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SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data

Analysis of time-resolved data typically involves discriminating noise against the signal and extracting time-independent components and their time-dependent contributions. Singular value decomposition (SVD) serves this purpose well, but the extracted time-independent components are not necessarily...

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Detalles Bibliográficos
Autores principales: Ki, H., Lee, Y., Choi, E. H., Lee, S., Ihee, H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Crystallographic Association 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435371/
https://www.ncbi.nlm.nih.gov/pubmed/30931347
http://dx.doi.org/10.1063/1.5085864
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author Ki, H.
Lee, Y.
Choi, E. H.
Lee, S.
Ihee, H.
author_facet Ki, H.
Lee, Y.
Choi, E. H.
Lee, S.
Ihee, H.
author_sort Ki, H.
collection PubMed
description Analysis of time-resolved data typically involves discriminating noise against the signal and extracting time-independent components and their time-dependent contributions. Singular value decomposition (SVD) serves this purpose well, but the extracted time-independent components are not necessarily the physically meaningful spectra directly representing the actual dynamic or kinetic processes but rather a mathematically orthogonal set necessary for constituting the physically meaningful spectra. Converting the orthogonal components into physically meaningful spectra requires subsequent posterior analyses such as linear combination fitting (LCF) and global fitting (GF), which takes advantage of prior knowledge about the data but requires that all components are known or satisfactory components are guessed. Since in general not all components are known, they have to be guessed and tested via trial and error. In this work, we introduce a method, which is termed SVD-aided Non-Orthogonal Decomposition (SANOD), to circumvent trial and error. The key concept of SANOD is to combine the orthogonal components from SVD with the known prior knowledge to fill in the gap of the unknown signal components and to use them for LCF. We demonstrate the usefulness of SANOD via applications to a variety of cases.
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spelling pubmed-64353712019-03-29 SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data Ki, H. Lee, Y. Choi, E. H. Lee, S. Ihee, H. Struct Dyn ARTICLES Analysis of time-resolved data typically involves discriminating noise against the signal and extracting time-independent components and their time-dependent contributions. Singular value decomposition (SVD) serves this purpose well, but the extracted time-independent components are not necessarily the physically meaningful spectra directly representing the actual dynamic or kinetic processes but rather a mathematically orthogonal set necessary for constituting the physically meaningful spectra. Converting the orthogonal components into physically meaningful spectra requires subsequent posterior analyses such as linear combination fitting (LCF) and global fitting (GF), which takes advantage of prior knowledge about the data but requires that all components are known or satisfactory components are guessed. Since in general not all components are known, they have to be guessed and tested via trial and error. In this work, we introduce a method, which is termed SVD-aided Non-Orthogonal Decomposition (SANOD), to circumvent trial and error. The key concept of SANOD is to combine the orthogonal components from SVD with the known prior knowledge to fill in the gap of the unknown signal components and to use them for LCF. We demonstrate the usefulness of SANOD via applications to a variety of cases. American Crystallographic Association 2019-03-26 /pmc/articles/PMC6435371/ /pubmed/30931347 http://dx.doi.org/10.1063/1.5085864 Text en © 2019 Author(s). 2329-7778/2019/6(2)/024303/10 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle ARTICLES
Ki, H.
Lee, Y.
Choi, E. H.
Lee, S.
Ihee, H.
SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title_full SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title_fullStr SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title_full_unstemmed SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title_short SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
title_sort svd-aided non-orthogonal decomposition (sanod) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
topic ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435371/
https://www.ncbi.nlm.nih.gov/pubmed/30931347
http://dx.doi.org/10.1063/1.5085864
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