Design statistical experimental

In the development of a SE-HPLC method the variables that may be manipulated and optimized are the column (matrix type, particle and pore size, and physical dimension), buffer system (type and ionic strength), pH, and solubility additives (e.g., organic solvents, detergents). Once a column and mobile phase system have been selected the system parameters of protein load (amount of material and volume) and flow rate should also be optimized. A beneficial approach to the development of a SE-HPLC method is to optimize the multiple variables by the use of statistical experimental design. Also, information about the physical and chemical properties such as pH or ionic strength, solubility, and especially conditions that promote aggregation can be applied to the development of a SE-HPLC assay. Typical problems encountered during the development of a SE-HPLC assay are protein insolubility and column stationary phase... 

Application of Statistical Experimental Design to Development of Low-Density Polymer Foams... 

We are applying the principles of statistical experimental design to the development of low-density polymer foams for use as direct-drive high-gain targets for Laser Inertial Confinement Fusion (ICF). Both polystyrene (PS) and resorcinol-formaldehyde (RF) foams are being developed. 

Using experimental design such as Surface Response Method optimises the product formulation. This method is more satisfactory and effective than other methods such as classical one-at-a-time or mathematical methods because it can study many variables simultaneously with a low number of observations, saving time and costs [6]. Hence in this research, statistical experimental design or mixture design is used in this work in order to optimise the MUF resin formulation. 

Figure 11.7. Experimental combinations in statistical experimental design for three variables.

Statistical experimental design is characterized by the three basic principles Replication, Randomization and Blocking (block division, planned grouping). Latin square design is especially useful to separate nonrandom variations from random effects which interfere with the former. An example may be the identification of (slightly) different samples, e.g. sorts of wine, by various testers and at several days. To separate the day-to-day and/or tester-to-tester (laboratory-to-laboratory) variations from that of the wine sorts, an m x m Latin square design may be used. In case of m = 3 all three wine samples (a, b, c) are tested be three testers at three days, e.g. in the way represented in Table 5.8 ... 

In point of fact, the sciences of both statistics and chemometrics each have their own approach to how experiments should be designed, each with a view toward making experimental procedures better in some sense. There is a gradation between the two approaches, nevertheless there is also somewhat of a distinction between what might be thought of as classical statistical experimental design and the more currently fashionable experimental designs considered from a chemometric point of view. These differences in approach reflect differences in the nature of the information to be obtained from each. 

Experimental designs, and in particular statistical experimental designs, are used in order to achieve one or more of the following goals ... 

Increase efficiency of resource use, that is, obtain the desired information using the fewest possible necessary experiments (this is usually what is thought of when statistical experimental designs are considered). This aspect of experimentation is particularly important when the experiment is large to begin with, or if the experiment uses resources that are rare or expensive, or if the experiment is destructive, so that materials (especially expensive ones) are used up. 

The key to all statistical experimental designs is planning. A properly planned experiment can achieve all the goals set forth above, and in fewer runs than you might expect (that s where achieving the goal of efficiency comes in). However, there are certain requirements that must be met ... 

Blunders must be eliminated, and all specified data must be collected. The efficiency of these experimental designs has another side effect any missing or defective data has a disproportionate effect relative to the amount of information that can be extracted from the final data set. When simpler experimental designs are used, where each piece of data is collected for the sole purpose of determining the effect of one variable, loss of that piece of data results in the loss of only that one result. When the more efficient statistical experimental designs are used, each piece of data contributes to more than one of the final results, thus each one is used the equivalent of many times and any missing piece of data causes the loss of all the results that are dependent upon it. 

Thus, when statisticians got into the act, there saw a need to retain the information that was not included in the one-at-a-time plans, while still keeping the total number of experiments manageable the birth of statistical experimental designs . Several types of statistical experimental designs have been developed over the years, with, of course,... 

In order to cover these nine variables adequately, a statistical experimental design was calculated. The statistical experimental design requires the use of 15 simulations for each plenum size. Simulations were repeated using 3 and 10 plenums. 

Adequate resolution of the components of a mixture in the shortest possible time is nearly always a principal goal. Establishing the optimum conditions by trial and error is inefficient and relies heavily on the expertise of the analyst. The development of computer-controlled HPLC systems has enabled systematic automated optimization techniques, based on statistical experimental design and mathematical resolution functions, to be exploited. The basic choices of column (stationary phase) and detector are made first followed by an investigation of the mobile phase composition and possibly other parameters. This can be done manually but computer-controlled optimization has the advantage of releasing the analyst for other... 

This chapter will examine the application of statistical experimental design to designing a product or process that is robust to variation from environmental variables. It should be understood that the phrase environmental variables is to be viewed broadly and is not just limited to variables such as temperature and humidity. In this context, variation from environmental variables is variation that is external to the product and that is outside of the control of the manufacturer during production. Thus, it might also include variation in the conditions in which the customer uses the product, or in the conditions in which the product is stored, or in how the product is maintained and serviced. 

In this chapter the use of statistical experimental designs in designing products and processes to be robust to environmental conditions has been considered. The focus has been on two classes of experimental design, response surface designs and split-plot designs. 

R. H. Lochner, J.E. Matar, Designing for quality an introduction to the best of Taguchi and western methods of statistical experimental design. Chapman and Hall, London-Madras, 1990. 

For our validations, a CVp is a pooled estimate calculated from the particular type of statistical data set (36 samples) described earlier in the Statistical Experimental Design section of this report. A statistical procedure is given in Hald JL for determining an upper confidence limit for the coefficient of variation. This general theory had o be adapted appropriately for application to a pooled CVp estimate. For this design, and under the stated assumptions, there is a one-to-one correspondence between values of CVp and upper confidence limits for CVp. Therefore, the confidence limit criterion given above is equivalent to another criterion based on the relationship of CVp and its critical value. The... 

We have presented a statistical experimental design and a protocol to use in evaluating laboratory data to determine whether the sampling and analytical method tested meets a defined accuracy criterion. The accuracy is defined relative to a single measurement from the test method rather than for a mean of several replicate test results. Accuracy here is the difference between the test result and the "true value, and thus, must combine the two sources of measurement error ... 

It is very important to apply the same oxidation conditions in each batch, and vary only one parameter at a time, unless statistical experimental design is being applied. Be especially careful to assure constant liquid ozone concentration cL as well as constant pH. Both are often observed to vary within the reaction time in waste water ozonation. The liquid ozone concentration shows an increase whereas the pH normally decreases due to the formation... 

Muir, B., Quick, S., Slater, B J. et al. (2005) Analysis of chemical warfare agents II. Use of thiols and statistical experimental design for the trace level determination of vesicant compounds in air samples. Journal of Chromatography A, 1068(2), 315-26. 

Table 4 Analysis of Statistical Experimental Design Data...

Nguyen NAT, Wells ML, Cooper DC. Identification of factors affecting preservative efficacy and chemical stability of lamivudine oral solution through statistical experimental design. Drug Dev Ind Pharm 1995 21 1671-1682. 

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