Decomposition of overlapping plasmagram peaks by spectral subtraction

Abstract

Low field atmospheric pressure Ion Mobility Spectroscopy (IMS) involves the careful analysis of plasmagrams with multiple peaks which can mask one another when they are closely spaced in drift time or corresponding reduced mobility. A typical signal processing approach to decomposing overlapped peaks would be to use an orthogonal decomposition technique, but unfortunately Gaussian-like functions are not orthogonal, so no unique decomposition can be guaranteed. However, each ion species in the drift tube will arrive at the Faraday plate with a known statistical distribution determined by the IMS instrument's drift tube design, electric field strength, reagent gas flow and other instrument-specific factors such as the ion gate function. This paper presents a straightforward algorithm for decomposing plasmagrams into distinct peaks using a subtractive technique that independently estimates the statistical parameters of each peak, rejecting spurious peaks and electrical noise. The results show that for relatively short gate times, the plasmagram peaks are nearly Gaussian-shaped, but slightly fatter and asymmetric. We show that including of the gate rise and fall times is also significant in matching the plasmagram peak shape. We also show that the diffusion effects on resolution can be attributed to combinations of non-uniform ion distributions in the reaction chamber as well as detritus effects in the drift tube. Given the known peaks statistical parameters, one can then separate overlapping peaks using a straightforward spectral subtractive technique.

A compact high resolution ion mobility spectrometer for fast trace gas analysis

“…Therefore, the improvement of the analytical performance of IMS both through operational and instrumental developments has been an ongoing task over the past decades. Operational improvements may include techniques such as post-processing of the acquired spectra to separate conjoined peaks, 14 sweeping of the dri voltage for dri tubes with a mobility dependent separation performance, 15 or using ion-ion recombination dynamics as an orthogonal pre-separation method. 16 However, such techniques are always limited by the overall performance of the dri tube itself.…”

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm

“…It thereby determines the range of particle positions [ x min , x max ], excludes impossible single‐peak combinations, and reduces the difficulty of identification. The relationship between FWHM and drift time t d is described in Equation (), where σ is the standard deviation of the Gaussian function and D is the diffusion coefficient: 46 The inertial weight w controls the impact of the particle's previous velocity on the current velocity. A larger value of w encourages global search capability, while a smaller w value enhances the local search ability; w often is decreased linearly from about 0.9 to 0.4 during a run 47…”

“…It thereby determines the range of particle positions [x min , x max ], excludes impossible single-peak combinations, and reduces the difficulty of identification. The relationship between FWHM and drift time t d is described in Equation(7), where σ is the standard deviation of the Gaussian function and D is the diffusion coefficient:46…”