oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish
Department of Aquaculture, Faculty of Agriculture, University Putra Malaysia, Selangor, Malaysia.
Department of Ecosystem Analysis, Institute for Environmental Research (Biology V), RWTH Aachen University, Aachen, Germany. (MTM)ORCID iD: 0000-0002-2356-6686
Department of Ecosystem Analysis, Institute for Environmental Research (Biology V), RWTH Aachen University, Aachen, Germany.
Fisheries and Oceans Canada at the Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton NB, Canada.
2013 (English)In: Environmental science and pollution research international, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 20, no 3, p. 1586-1595Article in journal (Refereed) Published
Abstract [en]

This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A “data trimming”approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.

Place, publisher, year, edition, pages
Heidelberg, Germany: Springer, 2013. Vol. 20, no 3, p. 1586-1595
Keywords [en]
Modeling . Fish biomarkers . Fuzzy inference system (FIS) . Adaptive neuro-fuzzy inference system (ANFIS) . Benzo[a]pyrene (BaP) . African catfish (Clarias gariepinus)
National Category
Environmental Sciences
Research subject
Enviromental Science
Identifiers
URN: urn:nbn:se:oru:diva-40134DOI: 10.1007/s11356-012-1027-5ISI: 000315442500036PubMedID: 22752811Scopus ID: 2-s2.0-84874287480OAI: oai:DiVA.org:oru-40134DiVA, id: diva2:776034
Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2018-05-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Keiter, Steffen

Search in DiVA

By author/editor
Keiter, Steffen
In the same journal
Environmental science and pollution research international
Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 411 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf