Document Type
Article
Publication Date
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.
Repository Citation
Aguilera, T.,
Lozano, J.,
Paredes, J. A.,
Alvarez, F. J.,
& Suárez, J. I.
(2012). Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction. Sensors, 12 (6), 8055-8072.
https://corescholar.libraries.wright.edu/ncbp/530
DOI
103390/s120608055
Included in
Medical Cell Biology Commons, Medical Neurobiology Commons, Medical Physiology Commons, Neurosciences Commons, Physiological Processes Commons
Comments
© 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/).