Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1877366 | Applied Radiation and Isotopes | 2007 | 7 Pages |
Abstract
Multivariate data analysis methods were used to recognize and classify soils of unknown geographic origin. A total of 103 soil samples were differentiated into classes, according to regions in Serbia and Montenegro from which they were collected. Their radionuclide (226Ra, 238U, 235U, 40K, 134Cs, 137Cs, 232Th and 7Be) activities detected by gamma-ray spectrometry were then used as the inputs in different pattern recognition methods. For the classification of soil samples using eight selected radionuclides, the prediction ability of linear discriminant analysis (LDA), k-nearest neighbours (kNN), soft independent modelling of class analogy (SIMCA) and artificial neural network (ANN) were 82.8%, 88.6%, 60.0% and 92.1%, respectively.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Radiation
Authors
Snezana Dragovic, Antonije Onjia,