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

Plant Physiol Biochem. 2016 Sep:106:264-8. doi: 10.1016/j.plaphy.2016.05.017. Epub 2016 May 13.

Abstract

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.

Keywords: Artificial neural network; Food technology; Topologic map; Weight map.

MeSH terms

  • Amino Acids / metabolism*
  • Glycine max / metabolism*
  • Neural Networks, Computer*

Substances

  • Amino Acids