A minimal neural network was applied to a large library of high-temperature mobility spectra drawn from 16 chemical classes including 154 substances with 2000 spectra at various concentrations. A genetic algorithm was used to create a representative subset of points from the mobility spectrum as input to a cascade-type back-propagation network. This network demonstrated that significant information specific to chemical class was located in the spectral region near the reactant ions. This network failed to generalize the solution to unfamiliar compounds necessitating the use of complete spectra in network processing. An extended back-propagation network classified unfamiliar chemicals by functional group with a mean for average values of 0.83 without sulfides and 0.79 with sulfides. Further experiments confirmed that chemical class information was resident in the spectral region near the reactant ions. Deconvolution of spectra demonstrated the presence of ions, merged with the reactant ion peaks that originated from introduced samples. The ability of the neural network to generalize the solution to unfamiliar compounds suggests that these ions are distinct and class specific.

The reduced mobilities in air, at 200~ of six isomeric C7H~sN2 protonated diamines, two triamines (caldine and spermidine), and two tetramines (thermine and spermine) were measured by ion mobility spectrometric (IMS) techniques. The results indicated that all these polyamines undergo proton-induced cyclization, with the proton forming a bridge between two amino groups. It appears as if the favored configuration of the protonated polyamines involves a six-or sevenmembered ring rather than a bridge between the terminal amino groups. It is believed that in the tetramines the cyclic structure is formed between the two central, more basic, secondary amine sites.