The results indicate that derivative methods provide the best selectivity, with minimum detectable peak separations of 0.20 msec. However, their susceptibility to noise results in poor sensitivity. Cross-correlation methods provide the best sensitivity, or detection limit, with minimum detectable RDX quantities of 0.01 nanogram. Despite their good detection limit, correlation methods suffer from a poor selectivity due to their smoothing effect. A multiresolution algorithm combining derivative methods with correlation methods is presented in this paper, and is shown to provide the advantages of both. In the proposed algorithm cross correlation methods are applied to the original signal in order to determine potential peak locations. Derivative methods are then applied within specific time windows with proper smoothing at the edges.

The paper also discusses the applicability of neural networks to the peak detection problem in IMS. A brief overview of neural networks is presented with an emphasis of the Hopfield network which was selected as the most appropriate structure for the IMS peak detection problem. Peak separations down to 0.18 msec were resolved.