Monday, May 5, 2008
12-09

Optimization of inductor concentration and fermentation conditions for lipolytic enzyme production by isolated bacterium strain from petroleum contamined soil

Ingrid C. Feitosa1, Igor G. Muratori1, Manoel M. do Prado1, Heizir F. de Castro2, Gisella M. Zanin3, Alvaro S. Lima4, and Cleide M. F. Soares4. (1) Universidade Tiradentes, Av. Murilo Dantas, 300, Farolandia, Aracaju - SE, 49032-490, Brazil, (2) Engineering School of Lorena, University of São Paulo, P.O.Box 116, Lorena - SP, 12602-810, Brazil, (3) Chemical Engineering Department, State University of Maringa, Av. Colombo, 5790, BL E-46, Maringa - PR, 87020-900, Brazil, (4) Instituto de Tecnologia e Pesquisa, Universidade Tiradentes, Av. Murilo Dantas, 300, Farolandia, Aracaju - SE, 49032-490, Brazil

A large number of microorganisms, including bacteria, yeasts and fungi produce different groups of enzymes. Lipolytic enzymes have been shown to have many applications in detergents, food processing, chemical industry, as well biodiesel production. The objective of this work is to present alternatives for lipolytic enzymes production by an isolated bacterium strain from petroleum contaminated soil under submerged fermentation (SmF). Experiments were performed in Erlenmeyer flasks with 200mL medium containing (%, w/v): KH2PO4 (0.1), MgSO4.7H2O (0.05), NaNO3 (0.3), yeast extract (0.6), peptone (0.13), and starch (2%) as carbon source. Coconut oil was used as lipolytic enzyme inducer and was added at different concentrations after 72h cultivation. Experimental design methodology (DOE) was used to optimize the fermentations conditions as a function of temperature (24, 37 or 50°C), pH (5, 7 or 9) and inducer concentration (1, 2, 3 or 4%, v/v). Maxima activities for lipase were approximately 1,700 U/mL using high concentration level of coconut oil 4% (v/v) at pH 7.0 and 37°C. Validation of the experimental results based on DOE methodology allowed to enhance the enzyme production by this isolated bacterium, indicating the importance of this methodology to evaluate the main variables (pH and temperature) and their interaction effects for process optimization.

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