Document Detail

Cluster analysis and artificial neural networks multivariate classification of onion varieties.
MedLine Citation:
PMID:  20949919     Owner:  NLM     Status:  In-Data-Review    
Eight cultivars of different colored onions (white, golden, and red) were evaluated for fresh bulbs cultivated and grown under the same environmental and agronomical conditions. Cluster analysis and principal component analysis, based on different flavonoids, total phenols, and pungency, data showed that the onions were not clustered according to variety (genetic similarity degree), whereas the color was the variable with the highest influence, ranging between 50 and 70%. Artificial neural networks were applied to study the possibility of discriminating among onion varieties. Characterization of the onion according to variety and procedence of the seeds was around 95-100%. Samples belonging to the Carrizal Alto procedence had an incorrect classification for 25% of the data.
Beatriz Rodríguez Galdón; Eladia Peña-Méndez; Josef Havel; Elena María Rodríguez Rodríguez; Carlos Díaz Romero
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Publication Detail:
Type:  Journal Article     Date:  2010-10-15
Journal Detail:
Title:  Journal of agricultural and food chemistry     Volume:  58     ISSN:  0021-8561     ISO Abbreviation:  J. Agric. Food Chem.     Publication Date:  2010 Nov 
Date Detail:
Created Date:  2014-02-05     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0374755     Medline TA:  J Agric Food Chem     Country:  United States    
Other Details:
Languages:  eng     Pagination:  11435-40     Citation Subset:  IM    
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