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Rekha S. Singhal - One of the best experts on this subject based on the ideXlab platform.
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Fermentative Production of glycine betaine and trehalose from acid whey using Actinopolyspora halophila (MTCC 263)
Environmental Technology and Innovation, 2015Co-Authors: John E. Hallsworth, Rekha S. SinghalAbstract:Abstract Acid whey has become a major concern especially in dairy industry manufacturing Greek yoghurt. Proper disposal of acid whey is essential as it not only increases the BOD of water but also increases the acidity when disposed of in landfill, rendering soil barren and unsuitable for cultivation. Effluent (acid-whey) treatment increases the cost of Production. The vast quantities of acid whey that are produced by the dairy industry make the treatment and safe disposal of effluent very difficult. Hence an economical way to handle this problem is very important. Biogenic glycine betaine and trehalose have many applications in food and confectionery industry, medicine, bioprocess industry, agriculture, genetic engineering, and animal feeds (etc.), hence their Production is of industrial importance. Here we used the extreme, obligate halophile Actinopolyspora halophila (MTCC 263) for Fermentative Production of glycine betaine and trehalose from acid whey. Maximum yields were obtained by implementation of a sequential media optimization process, identification and addition of rate-limiting enzyme cofactors via a bioinformatics approach, and manipulation of nitrogen substrate supply. The implications of using glycine as a precursor were also investigated. The core factors that affected Production were identified and then optimized using orthogonal array design followed by response surface methodology. The maximum Production achieved after complete optimization was 9.07 ± 0.25 g/L and 2.49 ± 0.14 g/L for glycine betaine and trehalose, respectively.
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cyclosporin a a review on Fermentative Production downstream processing and pharmacological applications
Biotechnology Advances, 2011Co-Authors: Shrikant A. Survase, Lalit D. Kagliwal, Uday S Annapure, Rekha S. SinghalAbstract:In present times, the immunosuppressants have gained considerable importance in the world market. Cyclosporin A (CyA) is a cyclic undecapeptide with a variety of biological activities including immunosuppressive, anti-inflammatory, antifungal and antiparasitic properties. CyA is produced by various types of fermentation techniques using Tolypocladium inflatum. In the present review, we discuss the biosynthetic pathway, Fermentative Production, downstream processing and pharmacological activities of CyA.
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comparison of artificial neural network ann and response surface methodology rsm in fermentation media optimization case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Shrikant A. Survase, Parag S. Saudagar, S S Lele, Rekha S. SinghalAbstract:Abstract Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.
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Statistical approach to optimization of Fermentative Production of gellan gum from Sphingomonas paucimobilis ATCC 31461.
Journal of Bioscience and Bioengineering, 2006Co-Authors: Ishwar B. Bajaj, Parag S. Saudagar, Rekha S. Singhal, Ashok PandeyAbstract:Gellan gum, a high-molecular-weight anionic linear polysaccharide produced by pure-culture fermentation from Sphingomonas paucimobilis ATCC 31461, has elicited industrial interest in recent years as a high-viscosity biogum, a suspending agent, a gelling agent, and an agar substitute in microbial media. In this paper we report on the optimization of gellan gum Production using a statistical approach. In the first step, the one factor-at-a-time method was used to investigate the effect of medium constituents such as carbon and nitrogen sources; subsequently, the intuitive analysis based on statistical calculations carried out using the L16-orthogonal array method. The design for the L16-orthogonal array was developed and analyzed using MINITAB 13.30 software. All the fermentation runs were carried out at 30±2°C on a rotary orbital shaker at 180 rpm for 48 h. In the second step, the effects of amino acids and gellan precursors such as uridine-5′-diphospate (UDP) and adenosine-5′-diphospate (ADP) on the Fermentative Production of gellan gum were studied. Media containing 4% soluble starch, 0.025% yeast extract, 1.0 mM ADP and 0.05% tryptophan gave a maximum yield of 43.6 g l−1 starch-free gellan gum, which was significantly higher than reported values in the literature.
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Fermentative Production of curdlan.
Applied Biochemistry and Biotechnology, 2004Co-Authors: Parag S. Saudagar, Rekha S. SinghalAbstract:Curdlan was produced by pure culture fermentation using Agrobacterium radiobacter NCIM 2443. Three different carbon sources (glucose, sucrose, maltose) were selected for study. Sucrose was found to be the most efficient. Utilization of sugar during the course of fermentation was studied, and the data were correlated to the Production of curdlan. Curdlan mimics a secondary metabolite, in that its synthesis is associated with the poststationary growth phase of nitrogen-depleted batch culture. This was inferred from the results obtained from utilization of nitrogen. Regulation of pH at 6.1±0.3 resulted in an increased yield of curdlan from 2.48 to 4.8 g/L, and the corresponding increase in succinoglucan Production was from 1.78 to 2.8 g/L. An attempt was made to increase curdlan Production by the addition of the uridine nucleotides UMP and UDP-glucose to the fermentation broth. It was found that UDP-glucose at 0.8 µg/mL and UMP at 0.6 µg/mL served as precursors for curdlan and succinoglucan Production when added after 18 h of nitrogen depletion in the fermentation broth.
Rekha Satishchandra Singhal - One of the best experts on this subject based on the ideXlab platform.
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Cyclosporin A - A review on Fermentative Production, downstream processing and pharmacological applications
Biotechnology Advances, 2011Co-Authors: Shrikant A. Survase, Lalit D. Kagliwal, Uday S Annapure, Rekha Satishchandra SinghalAbstract:In present times, the immunosuppressants have gained considerable importance in the world market. Cyclosporin A (CyA) is a cyclic undecapeptide with a variety of biological activities including immunosuppressive, anti-inflammatory, antifungal and antiparasitic properties. CyA is produced by various types of fermentation techniques using Tolypocladium inflatum. In the present review, we discuss the biosynthetic pathway, Fermentative Production, downstream processing and pharmacological activities of CyA. © 2011 Elsevier Inc.
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Microbial cellulose: Fermentative Production and applications
Food Technology and Biotechnology, 2009Co-Authors: Prashant R. Chawla, Ishwar B. Bajaj, Shrikant A. Survase, Rekha Satishchandra SinghalAbstract:Bacterial cellulose, an exopolysaccharide produced by some bacteria, has unique structural and mechanical properties and is highly pure as compared to plant cellulose. This article presents a critical review of the available information on the bacterial cellulose with special emphasis on its Fermentative Production and applications. Information on the biosynthetic pathway of bacterial cellulose, enzymes and precursors involved in bacterial cellulose synthesis has been specified. Characteristics of bacterial cellulose with respect to its structure and physicochemical properties are discussed. Current and potential applications of bacterial cellulose in food, pharmaceutical and other industries are also presented.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Parag S. Saudagar, Shrikant A. Survase, S S Lele, Rekha Satishchandra SinghalAbstract:Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively. © 2008 Elsevier B.V. All rights reserved.
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Gellan gum: Fermentative Production, downstream processing and applications
Food Technology and Biotechnology, 2007Co-Authors: Ishwar B. Bajaj, Shrikant A. Survase, Parag S. Saudagar, Rekha Satishchandra SinghalAbstract:The microbial exopolysaccharides are water-soluble polymers secreted by microorganisms during fermentation. The biopolymer gellan gum is a relatively recent addition to the family of microbial polysaccharides that is gaining much importance in food, pharmaceutical and chemical industries due to its novel properties. It is commercially produced by C. P. Kelco in Japan and the USA. Further research and development in biopolymer technology is expected to expand its use. This article presents a critical review of the available information on the gellan gum synthesized by Sphingomonas paucimobilis with special emphasis on its Fermentative Production and downstream processing. Rheological behaviour of fermentation broth during Fermentative Production of gellan gum and problems associated with mass transfer have been addressed. Information on the biosynthetic pathway of gellan gum, enzymes and precursors involved in gellan gum Production and application of metabolic engineering for enhancement of yield of gellan gum has been specified. Characteristics of gellan gum with respect to its structure, physicochemical properties, rheology of its solutions and gel formation behaviour are discussed. An attempt has also been made to review the current and potential applications of gellan gum in food, pharmaceutical and other industries.
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Scleroglucan: Fermentative Production, downstream processing and applications
Food Technology and Biotechnology, 2007Co-Authors: Shrikant A. Survase, Ishwar B. Bajaj, Parag S. Saudagar, Rekha Satishchandra SinghalAbstract:Exopolysaccharides produced by a variety of microorganisms find multifarious industrial applications in foods, pharmaceutical and other industries as emulsifiers, stabilizers, binders, gelling agents, lubricants, and thickening agents. One such exopolysaccharide is scleroglucan, produced by pure culture fermentation from filamentous fungi of genus Sclerotium. The review discusses the properties, Fermentative Production, downstream processing and applications of scleroglucan.
Shrikant A. Survase - One of the best experts on this subject based on the ideXlab platform.
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cyclosporin a a review on Fermentative Production downstream processing and pharmacological applications
Biotechnology Advances, 2011Co-Authors: Shrikant A. Survase, Lalit D. Kagliwal, Uday S Annapure, Rekha S. SinghalAbstract:In present times, the immunosuppressants have gained considerable importance in the world market. Cyclosporin A (CyA) is a cyclic undecapeptide with a variety of biological activities including immunosuppressive, anti-inflammatory, antifungal and antiparasitic properties. CyA is produced by various types of fermentation techniques using Tolypocladium inflatum. In the present review, we discuss the biosynthetic pathway, Fermentative Production, downstream processing and pharmacological activities of CyA.
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Cyclosporin A - A review on Fermentative Production, downstream processing and pharmacological applications
Biotechnology Advances, 2011Co-Authors: Shrikant A. Survase, Lalit D. Kagliwal, Uday S Annapure, Rekha Satishchandra SinghalAbstract:In present times, the immunosuppressants have gained considerable importance in the world market. Cyclosporin A (CyA) is a cyclic undecapeptide with a variety of biological activities including immunosuppressive, anti-inflammatory, antifungal and antiparasitic properties. CyA is produced by various types of fermentation techniques using Tolypocladium inflatum. In the present review, we discuss the biosynthetic pathway, Fermentative Production, downstream processing and pharmacological activities of CyA. © 2011 Elsevier Inc.
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Microbial cellulose: Fermentative Production and applications
Food Technology and Biotechnology, 2009Co-Authors: Prashant R. Chawla, Ishwar B. Bajaj, Shrikant A. Survase, Rekha Satishchandra SinghalAbstract:Bacterial cellulose, an exopolysaccharide produced by some bacteria, has unique structural and mechanical properties and is highly pure as compared to plant cellulose. This article presents a critical review of the available information on the bacterial cellulose with special emphasis on its Fermentative Production and applications. Information on the biosynthetic pathway of bacterial cellulose, enzymes and precursors involved in bacterial cellulose synthesis has been specified. Characteristics of bacterial cellulose with respect to its structure and physicochemical properties are discussed. Current and potential applications of bacterial cellulose in food, pharmaceutical and other industries are also presented.
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comparison of artificial neural network ann and response surface methodology rsm in fermentation media optimization case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Shrikant A. Survase, Parag S. Saudagar, S S Lele, Rekha S. SinghalAbstract:Abstract Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Parag S. Saudagar, Shrikant A. Survase, S S Lele, Rekha Satishchandra SinghalAbstract:Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively. © 2008 Elsevier B.V. All rights reserved.
Kiran M. Desai - One of the best experts on this subject based on the ideXlab platform.
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comparison of artificial neural network ann and response surface methodology rsm in fermentation media optimization case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Shrikant A. Survase, Parag S. Saudagar, S S Lele, Rekha S. SinghalAbstract:Abstract Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Parag S. Saudagar, Shrikant A. Survase, S S Lele, Rekha Satishchandra SinghalAbstract:Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively. © 2008 Elsevier B.V. All rights reserved.
Parag S. Saudagar - One of the best experts on this subject based on the ideXlab platform.
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comparison of artificial neural network ann and response surface methodology rsm in fermentation media optimization case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Shrikant A. Survase, Parag S. Saudagar, S S Lele, Rekha S. SinghalAbstract:Abstract Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.
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Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of Fermentative Production of scleroglucan
Biochemical Engineering Journal, 2008Co-Authors: Kiran M. Desai, Parag S. Saudagar, Shrikant A. Survase, S S Lele, Rekha Satishchandra SinghalAbstract:Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative Production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium. Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively. © 2008 Elsevier B.V. All rights reserved.
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Gellan gum: Fermentative Production, downstream processing and applications
Food Technology and Biotechnology, 2007Co-Authors: Ishwar B. Bajaj, Shrikant A. Survase, Parag S. Saudagar, Rekha Satishchandra SinghalAbstract:The microbial exopolysaccharides are water-soluble polymers secreted by microorganisms during fermentation. The biopolymer gellan gum is a relatively recent addition to the family of microbial polysaccharides that is gaining much importance in food, pharmaceutical and chemical industries due to its novel properties. It is commercially produced by C. P. Kelco in Japan and the USA. Further research and development in biopolymer technology is expected to expand its use. This article presents a critical review of the available information on the gellan gum synthesized by Sphingomonas paucimobilis with special emphasis on its Fermentative Production and downstream processing. Rheological behaviour of fermentation broth during Fermentative Production of gellan gum and problems associated with mass transfer have been addressed. Information on the biosynthetic pathway of gellan gum, enzymes and precursors involved in gellan gum Production and application of metabolic engineering for enhancement of yield of gellan gum has been specified. Characteristics of gellan gum with respect to its structure, physicochemical properties, rheology of its solutions and gel formation behaviour are discussed. An attempt has also been made to review the current and potential applications of gellan gum in food, pharmaceutical and other industries.
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Scleroglucan: Fermentative Production, downstream processing and applications
Food Technology and Biotechnology, 2007Co-Authors: Shrikant A. Survase, Ishwar B. Bajaj, Parag S. Saudagar, Rekha Satishchandra SinghalAbstract:Exopolysaccharides produced by a variety of microorganisms find multifarious industrial applications in foods, pharmaceutical and other industries as emulsifiers, stabilizers, binders, gelling agents, lubricants, and thickening agents. One such exopolysaccharide is scleroglucan, produced by pure culture fermentation from filamentous fungi of genus Sclerotium. The review discusses the properties, Fermentative Production, downstream processing and applications of scleroglucan.
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Statistical approach to optimization of Fermentative Production of gellan gum from Sphingomonas paucimobilis ATCC 31461.
Journal of Bioscience and Bioengineering, 2006Co-Authors: Ishwar B. Bajaj, Parag S. Saudagar, Rekha S. Singhal, Ashok PandeyAbstract:Gellan gum, a high-molecular-weight anionic linear polysaccharide produced by pure-culture fermentation from Sphingomonas paucimobilis ATCC 31461, has elicited industrial interest in recent years as a high-viscosity biogum, a suspending agent, a gelling agent, and an agar substitute in microbial media. In this paper we report on the optimization of gellan gum Production using a statistical approach. In the first step, the one factor-at-a-time method was used to investigate the effect of medium constituents such as carbon and nitrogen sources; subsequently, the intuitive analysis based on statistical calculations carried out using the L16-orthogonal array method. The design for the L16-orthogonal array was developed and analyzed using MINITAB 13.30 software. All the fermentation runs were carried out at 30±2°C on a rotary orbital shaker at 180 rpm for 48 h. In the second step, the effects of amino acids and gellan precursors such as uridine-5′-diphospate (UDP) and adenosine-5′-diphospate (ADP) on the Fermentative Production of gellan gum were studied. Media containing 4% soluble starch, 0.025% yeast extract, 1.0 mM ADP and 0.05% tryptophan gave a maximum yield of 43.6 g l−1 starch-free gellan gum, which was significantly higher than reported values in the literature.