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Oi Ming Lai ; Afshin Ebrahimpour ; Carynn Josue Tanduba ; Tan Siow Voon ; Suriati Mukhlis ;Electronic Journal of Biotechnology 2010, 13 5

Autor: Anahita Khoramnia

Fuente: http://www.redalyc.org/


Introducción



Electronic Journal of Biotechnology E-ISSN: 0717-3458 edbiotec@ucv.cl Pontificia Universidad Católica de Valparaíso Chile Khoramnia, Anahita; Ming Lai, Oi; Ebrahimpour, Afshin; Tanduba, Carynn Josue; Voon, Tan Siow; Mukhlis, Suriati Thermostable lipase from a newly isolated Staphylococcus xylosus strain; process optimization and characterization using RSM and ANN Electronic Journal of Biotechnology, vol.
13, núm.
5, 2010, pp.
1-16 Pontificia Universidad Católica de Valparaíso Valparaíso, Chile Available in: http:--www.redalyc.org-articulo.oa?id=173318799015 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Electronic Journal of Biotechnology ISSN: 0717-3458 http:--www.ejbiotechnology.info DOI: 10.2225-vol13-issue5-fulltext-22 Thermostable lipase from a newly isolated Staphylococcus xylosus strain; process optimization and characterization using RSM and ANN 1 1 Anahita Khoramnia · Oi Ming Lai 1 1 · Afshin Ebrahimpour · Carynn Josue Tanduba 1 1 Tan Siow Voon · Suriati Mukhlis 1 Faculty of Biotechnology and Biomolecular Sciences, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Corresponding author: omlai@biotech.upm.edu.my Received November 27, 2009 - Accepted June 16, 2010 Published online: September 15, 2010 © 2010 by Pontificia Universidad Católica de Valparaíso, Chile Abstract Normal feed forward back-propagation artificial neural network (ANN) and cubic backward elimination response surface methodology (RSM) were used to build a predictive model of the combined effects and optimization of culture parameters for the lipase production of a newly isolated Staphylococcus xylosus.
The results demonstrated a high predictive accuracy of artificial neural network compared to response surface metho...





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