title: Application of self-organising maps towards segmentation of soybean samples by determination of inorganic compounds content.
abstract: BACKGROUND
  In this study, 20 samples of soybean, both transgenic and conventional cultivars, which were planted in two different regions, Londrina and Ponta Grossa, both located at Paraná, Brazil, were analysed. In order to verify whether the inorganic compound levels in soybeans varied with the region of planting, K, P, Ca, Mg, S, Zn, Mn, Fe, Cu and B contents were analysed by an artificial neural network self-organising map.
  
  
  RESULTS
  It was observed that with a topology 10 × 10, 8000 epochs, initial learning rate of 0.1 and initial neighbourhood ratio of 4.5, the network was able to differentiate samples according to region of origin. Among all of the variables analysed by the artificial neural network, the elements Zn, Ca and Mn were those which most contributed to the classification of the samples.
  
  
  CONCLUSIONS
  The results indicated that samples planted in these two regions differ in their mineral content; however, conventional and transgenic samples grown in the same region show no difference in mineral contents in the grain.
authors: Department of Chemistry, State University of Londrina, Londrina, PR 86057-970, Brazil.
authors: Hágata
authors: H
authors: Cremasco
authors: Department of Chemistry, State University of Londrina, Londrina, PR 86057-970, Brazil.
authors: Dionísio
authors: D
authors: Borsato
authors: Department of Chemistry, State University of Londrina, Londrina, PR 86057-970, Brazil.
authors: Karina Gomes
authors: KG
authors: Angilelli
authors: Department of Chemistry, State University of Londrina, Londrina, PR 86057-970, Brazil.
authors: Olívio Fernandes
authors: OF
authors: Galão
authors: Postgraduate Program in Food Technology, Federal Technological University of Paraná, Campo Mourão, PR 87301-899, Brazil.
authors: Evandro
authors: E
authors: Bona
authors: Department of Applied Mathematics, State University of Campinas, Campinas, SP 13083-859, Brazil.
authors: Marcos Eduardo
authors: ME
authors: Valle
mesh_terms: D000383:Agriculture; D001938:Brazil; D008903:Minerals / Q000032:analysis*; D016571:Neural Networks, Computer; D030821:Plants, Genetically Modified; D012639:Seeds / Q000737:chemistry*; D012987:Soil / Q000737:chemistry; D013025:Glycine max / Q000737:chemistry* / Q000145:classification; D013045:Species Specificity; D014131:Trace Elements / Q000032:analysis*
chemical_list: D008903:Minerals; D012987:Soil; D014131:Trace Elements
keywords: Kohonen self-organising map; artificial neural networks; soybean minerals; synaptic adaptation; topological neighbourhood; unsupervised learning