Statistical optimization of a high maltose-forming, hyperthermostable and Ca2+-independent alpha-amylase production by an extreme thermophile Geobacillus thermoleovorans using response surface methodology

Abstract

Aim: Statistical optimization for maximum production of a hyperthermostable, Ca 2+ -independent and high maltose-forming a-amylase by Geobacillus thermoleovorans. Methods and Results: G. thermoleovorans was cultivated in 250 ml flasks containing 50 ml of chemically defined glucose-arginine medium (g l )1 : glucose 20; arginine 1AE2; riboflavin 150 lg ml )1 ; MgSO 4 . 7H 2 O 0AE2; NaCl 1AE0; pH 7AE0). The medium was inoculated with 5 h-old bacterial inoculum (1AE8 · 10 8 CFU ml )1 ), and incubated in an incubator shaker at 70°C for 12 h at 200 rev min )1 . The fermentation variables optimized by Ôone variable at a timeÕ approach were further optimized by response surface methodology (RSM). The statistical model was obtained using central composite design (CCD) with three variables: glucose, riboflavin and inoculum density. An over all 24 and 70% increase in enzyme production was attained in shake flasks and fermenter because of optimization by RSM, respectively. A good coverage of interactions could also be explained by RSM. The end products of the action of a-amylase on starch were maltose (62%), maltotriose (31%) and malto-oligosaccharides (7%). Conclusions: RSM allowed optimization of medium components and cultural parameters for attaining high yields of a-amylase, and further, a good coverage of interactions could be explained. The yield of maltose was higher than maltotriose and malto-oligosaccharides in the starch hydrolysate. Significance and Impact of the Study: By applying RSM, critical fermentation variables were optimized rapidly. The starch hydrolysate contained a high proportion of maltose, and therefore, the enzyme can find application in starch saccharification process for the manufacture of high maltose syrups. The use of this enzyme in starch saccharification eliminates the addition of Ca 2+ .

Hyperthermostable, Ca2+-Independent, and High Maltose-Forming α-Amylase Production by an Extreme Thermophile Geobacillus thermoleovorans: Whole Cell Immobilization

“…Source and Maintenance of Strain G. thermoleovorans NP 54 was isolated from a water sample collected from the hot water spring of the Waimangu volcanic valley (New Zealand) and maintained as described earlier [26][27][28][29][30]. The culture is deposited at Microbial Type Culture Collection, Institute of Microbial Technology, Chandigarh (India; MTCC 4220).…”

“…Immobilization matrices and conditions must be selected for each microbial strain [24]. We have been publishing several research papers on production, purification, and characterization of α-amylase from G. thermoleovorans [25][26][27][28][29][30][31]. However, there is still a need to develop a sustainable enzyme titer and continuous culture of G. thermoleovorans by using immobilization, aerobic, and anaerobic fermentation processes; for that, the present investigation was undertaken to understand the nature of synthesis of Ca 2+ -independent, high maltose-forming, and hyperthermostable α-amylase in free as well as immobilized cells of G. thermoleovorans in aerobic and anaerobic fermentation.…”

Optimization of critical process parameters for amylase production by Bacillus sp. using statistical approach (RSM)

“…Uma Maheswar Rao and T. Satyanarayana used the central composite design (CCD) resulting with three variables: glucose, riboflavin and inoculum density. An overall 24 and 70% increase in enzyme production was attained in shake flasks and fermenter because of optimization by RSM, respectively [27]. In another study by Bhaskara Rao RSM was employed to examine the combined interaction of different variables viz, starch, yeast extract, pH, NaCl and temperature [28].…”

Multiple Parameter Optimization for Maximization of Pectinase Production by Rhizopus sp. C4 under Solid State Fermentation

“…The R 2 value is always between 0 and 1. The closer the R 2 is to 1.0, the stronger the model and better it predicts the response [33]. The justification of the variability in observed response values by the experimental factors (variables) and their interactions was measured by R 2 (coefficient of determination).…”