期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 118, 期 -, 页码 33-40出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2012.07.009
关键词
Noisy data; Error structure; Multivariate Curve Resolution; Alternating Least Squares; Weighted Alternating Least Squares; Maximum Likelihood Principal Component Analysis
类别
资金
- Ministerio de Ciencia y Innovacion, Spain [CTQ 2009-11572]
- Institute for Advanced Studies in Basic Sciences [G2011IASBS117]
A comparison of the results obtained using three algorithms, Multivariate Curve Resolution Alternating Least Squares (MCR-ALS), Multivariate Curve Resolution Weighted Alternating Least Squares (MCR-WALS) and Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS), is presented. The three approaches are applied to the analysis of a simulated environmental data set with error structures of different types and sizes. Special attention is paid to the case of highly heteroscedastic correlated noise. In all cases, the results show that the solutions provided by MLPCA-MCR-ALS are practically identical to those obtained by MCR-WALS. (C) 2012 Elsevier B.V. All rights reserved.
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