Linked Life Data

8936424

Source:http://linkedlifedata.com/resource/pubmed/id/8936424

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1996-12-31
pubmed:abstractText
Dystal, an artificial neural network, was used to classify orange juice products. Nine varieties of oranges collected from six geographical regions were processed into single-strength, reconstituted or frozen concentrated orange juice. The data set represented 240 authentic and 173 adulterated samples of juices; 16 variables [8 flavone and flavanone glycoside concentrations measured by high-performance liquid chromatography (HPLC) and 8 trace element concentrations measured by inductively coupled plasma spectroscopy] were selected to characterize each juice and were used as input to Dystal. Dystal correctly classified 89.8% of the juices as authentic or adulterated. Classification performance increased monotonically as the percentage of pulpwash in the sample increased. Dystal correctly identified 92.5% of the juices by variety (Valencia vs non-Valencia).
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0097-8485
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
261-6
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1996
pubmed:articleTitle
Orange juice classification with a biologically based neural network.
pubmed:affiliation
Environmental Research Institute of Michigan, Arlington, VA 22209, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't