Abstract Real-time data analysis is important in many applications. However, many chemometric algorithms have difficulty processing data in real-time. A novel real-time two-dimensional wavelet compression (WC2) has been developed to compress data as it is acquired from analytical instrumentation. The WC algorithm was enhanced so that data with an arbitrary number of points were compressed, and truncation or padding to a dyadic number was avoided. After compression, the noise level is reduced while useful chemical information is retained. A modified simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) algorithm was applied to the wavelet-compressed data and the model was transformed back to the original representation while leaving the data compressed. The reduced size of the wavelet-compressed data furnished a faster implementation of SIMPLISMA that facilitates real-time acquisition. This real-time WC2-SIMPLISMA algorithm was applied to the rapid identification of explosives by ion mobility spectrometry (IMS). SIMPLISMA resolved concentration profiles and component spectra were displayed simultaneously while the data was acquired from an ion mobility spectrometer with a LabVIEW virtual instrument (VI). Author Keywords: Real-time; Multi-dimensional; Wavelet compression; SIMPLISMA; FIR; Ion mobility spectrometry; Explosives; Chemometrics

Reduction of ion mobility spectrometry data by clustering characteristic peak structures Bader, Sabine | Urfer, Wolfgang | Baumbach, Jorg Ingo Journal of Chemometrics. Vol. 20, no. 3-4, pp. 128-135. Mar.-Apr. 2006 Spectrometric methods generate a large amount of data that are difficult to process due to high dimensionality and considerable variability. Therefore, a method for the detection of characteristic peak structures was established on the base of clustering techniques and a merging regions algorithm that enabled the reduction of data in the size of one million to about one hundred meaningful values with respect to fluctuating axis allocations. The effectiveness of the method was exemplified considering a set of ion mobility spectrometry measurements of human breath. In questions of comparative nature, the reduction even could be upgraded by multiple test procedures. Afterwards statistical analyses could be arranged easily for various targets. Descriptors: Ionic mobility | Spectroscopy | Reduction | Spectrometry | Ion mobility | Clustering | Allocations | Merging | Mathematical analysis | Fluctuation | Positioning |

Abstract This initial study evaluates the use of ion mobility spectrometry (IMS) as a rapid test procedure for potential detection of adulterated perfumes and speciation of plant life. Sample types measured consist of five genuine perfumes, two species of sagebrush, and four species of flowers. Each sample type is treated as a separate classification problem. It is shown that discrimination using principal component analysis with K-nearest neighbors can distinguish one class from another. Discriminatory models generated using principal component regressions are not as effective. Results from this examination are encouraging and represent an initial phase demonstrating that perfumes and plants possess characteristic chemical signatures that can be used for reliable identification. Keywords: Ion mobility spectrometry; Chemometrics; Perfumes; Plant speciation

Ion mobility spectrometry (IMS) has recently been gaining attention due to its low cost, light weight, durability, versatility, and portability. IMS combined with time series analysis (TSA) has proved useful for identification of individual chemical species. These methods require the vapor pressure concentration to vary independently with respect to time. When sampling vapors are obtained from liquid mixtures, the analyte vapor compositions may not vary independently. A vapor mixture may be resolved by differential cleardown rates in IMS instruments. Cleardown is the decay in analyte signal when the sample is removed from the instrument. If different mixture components exhibit different affinities in the instrument, then the concentration of the mixture components will vary independently during cleardown, and SIMPLISMA, a feature extraction method, may resolve the components. This approach is an example of reverse frontal chromatography for which the sample is introduced into the detector (the IMS) and removed chromatographically by adsorption onto the molecular sieves. In this research, mixtures of three nerve agent simulants are used to demonstrate this new data extraction method with SIMPLISMA.

Discriminant Analysis of Fused Positive and Negative Ion Mobility Spectra Using Multivariate Self-Modeling Mixture Analysis and Neural Networks Chen,; Harrington, ims neural networks Discriminant Analysis chemomemtrics