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

Masoum, S ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1016/j.aca.2005.11.015
  3. Publisher: 2006
  4. Abstract:
  5. The learning vector quantization (LVQ) neural network is a useful tool for pattern recognition. Based on the network weights obtained from the training set, prediction can be made for the unknown objects. In this paper, discrimination of wines based on 2D NMR spectra is performed using LVQ neural networks with orthogonal signal correction (OSC). OSC has been proposed as a data preprocessing method that removes from X information not correlated to Y. Moreover, the partial least squares discriminant analysis (PLS-DA) method has also been used to treat the same data set. It has been found that the OSC-LVQ neural networks method gives slightly better prediction results than OSC-PLS-DA © 2005 Elsevier B.V. All rights reserved
  6. Keywords:
  7. 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 ; Artificial neural network ; Carbon nuclear magnetic resonance ; Discriminant analysis ; Food analysis ; Mathematical analysis ; Nuclear magnetic resonance ; Priority journal ; Proton nuclear magnetic resonance ; Regression analysis ; Statistical model
  8. Source: Analytica Chimica Acta ; Volume 558, Issue 1-2 , 2006 , Pages 144-149 ; 00032670 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0003267005018751