Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

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 Ne...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 12; no. 6; pp. 8055 - 8072
Main Authors: Aguilera, Teodoro, Lozano, Jesús, Paredes, José A., Álvarez, Fernando J., Suárez, José I.
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.06.2012
Molecular Diversity Preservation International (MDPI)
Subjects:
Artificial neural networks
Biosensing Techniques
Classification
Clustering
Electronic Nose
Electronic noses
Independent component analysis
Least squares method
Least-Squares Analysis
Neural networks
Neural Networks, Computer
Panels
partial least squares
Pattern recognition
Principal Component Analysis
Sensors
Signal Processing, Computer-Assisted
Time Factors
Wine - analysis
Wine - classification
Wines
Madrid Spain
Spain
artificial neural networks
independent component analysis
electronic nose
partial least squares
wine classification
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s120608055