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Widjaja E, Garland M. 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. J Comput Chem. 2002;23(9):911-9doi: 10.1002/jcc.10080.
Widjaja, E., & Garland, M. (2002). 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), 911-9. https://doi.org/10.1002/jcc.10080
Widjaja, Effendi, and Garland, Marc. "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 vol. 23,9 (2002): 911-9. doi: https://doi.org/10.1002/jcc.10080
Widjaja E, Garland M. 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. J Comput Chem. 2002 Jul 15;23(9):911-9. doi: 10.1002/jcc.10080. PMID: 11984852.
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J Comput Chem. 2002 Jul 15;23(9):911-9. doi: 10.1002/jcc.10080.

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

Effendi Widjaja, Marc Garland

Affiliations

  1. Department of Chemical and Environmental Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 119260.

PMID: 11984852 DOI: 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.

Copyright 2002 Wiley Periodicals, Inc.

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