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The influence of media and process parameters (aeration and agitation) on fermentation broth rheology and biomass formation has been studied in 1.5-l stirred tank reactor for lipase production using Rhodotorula mucilaginosa MTCC 8737. Molasses, as sole production medium, is used for lipase production by varying aeration (1, 2, and 3 vvm) and agitation speeds (100, 200, and 300 rpm). Maximum lipase activity of 72 U/ml was obtained during 96 h of fermentation at 2 vvm, 200 rpm, pH 7, and 25 +/- 2 degrees C temperature. Lipase production kinetics with respect to dry cell weight of biomass showed Y (P/S) of 25.71 U/mg, specific product formation of 10.9 U/mg DC, and Y (X/S) 2.35 mg/mg. Maximum lipase activity (MC 2) of 56 U/ml was observed at 1% molasses, and a further increase in the molasses concentration of (%) 1.5 and 2 inhibited the product formation of lipase with 15 and 8.5 U/ml, respectively. The production kinetics of molasses media showed Y (P/X) was 14 U/mg DC, Y (P/S) 16 U/mg, and Y (X/S) 1.14 mg/mg during 96 h of bioreactor operation. The k(L)a values for all batches (MC 1-MC 4) at 96 h of fermentation were 32, 28, 21, and 19/h, and the |oxygen transfer rate were 54.4, 56, 35.7, and 17.29 mg/l h, respectively. Increase in molasses concentration resulted in decreased lipase activity by increase in viscosity of the fermentation broth.

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Response surface methodology was employed to optimize culture medium for production of lipase with Rhodotorula sp. MTCC 8737. In the first step, a Plackett-Burman design was used to evaluate the effects of different inducers qualitatively. Of all the seven inducers tested, soybean oil showed significant influence on the lipase production. Further, response surface studies were conducted to quantitatively optimize by considering linear, interactive, and quadratic effects of test variables. A novel approach was proposed to optimize the lipase production system by optimizing the responses in terms of yield kinetics rather than optimizing the direct responses like lipase titer and biomass growth. The coefficient of determination (R(2)) calculated for Y (P/S) (0.769), Y (P/X) (0.799), and Y (X/S) (0.847) indicated that the statistical model could explain 76.9%, 79.99%, and 84.7% of variability in the response.