Spectram:用于动态光谱数据的过渡模型引导反卷积的MATLAB®和GNU倍频工具箱

Q1 Social Sciences Journal of Open Research Software Pub Date : 2020-06-09 DOI:10.5334/jors.323
M. Rabe
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引用次数: 1

摘要

光谱数据依赖于实验可控变量,为研究人员提供了丰富的信息。然而,具有重叠峰和多次跃迁的复杂光谱使其解释变得复杂,往往无法提取出包含的全部信息。本文介绍了MATLAB®和GNU Octave的Spectram工具箱,该工具箱的开发是为了通过基于奇异值分解(SVD)和转换模型耦合重组的方法来分析这些数据。该方法使用用户定义的转换模型,该模型依赖于控制变量并且通常是已知的,或者使用转换的经验描述(通常可以猜测)来反卷积这些数据。结果是与转换和模型参数相关的谱分量。两者都可以直接用物理意义来解释。Spectram可以应用于任何所需的光谱技术,并在选择应用模型时给予充分的自由,使其高度可重复使用。
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Spectram: A MATLAB® and GNU Octave Toolbox for Transition Model Guided Deconvolution of Dynamic Spectroscopic Data
Spectroscopic data, depending on an experimentally controllable variable, contains a wealth of information for researchers. However, complex spectra with overlapping peaks and multiple transitions complicate its straightforward interpretation and often the full contained information cannot be extracted. Here, the Spectram toolbox for MATLAB® and GNU Octave is described which was developed to analyse such data by a method based on singular value decomposition (SVD) and transition model coupled recombination. The method employs user-defined transition models, which depend on the control variable and are often known, or empirical descriptions of the transitions, which often can be guessed, to deconvolute such data. The outcome are the spectral components associated to the transitions and the model parameters. Both can be directly interpreted in terms of their physical meaning. Spectram can be applied to any desired spectroscopic technique and gives full freedom in the choice of the applied models, making it highly reusable.
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来源期刊
Journal of Open Research Software
Journal of Open Research Software Social Sciences-Library and Information Sciences
CiteScore
6.50
自引率
0.00%
发文量
7
审稿时长
21 weeks
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