Unsupervised pattern-recognition techniques to investigate metal pollution in estuaries

被引：22
作者：
Gredilla, A. [1 ]
Fdez-Ortiz de Vallejuelo, S. [1 ]
Amigo, J. M. [2 ]
de Diego, A. [1 ]
Madariaga, J. M. [1 ]
机构：
[1] Univ Basque Country UPV EHU, Dept Analyt Chem, Bilbao 48080, Basque Country, Spain
[2] Univ Copenhagen, Fac Life Sci, Dept Food Sci Qual & Technol, DK-1958 Frederiksberg C, Denmark
来源：
TRAC-TRENDS IN ANALYTICAL CHEMISTRY | 2013年 / 46卷
关键词：
Artificial Neural Networks (ANN); Chemometrics; Cluster Analysis (CA); Estuary; Factor model; Metal pollution; Multivariate data; Pattern recognition; Principal Component Analysis (PCA); Unsupervised technique; LEACHABLE TRACE-METALS; HEAVY-METALS; MULTIVARIATE-ANALYSIS; ENVIRONMENTAL-QUALITY; EXTRACTION PROCEDURES; SEDIMENT SAMPLES; RIVER; ELEMENTS; CONTAMINATION; BEHAVIOR;
D O I：
10.1016/j.trac.2013.01.014
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
There has been a significant increase in the application of unsupervised pattern-recognition techniques to the analysis of long datasets emerging from the monitoring of metal pollution in estuaries. In this work, we thoroughly review the most important articles published on this topic in recent years. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：59 / 69
页数：11
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