Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

  • Bruno Baruque
  • Emilio Corchado
  • Hujun Yin
  • Jordi Rovira
  • Javier González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)


This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way.


topology preserving mappings ensembles electronic nose food industry 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bruno Baruque
    • 1
  • Emilio Corchado
    • 1
  • Hujun Yin
    • 2
  • Jordi Rovira
    • 3
  • Javier González
    • 3
  1. 1.Department of Civil Engineering. University of BurgosSpain
  2. 2.School of Electrical and Electronic Engineering. University of ManchesterUK
  3. 3.Department of Biotechnology and Food Science, University of BurgosSpain

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