Onion sour skin detection using a gas sensor array and support vector machine

  • Changying Li
  • Ron Gitaitis
  • Bill Tollner
  • Paul Sumner
  • Dan MacLean
Original Paper


Onion is a major vegetable crop in the world. However, various plant diseases, including sour skin caused by Burkholderia cepacia, pose a great threat to the onion industry by reducing shelf-life and are responsible for significant postharvest losses in both conventional and controlled atmosphere (CA) storage. This study investigated a new sensing approach to detect sour skin using a gas sensor array and the support vector machine (SVM). Sour skin infected onions were put in a concentration chamber for headspace accumulation and measured three to six days after inoculation. Principal component analysis (PCA) score plots showed two distinct clusters formed by healthy and sour skin infected onions. The MANOVA statistical test further proved the hypothesis that the responses of the gas sensor array to healthy onion bulbs and sour skin infected onion bulbs are significantly different (P < 0.0001). The support vector machine was employed for the classification model development. The study was undertaken in two phases: model training and cross-validation within the training datasets and model validation using new datasets. The performances of three feature selection schemes were compared using the trained SVM model. The classification results showed that although the six-sensor scheme (with 81% sensor reduction) had a slightly lower correct classification rate in the training phase, it significantly outperformed its counterparts in the validation phase (85% vs. 69% and 67%). This result proved that effective feature selection strategy could improve the discrimination power of the gas sensor array. This study demonstrated the feasibility of using a gas sensor array coupled with the SVM for the detection of sour skin in sweet onion bulbs. Early detection of sour skin will help reduce postharvest losses and secondary spread of bacteria in storage.


Vidalia onion Sour skin Burkholderia cepacia Gas sensor array Support vector machine Feature selection 



This research was funded by the University of Georgia Research Foundation, Georgia FoodPAC, Georgia Fruit and Vegetable Grower Association, and the Vidalia Onion Committee. Authors would like to thank the technical support provided by Mr. Timothy P. Rutland.


  1. 1.
    National Onion Association, About onions: bulb onion production (2008), Cited 2008 Aug 2
  2. 2.
    Georgia Cooperative Extension County Agents, 2006 Georgia farm gate vegetable report (2006), Cited 2007 Sept 25
  3. 3.
    R. Clements, Why can’t Vidalia onions be grown in Iowa? Developing a branded agricultural product (2002), Cited 2007 Sept 25
  4. 4.
    B.W. Maw, Y.C. Hung, E.W. Tollner, Some physical properties of sweet onions, in ASAE Paper No. 896007 (ASAE, St. Joseph, MI, 1989)Google Scholar
  5. 5.
    R.D. Gitaitis, Bacteriology Results 1993 Onion Trials. in Georgia Onion Research Extension Report (Cooperative Extension Service, University of Georgia, College of Agriculture and Environmental Sciences, Tifton, Georgia, 1994)Google Scholar
  6. 6.
    G. Boyhan, R.L. Torrance, Vidalia Onions––Sweet Onion Production in Southeast Georgia, in Comprehensive Crop Reports (University of Georgia, East Georgia Extension Center, Statesboro, GA, 2002)Google Scholar
  7. 7.
    E.W. Tollner, R.R. Gitaitis, K.W. Seebold, B.W. Maw, Experiences with a food product X-ray inspection system for classifying onions. Appl. Eng. Agric. 21(5), 907–912 (2005)Google Scholar
  8. 8.
    J.E. Simon, A. Hetzroni, B. Bordelon, G.E. Miles, D.J. Charles, Electronic sensing of aromatic volatiles for quality sorting of blueberries. J. Food Sci. 61(5), 967–969 (1996)CrossRefGoogle Scholar
  9. 9.
    B. Prithiviraj, A. Vikram, A.C. Kushalappa, V. Yaylayan, Volatile metabolite profiling for the discrimination of onion bulbs infected by Erwinia carotovora ssp. carotovora, Fusarium oxysporum and Botrytis allii. Eur. J. Plant Pathol. 110, 371–377 (2004)CrossRefGoogle Scholar
  10. 10.
    J.W. Gardner, P.N. Bartlett, A brief history of electronic noses. Sens. Actuator B: Chem. 18(1–3), 210–211 (1994)CrossRefGoogle Scholar
  11. 11.
    C. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, New York, 1995), p. 482Google Scholar
  12. 12.
    V. Vapnik, The Nature of Statistical Learning Theory, 2nd edn. (Springer-Verlag, New York, 1995), p. 314Google Scholar
  13. 13.
    C. Distante, N. Ancona, P. Siciliano, Support vector machines for olfactory signals recognition. Sens. Actuator B: Chem. 88(1), 30 (2003)CrossRefGoogle Scholar
  14. 14.
    R.F. Machado, D. Laskowski, O. Deffenderfer, T. Burch, S. Zheng, P.J. Mazzone, T. Mekhail, C. Jennings, J.K. Stoller, J. Pyle, J. Duncan, R.A. Dweik, S.C. Erzurum, Detection of lung cancer by sensor array analyses of exhaled breath. Am. J. Respir. Crit. Care Med. 171, 1286–1291 (2005)CrossRefGoogle Scholar
  15. 15.
    E. Llobet, E.L. Hines, J.W. Gardner, S. Franco, Non-destructive banana ripeness determination using a neural network-based electronic nose. Meas. Sci Technol. 10(6), 538–548 (1999)CrossRefGoogle Scholar
  16. 16.
    S. Oshita, K. Shima, T. Haruta, Y. Seo, Y. Kawagoe, S. Nakayama, H. Takahara, Discrimination of odors emanating from ‘La France’ pear by semi-conducting polymer sensors. Comput. Electron. Agric. 26(2), 209 (2000)CrossRefGoogle Scholar
  17. 17.
    J. Brezmes, E. Llobet, X. Vilanova, J. Orts, G. Saiz, X. Correig, Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with Pink Lady apples. Sens. Actuator B: Chem. 80(1), 41 (2001)CrossRefGoogle Scholar
  18. 18.
    S. Balasubramanian, S. Panigrahi, C.M. Logue, M. Marchello, C. Doetkott, H. Gu, J. Sherwood, L. Nolan, Spoilage identification of beef using an electronic nose system. Trans. ASAE 47(5), 1625–1633 (2004)Google Scholar
  19. 19.
    C. Li, P. Heinemann, J. Irudayaraj, Detection of apple defects using an electronic nose and zNose. Trans. ASABE 50(4), 1417–1425 (2007)Google Scholar
  20. 20.
    C. Li, P. Heinemann, A comparative study of three evolutionary algorithms for a surface acoustic wave sensor wavelength selection. Sens. Actuator B: Chem. 125(1), 311–320 (2007)CrossRefGoogle Scholar
  21. 21.
    C. Li, P. Heinemann, R. Sherry, Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sens. Actuator B: Chem. 125(1), 301–310 (2007)CrossRefGoogle Scholar
  22. 22.
    A. Jonsson, F. Winquist, J. Schnurer, H. Sundgren, I. Lundstrom, Electronic nose for microbial quality classification of grains. Int. J. Food Microbiol. 35(2), 187 (1997)CrossRefGoogle Scholar
  23. 23.
    L. Abbey, J. Aked, D.C. Joyce, Discrimination amongst Alliums using an electronic nose. Annal. Appl. Biol. 139, 337–342 (2001)CrossRefGoogle Scholar
  24. 24.
    L. Abbey, D.C. Joyce, J. Aked, B. Smith, Evaluation of eight spring onion genotypes, sulphur nutrition and soil-type effects with an electronic nose. J. Hortic. Sci. Biotechnol. 80(3), 375–381 (2005)Google Scholar
  25. 25.
    C.-W. Hsu, C.-C. Chang, C.-J. Lin, A Practical Guide to Support Vector Classification (Department of Computer Science, National Taiwan University, Taiwan, 2007)Google Scholar
  26. 26.
    T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner, Handbook of Machine Olfaction-Electronic Nose Technology (Wiley-VCR, Germany, 2003)Google Scholar
  27. 27.
    B.P.J. de Lacy Costello, P. Evans, R.J. Ewen, H.E. Gunson, N.M. Ratcliffe, P.T.N. Spencer-Phillips, Identification of volatiles generated by potato tubers (Solanum tuberosum cv: Maris Piper) infected by Erwinia carotovora, Bacillus polymyxa and Arthrobacter sp. Plant. Pathol. 48(3), 345–351 (1999)CrossRefGoogle Scholar
  28. 28.
    G. Keshri, P. Voysey, N. Magan, Early detection of spoilage moulds in bread using volatile production patterns and quantitative enzyme assays. J. Appl. Microbiol. 92(1), 165–172 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Changying Li
    • 1
  • Ron Gitaitis
    • 2
  • Bill Tollner
    • 3
  • Paul Sumner
    • 1
  • Dan MacLean
    • 4
  1. 1.Department of Biological and Agricultural EngineeringUniversity of GeorgiaTiftonUSA
  2. 2.Department of Plant PathologyUniversity of GeorgiaTiftonUSA
  3. 3.Department of Biological and Agricultural EngineeringUniversity of GeorgiaAthensUSA
  4. 4.Department of HorticultureUniversity of GeorgiaTiftonUSA

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