Ion mobility spectrometry (IMS) has been successfully developed to yield an advanced portable instrument. However, the formation of pure or heterogeneous cluster ions introduces nonlinear variances into the data. Cluster ions may arise from the sample in addition, and competition to the standard anticipated product ions and may deleteriously affect quantitative determinations. The SIMPLISMA (simple-to-use interactive self-modeling mixture analysis) method is demonstrated for detecting and modeling these nonlinear variances in IMS data, which is especially useful when vapor mixtures are encountered. Furthermore, SIMPLISMA may assist in the resolution of overlapping peaks that are characteristic of low-resolution IMS drift tubes. The synergistic combination of IMS and SIMPLISMA is shown for the detection of heterogeneous cluster ions produced from vapor mixtures of 1-pentanol and 1-octanol.

A discrete sine transform (DST) method has been devised for the Fourier compression of ion mobility spectra. The DST allows the calculation of eigenvalues with correct scale directly from the compressed data. A novel procedure for transforming the variable loadings from Fourier to native domains has been devised. For the first time, data may be interpreted in their native domain without decompression of the entire data set. This achievement is significant because results generated from the analysis of compressed data had been restricted to an abstract mathematical form. Methodology to convert these abstract results to a form that is understandable by analytical chemists is presented. This capability has important applications to the analysis of large data sets and embedded data processing under the constraints of miniaturized instrumentation. Five sets of evaluation data were obtained from vapor samples of formamide, 1-pentanol, and a mixture of two chemical warfare simulants (DIMP and DMMP) with a handheld ion mobility spectrometer (IMS). These data sets were compressed by 90% or more and reconstructed with 1% or less relative error. IMS instruments can generate thousands of spectra per hour. MATLAB functions for the DST, the inverse DST, and the two-dimensional DST are furnished in the Appendix.

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