Advertisement

Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry

  • Bruno Baruque
  • Emilio Corchado
  • Jordi Rovira
  • Javier González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

Weighted Voting Superposition (WeVoS) is a novel summarization algorithm that may be applied to the results of an ensemble of topology preserving maps in order to identify the lowest topographical error in a map and thereby, to calculate the best possible visualization of the internal structure of its datasets. It is applied in this research to the food industry field that is studying the olfactory properties of Spanish dry-cured ham. The datasets used for the analysis are taken from the readings of an electronic nose, a device that can be used to recognize the sensory smellprints of Spanish dry-cured ham samples. They are then automatically analyzed using the previously mentioned techniques, in order to detect those batches with an anomalous smell (acidity, rancidity and different type of taints).. The Weighted Voting Superposition of ensembles of Self-Organising Maps (SOMs) is used here for visualization purposes, and is compared with the simple version of the SOM. The results clearly demonstrate how the WeVoS-SOM outperforms the simple SOM method.

Keywords

Electronic Nose Sensory Assessment Odour Recognition Topographic Error Topology Preserve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)CrossRefzbMATHGoogle Scholar
  2. 2.
    Kohonen, T.: The self-organizing map. Neurocomputing 21, 1–6 (1998)CrossRefzbMATHGoogle Scholar
  3. 3.
    Monahan, R.L., Brunton, N.P., Cronin, D.A., Durcan, R.: Determination of hexanal in cooked turkey using solid phase microextraction (SPME)/GC). In: 44th International Congress of Meat Science and Technology (ICoMST), vol. 1, pp. 586–587 (1998)Google Scholar
  4. 4.
    Kohonen, T., Lehtio, P., Rovamo, J., Hyvarinen, J., Bry, K., Vainio, L.: A principle of neural associative memory. Neuroscience 2, 1065–1076 (1977)CrossRefGoogle Scholar
  5. 5.
    Kraaijveld, M.A., Mao, J., Jain, A.K.: A nonlinear projection method based on Kohonen’s topology preserving maps. IEEE Transactions, Neural Networks 6, 548–559 (1995)CrossRefGoogle Scholar
  6. 6.
    Corchado, E., Baruque, B., Yin, H.: Boosting Unsupervised Competitive Learning Ensembles. In: International Conference of Neural Network (ICANN 2007), pp. 339–348 (2007)Google Scholar
  7. 7.
    Baruque, B., Corchado, E., Yin, H.: Quality of Adaptation of Fusion ViSOM. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 728–738. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Hotelling, H.: Analysis of a Complex of Statistical Variables into Principal Components. Journal of Education Psychology 24, 417–444 (1933)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pölzlbauer, G.: Survey and Comparison of Quality Measures for Self-Organizing Maps. In: Fifth Workshop on Data Analysis (WDA 2004), pp. 67–82 (2004)Google Scholar
  10. 10.
    Kaski, S., Lagus, K.: Comparing Self-Organizing Maps. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, pp. 809–814. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bruno Baruque
    • 1
  • Emilio Corchado
    • 1
  • Jordi Rovira
    • 2
  • Javier González
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosSpain
  2. 2.Department of Biotechnology and Food ScienceUniversity of BurgosSpain

Personalised recommendations