Methods, vol. 1, pp. 45-54, 2000.
[12] T. Kohonen, "The self -organizing map", IEEE Proceedings, vol. 78, no.
9, pp. 1464-1480, 1990.
[13] M. P. Lechevalier, "Lipids in bacterial taxonomy: a taxonomist-s view",
Clinical Microbiology Reviews, vol. 5, pp. 109-210, 1977.
[14] L. T. Miller, "Single derivatization method for routine analysis of
bacterial whole-cell fatty acid methyl esters, including hydroxy acids",
Journal of Clinical Microbiology, vol. 16, pp. 584-586, 1982.
[15] L. A. Briganti, and S. C. Wacker, "Fatty acid profiling and the
identification of environmental bacteria for drinking water utilities",
American Water Works Association Research Foundation and American
Water Works Association, Denver, Colorado, 1995.
[16] C. D. Norton, and M. W. LeChevallier, "A pilot study of bacteriological
population changes through potable water treatment and distribution",
Applied Environmental Microbiology, vol. 66, no.1, pp.268-276, 2000
[17] D. L. Hudson and M. E. Cohen, Neural networks and artificial
intelligence for biomedical engineering. New York: IEEE Press, 2000.
@article{"International Journal of Biological, Life and Agricultural Sciences:60614", author = "Mauro Giacomini and Stefania Bertone and Federico Caneva Soumetz and Carmelina Ruggiero", title = "An Advanced Approach Based on Artificial Neural Networks to Identify Environmental Bacteria", abstract = "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.
", keywords = "Cellular fatty acid methyl esters, environmental bacteria, gas-chromatography, unsupervised ANN.", volume = "1", number = "5", pages = "59-8", }
