Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1179888 | Chemometrics and Intelligent Laboratory Systems | 2011 | 7 Pages |
Abstract
A recent type of receptor modelling technique the Positive Matrix Factorization (PMF) has been applied to a geochemical dataset obtained by XRF analysis on sediments from 11 alpine lakes located in Italy. Also, two usual pattern recognition techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA), were investigated. Four interpretable factors were identified through PMF analysis, in connection with the mineralogical/chemical features of lake sediments in the catchment areas: phosphate and sulphur source, carbonates, silicates and heavy metal-bearing minerals. Also, to properly modify individual uncertainty estimates, a new PMF factor was identified, explaining a possible Pb contamination source.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Sara Comero, Giovanni Locoro, Gary Free, Stefano Vaccaro, Luisa De Capitani, Bernd Manfred Gawlik,