Here’s a representation of a classic central composite design for 2 factors.
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The central composite design and three-level full factorial design created significantly better models comparing to the other methods. As the central composite design requires a smaller number of experiments, its models were used for theoretical examination of experimental space.
Numeric Factors: The number of numeric factors involved in the experiment
Categoric Factors: The number of categoric factors involved in the experiment
Note
For CCD’s the number of runs required for the numeric factors will be multiplied by the number of categoric combinations. If two categoric factors having three levels each are added to this design the number of runs will be multiplied by nine. Consider optimal designs when you have both numeric and categoric factors.
Enter factor ranges in terms of …
+/-1 to define the limits for the area of interest where the optimum is believed to exist. Axial points will typically be outside this limit. Enter the limits in the Low and High columns.
alphas to ensure that even the extreme axial runs are within the area of operability. The area of interest must be within the area of operability. Enter the limits in the -alpha and +alpha columns.
Type: There will be different options available in this pull down based on how many factors are to be included in the experiment. The designs listed here form the factorial core of the central composite design. The default suggestion is the largest fraction that will produce a design under 1000 runs or maintain at least resolution V behavior. Smaller fractional cores can be used to save on the budget. For six or more factors we recommend using the Min Run Res V type to get both good estimates while keeping the number of runs under control. Small designs are the smallest recognized central composite design and are not recommended due to possible analysis issues.
Blocks: Central composite designs can be split into blocks. The factorial design is split into sub-fractions that support the two-factor interaction model and the axial points in the final block if needed.
Options button: Click on the options button to change the axial (alpha) distance which is how far the star points will be from the center in coded units. Use the help button on the options dialog for more information.
The Points area provides a preview of the number and type of runs that will be in the design.
This paper presents the development and optimization of a liquid chromatographic method for the determination of fluconazole and its impurities by experimental design methodology. Four experimental design types were applied: two-level full factorial design, central composite design, Box-Behnken design, and three-level full factorial design. The advantages and drawbacks of each design are described and detailed statistical evaluation of mathematical models was performed. The central composite design and three-level full factorial design created significantly better models comparing to the other methods. As the central composite design requires a smaller number of experiments, its models were used for theoretical examination of experimental space. Multiobjective optimization aiming to achieve maximal separation of all investigated substances and minimal analysis duration was performed by a grid point search. The defined optimal separation was achieved on a C18 (125 mm × 4 mm, 5 µm particle size) column with a mobile phase consisting of acetonitrile and water (5 mM ammonium formate) (15:85, v/v); a column temperature of 25°C; a flow rate of 1.2 mL min−1; and a detection wavelength of 260 nm.
Keywords: Chemometrics, Experimental design, Fluconazole, Impurities, Liquid chromatography
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