Linked Life Data

12083885

Source:http://linkedlifedata.com/resource/pubmed/id/12083885

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
14
pubmed:dateCreated
2002-6-26
pubmed:abstractText
The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0021-8561
pubmed:author
pubmed:issnType
Print
pubmed:day
3
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4069-75
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.
pubmed:affiliation
Institute of Theoretical Chemistry and Molecular Biology, University of Vienna, Waehringer Strasse 17, A-1090 Vienna, Austria.
pubmed:publicationType
Journal Article