Linked Life Data

17059674

Source:http://linkedlifedata.com/resource/pubmed/id/17059674

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2006-10-24
pubmed:abstractText
Analogous to the situation found in calibration, a classification model constructed from spectra measured on one instrument may not be valid for prediction of class from spectra measured on a second instrument. In this paper, the transfer of multivariate classification models between laboratory and process near-infrared spectrometers is investigated for the discrimination of whole, green Coffea arabica (Arabica) and Coffea canefora (Robusta) coffee beans. A modified version of slope/bias correction, orthogonal signal correction trained on a vector of discrete class identities, and model updating were found to perform well in the preprocessing of data to permit the transfer of a classification model developed on data from one instrument to be used on another instrument. These techniques permitted development of robust models for the discrimination of green coffee beans on both spectrometers and resulted in misclassification errors for the transfer process in the range of 5-10%.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0003-7028
pubmed:author
pubmed:issnType
Print
pubmed:volume
60
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1198-203
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Transfer of multivariate classification models between laboratory and process near-infrared spectrometers for the discrimination of green Arabica and Robusta coffee beans.
pubmed:affiliation
Laboratory for Chemometrics, Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't