Investigation of Arctic and Antarctic spatial and depth patterns of sea water in CTD profiles using chemometric data analysis

被引:6
|
作者
Kotwa, Ewelina [1 ]
Lacorte, Silvia [2 ]
Duarte, Carlos [3 ]
Tauler, Roma [2 ]
机构
[1] Tech Univ Denmark, Dept Informat & Math Modelling, DK-2800 Lyngby, Denmark
[2] Spanish Council Res, Inst Environm Assessment & Water Res, Barcelona 08034, Spain
[3] Spanish Council Res, Mediterranean Inst Adv Studies, Esporles 2107190, Mallorca, Spain
关键词
Arctic Antarctic; CTD; Multi-way analysis; Fluorescence; PRINCIPAL COMPONENT ANALYSIS; MULTILINEAR PLS; MISSING ELEMENTS; REGRESSION; OUTLIERS; MODELS; RANK;
D O I
10.1016/j.polar.2014.05.003
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
In this paper we examine 2- and 3-way chemometric methods for analysis of Arctic and Antarctic water samples. Standard CTD (conductivity-temperature-depth) sensor devices were used during two oceanographic expeditions (July 2007 in the Arctic; February 2009 in the Antarctic) covering a total of 174 locations. The output from these devices can be arranged in a 3-way data structure (according to sea water depth, measured variables, and geographical location). We used and compared 2- and 3-way statistical tools including PCA, PARAFAC, PLS, and N-PLS for exploratory analysis, spatial patterns discovery and calibration. Particular importance was given to the correlation and possible prediction of fluorescence from other physical variables. MAT-LAB's mapping toolbox was used for geo-referencing and visualization of the results. We conclude that: 1) PCA and PARAFAC models were able to describe data in a satisfactory way, but PARAFAC results were easier to interpret; 2) applying a 2-way model to 3-way data raises the risk of flattening the covariance structure of the data and losing information; 3) the distinction between Arctic and Antarctic seas was revealed mostly by PC1, relating to the physico-chemical properties of the water samples; and 4) we confirm the ability to predict fluorescence values from physical measurements when the 3-way data structure is used in N-way PLS regression. (C) 2014 Elsevier B.V. and NIPR. All rights reserved.
引用
收藏
页码:242 / 254
页数:13
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