Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins

Journal of Microbiological Methods

Volumn 66, Issue 2, 2006, Pages 336-346

  Piraino, P ^a   Ricciardi, A ^a   Salzano, G ^a   Zotta, T ^a   Parente, E ^a  

^a UNIVERSITY OF BASILICATA   (Italy)

Author keywords

Artificial neural networks; Cluster analysis; Discriminant analysis; Kohonen networks; SDS PAGE of whole cell proteins

Indexed keywords

BACTERIAL PROTEIN; CELL PROTEIN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACTERIAL STRAIN; BACTERIUM IDENTIFICATION; BAYES THEOREM; CLASSIFICATION; CLUSTER ANALYSIS; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; ELECTROPHORESIS; INTERMETHOD COMPARISON; LACTIC ACID BACTERIUM; LACTOBACILLUS ACIDOPHILUS; LACTOBACILLUS CURVATUS; LACTOBACILLUS DELBRUECKII; LACTOBACILLUS HELVETICUS; LACTOBACILLUS PLANTARUM; LEUCONOSTOC; MICROBIOLOGICAL EXAMINATION; MULTIVARIATE ANALYSIS; NONHUMAN; POLYACRYLAMIDE GEL ELECTROPHORESIS; PRIORITY JOURNAL; SPECIES DIFFERENCE; SPECIES IDENTIFICATION; STATISTICAL MODEL; WHOLE CELL;

BACTERIAL PROTEINS; CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; ELECTROPHORESIS, POLYACRYLAMIDE GEL; FOOD MICROBIOLOGY; LACTOBACILLACEAE; MULTIVARIATE ANALYSIS; NEURAL NETWORKS (COMPUTER);

ENTEROCOCCUS; LACTOBACILLUS; LACTOCOCCUS; LEUCONOSTOC; STREPTOCOCCUS;

EID: 33746862912     PISSN: 01677012     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mimet.2005.12.007     Document Type: Article

References (56)

1. Al-Haddad L., Morris C.W., and Boddy L. Training radial basis function neural networks: effects of training set size and imbalanced training sets. J. Microbiol. Methods 43 (2000) 33-44
2. Almeida J.S., Sonesson A., Ringelberg D.B., and White D.C. Application of artificial neural networks to the detection of Mycobacterium tuberculosis, its antibiotic resistance and prediction of pathogenicity amongst Mycobacterium spp. based on signature lipid biomarkers. Bin. Comput. Microbiol. 7 (1995) 159-166
3. Andrighetto C., Knijff E., Lombardi A., Torriani S., Vancanneyt M., Kersters K., Swings J., and Dellaglio F. Phenotypic and genetic diversity of enterococci isolated from Italian cheeses. J. Dairy Res. 68 (2001) 303-316
4. Basheer I.A., and Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43 (2000) 3-31
5. Blaiotta G., Pepe O., Mauriello G., Villani F., Andolfi R., and Moschetti G. 16S-23S rDNA intergenic spacer region polymorphism of Lactococcus garviae, Lactococcus raffinolactis and Lactococcus lactis as revealed by PCR and nucleotide sequence analysis. Syst. Appl. Microbiol. 25 (2002) 520-527
6. Carson C.A., Keller J.M., McAdoo K.K., Wang D., Higgins B., Bailey C.W., Thorne J.G., Payne B.J., Skala M., and Hahn A.W. Escherichia coli O157:H7 restriction pattern recognition by artificial neural network. J. Clin. Microbiol. 33 (1995) 2894-2898
7. Cheroutre-Vialette M., and Lebert A. Application of recurrent neural network to predict bacterial growth in dynamic conditions. Int. J. Food Microbiol. 73 (2002) 107-118
8. Corsetti A., De Angelis M., Dellaglio F., Paparella A., Fox P.F., Settanni L., and Gobbetti M. Characterization of sourdough lactic acid bacteria based on genotypic and cell-wall protein analyses. J. Appl. Microbiol. 94 (2003) 651-654
9. Dalgaard P., Vancanneyt M., Euras Vilalta N., Swings J., Fruekilde P., and Leisner J.J. Identification of lactic acid bacteria from spoilage associations of cooked and brined shrimps stored under modified atmosphere between 0 °C and 25 °C. J. Appl. Microbiol. 94 (2003) 80-89
10. De Angelis M., Corsetti A., Tosti N., Rossi J., Corbo M.R., and Gobbetti M. Characterization of non-starter lactic acid bacteria from Italian ewe cheeses based on phenotypic, genotypic and cell wall protein analyses. Appl. Environ. Microbiol. 67 (2001) 2011-2020
11. Du Toit M., Dicks L.M.T., and Holzapfel W.H. Identification of heterofermentative lactobacilli isolated from pig faeces by numerical analysis of total soluble cell protein patterns and RAPD-PCR. Lett. Appl. Microbiol. 37 (2003) 12-16
12. Everitt B.S., and Dunn G. Applied Multivariate Data Analysis (2001), Arnold, London
13. Everitt B.S., Landau S., and Dunn G. Cluster Analysis (2001), Arnold, London
14. Freeman R., Goodacre R., Magee P.R., Ward A.C., and Lightfoot N.F. Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural networks analysis of pyrolysis mass spectra. J. Med. Microbiol. 40 (1994) 170-173
15. García-Gimeno R.M., Hervás-Martínez C., and de Silóniz M.I. Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. Int. J. Food Microbiol. 72 (2002) 19-30
16. Gatti M., Fornasari M.E., and Neviani E. Differentiation of Lactobacillus delbrueckii subsp. bulgaricus and Lactobacillus delbrueckii subsp. lactis by SDS-PAGE of cell-wall proteins. Lett. Appl. Microbiol. 32 (2001) 352-356
17. Gatti M., Lazzi C., Rossetti L., Mucchetti G., and Neviani E. Biodiversity of Lactobacillus helveticus strains present in natural whey starter used for Parmigiano Reggiano cheese. J. Appl. Microbiol. 95 (2003) 463-470
18. Giacomini M., Ruggiero C., Bertone S., and Calegari L. Artificial neural network identification of heterotrophic marine bacteria based on their fatty-acid composition. IEEE Trans. Biomed. Eng. 44 (1997) 1185-1191
19. Giacomini M., Ruggiero C., Calegari L., and Bertone S. Artificial neural network based identification of environmental bacteria by gas-chromatographic and electrophoretic data. J. Microbiol. Methods 43 (2000) 45-54
20. Gómez-Zavaglia A., Abraham A., Giorgieri S., and De Antoni G. Application of polyacrylamide gel electrophoresis and capillary gel electrophoresis to the analysis of Lactobacillus delbrueckii whole-cell proteins. J. Dairy Sci. 82 (1999) 870-877
21. Goodacre R., Howell S.A., Noble W.C., and Neal M.J. Sub-species discrimination using pyrolysis mass spectrometry and self-organizing neural networks of Propionibacterium acnes isolated from normal human skin. Zbl. Bakt. 22 (1994) 124-136
22. Goodacre R., Neal M.J., Kell D.B., Greenham L.W., Noble W.C., and Harvey R.G. Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. J. Appl. Bacteriol. 76 (1994) 124-134
23. Goodacre R., Rooney P.J., and Kell D.B. Discrimination between methicillin-resistant and methicillin susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks. J. Antimicrob. Chemother. 41 (1998) 27-34
24. Goodacre R., Timmins É.M., Burton R., Kaderbhai N., Woodward A.M., Kell D.B.P., and Rooney P. Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 144 (1998) 1157-1170
25. Hajmeer M., and Basheer I. A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. J. Microbiol. Methods 51 (2002) 217-226
26. Haykin S. Neural Networks. A Comprehensive Foundation (1999), Prentice Hall International, London
27. Hérbert E.M., Raya R.R., and de Giori G.S. Use of SDS-PAGE of cell-wall proteins for rapid differentiation of Lactobacillus delbrueckii subsp. lactis and Lactobacillus helveticus. Biotechnol. Lett. 22 (2000) 1003-1006
28. Jeyamkondan S., Jayas D.S., and Holley R.A. Microbial growth modelling with artificial neural networks. Int. J. Food Microbiol. 64 (2001) 343-354
29. Kennedy M.J., and Thakur M.S. The use of artificial neural networks to aid microorganism identification: a case study of Haemophilus species identification. Antonie van Leeuwenhoek 63 (1993) 35-38
30. Klein G., Pack A., Bonaparte C., and Reuter G. Taxonomy and physiology of probiotic lactic acid bacteria. Int. J. Food Microbiol. 41 (1998) 103-125
31. Kunene N.F., Geornaras I., Von Holy A., and Hastings J.W. Characterization and determination of origin of lactic acid bacteria from a sorghum-based fermented weaning food by analysis of soluble proteins and amplified fragment length polymorphism fingerprinting. Appl. Environ. Microbiol. 66 (2000) 1084-1092
32. Laursen B.G., Bay L., Cleenwerck I., Vancanneyt M., Swings J., Dalgaard P., and Leisner J.J. Carnobacterium divergens and Carnobacterium maltaromicum as spoilers or protective cultures in meat and seafood: phenotypic and genotypic characterization. Syst. Appl. Microbiol. 28 (2005) 151-164
33. Leflaive J., Céréghino R., Danger M., Lacroix G., and Ten-Hage L. Assessment of self-organizing maps to analyze sole-carbon source utilization profiles. J. Microbiol. Methods 62 (2005) 89-102
34. Leisner J.J., Vancanneyt M., Rusul G., Pot B., Lefebvre K., Fresi A., and Tee L.K. Identification of lactic acid bacteria constituting the predominating microflora in an acid-fermented condiment (tempoyak) popular in Malaysia. Int. J. Food Microbiol. 63 (2001) 149-157
35. Lopes M.F.S., Pereira C.I., Rodrigues F.M.S., Martins M.P., Mimoso M.C., Barros T.C., Figueiredo Marques J.J., Tenreiro R.P., Almeida J.S., and Barreto Crespo M.T. Registered designation of origin areas of fermented food products defined by microbial phenotypes and artificial neural networks. Appl. Environ. Microbiol. 65 (1999) 4484-4489
36. Lou W., and Nakai S. Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity. Food Res. Int. 34 (2001) 573-579
37. Lovell C.R., Bagwell C.E., Czákó M., Márton L., Piceno Y.M., and Ringelberg D.B. Stability of a rizosphere microbial community exposed to natural and manipulated environmental variability. FEMS Microbiol. Ecol. 38 (2001) 69-76
38. Moschetti G., Blaiotta G., Villani F., Coppola S., and Parente E. A comparison of statistical methods for the identification of Streptococcus thermophilus, Enterococcus faecalis and Enterococcus faecium from RAPD-PCR patterns. Appl. Environ. Microbiol. 67 (2001) 2156-2166
39. Noble P.A., Bidle K.D., and Fletcher M. Natural microbial community compositions compared by a back-propagating neural network and cluster analysis of 5S rRNA. Appl. Environ. Microbiol. 63 (1997) 1762-1770
40. Noble P.A., Jonas S., Almeida C., and Lovell R. Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities. Appl. Environ. Microbiol. 66 (2000) 694-699
41. Parente E., Rota M.A., Ricciardi A., and Clementi F. Characterization of natural starter cultures used in the manufacture of Pasta Filata cheese in Basilicata (Southern Italy). Int. Dairy J. 7 (1997) 775-783
42. Piraino P., Ricciardi A., Lanorte M.T., Malkhazova I., and Parente E. A new procedure for data reduction in electrophoretic fingerprints of whole-cell proteins. Biotechnol. Lett. 24 (2002) 1477-1482
43. Piraino P., Parente E., and McSweeney P.L.H. A new approach for preprocessing of chromatographic data and chemometrical analysis of proteolytic profiles. J. Agric. Food Chem. 52 (2004) 6904-6911
44. Piraino P., Zotta T., Ricciardi A., and Parente E. Discrimination of commercial Caciocavallo cheeses on the basis of the diversity of lactic microflora and primary proteolysis. Int. Dairy J. 15 (2005) 1138-1149
45. Pot B., and Janssens D. The potential role of a culture collection for the identification and maintenance of lactic acid bacteria. In: Foo A.L., Griffin H.G., Molby R., and Heden C.G. (Eds). The Lactic Acid Bacteria, Proceeding of the first Lactic Acid Bacteria Computer Conference (1993), Horizon Scientific Press, Norfolk, UK 81-87
46. Pot B., Ludwig W., Kersters K., and Schleifer K.-H. Taxonomy of lactic acid bacteria. In: De Vuyst L., and Vandamme E.J. (Eds). Taxonomy of Lactic Acid Bacteria. Bacteriocins of Lactic Acid Bacteria (1994), Blackie, London, UK 13-89
47. Pot B., Vandamme P., and Kersters K. Analysis of electrophoretic whole-organism protein fingerprints. In: Goodfellow M., and O'Donnell A.G. (Eds). Modern Microbiological Methods. Chemical Methods in Prokaryotic Systematics (1994), John Wiley and Sons, Chichester, England 493-521
48. Sánchez I., Seseña S., and Palop L. Identification of lactic acid bacteria from spontaneous fermentation of 'Almagro' eggplants by SDS-PAGE whole cell protein fingerprinting. Int. J. Food Microbiol. 82 (2003) 181-189
49. Sperber W.H., and Swan J. Hot-loop test for the determination of carbon dioxide production from glucose by lactic acid bacteria. Appl. Environ. Microbiol. 31 (1976) 990-991
50. SPSS. Neural Connection 2.0. User's Guide (1997), SPSS Inc, Chicago, IL
51. Stiles M.E., and Holzapfel W.H. Lactic acid bacteria of foods and their current taxonomy. Int. J. Food Microbiol. 36 (1997) 1-29
52. Tuang F.N., Rademaker J.L.W., Alocilja E.C., Louws F.J., and de Brujin F.J. Identification of bacterial rep-PCR genomic fingerprints using a backpropagation neural network. FEMS Microbiol. Lett. 177 (1999) 249-256
53. Vandamme P., Pot B., Gillis M., Kersters K., and Swings J. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiol. Rev. 60 (1996) 407-438
54. Vauterin L., Swings J., and Kersters K. Protein electrophoresis and classification. In: O'Donnell J. (Ed). Handbook of New Bacterial Systematics (1993), Academic Press, London, England 251-280
55. Voisin S., Terreux R., Renaud F.N.R., Freney J., Domard M., and Deruaz D. Pyrolysis patterns of 5 close Corynbacterium species analyzed by artificial neural networks. Antonie van Leeuwenhoek 85 (2004) 287-296
56. Wilkins M.F., Boddy L., Morris C.W., and Jones R.R. Identification of phytoplankton from flow cytometry data by using radial basis function neural networks. Appl. Environ. Microbiol. 65 (1999) 4404-4410