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首页> 外文会议>International Conference on Advances in Mass Data Analysis of Signals and Images in Medicine Biotechnology and Chemistry(MDA 2006/2007); 200707018; Leipzig(DE) >Statistical Analysis of Microspectroscopy Signals for Algae Classification and Phylogenetic Comparison
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Statistical Analysis of Microspectroscopy Signals for Algae Classification and Phylogenetic Comparison

机译：用于藻类分类和系统发育比较的显微光谱信号的统计分析

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We performed microspectroscopic evaluation of the pigment composition of the photosynthetic compartments of algae belonging to different taxonomic divisions and higher plants. In [11], a supervised Gaussian bands decompositions was performed for the pigment spectra, the algae spectrum was modelled as the linear mixture, with unknown coefficients, of the pigment spectra, and a user-guided fitting algorithm was employed. The method provided a reliable discrimination among chlorophylls a, b and c, phycobiliproteins and carotenoids. Comparative analysis of absorption spectra highlighted the evolutionary grouping of the algae into three main lineages in accordance with the most recent endosymbiotic theories. In this paper, we adopt an unsupervised statistical estimation approach to automatically perform both Gaussian bands decomposition of the pigments and algae fitting. In a fully Bayesian setting, we propose estimating both the algae mixture coefficients and the parameters of the pigment spectra decomposition, on the basis of the alga spectrum alone. As a priori information to stabilize this highly un-derdetermined problem, templates for the pigment spectra are assumed to be available, though, due to their measurements outside the protein moiety, they differ in shape from the real spectra of the pigments present in nature by unknown, slight displacements and contraction/dilatation factors. We propose a classification system subdivided into two phases. In the first, the learning phase, the parameters of the Gaussians decomposition and the shape factors are estimated. In the second phase, the classification phase, the now known real spectra of the pigments are used as a base set to fit any other spectrum of algae. The unsupervised method provided results comparable to those of the previous, supervised method.
机译：我们对属于不同分类部门和高等植物的藻类光合作用区的色素组成进行了显微光谱评估。在[11]中，对颜料光谱进行了监督的高斯谱带分解，将藻类光谱建模为颜料光谱的未知系数的线性混合物，并采用了用户指导的拟合算法。该方法对叶绿素a，b和c，藻胆蛋白和类胡萝卜素提供了可靠的区分。吸收光谱的比较分析突出了根据最新的内共生理论将藻类分为三个主要谱系的进化分组。在本文中，我们采用无监督统计估计方法来自动执行色素的高斯谱带分解和藻类拟合。在完全的贝叶斯环境中，我们建议仅基于藻类光谱来估计藻类混合系数和色素光谱分解的参数。作为稳定此高度不确定的问题的先验信息，假定可以使用颜料光谱的模板，尽管由于它们在蛋白质部分之外的测量，它们的形状与自然界中存在的颜料的真实光谱有所不同未知，轻微位移和收缩/扩张因子。我们提出了一个分为两个阶段的分类系统。在第一个学习阶段，估计高斯分解的参数和形状因子。在第二阶段，即分类阶段，现在已知的色素真实光谱被用作适合任何其他藻类光谱的基础。无监督方法提供的结果可与以前的有监督方法相比。

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