Optimization strategy interpretive

The efficient utilization of most, if not all, systematic optimization strategies requires an understanding of basic chromatographic principles. Such an understanding also greatly facilitates the interpretation of the results. 

The report commented above for the determination of sulfonamides in pharmaceuticals [11] is a useful example of the development of an analytical procedure, where a sequential optimization is made. Next, the development of a procedure for the analysis of mixtures of p-blockers and diuretics [14] will show the usefulness of the interpretive optimization strategy shown in Chapter 8, which was assisted by the software (see Appendix I). 

Although the U-like shape of the warning NNTs aUow for an easy determination of the optimum range with respect to the parameter in variation (i.e., warning TTC in this example), the L-like shape of the intervention NNT requires a more subtle interpretation in order to find an optimum. The question is how both NNTs have to be interpreted in combination to find the optimal strategy and the operating point for the whole system. It is evident that a quantity including both of these characteristics is needed. 

Typically, resolution diagrams in MLC are complex, with several local maxima, frequently denoting interaction between factors. For this reason, reliable optimal conditions require considering all factors simultaneously, by applying an interpretive optimization strategy (i.e., based on the description of the retention behavior and peak shape of solutes). In this task, the product of free peak areas or purities has proved to be the best optimization criterion. An interactive computer program is available to obtain the best separation conditions in... 

The CASVB strategy [1-9] uses a very efficient algorithm for the transformation of CASSCF [10] structure spaces, for the interpretation of CASSCF wavefunctions, and for the fully-variational optimization of VB wavefunctions. Important features for the quality of the final description include the unbiased optimization of both the VB orbitals and the mode of spin coupling, and also flexibility in the choice of the form of wavefunction. 

Selecting the right variables often improves the models and makes interpretation easier. When there are too many descriptors, and especially when these descriptors do not have a clear physico-chemical meaning (e.g., connectivity indices and other 2D descriptors), stochastic methods such as genetic algorithms and evolutionary strategies can be used for finding an optimal subset of descriptors [91,92]. 

The strategy used to reach an accurate interpretation of the STM experimental results is to, whenever possible, perform DFT calculations on appropriate model adsorption systems and then to use the optimized DFT structures as input for subsequent STM image simulations [1]. We now briefly describe the details of both of these types of calculation. 

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