Band target entropy minimization for retrieving the information of individual components from overlapping chromatographic data
Band target entropy minimization for retrieving the information of individual components from overlapping chromatographic data
复制标题
带目标熵最小化，用于从重叠色谱数据中检索各个组分的信息
DOI：
10.1016/j.chroma.2015.07.124
复制
发表时间：
2015-09-11
影响因子：
4.1
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Xia, Zhenzhen;Liu, Yan;Shao, Xueguang
频带目标熵最小化方法是一种基于非负准则和Shannon熵最小化的自建模曲线分解方法。本研究应用BTEM算法从重叠的气相色谱-质谱图(GC-MS)数据中提取单个组分的信息。该算法首先沿着保留时间将整个数据划分为多个频段。在每个频段中，使用奇异值分解(SVD)将数据分解为分数和负荷。由于纯色谱信号具有最低的Shannon熵，在非负准则下，通过优化负载量与最小Shannon熵的组合，可以构造出各组分的色谱信号。为了验证该算法的有效性，对模拟的四组分重叠GC-MS数据和18种有机磷农药混合物的实验GC-MS数据进行了研究。结果表明，从重叠信号中可以成功地提取出各组分的色谱图和质谱图。(C)2015爱思唯尔B.V.保留所有权利。
Band target entropy minimization (BTEM) is a self-modeling curve resolution (SMCR) approach relying on non-negative criterion and minimization of Shannon entropy. In this study, BTEM algorithm was applied to retrieving the information of individual components from overlapping gas chromatography-mass spectrometry (GC-MS) data. The algorithm starts with dividing the whole data into bands along the retention time. In each band, singular value decomposition (SVD) is used to decompose the data into scores and loadings. Because the pure chromatographic signal possesses the lowest Shannon entropy, the chromatographic signal of each component can be constructed by optimizing the combination of the loadings with minimal Shannon entropy under non-negative criterion. To show the efficiency of the algorithm, a simulated four-component overlapping GC-MS data and an experimental GC-MS data of 18 organophosphorus pesticide mixture are investigated. The results show that both the chromatographic profiles and mass spectra of the components can be successfully extracted from the overlapping signals. (C) 2015 Elsevier B.V. All rights reserved.