Statistical optimization of alkaline xylanase production from Streptomyces violaceoruber under submerged fermentation using response surface methodology

Abstract

Response surface methodology employing central composite design (CCD) was used to optimize fermentation medium for the production of cellulase-free, alkaline xylanase from Streptomyces violaceoruber under r submerged fermentation. The design was employed by selecting wheat bran, peptone, beef extract, incubation time and agitation as model factors. A second-order quadratic model and response surface method showed that the optimum conditions for xylanase production (wheat bran 3.5 % (w/v), peptone 0.8 % (w/v), beef extract 0.8 % (w/v), incubation time 36 h and agitation 250 rpm) results in 3.0-fold improvement in alkaline xylanase production (1500.0 IUml -1 ) as compared to initial level (500.0 IUml -1 ) after 36 h of fermentation, whereas its value predicted by the quadratic model was 1347 IUml -1 . Analysis of variance (ANOVA) showed a high coefficient of determination (R fi 2 ) value of 0.9718, ensuring a satisfactory adjustment of the quadratic model with the experimental data.The economical and cellulase-free nature of xylanase would enhance its applicability in pulp and paper industry.

Studies on Exo-Chitinase Production from Trichoderma asperellum UTP-16 and Its Characterization

“…On the supplementation of wheat bran with most covenant carbon and nitrogen source for maximum enzyme production, maximum activity was observed on supplementation with starch (2.05 ± 0.04 U gds -1 ) as carbon source and yeast extract (2.09 ± 0.04 U gds -1 ) as nitrogen source as compared to others. This is in close conformity of the earlier report on utilization of cheap agricultural wastes for enzyme production such as rice bran and wheat bran supplemented with organic additives for many microorganism like Streptomyces violaceoruber, Trichoderma viride, and Penicillium chrysogenum [14][15][16].…”

“…However, this approach is not only cumbersome and time consuming, but also has the limitation of ignoring the importance of interaction of various physiological parameters. The statistical approach using RSM can be efficiently used for multivariable optimization in biological systems [14,17]. The RSM model developed in present study satisfied all the necessary arguments for its use in optimization with 21.15% increase in exo-chitinase activity.…”

Bioprocessing of wheat bran for the production of lignocellulolytic enzyme cocktail byCotylidia pannosaunder submerged conditions

“…The bran is rich in nutritional contents with 27% of total carbohydrate, 14% protein, 6% lipids and around 64% digestible nitrogen. 21 Additionally, no prior pretreatment is necessary for wheat bran for utilization in enzyme production. 22 The list of experiments planned and executed based on the Central Composite Design (CCD) of RSM are tabulated in Table 1.…”

“…The shear stress sensitivity of mycelium has also been reported in the case of xylanase production by Thermactinomyces thalophile subgroup C. 32 Furthermore, the shearing of mycelium at high agitation rates also release intracellular proteins in broth, which increase foam generation during the fermentation process and thus reduce xylanase yield by affecting the oxygen transfer ratios. 21 Response optimizer function of MINITAB 16 was used to predict the submerged fermentation variables for optimal cellulase, xylanase and laccase activities. The predicted variables seems to be equivalent for the SmF performance using a substrate pH of 5.0 at 31 C, 140 rpm for 77 h. The experimental results obtained through the triplicate runs by utilizing the predicted variables i.e 20.6 U/ml of cellulase activity, 17.3 U/ml of xylanase activity and 14 U/ml of laccase activity, were in close agreement with the the predicted outputs i.e cellulase: 20.1 U/ml; xylanase: 17.2 U/ml; laccase: 13.1 U/ml.…”

Cyclodextrin glycosyltransferase biosynthesis improvement by recombinantLactococcus lactisNZ:NSP:CGT: medium formulation and culture condition optimization

“…Ravinder et al [19], Ahmed and El-Refai [7], Kuo et al [20], Menocci and Goulart [21] and Illias et al [10], conducted for the purpose of fermentation optimization, which reflects the importance of this issue. According to Khurana et al [22], the univariate technique is not suitable for use in all cases of optimized factor level determination in the enzyme production process. It is a time-consuming and difficult technique; it neglects the combinatorial effects and there is no guarantee that optimum results will be obtained.…”