Identifikacija bakterija mliječne i propionske kiseline pomoću FTIR spektroskopije i umjetnih neuronskih mreža

Other literature type English OPEN
Dziuba, Bartłomiej ; Nalepa, Beata (2012)
  • Publisher: Faculty of Food Technology and Biotechnology, University of Zagreb
  • Journal: Food Technology and Biotechnology, volume 50, issue 4 (issn: 1330-9862, eissn: 1334-2606)
  • Subject: lactic acid bacteria; propionic acid bacteria; FTIR spectroscopy; artificial neural networks | bakterije mliječne kiseline; bakterije propionske kiseline; FTIR spektroskopija; umjetne neuronske mreže

In the present study, lactic acid bacteria and propionic acid bacteria have been identified at the genus level with the use of artificial neural networks (ANNs) and Fourier transform infrared spectroscopy (FTIR). Bacterial strains of the genera Lactobacillus, Lactococcus, Leuconostoc, Streptococcus and Propionibacterium were analyzed since they deliver health benefits and are routinely used in the food processing industry. The correctness of bacterial identification by ANNs and FTIR was evaluated at two stages. At first stage, ANNs were tested based on the spectra of 66 reference bacterial strains. At second stage, the evaluation involved 286 spectra of bacterial strains isolated from food products, deposited in our laboratory collection, and identified by genus-specific PCR. ANNs were developed based on the spectra and their first derivatives. The most satisfactory results were reported for the probabilistic neural network, which was built using a combination of W5W4W3 spectral ranges. This network correctly identified the genus of 95 % of the lactic acid bacteria and propionic acid bacteria strains analyzed.
  • References (33)
    33 references, page 1 of 4

    1. D. Naumann, H. Labischinski, P. Giesbrecht: The Characterization of Microorganisms by Fourier-Transform Infrared Spectroscopy (FT-IR). In: Modern Techniques for Rapid Microbiological Analysis, W.H. Nelson (Ed.), VCH Publishers, New York, NY, USA (1991) pp. 43−96.

    2. J. Samelis, A. Bleicher, C. Delbès-Paus, A. Kakouri, K. Neuhaus, M.C. Montel, FTIR-based polyphasic identification of lactic acid bacteria isolated from traditional Greek Graviera cheese, Food Microbiol. 28 (2011) 76-83.

    3. C. Amiel, L. Mariey, M.C. Curk−Daubié, P. Pichon, J. Travert, Potentiality of fourier transform infrared spectroscopy (FTIR) for discrimination and identification of dairy lactic acid bacteria, Lait, 80 (2000) 445-459.

    4. C. Amiel, L. Mariey, C. Denis, P. Pichon, J. Travert, FTIR spectroscopy and taxonomic purpose: Contribution to the classification of lactic acid bacteria, Lait, 81 (2001) 249-255.

    5. R. Goodacre, E.M. Timmins, R. Burton, N. Kaderbhai, A.M. Woodward, D.B. Kell P.J. Rooney, Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks, Microbiology, 144 (1998) 1157−1170.

    6. C. Kirschner, K. Maquelin, P. Pina, N.A. Ngo-Thi, L.P. Choo-Smith, G.D. Sockalingum, Classification and identification of enterococci: A comparative phenotypic, genotypic, and vibrational spectroscopic study, J. Clin. Microbiol. 39 (2001) 1763-1770.

    7. K. Tintelnot, G. Haase, M. Seibold, F. Bergmann, M. Staemmler, T. Franz, D. Naumann, Evaluation of phenotypic markers for selection and identification of Candida dubliniensis, J. Clin. Microbiol. 38 (2000) 1599−1608.

    8. T. Udelhoven, D. Naumann, J. Schmitt, Development of hierarchical classification systems with artificial neural networks and FT-IR spectra for the identification of bacteria, Appl. Spectrosc. 54 (2000) 1471-1479.

    9. M. Wenning, N.R. Büchl, S. Scherer, Species and strain identification of lactic acid bacteria using FTIR spectroscopy and artificial neural networks, J. Biophotonics, 3 (2010) 493-505.

    10. R. Tadeusiwicz, P. Lula, STATISTICA Neural Network v. 4.01: Course Materials, StatSoft Inc, Warsaw, Poland (2002).

  • Metrics
    No metrics available
Share - Bookmark