Three data pairs method

Several studies have employed chemometric designs in CZE method development. In most cases, central composite designs were selected with background electrolyte pH and concentration as well as buffer additives such as methanol as experimental factors and separation selectivity or peak resolution of one or more critical analyte pairs as responses. For example, method development and optimization employing a three-factor central composite design was performed for the analysis of related compounds of the tetracychne antibiotics doxycycline (17) and metacychne (18). The separation selectivity between three critical pairs of analytes were selected as responses in the case of doxycycline while four critical pairs served as responses in the case of metacychne. In both studies, the data were htted to a partial least square (PLS) model. The factors buffer pH and methanol concentration proved to affect the separation selectivity of the respective critical pairs differently so that the overall optimized methods represented a compromise for each individual response. Both methods were subsequently validated and applied to commercial samples. 

Absolute rate constants for electron transfer reactions of aromatic molecules in solution have been determined by the pulse radiolysis method for three additional pairs of aromatic compounds. In two of these cases in which an electron transfer equilibrium is established, the rate constant for the back reaction has also been determined. The equilibrium constant has been estimated from the kinetic data. A correlation of the experimental rate constants with the theory for homogeneous electron transfer rates is considered. 

The Coats and Redfem equation (either in original form or as modified by Reich and Stivala) provides the basis for several numerical procedures for determining a value of n. One such method requires only three (a,T) data pairs when starting with an equation in the form derived by Reich and Stivala. An examination of Eq. (8.16) reveals that when calculations are performed using pairs of (o ,T) data a constant value for Ea/R will be obtained only if n has the correct value. Any other (incorrect) value for n will result in the calculated values for Ej k showing a trend. 

Section 15.4 provides a discussion of similarity measures, which depend on three factors (1) the representation used to encode the desired molecular and chemical information, (2) whether and how much information is weighted, and (3) the similarity function (sometimes called the similarity coefficient) that maps the set of ordered pairs of representations onto the unit interval of the real line. Each of these factors is discussed in separate subsections. Section 15.5 presents a discussion of a number of questions that address significant issues associated with MSA Does asymmetric similarity have a role to play Do two-dimensional (2D) similarity methods perform better than three-dimensional (3D) methods Do data fusion and consensus similarity methods exhibit improved results Are different similarity measures statistically independent How do we compare similarity methods Can similarity measures be validated S ection 15.6 provides a discussion of activity landscapes... 

A straightforward extension of the three-point technique is to utilize a larger number of measured AE-i data pairs, and to analyze the data by using some kind of numerical curve-fitting procedure, usually a nonlinear least-squares method. This increases... 

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