Kopriva Ivica, Jerić Ivanka
Division of Laser and Atomic Research and Development, Ruder Bosković Institute, Bijenicka cesta 54, HR-10000, Zagreb, Croatia.
J Mass Spectrom. 2009 Sep;44(9):1378-88. doi: 10.1002/jms.1627.
The paper presents sparse component analysis (SCA)-based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on l(1) norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds.
本文提出了基于稀疏分量分析（SCA）的质谱混合物盲分解方法，将其分解为纯组分，其中混合物的数量少于纯组分的数量。公开文献中发表的相关盲源分离（BSS）问题的标准解决方案要求混合物的数量大于或等于未知纯组分的数量。具体而言，我们通过实验证明了SCA仅从两种混合物中盲目提取五种纯组分质谱的能力。测试了两种SCA方法：第一种基于通过线性规划实现的l(1)范数最小化，第二种通过对纯组分谱施加稀疏约束的多层分层交替最小二乘非负矩阵分解来实现。与许多现有的盲分解方法不同，该方法不需要关于纯组分数量的先验信息。它使用鲁棒数据聚类算法从混合物中估计，同时得到纯组分浓度矩阵。所提出的方法可以作为用于质谱分析和化合物鉴定的软件包的一部分来实现。
