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Statistical approach for production of PUFA from Kocuria sp. BRI 35 isolated from marine water sample.


ABSTRACT: In this study, Plackett-Burman design was used to identify the most influential parameters affecting PUFA production by Kocuria sp. BRI 35 isolated from Antarctic water sample. Amongst 10 variables evaluated, magnesium chloride, protease peptone, glucose, and temperature were significant. Response surface methodology consisting of a central composite design was developed to study the interactions between the variables and to determine optimal values of significant variables. A quadratic model (R = 0.9652, F = 14.64, P < 0.0001) was built. The contour plots indicated that the isolate produced maximum PUFA at lower concentrations of magnesium sulfate (0.9 g/L) and higher concentrations of protease peptone (5 g/L) and glucose (10 g/L) at 15°C. MgSO4 and glucose exhibited quadratic as well as interactive effect on PUFA production whereas protease peptone and temperature showed interactive effects only. After optimization, PUFA production per unit biomass increased from 0.94 mg/g to 11.12 mg/g. This represented an increase from 3% to 58.62% of the total fatty acids. Among PUFAs, the yield of ? -6 fatty acids increased from 9.66 mg/L to 107.71 mg/L with significant increase in linoleic acid (20.36 mg/L) whereas ? -3 fatty acids increased up to 12.37 mg/L with DHA being the major ? -3 fatty acid produced.

SUBMITTER: Pote S 

PROVIDER: S-EPMC4074494 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Statistical approach for production of PUFA from Kocuria sp. BRI 35 isolated from marine water sample.

Pote Swanandi S   Bhadekar Rama R  

BioMed research international 20140525


In this study, Plackett-Burman design was used to identify the most influential parameters affecting PUFA production by Kocuria sp. BRI 35 isolated from Antarctic water sample. Amongst 10 variables evaluated, magnesium chloride, protease peptone, glucose, and temperature were significant. Response surface methodology consisting of a central composite design was developed to study the interactions between the variables and to determine optimal values of significant variables. A quadratic model (R  ...[more]

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