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A novel exploratory chemometric approach to environmental monitorring by combining block clustering with Partial Least Square (PLS) analysis

Overview of attention for article published in BMC Chemistry, August 2013
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Title
A novel exploratory chemometric approach to environmental monitorring by combining block clustering with Partial Least Square (PLS) analysis
Published in
BMC Chemistry, August 2013
DOI 10.1186/1752-153x-7-145
Pubmed ID
Authors

Dragos V Nica, Despina Maria Bordean, Ioan Pet, Elena Pet, Simion Alda, Iosif Gergen

Abstract

Given the serious threats posed to terrestrial ecosystems by industrial contamination, environmental monitoring is a standard procedure used for assessing the current status of an environment or trends in environmental parameters. Measurement of metal concentrations at different trophic levels followed by their statistical analysis using exploratory multivariate methods can provide meaningful information on the status of environmental quality. In this context, the present paper proposes a novel chemometric approach to standard statistical methods by combining the Block clustering with Partial least square (PLS) analysis to investigate the accumulation patterns of metals in anthropized terrestrial ecosystems. The present study focused on copper, zinc, manganese, iron, cobalt, cadmium, nickel, and lead transfer along a soil-plant-snai food chain, and the hepatopancreas of the Roman snail (Helix pomatia) was used as a biological end-point of metal accumulation.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Unspecified 1 13%
Professor 1 13%
Student > Bachelor 1 13%
Student > Master 1 13%
Other 1 13%
Unknown 1 13%
Readers by discipline Count As %
Environmental Science 4 50%
Unspecified 1 13%
Agricultural and Biological Sciences 1 13%
Engineering 1 13%
Unknown 1 13%