Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

Authors:
Bruno Baruque;Emilio Corchado;Hujun Yin;Jordi Rovira;Javier González
Affiliations:
Department of Civil Engineering. University of Burgos, Spain.;Department of Civil Engineering. University of Burgos, Spain.;School of Electrical and Electronic Engineering. University of Manchester, UK;Department of Biotechnology and Food Science, University of Burgos, Spain;Department of Biotechnology and Food Science, University of Burgos, Spain
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

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.