Linked Life Data

19576843

Source:http://linkedlifedata.com/resource/pubmed/id/19576843

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2009-9-7
pubmed:abstractText
A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis-NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x-y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard-Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1873-3557
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
74
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
344-8
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine.
pubmed:affiliation
Hunan Agricultural Product Processing Institute, Changsha 410125, PR China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies