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

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Abstract

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.

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References

  1. National Onion Association, About onions: bulb onion production (2008), http://www.onions-usa.org/about/season.asp. Cited 2008 Aug 2

  2. Georgia Cooperative Extension County Agents, 2006 Georgia farm gate vegetable report (2006), www.caed.uga.edu. Cited 2007 Sept 25

  3. R. Clements, Why can’t Vidalia onions be grown in Iowa? Developing a branded agricultural product (2002), www.matric.iastate.edu. Cited 2007 Sept 25

  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)

  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. 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)

  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. 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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  10. J.W. Gardner, P.N. Bartlett, A brief history of electronic noses. Sens. Actuator B: Chem. 18(1–3), 210–211 (1994)

    Article  Google Scholar 

  11. C. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, New York, 1995), p. 482

    Google Scholar 

  12. V. Vapnik, The Nature of Statistical Learning Theory, 2nd edn. (Springer-Verlag, New York, 1995), p. 314

  13. C. Distante, N. Ancona, P. Siciliano, Support vector machines for olfactory signals recognition. Sens. Actuator B: Chem. 88(1), 30 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  CAS  Google Scholar 

  23. L. Abbey, J. Aked, D.C. Joyce, Discrimination amongst Alliums using an electronic nose. Annal. Appl. Biol. 139, 337–342 (2001)

    Article  Google Scholar 

  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)

    CAS  Google Scholar 

  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. 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. 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)

    Article  CAS  Google Scholar 

  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)

    Article  CAS  Google Scholar 

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Acknowledgement

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.

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Authors and Affiliations

  1. Department of Biological and Agricultural Engineering, University of Georgia, P.O. Box 748, Tifton, GA, 31793, USA

    Changying Li & Paul Sumner

  2. Department of Plant Pathology, University of Georgia, Tifton, GA, 31793, USA

    Ron Gitaitis

  3. Department of Biological and Agricultural Engineering, University of Georgia, Athens, GA, 30602, USA

    Bill Tollner

  4. Department of Horticulture, University of Georgia, Tifton, GA, 31793, USA

    Dan MacLean

Authors
  1. Changying Li
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  2. Ron Gitaitis
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  5. Dan MacLean
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Correspondence to Changying Li.

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Li, C., Gitaitis, R., Tollner, B. et al. Onion sour skin detection using a gas sensor array and support vector machine. Sens. & Instrumen. Food Qual. 3, 193–202 (2009). https://doi.org/10.1007/s11694-009-9085-1

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  • DOI: https://doi.org/10.1007/s11694-009-9085-1

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