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Electronic Journal of Biotechnology
Universidad Católica de Valparaíso
ISSN: 0717-3458
Vol. 13, No. 5, 2010
Bioline Code: ej10059
Full paper language: English
Document type: Research Article
Document available free of charge

Electronic Journal of Biotechnology, Vol. 13, No. 5, 2010

 en Thermostable lipase from a newly isolated Staphylococcus xylosus check for this species in other resources strain; process optimization and characterization using RSM and ANN
Khoramnia, Anahita; Lai, Oi Ming; Ebrahimpour, Afshin; Tanduba, Carynn Josue; Voon, Tan Siow & Mukhlis, Suriati

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 check for this species in other resources . The results demonstrated a high predictive accuracy of artificial neural network compared to response surface methodology. The optimum operating condition obtained from the ANN model was found to be at 30°C incubation temperature, pH 7.5, 60 hrs incubation period, 1.8% inoculum size and 60 rpm agitation. The lipase production increased 3.5 fold for optimal medium. The produced enzyme was characterized biochemically and this is the first report about a mesophilic staphylococci bacterium with a high thermostable lipase which is able to retain 50% of its activity at 70°C after 90 min and at 60°C after 120 min. This lipase is also acidic and alkaline resistant which remains active after 24 hrs in a broad range of pH (4-11).

Keywords
artificial neural network, characterization, lipase, optimization, response surface methodology, Staphylococcus xylosus.

 
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