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Thermostable lipase from a newly isolated Staphylococcus xylosus strain; process optimization and characterization using RSM and ANN Anahita Khoramnia Oi Ming Lai* Afshin Ebrahimpour Carynn Josue Tanduba Tan Siow Voon Suriati Mukhlis *Corresponding author Financial support: University Putra Malaysia. Keywords: artificial neural network, characterization, lipase, optimization, response surface methodology, Staphylococcus xylosus.
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 methodology. The optimum operating condition obtained from the ANN model was found to be at 30ºC 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 70ºC after 90 min and at 60ºC 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). |