Statistical optimization of l-leucine amino peptidase production from Streptomyces gedanensis IFO 13427 under submerged fermentation using response surface methodology

Rahulan, Raji; Nampoothiri, K. Madhavan; Szakács, George; Nagy, Viviána; Pandey, Ashok

doi:10.1016/j.bej.2008.08.011

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…strain P34 [34], cephalosporin C from Acremonium chrysogenum [35] and citric acid from Aspergillus niger [36] etc. Recently, Rahulana et al [37] successfully used central composite experimental design to optimize the heterogeneous process parameters for L-leucine amino peptidase production from Streptomyces gedanensis and achieved twofold enhancement in the production. To the best of our knowledge, we could not find any report in the literature on the optimization of physical parameters for the production of glutaminase-free L-asparaginase form P. carotovorum MTCC 1428 in submerged fermentation.…”

Section: Optimization Of Physical Process Parameters For Maximizationmentioning

confidence: 99%

Assessment of Physical Process Conditions for Enhanced Production of Novel Glutaminase-Free L-Asparaginase from Pectobacterium carotovorum MTCC 1428

Kumar

Dasu

Pakshirajan

2010

Appl Biochem Biotechnol

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Statistically based experimental design was applied to maximize the production of glutaminase-free L-asparaginase from Pectobacterium carotovorum MTCC 1428. The effect of physical process parameters (initial pH of the medium, temperature, rpm of the shaking incubator, and inoculum size) on the production of L-asparaginase from P. carotovorum MTCC 1428 was studied using central composite design technique. The individual optimum levels of initial pH of the medium, temperature, rpm of shaking incubator, and inoculum size were found to be 6.90, 29.8 °C, 157 rpm, and 2.61% (v/v), respectively, for the production of L-asparaginase. After physical process parameters optimization, the production and productivity of L-asparaginase was enhanced by 26.39% (specific activity) and 10.19%, respectively. Maximization of L-asparaginase production was achieved at 12 h under optimal levels of physical process parameters in shake flask level.

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Section: Optimization Of Physical Process Parameters For Maximizationmentioning

confidence: 99%

Assessment of Physical Process Conditions for Enhanced Production of Novel Glutaminase-Free L-Asparaginase from Pectobacterium carotovorum MTCC 1428

Kumar

Dasu

Pakshirajan

2010

Appl Biochem Biotechnol

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“…Statistical experimental designs, such as PB (Plackett-Burman) and RSM (Response surface methodology), are alternative strategies that involve a minimum number of experiments for a large number of factors. These methods have been employed to improve the production of enzymes such as a-amylase [17], lipase [18], xylanase [3] and L-leucine amino peptidase [19].…”

Section: Introductionmentioning

confidence: 99%

Enhanced production of xylanase by Aspergillus carneus M34 in solid-state fermentation with agricultural waste using statistical approach

2010

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“…28 An RSM-integrated GA strategy was carried out with the aim of modeling and optimizing the lipase-based detergent formulation. 29 The oil removal of S. arlettae JPBW-1 was expressed as a nonlinear function of the input process parameters in coded form as follows (2)…”

Section: Resultsmentioning

confidence: 99%

Modeling Embedded Optimization Strategy for the Formulation of Bacterial Lipase-Based Biodetergent

Chauhan

Garlapati

2013

Ind. Eng. Chem. Res.

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Lipase-based detergent formulations are a viable substitute for chemical detergents that pose health and environmental hazards to customers and society. In this study, the efficacy of Staphylococcus arlettae JPBW-1 lipase as an additive in laundry detergent was assessed for oil removal through modeling and optimization using a response-surface-methodologyintegrated genetic algorithm. A three-level five-factorial central composite design was used to evaluate the interactive effects on oil removal percentage from cotton fabric of process conditions, namely, detergent concentration, lipase concentration, buffer pH, washing temperature, and washing time. The input space of the validated response surface methodology (RSM) model (R 2 value of 97.7%) was utilized for genetic algorithm (GA) optimization. An optimum value of 79.6% oil removal was achieved with the GA-predicted process variables of 0.69% detergent, 47.37 U of lipase, buffer pH of 7.2, and washing temperature of 37.18°C in 26.11 min, which was 27% more than the oil removal without lipase. Hence, lipase from S. arlettae JPBW-1 can be effectively used as an additive in laundry detergent for oil removal from soiled fabric and introduces a new lipase into the biobased detergent industry.

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Statistical optimization of l-leucine amino peptidase production from Streptomyces gedanensis IFO 13427 under submerged fermentation using response surface methodology

Cited by 24 publications

References 27 publications

Assessment of Physical Process Conditions for Enhanced Production of Novel Glutaminase-Free L-Asparaginase from Pectobacterium carotovorum MTCC 1428

Assessment of Physical Process Conditions for Enhanced Production of Novel Glutaminase-Free L-Asparaginase from Pectobacterium carotovorum MTCC 1428

Enhanced production of xylanase by Aspergillus carneus M34 in solid-state fermentation with agricultural waste using statistical approach

Modeling Embedded Optimization Strategy for the Formulation of Bacterial Lipase-Based Biodetergent

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