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Using color histograms and SPA-LDA to classify bacteria

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Abstract

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red–green–blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.

Summary of the new proposed methodology for bacteria classification by using color histograms and SPA-LDA

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References

  1. van der Merwe RG, van Helden PD, Warren RM, Sampson SL, van Pittius NCG (2014) Phage-based detection of bacterial pathogens. Analyst. doi:10.1039/C4AN00208C

    Google Scholar 

  2. Hossain Z (2014) In: Motarjemi Y (ed) Encyclopedia of food safety, 1st edn. Academic, San Diego

    Google Scholar 

  3. Codina MG, de Cueto M, Vicente D, Echevarría JE, Prats G (2011) Microbiological diagnosis of central nervous system infections. Enferm Infecc Microbiol Clin 29:127–134

    Article  Google Scholar 

  4. Guibet F, Amiel C, Cadot P, Cordevant C, Desmonts MH, Lange M, Marecat A, Travert J, Denis C, Mariey L (2003) Discrimination and classification of Enterococci by Fourier transform infrared (FT-IR) spectroscopy. Vib Spectrosc 33:133–142

    Article  CAS  Google Scholar 

  5. Schleifer KH (2009) Classification of bacteria and Archaea: past, present and future. Syst Appl Microbiol 32:533–542

    Article  Google Scholar 

  6. Xiao D, Zhao F, Lv M, Zhang H, Zhang Y, Huang H, Su P, Zhang Z, Zhang J (2012) Rapid identification of microorganisms isolated from throat swab specimens of community-acquired pneumonia patients by two MALDI-TOF MS systems. Diagn Microbiol Infect Dis 73:301–307

    Article  CAS  Google Scholar 

  7. Nakai S, Wang ZH, Dou J, Nakamura S, Ogawa M, Nakai E, Vanderstoep J (1999) Gas chromatography/principal component similarity system for detection of E. coli and S. aureus contaminating salmon and hamburger. J Agric Food Chem 47:576–583

    Article  CAS  Google Scholar 

  8. Li D, Truong TV, Bills TM, Holt BC, Van Derwerken DN, Williams JR, Acharya A, Robison RA, Tolley HD, Lee ML (2012) GC/MS method for positive detection of Bacillus anthracis endospores. Anal Chem 84:1637–1644

    Article  CAS  Google Scholar 

  9. Hantula J, Kurki A, Vuoriranta P, Bamford DH (1991) Rapid classification of bacterial strains by SDS-polyacrylamide gel electrophoresis: population dynamics of the dominant dispersed phase bacteria of activated sludge. Appl Microbiol Biotechnol 34:551–555

    CAS  Google Scholar 

  10. Veloo ACM, Erhard M, Welker M, Welling GW, Degener JE (2011) Identification of Gram-positive anaerobic cocci by MALDI-TOF mass spectrometry. Syst Appl Microbiol 34:58–62

    Article  CAS  Google Scholar 

  11. Beier BD, Quivey RG Jr, Berger AJ (2010) Identification of different bacterial species in biofilms using confocal Raman microscopy. J Biomed Opt 15:066001

    Article  Google Scholar 

  12. Oust A, Møretrø T, Kirschner C, Narvhus JA, Kohler A (2004) FT-IR spectroscopy for identification of closely related lactobacilli. J Microbiol Methods 59:149–162

    Article  CAS  Google Scholar 

  13. Preisner O, Lopes JA, Menezes JC (2008) Uncertainty assessment in FT-IR spectroscopy based bacteria classification models. Chemom Intell Lab Syst 94:33–42

    Article  CAS  Google Scholar 

  14. Marques AS, de Melo MCN, Cidral TA, de Lima KMG (2014) Feature selection strategies for identification of Staphylococcus aureus recovered in blood cultures using FT-IR spectroscopy successive projections algorithm for variable selection: a case study. J Microbiol Methods 98:26–30

    Article  CAS  Google Scholar 

  15. Giana HE, Silveira L Jr, Zângaro RA, Pacheco MTT (2003) Rapid identification of bacterial species by fluorescence spectroscopy and classification through principal components analysis. J Fluoresc 13:489–493

    Article  CAS  Google Scholar 

  16. Sohn M, Himmelsbach DS, Barton FE, Fedorka-Cray PJ (2009) Fluorescence spectroscopy for rapid detection and classification of bacterial pathogens. Appl Spectrosc 63:1251–1255

    Article  CAS  Google Scholar 

  17. Kim SW, Ban SH, Ahn CY, Oh HM, Chung H, Cho SH, Park YM, Liu JR (2006) Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis. J Plant Biol 49:271–275

    Article  CAS  Google Scholar 

  18. Dubuisson M-P, Jain AK, Jain MK (1994) Segmentation and classification of bacterial culture images. J Microbiol Methods 19:279–295

    Article  Google Scholar 

  19. Kumar S, Mittal GS (2008) Geometric and optical characteristics of five microorganisms for rapid detection using image processing. Biosyst Eng 99:1–8

    Article  Google Scholar 

  20. Huff K, Aroonnual A, Littlejohn AE, Rajwa B, Bae E, Banada PP, Patsekin V, Hirleman ED, Robinson JP, Richards GP, Bhunia AK (2012) Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate. Microb Biotechnol 5:607–620

    Article  Google Scholar 

  21. Suchwałko A, Buzalewicz I, Wieliczko A, Podbielska H (2013) Bacteria species identification by the statistical analysis of bacterial colonies Fresnel patterns. Opt Express 21:11322–11337

    Article  Google Scholar 

  22. Diniz PHGD, Dantas HV, Melo KDT, Barbosa MF, Harding DP, Nascimento ECL, Pistonesi MF, Band BSF, Araújo MCU (2012) Using a simple digital camera and SPA-LDA modeling to screen teas. Anal Methods 4:2648–2652

    Article  CAS  Google Scholar 

  23. Domínguez MA, Diniz PHGD, Di Nezio MS, Araújo MCU, Centurión ME (2014) Geographical origin classification of Argentinean honeys using a digital image-based flow-batch system. Microchem J 112:104–108

    Article  Google Scholar 

  24. Milanez KDTM, Pontes MJC (2014) Classification of edible vegetable oil using digital image and pattern recognition techniques. Microchem J 113:10–16

    Article  CAS  Google Scholar 

  25. Soares SFC, Gomes AA, Galvão Filho AR, Araújo MCU, Galvão RKH (2013) The successive projections algorithm. Trends Anal Chem 42:84–98

    Article  CAS  Google Scholar 

  26. Agência Nacional de Vigilância Sanitária (2010) Farmacopeia brasileira, Brasil

  27. Clinical and Laboratory Standards Institute (2005) CLSI document M100-S15, EUA

  28. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148

    Article  Google Scholar 

  29. Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods 5:3790–3798

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the Universidade Estadual da Paraíba for the financial support. The authors thank also Capes and CNPq Brazil scholarships and research fellowships. Paulo Henrique Gonçalves Dias Diniz also thanks to Fundação de Apoio à Pesquisa do Estado da Paraíba – FAPESQ-PB.

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Authors and Affiliations

  1. Centro de Ciência e Tecnologia, Laboratório de Química Analítica e Quimiometria (LQAQ), Universidade Estadual da Paraíba, CEP 58.429-500, Campina Grande, Paraíba, Brazil

    Valber Elias de Almeida, Gean Bezerra da Costa, David Douglas de Sousa Fernandes, Paulo Henrique Gonçalves Dias Diniz & Germano Véras

  2. Centro de Ciências Biológicas e da Saúde, Laboratório de Desenvolvimento e Ensaios de Medicamentos (LABDEM), Universidade Estadual da Paraíba, CEP 58.429-500, Campina Grande, Paraíba, Brazil

    Deysiane Brandão & Ana Claudia Dantas de Medeiros

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  1. Valber Elias de Almeida
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  2. Gean Bezerra da Costa
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  3. David Douglas de Sousa Fernandes
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  4. Paulo Henrique Gonçalves Dias Diniz
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  5. Deysiane Brandão
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  6. Ana Claudia Dantas de Medeiros
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  7. Germano Véras
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Correspondence to Germano Véras.

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de Almeida, V.E., da Costa, G.B., de Sousa Fernandes, D.D. et al. Using color histograms and SPA-LDA to classify bacteria. Anal Bioanal Chem 406, 5989–5995 (2014). https://doi.org/10.1007/s00216-014-8015-1

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  • DOI: https://doi.org/10.1007/s00216-014-8015-1

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