Linked Life Data

18398603

Source:http://linkedlifedata.com/resource/pubmed/id/18398603

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-11-30
pubmed:abstractText
An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe(3)O(4)-SiO(2)) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5?×?10(-7) to 4.4?×?10(-4) M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1618-2650
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
391
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
679-85
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks.
pubmed:affiliation
College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China. etanglin@126.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't