Automated Peak Detection and Matching Algorithm for Gas Chromatography−Differential Mobility Spectrometry

Abstract

A gas chromatography–differential mobility spectrometer (GC-DMS) involves a portable and selective mass analyzer that may be applied to chemical detection in the field. Existing approaches examine whole profiles and do not attempt to resolve peaks. A new approach for peak detection in the 2D GC-DMS chromatograms is reported. This method is demonstrated on three case studies: a simulated case study; a case study of headspace gas analysis of Mycobacterium tuberculosis (MTb) cultures consisting of three matching GC-DMS and GC-MS chromatograms; a case study consisting of 41 GC-DMS chromatograms of headspace gas analysis of MTb culture and media.

Supervised semi-automated data analysis software for gas chromatography / differential mobility spectrometry (GC/DMS) metabolomics applications

“…Occasionally, we also may be interested in detecting groups of several chemicals from a complex mixtures, but still only identifying a handful of major chemical features which is a small finite number (Camara et al 2013; Lu and Harrington 2007; Rearden et al 2007). In some examples, signal alignment has been used (Krebs et al 2006a), along with feature selection procedures (Fong et al 2011; Zhao et al 2009) to assist in chemical identification. DMS data can also be augmented by mass spectrometry to confirm chemical identity (Lu et al 2009), and in general these approaches are still numerically relatively straight forward to interpret.…”

Chromophoreasy, an Excel-Based Program for Detection and Integration of Peaks from Chromatographic and Electromigration Techniques

“…This issue becomes more critical with increasing demand for faster analysis and narrower peaks, which mobilizes the developers of algorithms for chromatographic data treatment. Therefore, various peak recognition methods [10][11][12][13][14][15] and mathematical models for deconvolution of overlapping peaks and integration in noisy and complex systems 16,17 have been developed and evaluated. However, a deficiency in the availability of programs dedicated to chromatographic or electrophoretic data processing, which are simple, practical and accessible to researchers, students and specialized laboratories, is still noticed.…”

Automated algorithm for measurement of spontaneous adenosine transients in large electrochemical data sets

“…Adenosine modulates numerous important physiological functions including sleep 12 , breathing 13 , and heart rate 14 . Furthermore, adenosine is directly involved in pathologies like inflammation and cerebral ischemia.…”

“…Historically, microdialysis coupled with HPLC has been one of the most employed techniques for measuring adenosine 12,18,19 . Microdialysis is a sampling technique that is used frequently in neurobiology because it is minimally invasive, has the ability to sample continuously, and measures basal levels of analytes.…”

“…In conclusion, this thesis will describe a new method for automatically identifying spontaneous adenosine transients, with minimal analyst input, and show how an algorithm can be developed for neurotransmitter identification in in vivo FSCV analysis. Algorithms have been developed to automate identification of molecules in chemical data [6][7][8][9][10][11][12][13][14][15][16] . For in vivo electrochemical data, peak identification has used the cyclic voltammogram (CV) as a chemical fingerprint to identify which peak is detected.…”