Article ID Journal Published Year Pages File Type
1172532 Analytica Chimica Acta 2006 6 Pages PDF
Abstract

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

Related Topics
Physical Sciences and Engineering Chemistry Analytical Chemistry
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