MEDIUM OPTIMIZATION FOR THE PRODUCTION OF BIOMASS BY Cunninghamella sp. 2A1 USING RESPONSE SURFACE METHODOLOGY

Abstract

A statistical design approach has been used to optimize the production of biomass by Cunninghamella sp. 2A1, evaluated based on lipidless biomass. A 2 3 full factorial central composite design (CCD) was chosen to study the combined effects of three factors; ammonium tartrate, peptone and glucose concentrations. The p-value for each factors was < 0.05 suggesting that these factors have significant effect on the production of lipidless biomass. The production is represented by a linear model with p-value < 0.0001. The optimized medium consists of 3.86 g/l ammonium tartrate, 55.84 g/l glucose and 7.73 g/l peptone predicted 16.83 g/l lipidless biomass. Results from four replications based on the optimized medium produced 18.48 g/l lipidless biomass, which are in close agreement with the predicted value. The coefficient for glucose was the highest indicating it as the most significant factor affecting lipidless biomass production.

Directed‐Vat‐Set starter producing ACE ‐inhibitory peptides: Optimization and evaluation of stability

“…The effects of variables were determined by the F ‐test and the more obvious effect on the variables with the lower p ‐value. The R ‐squared value provided a measure of the variability of the response values that could be explained by the experimental factors and their interactions (Sulaiman et al, 2005).…”

“…The effects of variables were determined by the F-test and the more obvious effect on the variables with the lower p-value. The R-squared value provided a measure of the variability of the response values that could be explained by the experimental factors and their interactions(Sulaiman et al, 2005).The model of response OD was highly significant (p < .001) and lack of fit was not significant (p = .3367 > .05), indicating that the quadratic regression model could be used to analyze and predict the response OD. The coefficient of determination R 2 reached .975, manifesting that the model could be illustrated the change of 97.50% of the response value.…”

Application of RSM for optimization of glutamic acid production by Corynebacterium glutamicum in bath culture

“…Overcome these problems, the optimization by RSM is an effective strategy to access optimum conditions in multiple variable systems. RSM reduces the number of tests, save time and need for complex measurements to analyze data [16] and used in a lot of researches [28,29,30,31]. RSM successfully have been used to optimize fermentation of C. glutamicum to improve different metabolites [26,32,33] and Glu production [34,35,36].…”