title: Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction.
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.
authors.affiliation: Sensory Systems Research Group, University of Extremadura, 06006 Badajoz, Spain. teoaguibe@unex.es
authors.forename: Teodoro
authors.initials: T
authors.lastname: Aguilera
authors.forename: Jesús
authors.initials: J
authors.lastname: Lozano
authors.forename: José A
authors.initials: JA
authors.lastname: Paredes
authors.forename: Fernando J
authors.initials: FJ
authors.lastname: Alvarez
authors.forename: José I
authors.initials: JI
authors.lastname: Suárez
mesh_terms: D015374:Biosensing Techniques; D062609:Electronic Nose; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014920:Wine / Q000032:analysis* / Q000145:classification