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The optimum process conditions were determined by analyzing the response surface of a three-dimensional plot and by solving the regression model equation.
The optimum process conditions were determined by analyzing response surface three-dimensional surface plot and contour plot and by solving the regression model equation with Design Expert software.
By solving the regression equation and analyzing 3-D plots, the optimum condition was at extraction temperature 100 °C, time 3.5 h, numbers 4 and ratio of water to raw material 13.6.
By solving the regression equation and analyzing the data, the optimum conditions were obtained as follows: extracting time 1.52 h, extraction temperature 91.40 °C, and the ratio of water to raw material at 24.78.
By solving the regression equation and also by analyzing the response surface contour plots, the optimal process parameters were determined: fermentation temperature 28.57 °C, fermentation time 7.82 d and inoculum volume 12.57 ml.
By solving the regression equation and analyzing 3D plots, the optimum condition was at extraction temperature 70 °C, time 3 h, numbers 3 and ratio of water to raw material 18.5 mL/g.
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The optimum values of the selected variables were obtained by solving the quadratic regression model, as well as by analysing the response surface contour plots.
The optimum values of the selected variables were found by solving the quadratic regression model and analyzing the response surface plots.
By solving the quadratic regression equation using appropriate statistic methods, the optimum concentration values for obtaining 876.32 μg extracellular polysaccharide per milliliter of cultivation medium were determined: yeast extract 6.0 g l−1, fructose 11.5 g l−1, magnesium sulfate 0.5 g l−1, maltose 9.6 g l−1, zinc chloride 38.6 mg l−1 and initial pH value 5.3.
Nevertheless, this assumption is not crucial in solving the regression.
Then solving the regression Y = b1X1+.+bhXh yields the estimated haplotype frequencies.
More suggestions(15)
by solving the problem
by solving the normalization
by solving the set
by solving the scalar
by exponentiating the regression
by solving the system
by solving the master
by solving the convex
by solving the cholera
by solving the model
by solving the inverse
by solving the right
by using the regression
by repeating the regression
by dividing the regression
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