Effect of Experimental Factors

From the above statement the information content of a chemical measurement system can be evaluated by studying the effects of experimental factors on the degree of achievement of the informational goal(s). This is elaborated below. 

A study designed to determine the effects of experimental factors on information content might be based on the relationship defined by Equation (3) ... 

To estimate the effect of experimental factors on the basis of univariate and multivariate analyses of variance. 

Table 6.3 Effect of experimental factors on the sensory perception of strawberry yognrts...

In contrast to the situation for aluminum, electrochemical studies on the pitting of stainless steels are fi aught with contradictions because of the effect of experimental factors. Several investigations have used breakthrough potential or breakdown potential, as a measure of pitting susceptibility. As pointed out earlier, the current increases rapidly at the breakdown potential (Fig. 4.24), this is considered as an indication of a breakdown of the passive, corrosion-resistant steel film and initiation of pitting. 

There is an extensive amount of data in the literature on the effect of many factors (e.g. temperature, monomer and surfactant concentration and types, ionic strength, reactor configuration) on the time evolution of quantities such as conversions, particle number and size, molecular weight, composition. In this section, EPM predictions are compared with the following limited but useful cross section of isothermal experimental data ... 

The experimental studies of the surface properties of monocrystals of oxides of various metals recently conducted at well-controlled conditions [32, 210] enable one to proceed with detailed analysis of separate effects of various factors on characteristics of semiconductor gas sensors. In this direction numerous interesting results have been obtained regarding the fact of various electrophysical characteristics of monocrystalline adsorbents on the value of adsorption-related response. Among these characteristics there are crystallographic orientation of facets [211], availability of structural defects, the disorder in stoichiometry [32], application of metal additives, etc. These results are very useful while manufacturing sensors for specific gases with required characteristics. 

Also, a chromatographic profile or fingerprint of trace unknowns can be established and monitored, so that if product performance unexpectedly changes, there will be a starting point for troubleshooting. The effects of experimental variables on sample recoveries should be measured directly by controlled variation of an experimental factor, using the reference standard, or suitable external standards, or spiked addition of an external standard to the reference standard. A detailed example of the use of internal and external standards is presented in Chapter 4. 

The target for optimization in FTA with CL detection is to adjust all experimental factors in such a way so that the detector views as much radiation as possible while the chemiluminescent solution flows through the cell. Hence the kinetics of the flow and detector system should be monitored to match the kinetics of the reaction and generate maximum intensity inside the cell. The effect of experimental variables on the CL signal cannot be exactly predicted in advance and there is not enough theoretical background to support any suggestion. 

Consideration of the effect of experimental design on the elements of the variance-covariance matrix leads naturally to the area of optimal design [Box, Hunter, and Hunter (1978), Evans (1979), and Wolters and Kateman (1990)]. Let us suppose that our purpose in carrying out two experiments is to obtain good estimates of the intercept and slope for the model yj, = Po + Pi i, + r,. We might want to know what levels of the factor x , we should use to obtain the most precise estimates of po and... 

Although the coefficients of determination and the correlation coefficients are conceptually simple and attractive, and are frequently used as a measure of how well a model fits a set of data, they are not, by themselves, a good measure of the effectiveness of the factors as they appear in the model, primarily because they do not take into account the degrees of freedom. Thus, the value of R can usually be increased by adding another parameter to the model (until p =J), but this increased R value does not necessarily mean that the expanded model offers a significantly better fit. It should also be noted that the coefficient of determination gives no indication of whether the lack of perfect prediction is caused by an inadequate model or by purely experimental uncertainty. 

One purpose of a good design is to minimize uncertainty and maximize information over the region of interest. We will use both normalized uncertainty and normalized information to discuss the effect of experimental design on the quality of information obtained from a two-factor FSOP model. 

Coding is sometimes used as a way of sweeping these considerations under the rug, of ignoring this effect of units. It is often assumed that the researcher will use an experimentally relevant domain or meaningful coded levels of the factors so that the bare 8y will show the effect of the factor at the extremes of this domain. If this is understood explicitly, then no harm comes of it. But if the experimenter is unaware of these influences of units and coding, misinterpretation of the results is easily possible. 

Experimental Design 1. Due to the large scale of each run, an experimental design with a minimum number of runs was employed to obtain the desired information. The one half fractional factorial in Table II was set up to determine the effect of each factor. 

The overall effect of these factors can be determined experimentally and it is convenient to reduce them in order to increase the operational stability of the biocatalyst. 

Computation of the effect of the factors on the response(s) of the method, to derive which factors might have experimentally relevant effects ... 

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