ArticleStructural dynamics (Melville, N.Y.)2019
SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data.
Article in Structural dynamics (Melville, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A light-triggered Time-Resolved X-ray Solution Scattering (TR-XSS) workflow with application to protein conformational dynamics.FEBS open bio · 2026Article
- A new reference for changes in X-ray scattering from water due to temperature changes at ambient conditions.Journal of synchrotron radiation · 2026Article
- Tracking polar solvation dynamics of a photoexcited organic chromophore with ultrafast X-ray scattering.Nature communications · 2026Article
- Ultrafast Interfacial Charge Transfer Initiates Mechanical Stress and Heat Transport at the Au-TiOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Projection to extract the perpendicular component (PEPC) method for extracting kinetics from time-resolved data.Structural dynamics (Melville, N.Y.) · 2023Article
- Reaction dynamics studiedChemical science · 2022Review
- Determining the charge distribution and the direction of bond cleavage with femtosecond anisotropic x-ray liquidography.Nature communications · 2022Article
- Filming ultrafast roaming-mediated isomerization of bismuth triiodide in solution.Nature communications · 2021Article
- Estimating signal and noise of time-resolved X-ray solution scattering data at synchrotrons and XFELs.Journal of synchrotron radiation · 2020Article
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Authors and funding
5 authors.
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Abstract
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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