2013
DOI: 10.5402/2013/528708
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The Use of Response Surface Methodology as a Statistical Tool for Media Optimization in Lipase Production from the Dairy Effluent Isolate Fusarium solani

Abstract: The optimization of extracellular lipase production by Fusarium isolani strain SKWF7 isolated from dairy wastewater was carried out in this study. Initially, the physicochemical factors significantly influencing enzyme production were studied by varying one-factor-at-a-time (OFAT). A mesophilic temperature of 40°C, alkaline pH of 8, and incubation period of 72 hours were found to be the optimal conditions for lipase production. Among the media components, the disaccharide sucrose acted as the best carbon sourc… Show more

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Cited by 33 publications
(22 citation statements)
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References 26 publications
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“…The analysis indicated significant quadratic models for the respective responses (< 0.0001), and adequate to denoting the mathematical relationship between variables and to measuring the degree of variability in response variables accounted for by the experimental process variables and their specific interactions [39]. The quadratic models were acknowledged good and adequate for the prediction of the responses, owing to their high R 2 values, low p values and high F values, respectively [38,40]. It was reported that the magnitude of adjusted and predicted R 2 values can be applied as a measure of proximity of experimental and predicted values [40,41].…”
Section: Discussionmentioning
confidence: 83%
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“…The analysis indicated significant quadratic models for the respective responses (< 0.0001), and adequate to denoting the mathematical relationship between variables and to measuring the degree of variability in response variables accounted for by the experimental process variables and their specific interactions [39]. The quadratic models were acknowledged good and adequate for the prediction of the responses, owing to their high R 2 values, low p values and high F values, respectively [38,40]. It was reported that the magnitude of adjusted and predicted R 2 values can be applied as a measure of proximity of experimental and predicted values [40,41].…”
Section: Discussionmentioning
confidence: 83%
“…The quadratic models were acknowledged good and adequate for the prediction of the responses, owing to their high R 2 values, low p values and high F values, respectively [38,40]. It was reported that the magnitude of adjusted and predicted R 2 values can be applied as a measure of proximity of experimental and predicted values [40,41]. In the present study, the adjusted and predicted R 2 values for the respective responses were in close conformity, with indication of high correlation between the experimental and predicted data (Table 3).…”
Section: Discussionmentioning
confidence: 99%
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“…Statistical optimizations such as RSM are studies where participating parameters are varied simultaneously for optimization experiments (Greasham and Inamine, 1986; Greasham and Herber, 1997; Zhang et al, 2000; Margarita et al, 2005). RSM can collectively optimize all the affecting parameters to eliminate the limitations with single-factor optimization process (Gough et al, 1996; Dubey et al, 2011; Kanmani et al, 2013). We therefore, attempted to decipher and model glucose and NaCl content of the growth medium, using R. toruloides.…”
Section: Introductionmentioning
confidence: 99%
“…This method is feasible and practical when only a limited number of variables are involved in the experiment, but when the variables involved are large then this method becomes laborious and necessitates a daunting number of trials compounded with a drawback as it fails to incorporate the interaction effects between the variables on the process (Kanmani et al 2013).…”
Section: Introductionmentioning
confidence: 99%