Author(s)

Artur Scheinemann, Stefanie Sielemann, Jorg Walter, Theodor Doll

Gesellschaft für Analytische Sensorsysteme mbH G.A.S, Dortmund, Germany.
Institute of Physics, Johannes Gutenberg-University Mainz, Mainz, Germany.

Data evaluation strategies for the novel coupled MCC-IMS sensory system are developed. Mayor attention to the plausibility of applied procedures and the feasibility of automation was paid. Three stages of extraction levels with increasing data reduction are presented for several fields of application. According to suitable extraction levels, real data were tested on various structures of artificial neural networks (ANN) with the result, that the computational levels must still be chosen by expertise, but subsequent processing and training can be fully automated. For the training of larger net- works a method of automated generation of secondary training data is presented which exceeds the quality of previous noise models by far. It is concluded that the combination of MCC-IMS as measuring instrument and ANNs as evalua- tion technique have high potential for industrial use in process monitoring.

Gas Chromatography; Ion Mobility Spectrometry; GC-IMS; MCC-IMS; Artificial Neural Network; Measurement Evaluation

A. Scheinemann, S. Sielemann, J. Walter and T. Doll, "Evaluation Strategies for Coupled GC-IMS Measurement including the Systematic Use of Parametrized ANN," Open Journal of Applied Sciences, Vol. 2 No. 4, 2012, pp. 257-266. doi: 10.4236/ojapps.2012.24038.