Discrimination of wines based on 2D NMR spectra using learning vector quantization neural networks and partial least squares discriminant analysis

Analytica Chimica Acta

Volumn 558, Issue 1-2, 2006, Pages 144-149

  Masoum, Saeed ^a   Bouveresse, Delphine Jouan Rimbaud ^b   Vercauteren, Joseph ^c   Jalali Heravi, Mehdi ^a   Rutledge, Douglas Neil ^b  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^b AGROPARISTECH   (France)

^c UNIV MONTPELLIER   (France)

Author keywords

2D NMR spectra; Learning vector quantization (LVQ) neural networks; Orthogonal signal correction (OSC); Partial least squares (PLS) discriminant analysis; Principal component transform

Indexed keywords

LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; VECTOR QUANTIZATION;

2D NMR SPECTRA; LEARNING VECTOR QUANTIZATION (LVQ) NEURAL NETWORKS; ORTHOGONAL SIGNAL CORRECTION (OSC); PARTIAL LEAST SQUARES (PLS) DISCRIMINANT ANALYSIS; PRINCIPAL COMPONENT TRANSFORMS;

WINE;

PHENOL DERIVATIVE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CARBON NUCLEAR MAGNETIC RESONANCE; DISCRIMINANT ANALYSIS; FOOD ANALYSIS; MATHEMATICAL ANALYSIS; NUCLEAR MAGNETIC RESONANCE; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; REGRESSION ANALYSIS; STATISTICAL MODEL; WINE;

EID: 31044456284     PISSN: 00032670     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aca.2005.11.015     Document Type: Article

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