Chemometric exploration of sea water chemical component data sets with missing elements

• Autorzy: Smoliński A. , Falkowska L. , Pryputniewicz D.
• Języki publikacji: EN

Abstrakty

EN

The results of the application of chemometric methods, such as principal component analysis (PCA) and its generalization for N-way data, the Tucker3 model, for the analysis of an environmental data set are presented. The analyzed data consists of concentration values of chemical compounds of organic matter, and their transformed products, in a short-term study of a sea water column measured at the Gdańsk Deep (.[fi]= 55°1’N, [lambda] = 19°10’E). The main goal of this paper is to present improved approaches for exploration of data sets with missing elements, based on the expectation-maximization (EM) algorithm. The most common methods for dealing with missing data, generally consisting of setting the missing elements to zero or to mean values of the measured data, are often unacceptable as they destroy data correlations or influence interpretation of relationships between objects and variables. The EM algorithm may be built into different computational procedures used for exploratory analysis (i.e. EM/PCA or EM/TUCKER3).

Słowa kluczowe

EN

missing data; chemometric methods; expectation-maximization algorithm; principal component analysis; Tucker3 model

• Wydawca: Institute of Oceanography University of Gdańsk
• Czasopismo: Oceanological and Hydrobiological Studies
• Rocznik: 2008
• Tom: Vol. 37, No.3
• Strony: 49--62
• Opis fizyczny: bibliogr. 31 poz., tab., wykr.

Twórcy

autor

Smoliński A.

autor

Falkowska L.

autor

Pryputniewicz D.

• 1Central Mining Institute Pl. Gwarków 1, 40-166 Katowice, Poland,

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