Response surface optimization of xylanase production by indigenous thermoalkalophillic Bacillus sp.

Abstract

Xylanases are an important class of hydrolytic enzymes with a wide range of industrially important applications especially in paper and pulp industry. The present study aimed to take the advantage of statistical approach of optimization to investigate the interactive effects of prominent process factors involved in xylanase production. A novel bacterial isolate Bacillus sp. MCC 2727 was isolated from soil possessing xylanase producing ability at alkaline pH (9.2) and optimum temperature of 50°C. Using the conventional one-factor-at-a-time method, low cost agricultural waste; wheat bran, combination of peptone and yeast extract served as best carbon and nitrogen sources, respectively. MgSO 4 as metal salt and xylan as additive increased the xylanase productivity. Central composite design and response surface methodology were used to optimize these significant process parameters and for evaluation of interactive factors. Maximum xylanase activity of 205.3 IU/ml was obtained with 5% wheat bran, 1% each of yeast extract, peptone, xylan and MgSO 4 which was in consensus with the predicted value (207.2 IU/ml) which proved the validity and the accuracy of the statistical approach of optimization.

Optimization of Amylase Production from <i>Lactobacillus plantarum</i> CS by Submerged Fermentation Using Agro Wastes Substrates

“…In enzyme production, metals demonstrate varied actions in their ability to act as effectors [33]. The presence of specific metal ion along with essential nutrient sources can inhibit or enhance amylase activity [34]. Therefore, the supplementation of the test metal ions into the production medium showed that Hg 2+ , Cu 2+ and Pb 2+ inhibited amylase production (when compared to the activity of the control (fermentation without metal supplement) which is taken as 100% (Figure 4).…”

Statistical Optimization of Production Conditions of Alkaline Pectin Lyase from Bacillus cereus using Response Surface Methodology

“…The R 2 value indicated goodness of fit of model, but high R 2 may be due to other non-significant variables and adj. R-squared value must be considered to manage the R 2 [26]. Similarly, in other studies RSM was carried out for optimization and the adj.…”