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David F Gilmore

Publications and source records attributed to David F Gilmore.

2 recordsLinked to original sources

Modeling and optimization of biopolymer (polyhydroxyalkanoates) production from ice cream residue by novel statistical experimental design.

Polyhydroxyalkanoates (PHAs) are thermoplastic polyesters synthesized by Ralstonia eutropha and other bacteria as a form of intracellular carbon and energy storage and are accumulated as lipid inclusions in the cytoplasm of these bacteria. The modeling and optimization of PHA production by fermentation from industrial waste (ice cream residue) was studied by employing statistical experimental design methods. A series of iterative experimental designs was used to find optimal factor conditions (medium components and fermentation process time) in the order of fractional factorial design, path of steepest ascent, and full factorial augmented with axial design (rotational central composite design). An optimal range characterized by lipid (15 mg/mL) and % lipid (88%) values was found and further investigated to verify the optimal conditions for PHA production from ice cream (56.68 mL of ice cream or 56.68% ice cream in water [v/v], 5.03 mL of buffer, 1 mL of mineral salts solution, 100 muL of trace element solution, 100 mL of seed culture, and 213.76 h of fermentation time).

Analysis of Variance↗

Statistical experimental design for bioprocess modeling and optimization analysis: repeated-measures method for dynamic biotechnology process.

The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (time split-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.

Analysis of Variance↗