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Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination

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Abstract

Rapid and reliable discrimination among clinically relevant pathogenic organisms is a crucial task in microbiology. Microorganism resistance to antimicrobial agents increases prevalence of infections. The possibility of Fourier transform infrared (FT-IR) spectroscopy to assess the overall molecular composition of microbial cells in a non-destructive manner is reflected in the specific spectral fingerprints highly typical for different microorganisms. With the objective of using FT-IR spectroscopy for discrimination between diverse microbial species and strains on a routine basis, a wide range of chemometrics techniques need to be applied. Still a major issue in using FT-IR for successful bacteria characterization is the method for spectra pre-processing. We analyzed different spectra pre-processing methods and their impact on the reduction of spectral variability and on the increase of robustness of chemometrics models. Different types of the Enterococcus faecium bacterial strain were classified according to chromosomal DNA restriction patterns produced by pulsed-field gel electrophoresis (PFGE). Samples were collected from human patients. Collected FT-IR spectra were used to verify if the same classification was obtained. In order to further optimize bacteria classification we investigated whether a selected combination of the most discriminative spectral regions could improve results. Two different variable selection methods (genetic algorithms (GAs) and bootstrapping) were investigated and their relative merit for bacteria classification is reported by comparing with results obtained using the entire spectra. Discriminant partial least-squares (Di-PLS) models based on corrected spectra showed improved predictive ability up to 40% when compared to equivalent models using the entire spectral range. The uncertainty in estimating scores was reduced by about 50% when compared to models with all wavelengths. Spectral ranges with relevant chemical information for Enterococcus faecium bacteria discrimination were outlined.

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References

  1. Ausubel F, Brent M, Kingston R, Moore D, Seidman J, Smith J, Struhl K (eds) (1989) Current protocols in molecular biology. Wiley, New York, NY

    Google Scholar 

  2. Brown M, Dunn W, Ellis D, Goodacre R, Handl J, Knowles J, O’Hagan S, Spasic I, Kell D (2005) Metabolomics 1(1):39–51

    Article  CAS  Google Scholar 

  3. Neu HC (1992) Science 257:1064–1073

    Article  CAS  Google Scholar 

  4. Maquelin K, Kirschner C, Choo-Smith LP, Van den Braak N, Endtz H, Naumann D, Puppels GJ (2002) J Microbiol Methods 51:255–271

    Article  CAS  Google Scholar 

  5. Naumann D, Helm D, Labischinski H (1991) Nature 351:81–82

    Article  CAS  Google Scholar 

  6. Naumann D, Keller S, Helm D, Schultz Ch, Schrader B (1995) J Mol Struct 347:399–406

    Article  CAS  Google Scholar 

  7. Naumann D (2000) Infrared spectroscopy in microbiology. In: Meyers RA (ed) Encyclopedia of analytical chemistry. Wiley, Chichester, pp 102–131

    Google Scholar 

  8. Riddle JW, Kabler PW, Kenner BA, Bordner RH, Rockwood SW, Stevenson HJR (1956) J Bacteriol 72:593–603

    CAS  Google Scholar 

  9. Norris KP (1959) J Hyg 57:326–345

    Article  CAS  Google Scholar 

  10. Winder CL, Carr E, Goodacre R, Seviour R (2004) J Appl Microbiol 96:328–339

    Article  CAS  Google Scholar 

  11. Sandt C, Sockalingum GD, Aubert D, Lepan H, Lepouse C, Jaussaud M, Leon A, Pinon JM, Manfait M, Toubas D (2003) J Clin Microbiol Mar:954–959

  12. Consuelo López-Díez E, Goodacre R (2004) Anal Chem 76:585–591

    Article  CAS  Google Scholar 

  13. Wu Q, Hamilton T, Nelson WH, Elliott S, Sperry JF, Wu M (2001) Anal Chem 73:3432–3440

    Article  CAS  Google Scholar 

  14. Essendoubia M, Toubasb D, Bouzaggoua M, Pinonb JM, Manfaita M, Sockalingum GD (2005) Biochim Biophys Acta 1724:239–247

    Google Scholar 

  15. Rosch P, Harz M, Peschke K-D, Ronneberger O, Burkhardt H, Popp J (2006) Biopolymers 82:312–316

    Article  CAS  Google Scholar 

  16. Xie C, Mace J, Dinno MA, Li YQ, Tang W, Newton RJ, Gemperline PJ (2005) Anal Chem 77:4390–4397

    Article  CAS  Google Scholar 

  17. Maquelin K, Kirschner C, Choo-Smith L-P, Ngo-Thi NA,van Vreeswijk T, Stammler M, Endtz HP, Bruining HA, Naumann D, Puppels GJ (2003) J Clin Microbiol Jan:324–329

  18. Goodacre R, Adaoin E, Timmins M, Burton R, Kaderbhai N, Woodward AM, Kell DB, Rooney PJ (1998) Microbiology 144:1157–1170

    Article  CAS  Google Scholar 

  19. Mobley P, Kowalski B, Workman J, Bro R (1996) Appl Spectrosc Rev 31:347–368

    CAS  Google Scholar 

  20. Udelhoven T, Naumann D, Schmitt J (2000) Appl Spectrosc 54(10):1471–1479(9)

    Article  CAS  Google Scholar 

  21. Bakeev KA, Kurtyka B (2005) J Near Infrared Spectrosc 13(6):339–348

    CAS  Google Scholar 

  22. Martens H, Nielsen JP, Engelsen SB (2003) Anal Chem 75(3):394–404

    Article  CAS  Google Scholar 

  23. Jarvis RM, Goodacre R (2005) Bioinformatics 21(7):860–868

    Article  CAS  Google Scholar 

  24. Olivieri AC, Goicoechea HC (2003) J Chemom 17(6):338–345

    Article  CAS  Google Scholar 

  25. Geladi P, Kowalsky B (1986) Analytica Chimica Acta 185:1–17

    Article  CAS  Google Scholar 

  26. Alsberg B, Kell D, Goodacre R (1998) Anal Chem 70:4126–4133

    Article  CAS  Google Scholar 

  27. Martens H, Naes T (eds) (1992) Multivariate calibration. Wiley, UK

    Google Scholar 

  28. Mitchell M (1999) An introduction to genetic algorithms. The MIT Press

  29. Michalewicz Z (ed) (1997) Genetic algorithms+data structures=evolution programs. Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  30. Lazraq A, Cléroux R, Gauchi J-P (2003) Chemometr Intell Lab Syst 66:117–126

    CAS  Google Scholar 

  31. Graves L, Swaminathan B (2001) Int J Food Microbiol 65:55–62

    Article  CAS  Google Scholar 

  32. Brereton RG (ed) (2003) Chemometrics. Data analysis for the laboratory and chemical plant. Wiley

  33. Næs T, Isaksson T, Fearn T, Davies T (eds) (2002) A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, p 344

    Google Scholar 

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Acknowledgements

O.P. gratefully acknowledges financial support from the Portuguese Foundation for Science and Technology (research grant no. SFRH/BD/15218/2004).

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Authors and Affiliations

  1. Centre for Biological and Chemical Engineering, IST, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Ornella Preisner & José C. Menezes

  2. REQUIMTE, Department of Chemistry-Physics, Faculty of Pharmacy, University of Porto, Rua Anibal Cunha 164, 4050-047, Porto, Portugal

    João Almeida Lopes

  3. Enterobactereaceae Unity, Bacteriology Center, National Health Institute Ricardo Jorge, Avenida Padre Cruz, 1649–016, Lisbon, Portugal

    Raquel Guiomar & Jorge Machado

Authors
  1. Ornella Preisner
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  2. João Almeida Lopes
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  3. Raquel Guiomar
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  4. Jorge Machado
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  5. José C. Menezes
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Correspondence to Ornella Preisner.

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Preisner, O., Lopes, J.A., Guiomar, R. et al. Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination. Anal Bioanal Chem 387, 1739–1748 (2007). https://doi.org/10.1007/s00216-006-0851-1

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  • DOI: https://doi.org/10.1007/s00216-006-0851-1

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