Please use this identifier to cite or link to this item: https://doi.org/10.1002/jcc.10080
Title: Pure component spectral reconstruction from mixture data using SVD, global entropy minimization, and simulated annealing. Numerical investigations of admissible objective functions using a synthetic 7-species data set
Authors: Widjaja, E.
Garland, M. 
Keywords: Entropy minimization
Global optimization
Pure component spectral reconstruction
Simulated annealing
Singular value decomposition
Issue Date: 15-Jul-2002
Source: Widjaja, E., Garland, M. (2002-07-15). Pure component spectral reconstruction from mixture data using SVD, global entropy minimization, and simulated annealing. Numerical investigations of admissible objective functions using a synthetic 7-species data set. Journal of Computational Chemistry 23 (9) : 911-919. ScholarBank@NUS Repository. https://doi.org/10.1002/jcc.10080
Abstract: A combination of singular value decomposition, entropy minimization, and simulated annealing was applied to a synthetic 7-species spectroscopic data set with added white noise. The pure spectra were highly overlapping. Global minima for selected objective functions were obtained for the transformation of the first seven right singular vectors. Simple Shannon type entropy functions were used in the objective functions and realistic physical constraints were imposed in the penalties. It was found that good first approximations for the pure component spectra could be obtained without the use of any a priori information. The present method out performed the two widely used routines, namely Simplisma and OPA-ALS, as well as IPCA. These results indicate that a combination of SVD, entropy minimization, and simulated annealing is a potentially powerful tool for spectral reconstructions from large real experimental systems.
Source Title: Journal of Computational Chemistry
URI: http://scholarbank.nus.edu.sg/handle/10635/66778
ISSN: 01928651
DOI: 10.1002/jcc.10080
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