Skip to main content
SpringerLink
Log in

Hyperspectral fluorescence imaging using violet LEDs as excitation sources for fecal matter contaminate identification on spinach leaves

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spinach leaves (Spinacia oleracea) was evaluated. Violet fluorescence excitation was provided at 405 nm and light emission was recorded from 464 to 800 nm. Partial least square discriminant analysis and wavelength ratio methods were compared for detection accuracy for fecal contamination. Fluorescence emission profiles of spinach leaves were monitored over a 27 days storage period; peak emission blue-shifts were observed over the storage period accompanying a color change from green to green–yellow–brown hue. The PLSDA model developed correctly detected fecal contamination on 100 % of relatively fresh green spinach leaves used in this investigation, which also had soil contamination. The PLSDA model had 19 % false positives for non-fresh post storage leaves. A wavelength ratio technique using four wavebands (680, 688, 703 and 723 nm) was successful in identifying 100 % of fecal contaminates on both fresh and non-fresh leaves. An on-line fluorescence imaging inspection system for fecal contaminant detection has potential to allow fresh produce producers to reduce foodborne illnesses and prevent against the associated economic losses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. CDC, Centers for Disease Control and Prevention Estimates of Foodborne Illness in the United States (2011) http://www.cdc.gov/foodborneburden/surveillance-systems.html. Assessed 9 April 2015

  2. M.T. Brandl, Plant lesions promote the rapid multiplication of Escherichia coli O157:H7 on postharvest lettuce. Appl. Environ. Microbiol. 74(17), 5285–5289 (2008)

    Article  CAS  Google Scholar 

  3. USFDA, Guidance for Industry, Guide to Minimize Microbial Food Safety Hazards of Leafy Greens. (United States Department of Health and Human Services, United States Food and Drug Administration, Center for Food Safety and Applied Nutrition, Washington, DC, USA, 2009)

  4. D. Ivnitski, I. Abdel-Hamid, P. Atanasov, E. Wilkins, Biosensors for detection of pathogenic bacteria. Biosens. Bioelectron. 14(7), 599–624 (1999)

    Article  CAS  Google Scholar 

  5. M.T. Madigan, J.M. Martinko, J. Parker, Brock Biology of Microorganisms, 8th edn. (A Viacom Company, Upper Saddle River, 1997), p. 986

    Google Scholar 

  6. R.L. Buchanan, M.P. Doly, Foodborne disease significance of E. c oli O157:H7 and other enterohemorrhagic E. c oli. Food Technol. 51(10), 69–76 (1997)

    Google Scholar 

  7. P.C. Rowe, E. Orrbine, G.A. Well, Epidemiology of hemolytic-uremic syndrome in Canadian children from 1986 to 1988. J. Pediatr. 119, 218–224 (1991)

    Article  CAS  Google Scholar 

  8. A.E. Greenberg, R.R. Trussel, L.S. Clesceri, M.A.H. Franson, Standard Methods for the Examination of Water and Wastewater (American Public Health Association, Washington, 1992)

    Google Scholar 

  9. M. Tietjen, D.Y.C. Fung, Salmonella and food safety. Crit. Rev. Microbiol. 21, 53–83 (1995)

    Article  CAS  Google Scholar 

  10. M. Severgnini, P. Cremonesi, C. Consolandi, G. DeBellis, B. Castiglioni, Advances in DNA microarray technology for the detection of foodborne pathogens. Food Bioprocess Technol. 4, 936–953 (2011)

    Article  Google Scholar 

  11. O. Reichart, K. Szakmár, Á. Jozwiak, J. Felföldi, L. Baranyai, Redox potential measurement as a rapid method for microbiological testing and its validation for coliform determination. Int. J. Food Microbiol. 114, 143–148 (2007)

    Article  CAS  Google Scholar 

  12. D.I. Murdock, C.H. Brokaw, Some specific source of contamination in processing frozen concentrated orange juice. 1. Handling and preparing fruit for extraction. In: Proceedings of the Florida State Horticultural Society, 70:231–237 (1957)

  13. USFDA. Analysis and evaluation of prevention control measures for the control and reduction/elimination of microbial hazards on fresh and fresh-cut produce (2001). http://www.cfsan.fda.gov. Accessed 9 April 2015

  14. J. Xicohtencatl-Cort, E.S. Chacon, Interaction of Escherichia coli O157:H7 with leafy green produce. J. Food Prot. 72(7), 1531–1537 (2009)

    Google Scholar 

  15. S. Kang, K. Lee, J. Son, M.S. Kim, Detection of fecal contamination on leafy greens by hyperspectral imaging. Procedia Food Sci. 1, 953–959 (2011)

    Article  Google Scholar 

  16. M.S. Kim, Y.R. Chen, P.M. Mehl, Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Trans. ASAE 44(3), 721–729 (2001)

    Google Scholar 

  17. C.D. Everard, M.S. Kim, H. Lee, A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves. J. Food Eng. 143, 139–145 (2014)

    Article  CAS  Google Scholar 

  18. M.S. Kim, A.M. Lefcourt, Y.R. Chen, Y. Tao, Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion. J. Food Eng. 71, 85–91 (2005)

    Article  Google Scholar 

  19. M.S. Kim, A.M. Lefcourt, Y.R. Chen, Y. Tao, Optimal fluorescence excitation and emission bands for detection of fecal contamination. J. Food Prot. 66(7), 1198–1207 (2003)

    Google Scholar 

  20. X. Lu, H.M. Al-Qadiri, M. Lin, B.A. Rasco, Application of mid-infrared and Raman spectroscopy to the study of bacteria. Food Bioprocess Technol. 4, 919–935 (2011)

    Article  Google Scholar 

  21. A.A. Gowen, M. Taghizadeh, C.P. O’Donnell, Identification of mushrooms subjected to freeze damage using hyperspectral imaging. J. Food Eng. 93(1), 7–12 (2009)

    Article  Google Scholar 

  22. A.M. Vargas, M.S. Kim, Y. Tao, A.M. Lefcourt, Y.R. Chen, Y. Luo, Y. Song, R. Buchanan, Detection of fecal contamination on cantaloupes using hyperspectral fluorescence imagery. J. Food Sci. 70(8), 471–476 (2005)

    Article  Google Scholar 

  23. Y.R. Chen, K. Chao, M.S. Kim, Machine vision technology for agricultural applications. Comput. Electron. Agric. 36, 173–191 (2002)

    Article  Google Scholar 

  24. M.S. Kim, K. Lee, K. Chao, A.M. Lefcourt, W. Jun, D.E. Chan, Multispectral line-scan imaging system for simultaneous fluorescence and reflectance measurements of apples. Sens. Instrum. Food Qual. Saf. 2(2), 123–129 (2008)

    Article  Google Scholar 

  25. A. Walkley, I.A. Black, An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934)

    Article  CAS  Google Scholar 

  26. J. Xing, W. Saeys, J. DeBaerdemaeker, Combination of chemometric tools and image processing for bruise detection on apples. Comput. Electron. Agric. 56, 1–13 (2007)

    Article  Google Scholar 

  27. S. Roussel, V. Bellon-Maurel, J.M. Roger, P. Grenier, Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. J. Food Eng. 60, 407–419 (2003)

    Article  Google Scholar 

  28. D. Wang, F.E. Dowell, R.E. Lacey, Single wheat kernel color classification by using near-infrared reflectance spectra. Cereal Chem. 76(1), 30–33 (1999)

    Article  CAS  Google Scholar 

  29. C.D. Everard, C.P. O’Donnell, D.J. O’Callaghan, E.M. Sheehan, C.M. Delahunty, B.T. O’Kennedy, V. Howard, Prediction of sensory textural properties from rheological analysis for process cheeses varying in emulsifying salt, protein and moisture contents. J. Sci. Food Agric. 87, 641–650 (2007)

    Article  CAS  Google Scholar 

  30. M.S. Kim, J.E. McMurtrey, C.L. Mulchi, C.S.T. Daughtry, E.W. Chappelle, Y.R. Chen, Steady-state multispectral fluorescence imaging system for plant leaves. Appl. Opt. 40, 157–166 (2001)

    Article  CAS  Google Scholar 

  31. E.W. Chappelle, F.M. Wood, J.E. McMurtrey, W.W. Newcomb, Laser induced fluorescence of green plants: 1. A technique for the remote detection of plant stress and species differentiation. Appl. Opt. 23, 134–138 (1984)

    Article  CAS  Google Scholar 

  32. G. Papageorgiou, Chlorophyll fluorescence: an intrinsic probe of photosynthesis, in Bioenergetics of Photosynthesis, ed. by G. Govindjee (Academic Press, NewYork, 1975), pp. 320–371

    Google Scholar 

  33. B. Diezma, L. Lleó, J.M. Roger, A. Herrero-Langreo, L. Lunadei, M. Ruiz-Altisent, Examination of the quality of spinach leaves using hyperspectral imaging. Postharvest Biol. Technol. 85, 8–17 (2013)

    Article  Google Scholar 

  34. Z.Y. Liy, J.J. Shi, D.C. Wang, J.F. Huang, Discrimination and spectral response characteristic of stress leaves infected by rice Aphelenchoides besseyi Christie. Spectrosc. Spectr. Anal. 30, 710–714 (2010)

    Google Scholar 

  35. L. Xue, L. Yang, Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS J. Photogr. Remote Sens. 64, 97–106 (2009)

    Article  Google Scholar 

  36. C.S. Daughtry, C.L. Walthal, M.S. Kim, E.B. DeColstoun, J.E. McMurtrey, Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74(2), 229–239 (2000)

    Article  Google Scholar 

  37. G.A. Carter, W.G. Cibula, R.L. Miller, Narrow-band reflectance imagery compared with thermal imagery for early detection of plant stress. J. Plant Physiol. 148(5), 515–520 (1996)

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This publication has emanated from research conducted with the financial support of the European Union’s Seventh Framework Programme (FP7) under the Marie Curie International Outgoing Fellowships for Career Development (FP7-PEOPLE-2011-IOF).

Author information

Authors and Affiliations

  1. School of Biosystems Engineering, University College Dublin, Dublin 4, Ireland

    Colm D. Everard & Colm P. O’Donnell

  2. Environmental Microbial and Food Safety Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA

    Moon S. Kim

  3. Experiment and Research Institute, National Agricultural Products Quality Management Service, 5-3 Gimcheon innocity, Nam-myeon, Gimcheon City, Gyeongsangbuk-do Province, Republic of Korea

    Hyunjeong Cho

Authors
  1. Colm D. Everard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moon S. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyunjeong Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Colm P. O’Donnell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Colm D. Everard.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Everard, C.D., Kim, M.S., Cho, H. et al. Hyperspectral fluorescence imaging using violet LEDs as excitation sources for fecal matter contaminate identification on spinach leaves. Food Measure 10, 56–63 (2016). https://doi.org/10.1007/s11694-015-9276-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-015-9276-x

Keywords

Search

Navigation