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Article
Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction
Sensors
  • Teodoro Aguilera
  • Jesús Lozano
  • José A. Paredes
  • Francisco J. Alvarez, Wright State University - Main Campus
  • José I. Suárez
Document Type
Article
Publication Date
1-1-2012
Abstract
The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification.
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© 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

DOI
103390/s120608055
Citation Information
Teodoro Aguilera, Jesús Lozano, José A. Paredes, Francisco J. Alvarez, et al.. "Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction" Sensors Vol. 12 Iss. 6 (2012) p. 8055 - 8072 ISSN: 1424-8220
Available at: http://works.bepress.com/francisco_alvarez/15/