Plackett-Burman designs types, design

These two case studies are good examples of ruggedness tests that focus on the type of data analysis described in Section 3.4.6 and where one prefers not to carry out a statistical analysis. They will be described in detail later in this book (Chapter 5). The factors are examined at three levels in reflected Plackett-Burman designs. From the results of the designs normalized effects, Ex(%), are calculated. No statistical... 

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 ... 

Screening. These types of experiments involve seeing which factors are important for the success of a process. An example may be the study of a chemical reaction, dependent on proportion of solvent, catalyst concentration, temperature, pH, stirring rate, etc. Typically 10 or more factors might be relevant. Which can be eliminated, and which should be studied in detail Approaches such as factorial or Plackett-Burman designs (Sections 2.3.1-2.3.3) are useful in this context. 

The significance of the effects is evaluated on the basis of either (1) a separate estimate of the standard deviation of measurements of the same type (materials, conditions) as conducted for the Plackett-Burman design or (2) repetition of the design a second time to provide for two estimates of each factor effect. 

Several types of designs are available, and the choice of design depends on the number of variables and how detailed the information has to be. A full factorial design is a good choice when the number of variables is four or less. When more than four variables are of interest, a fractional design is applicable. With a large number of variables, a Plackett-Burman design (PBD) [164, 165] is the preferred choice. 

In both the Factorial Design and the Plackett-Burman Design all the variables are studied at two levels. An interesting property is that these designs can be applied to both types of variables. In the case of quantitative variables the — 1 level is usually (but not always) assigned to the lower level and the +r to the higher level in the case of qualitative variables the — 1 and -1-1 levels are arbitrarily assigned. 

The ruggedness of a method can be tested using two types of experimental designs. Procedure related factors at the one hand are examined mainly in screening designs of the Plackett-Burman or... 

A Plackett-Buiman experimental design for screening has been used to evaluate the influence of several variables on the analytical response. A Plackett-Burman type (III) resolution design for six factors was carried out. A central composite design was used to optimise the final conditions. 

HPLC-HG-AFS To establish which factors are statistically significant, a Plackett-Burman experimental design, with type III resolution and four degrees of freedom, was carried out. The optimisation of the two relevant variables was accomplished by an orthogonal central composite design ( +star). 

Various types of statistical experimental design (DoE) can, in principle, be used for the robustness test of an HPLC method. However, only a few of them are practicable, e.g. fractional factorial test schedules [13, 27, 31-33] and Plackett-Burman test schedules [15, 16, 18, 31],... 

In the following example, concerning the determination of the content of active components in pharmaceutical formulations, robustness tests of HPLC methods by means of statistical experimental design (DoE) of the Plackett-Burman type are explained in more detail. 

Strictly speaking, it is not the experimental design that is a Hadamard design but the model matrix X. But this notation is now common and we will use it. In order to build this type of experimental design, it is advised to use the mode of generation proposed by Plackett and Burman (6), which we review here Knowing the number of factors k, we determine the minimum number of experiments needed to study a polynomial model of degree 1 = it + I. and we seek the... 

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