Application of self-organising maps towards segmentation of soybean samples by determination of amino acids concentration.

Soybeans are widely used both for human nutrition and animal feed, since they are an important source of protein, and they also provide components such as phytosterols, isoflavones, and amino acids. In this study, were determined the concentrations of the amino acids lysine, histidine, arginine, asparagine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, tyrosine, phenylalanine present in 14 samples of conventional soybeans and 6 transgenic, cultivated in two cities of the state of Paraná, Londrina and Ponta Grossa. The results were tabulated and presented to a self-organising map for segmentation according planting regions and conventional or transgenic varieties. A network with 7000 training epochs and a 10 × 10 topology was used, and it proved appropriate in the segmentation of the samples using the data analysed. The weight maps provided by the network, showed that all the amino acids were important in targeting the samples, especially isoleucine. Three clusters were formed, one with only Ponta Grossa samples (including transgenic (PGT) and common (PGC)), a second group with Londrina transgenic (LT) samples and the third with Londrina common (LC) samples.

Application of self-organising maps towards segmentation of soybean samples by determination of inorganic compounds content.

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. CONCLUSION: 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.