RESUMO
The present work makes the first attempt to take into account adsorptions in the determination of partition coefficients by modeling the multiple headspace extraction (MHE) process. Modeling a six-step MHE procedure of a homologous series of methyl ketones revealed that their adsorption-desorption on the walls was the rate-limiting step. Moreover, a comparison between experimental and predicted MHE plots shows that only the last MHE points were affected by adsorption phenomena. Using cell materials with the lowest sorptive properties, partition coefficients were then accurately calculated from the first four MHE steps. This kinetic approach supports previous work in which adsorptions were lowered owing to the choice of sampling cell materials. It also justifies some reproducibility limitations of the MHE quantification procedure mentioned in the literature.
RESUMO
Two types of neural networks were used to establish relationships between chemical structure and musk odour of 79 nitrobenzenic compounds. Substituents on the five free sites of the benzene ring (one position was always occupied by a t-butyl group) were described using three volume descriptors and three electronegativity descriptors. Musk odour was coded by a binary variable. First a classical network with two hidden layers containing six and three neurons was used. This network gave a better classification (94%) than that obtained by linear discriminant analysis (81%). The odour was then predicted using a leave-ten-out procedure, with 77% of correct prediction for the whole sample. Then a dual two-way network was built to mimic the symmetry of the problem (two sides on a molecule, two muskophore patterns). This network recognized both patterns already known to chemists and gave 99% of correct classifications by taking into account substitution in all positions. As a side benefit of the modified network structure it was possible to evaluate the influence of each of 19 substituents in each of the five possible positions.