Factorial design, analytical methods

As an analytical method becomes more complex, the number of factors is likely to increase and the likelihood is that the simple approach to experimental design described above will not be successful. In particular, the possibility of interaction between factors that will have an effect on the experimental outcome must be considered and factorial design [2] allows such interactions to be probed. 

DQ is performed by the supplier of the equipment or system at the supplier s factory as part of the factory acceptance test (FAT). IQ (based on site acceptance test—SAT), OQ, and PQ are performed on-site at the GMP facility. For a GMP manufacturing facility, the validation activities include the facility design, FTVAC system, environment control, laboratory and production equipment, water system, gases and utilities, cleaning, and analytical methods. Validation protocols (IQ, QQ, and PQ) are prepared for each item, listing all critical steps and acceptance criteria. Deviations are reviewed and resolved before the validation activity proceeds to the next phase. 

Chemometric techniques have been frequently used for optimization of analytical methods, as they are faster, more economical and effective and allow more than one variable to be optimized simultaneously. Among these, two level fractional factorial design (2 ) is used mainly for preliminary evaluation of the significance of the variables and its interactions [1]. 

Abstract A preconcentration method using Amberlite XAD-16 column for the enrichment of aluminum was proposed. The optimization process was carried out using fractional factorial design. The factors involved were pH, resin amount, reagent/metal mole ratio, elution volume and samphng flow rate. The absorbance was used as analytical response. Using the optimised experimental conditions, the proposed procedure allowed determination of aluminum with a detection limit (3o/s) of 6.1 ig L and a quantification limit (lOa/s) of 20.2 pg L, and a precision which was calculated as relative standard deviation (RSD) of 2.4% for aluminum concentration of 30 pg L . The preconcentration factor of 100 was obtained. These results demonstrated that this procedure could be applied for separation and preconcentration of aluminum in the presence of several matrix. 

Factorial design, analytical methods, 624 Fast Blue B, Upid peroxidation visualization, 670... 

Halcon Process, t-butyl hydroperoxide, 428 Half-fractional factorial design, analytical methods, 624... 

The evaluation of robustness is normally considered during the development phase and depends on the type of procedure under study. Experimental design (e.g., fractional factorial design or Plackett-Burman design) is common and useful to investigate multiple parameters simultaneously. The result will help to identify critical parameters that will affect the performance of the method. Common method parameters that can affect the analytical procedure should be considered based on the analytical technique and properties of the samples ... 

Operating parameters usually need to be optimized when we develop an analytical method. The least efficient way to do this is to vary one parameter at a time while keeping everything else constant. More-efficient procedures are called fractional factorial experimental design7 and simplex optimizationSection 7-8 provides an example of the efficient design of a titration experiment. 

Bos CE, Bolhuis GK, Smilde AK, DeBoer JH. The use of a factorial design to evaluate the physical stability of tablets after storage under topical conditions. In Hendriks MMWB, DeBoer JH, Smilde AK, eds. Robustness of analytical chemical methods and pharmaceutical technological products. Amsterdam Elsevier, 1996 309-341. 

Figure A2.4 compares the CDFs for intake obtained from factorial design and DPD methods with the exact analytical solution for the CDF of intake. The 27 data points from the DPD and factorial methods were used to plot the empirical CDF shown in Figure A2.4. Figure A2.5 compares the CDF for intake obtained from 2000 Monte Carlo simulations with the exact analytical solution for the CDF of intake. Figure A2.6 compares the CDF obtained from 200 Latin hypercube sampling Monte Carlo simulations with the exact analytical solution for the CDF of intake. The Monte Carlo and Latin hypercube sampling empirical CDFs were plotted using all simulation outcomes.

Figure A2.4 Comparison of the CDFs for intake obtained from factorial design and DPD methods with the exact analytical solution for the CDF of intake and with the worst-case scenario...

There are several ways to study the effect of various experimental factors on an analytical method. The classical method of studying one variable at a time while holding others constant is extremely inefficient. Other approaches such as regression analysis and complete factorial designs involve a large number of experiments and are also inefficient. For example, a factorial design of seven factors at two levels requires 2 or 128 experiments. Therefore, alternate approaches which reduce the experimental work are very attractive. 

In observational studies, the main design issue is the choice of the sample size, whereas sample size determination and treatment choice are the primary design issues in factorial experiments. Sample size determination depends on the analytical method used to identify the genes with different expression and the optimality requirements selected for the study. These topics are examined in the next two sections. 

The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters and provides an indication of its reliability during normal usage. During the development phase of the analytical procedure, susceptible parameters should be identified, for example, stability of analytical solutions, extraction time, pH and composition of mobile phase, column lots and suppliers, temperature, flow rate, etc. A factorial design is encouraged. 

Ruggedness the capacity of an analytical method to remain unaffected and produce accurate data in spite of small but deliberately introduced changes in experimental conditions. Fractional factorial or Plackett Burman designs are frequently used to screen the impact of those changes. 

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