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Spectrum analysis by recursively pruned extended auto‐associative neural network
Author(s) -
Linker Raphael
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.955
Subject(s) - overfitting , artificial neural network , bottleneck , principal component analysis , computer science , pattern recognition (psychology) , variable (mathematics) , absorbance , associative property , artificial intelligence , algorithm , mathematics , biological system , chemistry , chromatography , biology , embedded system , mathematical analysis , pure mathematics
A method for automatic analysis of high‐dimensional spectra is presented. The method is based on a so‐termed extended auto‐associative neural network, which is an auto‐associative (bottleneck) neural network with an additional output that corresponds to the response variable of interest. The input of the neural network consists of the absorbance at selected wavelengths. Once the model has been calibrated, the contribution of each input is estimated, the least contributing input and the corresponding output are removed (pruned), and the training procedure is repeated. This procedure leads to a compact non‐linear mapping between the absorbance at a few wavelengths and the response variable of interest. The auto‐associative architecture of the model prevents the overparametrization and overfitting that occur in straightforward mapping between spectra and variable of interest. Overparametrization and overfitting are further reduced by retaining only the inputs required for predicting the variable of interest. The method is illustrated on two case studies involving mid‐infrared absorbance spectra. The results are compared to those obtained with partial least squares (PLS) and principal component analysis (PCA) followed by a neural network. In both case studies, the proposed approach leads to models with fewer parameters and smaller prediction errors. Copyright © 2006 John Wiley & Sons, Ltd.