2007
DOI: 10.1007/s00449-007-0172-6
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Application of reverse micelle extraction process for amylase recovery using response surface methodology

Abstract: The effect of different process variables of reverse micelle extraction process like pH, addition of surfactant (AOT) concentration and potassium chloride (KCl) concentration on amylase recovery has been studied and analysed. Solid-state fermentation was used for the production of amylase enzyme. Response surface methodology (RSM) using central composite rotatable design (CCRD) was employed to analyse and optimize the enzyme extraction process. The regression analysis indicates that the effect of AOT concentra… Show more

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Cited by 17 publications
(8 citation statements)
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“…This decrease in positive charge in turn reduces electrostatic interaction between the negatively charged surfactant head group and target protein. Similar observation of decrease in extraction of target protein as the pH reaches to its isoelectric point was reported for papain,27 amylase,26 and nattokinase 28…”
Section: Resultssupporting
confidence: 81%
See 1 more Smart Citation
“…This decrease in positive charge in turn reduces electrostatic interaction between the negatively charged surfactant head group and target protein. Similar observation of decrease in extraction of target protein as the pH reaches to its isoelectric point was reported for papain,27 amylase,26 and nattokinase 28…”
Section: Resultssupporting
confidence: 81%
“…The decrease in activity recovery beyond pH 6.0 is due to decrease in electrostatic interaction and in charge density on lactoperoxidase as pH reaches to its isoelectric point. Bera et al26 reported that as the pH of the protein approaches its isoelectric point the positive charge density of the protein decreases. This decrease in positive charge in turn reduces electrostatic interaction between the negatively charged surfactant head group and target protein.…”
Section: Resultsmentioning
confidence: 99%
“…A second-order polynomial equation model was used to fit the Y and PF data with interaction terms as given below: Y i = A 0 + A i Σx i + A ij Σx i x j + A ii Σx i 2 (i≠j), where Y i is the predicted response value; A 0 , A i , A ij and A ii are the regression coefficients of the model for the intercept, linear, cross-product and quadratic terms, respectively; and x i is the variable studied [ 47 , 48 ].…”
Section: Methodsmentioning
confidence: 99%
“…1) for the effects of the interaction variables on diosgenin yield were generated to determine the interactions among these factors and their optimum concentration values. In the plots, the responses were studied taking two variables at a time while keeping the other one at the '0' level [16][17][18][19][20][21][22][23]. Figure 1b showed the interaction of peptone and Tween 80.…”
Section: Optimization Of Medium Compositionmentioning
confidence: 99%
“…On the other hand, response surface methodology (RSM) could be used for such purposes. Hitherto, RSM has been widely used in the optimization of microbial transformation process by building models, designing experiments, evaluating the effects of variables and optimizing conditions [16][17][18][19][21][22][23].…”
Section: Introductionmentioning
confidence: 99%