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Differentiation of toxigenic fungi using hyperspectral imagery

  • Haibo YaoEmail author
  • Zuzana Hruska
  • Russell Kincaid
  • Robert L. Brown
  • Thomas E. Cleveland
Original Paper

Abstract

Some pathogenic fungi, Aspergillus flavus for example, produce mycotoxins that can contaminate grain products including wheat and corn. The contaminated grain poses a threat to the health of both humans and animals. Therefore, from the perspective of food safety and protection, it is important to detect and identify the different toxin-producing fungi encountered in food production. Earlier studies examined various spectral-based, non-destructive methods for the detection of fungi and toxins. The present report focused on the feasibility of using spectral image data for fungal species classification. A tabletop hyperspectral imaging system, VNIR-100E, was used for spectral and spatial data acquisition. A total of five fungal species were selected for a two-part experiment: Penicillium chrysogenum, Fusarium moniliforme (verticillioides), Aspergillus parasiticus, Trichoderma viride, and Aspergillus flavus. All fungal isolates were cultured on media under laboratory conditions and were imaged on day 5 of growth. The objective of the study was to use visible near-infrared hyperspectral imagery to differentiate fungal species. Results indicate that all five fungi are highly separable with classification accuracy of 97.7%. In addition, all five fungi could be classified by using only three narrow bands (bandwidth = 2.43 nm) centered at 743 nm, 458 nm, and 541 nm.

Keywords

Hyperspectral image Fungi Classification 

Notes

Acknowledgements

This research was carried out in accordance with the USDA Specific Cooperative Agreement No. 58-6435-3-0121. Funding for the current project was provided through the USDA Specific Cooperative Agreement No. 58-6435-3-0121. The authors gratefully acknowledge the expert assistance of Mr. David Ambrogio and Ms. Ahline Angeles with specimen preparation, and Mr. Kevin DiCrispino with data processing.

References

  1. 1.
    C.P. Wild, P.C. Turner, Mutagenesis 17, 471 (2002)CrossRefGoogle Scholar
  2. 2.
    H.P. van Egmond, M.A. Jonker, Worldwide regulations for mycotoxins in food and feed in 2003. Published by FAO as Food and Nutrition Paper No. 81 (2005)Google Scholar
  3. 3.
    R.L. Brown et al., J. Food Prot. 64, 396 (2001)Google Scholar
  4. 4.
    E. Collison et al., J. Hyg. Epidemiol. Microbiol. Immunol. 36, 338 (1992)Google Scholar
  5. 5.
    J. Duvick, Environ. Health Perspect. 109, 337 (2001)CrossRefGoogle Scholar
  6. 6.
    A.E. Desjardins, G. Manandhar et al., Appl. Environ. Microbiol. 66, 1020 (2000)Google Scholar
  7. 7.
    P.E. Nelson et al., Clin. Microbiol. Rev. 7, 479 (1994)Google Scholar
  8. 8.
    N.H. Aziz, A.A.M. Shahin, J. Food Prod. 17, 113 (1997)Google Scholar
  9. 9.
    B.R. Malone et al., J. AOAC Int. 83, 95 (2000)Google Scholar
  10. 10.
    H.J. Zeringue Jr. et al., Phytochemistry 52, 1391 (1999)CrossRefGoogle Scholar
  11. 11.
    I. Zachova et al., Folia Microbiol. 48, 817 (2003)CrossRefGoogle Scholar
  12. 12.
    T.K. Dutta, P. Das, Mycopathologia 151, 29 (2001)CrossRefGoogle Scholar
  13. 13.
    J. Jaimez Ordaz et al., Int. J. Food Microbiol. 83, 219 (2003)Google Scholar
  14. 14.
    C.A. Fente et al., Applied Environ. Microbiol. 67, 4858 (2001)Google Scholar
  15. 15.
    G.S. Birth, R.M. Johnson, J. Assoc. Off. Anal. Chem. 53, 931 (1970)Google Scholar
  16. 16.
    K. Yabe et al., Appl. Environ. Microbiol. 53, 230 (1987)Google Scholar
  17. 17.
    D.T. Wicklow, Plant Dis. 83, 1146 (1999)CrossRefGoogle Scholar
  18. 18.
    J. Aja-Nwachukwu, S.O. Emejuaiwe, Environ. Toxicol. Water Qual. An. Int. J. 9, 17 (1997)CrossRefGoogle Scholar
  19. 19.
    S.H. Gordon et al., Int. J. Food Microbiol. 35, 179 (1997)CrossRefGoogle Scholar
  20. 20.
    S.H. Gordon et al., J. Food Prot. 61, 221 (1998)Google Scholar
  21. 21.
    S.H. Gordon et al., J. Agric. Food. Chem. 47, 5267 (1999)CrossRefGoogle Scholar
  22. 22.
    R.V. Greene et al., J. Agric. Food Chem. 40, 1144 (1992)CrossRefGoogle Scholar
  23. 23.
    P. Yu, J. Struct. Biol. 150, 81 (2005)CrossRefGoogle Scholar
  24. 24.
    M.S. Kim et al., Trans. ASAE 44, 721 (2001)Google Scholar
  25. 25.
    R. Lu, Trans. ASAE 46, 523 (2003)Google Scholar
  26. 26.
    K.C. Lawrence et al., ASAE Paper No. 013129 (2001)Google Scholar
  27. 27.
    B. Park et al., Trans. ASAE 45, 2017 (2002)Google Scholar
  28. 28.
    I. Kim et al., Trans. ASAE 47, 1785 (2004)Google Scholar
  29. 29.
    B.A. Weinstock, Appl. Spectroc. 60, 9 (2006)CrossRefGoogle Scholar
  30. 30.
    H. Yao et al., in Proceedings of SPIE, Optic East, a SPIE Conference on Nondestructive Sensing for Food Safety, Quality, and Natural Resources (2004)Google Scholar
  31. 31.
    C. Mao, U.S. patent 6,166,373 (2000)Google Scholar
  32. 32.
    E.N. VI, ENVI Users Manual (Research Systems Inc, Boulder, CO, 2000)Google Scholar
  33. 33.
    G.F. Hughes, Trans. IEEE 14, 55 (1968)Google Scholar
  34. 34.
    SAS Users Manual, SAS Institute, Inc.Cary, NC (2003)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Haibo Yao
    • 1
    Email author
  • Zuzana Hruska
    • 1
  • Russell Kincaid
    • 1
  • Robert L. Brown
    • 2
  • Thomas E. Cleveland
    • 2
  1. 1.Institute for Technology DevelopmentStennis Space CenterUSA
  2. 2.SRRC, ARS, USDANew OrleansUSA

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