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Application of self‐organising maps towards segmentation of soybean samples by determination of inorganic compounds content
Author(s) -
Cremasco Hágata,
Borsato Dionísio,
Angilelli Karina Gomes,
Galão Olívio Fernandes,
Bona Evandro,
Valle Marcos Eduardo
Publication year - 2015
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7094
Subject(s) - cultivar , mathematics , chemistry , sowing , mineralogy , analytical chemistry (journal) , botany , biology , environmental chemistry
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. © 2015 Society of Chemical Industry