Showing papers by "Frank R. Burden published in 1997"

A chemically intuitive molecular index based on the eigenvalues of a modified adjacency matrix

Abstract: A new molecular index is proposed that is derived from the eigenvalues of a modified adjacency matrix. The construction of the matrix is chemically intuitive in that its elements relate to atomic and bonding properties. The index was tested by comparing its performance in predicting the experimental LogP of 230 compounds. The predictive power of the index is shown to be both superior than and complementary to traditional connectivity indices, χ, for LogP.

Cross-validatory Selection of Test and Validation Sets in Multivariate Calibration and Neural Networks as Applied to Spectroscopy

Abstract: Cross-validated and non-cross-validated regression models using principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) have been used to relate the concentrations of polycyclic aromatic hydrocarbon pollutants to the electronic absorption spectra of coal tar pitch volatiles. The different trends in the cross-validated and non-cross-validated results are discussed as well as a method for the production of a true cross-validated neural network regression model. It is shown that the methods must be compared through the errors produced in the validation sets as well as those given for the final model. Various methods for calculation of errors are described and compared. The separation of training, validation and test sets into fully independent groups is emphasized. PLS outperforms PCR using all indicators. ANNs are inferior to multivariate techniques for individual compounds but are reasonably effective in predicting the sum of PAHs in the mixture set.