Optimization of α-amylase production by Bacillus sp. using response surface methodology
TL;DR: The combined effects of macronutrients of media on α-amylase production by Bacillus sp.
About: This article is published in Process Biochemistry.The article was published on 2005-06-01. It has received 393 citations till now. The article focuses on the topics: Central composite design & Response surface methodology.
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TL;DR: This review focuses on the production of bacterial and fungal α-amylases, their distribution, structural-functional aspects, physical and chemical parameters, and the use of these enzymes in industrial applications.
587 citations
TL;DR: The production of alkaline protease was optimized using a newly isolated Bacillus sp.
308 citations
TL;DR: In this article, the authors present the state-of-the-art applications of RSM in the optimization of different food processes such as extraction, drying, blanching, enzymatic hydrolysis and clarification, production of microbial metabolites, and formulation.
Abstract: Response surface methodology (RSM) is a technique widely used to optimize various processes. This review presents the state-of-the-art applications of RSM in the optimization of different food processes such as extraction, drying, blanching, enzymatic hydrolysis and clarification, production of microbial metabolites, and formulation. The principles of RSM, its implementation steps, and different designs (full factorial design (FFD), Box-Behnken design (BBD), and central composite design (CCD)) are described. Furthermore, this work presents a comprehensive study of RSM literature recently published about the various food process fields and evaluating their RSM elements summarized in tables. Finally, the challenges and future prospects of using this statistical technique in the food industry processes are discussed. It can be concluded that appropriate selection of RSM design, independent variables (screening), and levels of the factors significantly influences the successful application of RSM. In addition, validity evaluation of the optimum conditions predicted through RSM is crucial too.
271 citations
01 Jan 2006
TL;DR: The article surveys the a-amylase family and the major characteristics, microbial sources, production aspects, downstream processing, salient biochemical properties, industrial applications, enzyme engineering and some recent research developments.
Abstract: Summary This review covers the progress made in research on microbial a-amylase, a highly demanded industrial enzyme in various sectors such as food, pharmaceuticals, textiles, detergents, etc. Amylases are of ubiquitous occurrence and hold the maximum market share of enzyme sales. The article surveys the a-amylase family and the major characteristics, microbial sources, production aspects, downstream processing, salient biochemical properties, industrial applications, enzyme engineering and some recent research developments.
265 citations
TL;DR: In this paper, the reaction of Reactive Black 5 (RB5) in aqueous solution by peanut hull was studied by using Placket-Burman (PB) and Central Composite Design (CCD) to develop mathematical model equation.
246 citations
References
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Book•
01 Jan 1937
TL;DR: The third edition, coming ten years after the first, emphasizes both the flowering of biochemical research and the prodigious effort by busy teachers and scientists to keep up to date this popular text and reference.
Abstract: Principles of biochemistry , Principles of biochemistry , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
5,830 citations
"Optimization of α-amylase productio..." refers background in this paper
...This effect of glycerol can result from conversion of glycerol into dihydroxy acetone by entering to gcoltic pathway for formation of metabolic energy [16,17] The coefficient for linear effect of starch and relatively linear effect of YE, peptone and the interactive effect of glycerol and peptone may be significant to some extent....
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TL;DR: In this article, the Response Surface Methodology (RSM) is used for scheduling and scheduling in response surface methodologies, and it is shown that it can be used in a variety of scenarios.
Abstract: (1996). Response Surface Methodology. IIE Transactions: Vol. 28, Scheduling and Logistics, pp. 1031-1032.
4,299 citations
Book•
01 Jan 1987
TL;DR: In this paper, the authors present a methodology for estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments with block effects mixture designs and analyses nonlinear response surface models.
Abstract: Introduction to response surface methodology matrix algebra, least squares, the analysis of variance, and principles of experimental design first-order models and designs second-order models and designs determining optimum conditions methods of estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments response surface models with block effects mixture designs and analyses nonlinear response surface models Taguchi's robust parameter design additional topics and some directions for future research. Appendix: solutions to selected exercises.
1,480 citations
TL;DR: In this paper, the authors present a methodology for estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments with block effects mixture designs and analyses nonlinear response surface models.
Abstract: Introduction to response surface methodology matrix algebra, least squares, the analysis of variance, and principles of experimental design first-order models and designs second-order models and designs determining optimum conditions methods of estimating response surfaces that rival least squares based on the integrated mean squared error criterion analysis of multiresponse experiments response surface models with block effects mixture designs and analyses nonlinear response surface models Taguchi's robust parameter design additional topics and some directions for future research. Appendix: solutions to selected exercises.
1,384 citations