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首页> 外文期刊>Journal of Fluorescence >Decomposition of complex fluorescence spectra containing components with close emission maxima positions and similar quantum yields. Application to fluorescence spectra of proteins
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Decomposition of complex fluorescence spectra containing components with close emission maxima positions and similar quantum yields. Application to fluorescence spectra of proteins

机译:分解复杂荧光光谱,其中包含具有接近最大发射位置和相似量子产率的组分。在蛋白质的荧光光谱中的应用

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摘要

Despite of widely application of multivariate analysis in chemometrics, problem of resolving closely positioned components in the fluorescence spectra remained unsolved, thus limiting the usage of fluorescence spectroscopy in analytical purpose. In this paper we have described a novel procedure, adapted especially for the analysis of complex fluorescence spectra with multiple, closely positioned components' maxima. The method was first tested on the simulated spectra and then applied on the spectra of proteins whose fluorophores have similar properties of both the excitation and the emission spectra. In this paper, simple but efficient modification of the method was applied. Instead of analyzing full size emission matrix (12 spectra), 9 spectra wide windows were analyzed, and 4 factors (greatest possible number of factors with physical meaning both for actin and simulated spectra) were extracted in each pass. Obtained factor scores were grouped by using the K-means algorithm. Groups of factor scores obtained from K-means algorithm were passed through the one more factor analysis (FA) in order to find one factor that represents each group. Our approach provides resolution of extremely closed spectral components, which is a vital data for protein conformation analysis based on fluorescence spectroscopy.
机译:尽管多元分析在化学计量学中得到了广泛应用,但解决荧光光谱中位置紧密的组分的问题仍未解决,因此限制了荧光光谱在分析中的用途。在本文中,我们描述了一种新颖的方法,该方法特别适用于分析具有多个紧密定位的组件最大值的复杂荧光光谱。该方法首先在模拟光谱上进行测试,然后应用于荧光团在激发光谱和发射光谱上具有相似特性的蛋白质的光谱。在本文中,对该方法进行了简单而有效的修改。代替分析完整尺寸的发射矩阵(12个光谱),而是分析9个光谱宽的窗口,并在每个过程中提取4个因子(对肌动蛋白和模拟光谱均具有物理意义的最大因子数)。通过使用K-means算法将获得的因子得分分组。从K均值算法获得的因子得分组经过一个以上的因子分析(FA),以便找到代表每个组的一个因子。我们的方法提供了非常封闭的光谱成分的分辨率,这对于基于荧光光谱的蛋白质构象分析是至关重要的数据。

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