PROJECTION OF PRIM MINIMAL SPANNING TREE INTO A KOHONEN NEURAL-NETWORK FOR IDENTIFICATION OF AIRBORNE PARTICLE SOURCES BY THEIR MULTIELEMENT TRACE PATTERNS

Authors
Citation
D. Wienke et Pk. Hopke, PROJECTION OF PRIM MINIMAL SPANNING TREE INTO A KOHONEN NEURAL-NETWORK FOR IDENTIFICATION OF AIRBORNE PARTICLE SOURCES BY THEIR MULTIELEMENT TRACE PATTERNS, Analytica chimica acta, 291(1-2), 1994, pp. 1-18
Citations number
25
Categorie Soggetti
Chemistry Analytical
Journal title
ISSN journal
00032670
Volume
291
Issue
1-2
Year of publication
1994
Pages
1 - 18
Database
ISI
SICI code
0003-2670(1994)291:1-2<1:POPMST>2.0.ZU;2-U
Abstract
A hybrid pattern recognition method has been developed as an alternati ve receptor modelling technique for the identification of sources of c oarse airborne particles. The Kohonen self-organizing neural network i s first applied to yield a topological map of an m-dimensional variabl es space. Unfortunately, during the projection into a low-dimensional subspace, most of the information about the correct distance between t he sample vectors is lost. However, the Kohonen network is a useful a priori step of data compression before application of the minimal span ning tree. Prim's minimal spanning tree partly compensates for this lo ss yielding the distance interrelationships between groups of the samp les. This combination of both projection techniques can overcome some of their individual deficiencies. Several illustrative examples are pr esented to demonstrate the nature of the analysis results. Then a set of airborne particle compositions for samples obtained at a single sam pling site were analysed. After transferring the combined map to a geo graphical unit circle (GUC), a correct pattern of the main industrial emission sources around a sampling site in Granite City (Illinois, USA ) has been obtained by decoding a 35-dimensional space of chemical-ana lytical variables into a visually and geographically interpretable 2-D space.