Statistical Optimization of Culture Conditions for Tannase Production by Aspergillus awamori MTCC 9299 under Submerged Fermentation

Abstract: Optimization of culture conditions for tannase production by Aspergillusawamori MTCC 9299 was studied using the response surface methodology.Maximum response for tannase production w as obtained at 5.0 pH, 35°C incubationtemperature, 125 rpm agitation speed and incubation period of 48 h. Under theproposed optimized conditions, the tannase experimental yield (1.45 U mL G 1 ) closelymatched the yield predicted by the statistical model (1.43 U mLG 1 ) with R 2 = 0.99 and highly significant F-value of 173.79. Key words: Tannase, response surface methodology, tannic acid, Aspergillus awamori MTCC 9299 INTRODUCTION Tannase or tannin acyl hydrolyase (EC 3.1.1.20) is an extrace llular inducible enzyme thatcatalyzes the hydrolysis of ester and depside bonds in hydrolysable tannins, releasingglucose and gallic acid. Gallic acid is extensively used as an ingredient of developer n iphotography and printing inks. It also serves as a precursor for the commercial productionof an anti-microbial drug-trimethoprim, a food preservati ve-propylgallate and some dyestuffs.Besides this, gallic acid possesses wide range of biological activities, such as antioxidant,antibacterial, antiviral, analgesic etc. As antioxidant gallic acid acts as an antiapoptotic agentand helps to protect human cells against oxidative damage. Gallic acid is also found to showcytotoxic activity against cancer cells, without harming normal cells (Bajpai and Patil, 2008).Besides gallic acid production, the enzyme is extensively used in the preparation of instanttea, wine, beer and coffee-flavored soft dr inks and also as additive for detannification of food(Lekha and Lonsane, 1997; Seth and Chand, 2000).In biotechnology, optimization of culture condition through Fractional Factorial Design(FFD) and Response Surface Methodology (RSM) is a common practice. The FFD and RSMare statistical techniques for designing experiments, building models, evaluating the effectsof factors and searching for the optimum conditions, have successfully been employed forscreening significant factors and optimization the medium composition in manybioprocess (Elibol, 2004; Lin