Abstract
The paper illustrates innovative ways of using the CARSO (Computer Aided Response Surface Optimization) procedure for response surfaces analyses derived by DCM4 experimental designs in multivariate spaces. Within this method, we show a new feature for optimization studies: the results of comparing their quadratic and linear models for discussing the best way to compute the most reliable predictions of future compounds.
Highlights
Following our recent papers [1]-[3], where we showed a new strategy for collecting the data needed for defining a response surface on the basis of an innovative method that requires only a very low number of experimental data, based on Double Circulant Matrices (DCMs)
The data reported in the Tables give a clear answer to our question: which of the models can be defined the most reliable for new predictions, to be computed by the data of Table 1. The discussion of these results clearly shows that the Q1 model is by far the best, with respect to L1, while L2 is the worst, because its predictions cover intervals from very high to very low, outside from those of the experimental data. Because of this the best way of describing the trends of a series of compounds appear to be a quadratic model, that finds out reliable results, usually within the explored space
The best way of describing the trends of a series of compounds is a quadratic model that finds out reliable results, usually within the explored space
Summary
Following our recent papers [1]-[3], where we showed a new strategy for collecting the data needed for defining a response surface on the basis of an innovative method that requires only a very low number of experimental data, based on Double Circulant Matrices (DCMs). They are similar to Central Composite Designs (CCDs), which represent the best way to generate response surfaces. (2015) The CARSO (Computer Aided Response Surface Optimization) Procedure in Optimization Studies. For example, reference [1]
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