Morphological weighted penalized least squares for background correction
Morphological weighted penalized least squares for background correction
复制标题
用于背景校正的形态加权惩罚最小二乘法
DOI：
10.1039/c3an00743j
复制
发表时间：
2013-01-01
期刊：
影响因子：
4.2
通讯作者：
Wang, Hong
中科院分区：
文献类型：
--
作者：
Li, Zhong;Zhan, De-Jian;Wang, Hong
分析信号中存在的背景往往会影响信号的有效性，影响分析方法的选择性和灵敏度。为了进行进一步的定性或定量分析，应采用合理的方法对背景进行校正。为此，本文提出了一种基于形态学运算和加权惩罚最小二乘（MPLS）的自动背景校正方法。它既不需要关于背景的先验知识，也不需要迭代过程或手动选择合适的局部最小值。该方法已成功地应用于模拟数据集以及来自不同仪器的实验数据集。实验结果表明，该方法具有很强的灵活性，可以处理不同的背景。所提出的MPLS方法在http://code.google.com/p/mpls上实现并作为开源包提供。
Backgrounds existing in the analytical signal always impair the effectiveness of signals and compromise selectivity and sensitivity of analytical methods. In order to perform further qualitative or quantitative analysis, the background should be corrected with a reasonable method. For this purpose, a new automatic method for background correction, which is based on morphological operations and weighted penalized least squares (MPLS), has been developed in this paper. It requires neither prior knowledge about the background nor an iteration procedure or manual selection of a suitable local minimum value. The method has been successfully applied to simulated datasets as well as experimental datasets from different instruments. The results show that the method is quite flexible and could handle different kinds of backgrounds. The proposed MPLS method is implemented and available as an open source package at http://code.google.com/p/mpls.