AnalyzerPro

Metabolomics has a great potential in addressing the metabolic gap in microbe-host systems and expanding knowledge on the human gut microbial metabolism. Metabolomics as a field is the analysis and unbiased relative quantification of all metabolites in a biological sample. However in order to study the entire metabolome of a biological system, it is essential that most, if not all, of the metabolites present in the system in question are extracted and identified through analytical platforms.

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To reduce the impact of non-biological variance introduced into untargeted metabolomics datasets by, among other factors, gradual changes in instrument performance, it is common practice to include the analysis of reference samples throughout an analytical sequence. There are few single software platforms for data processing, signal-correction and analysis/interpretation which are vendor neutral and support GC- and LC-MS data, and fewer implemented through a GUI. Here we present this software capability and have explored approaches for the validation of signal-corrected data.

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Advances in mass spectrometry are enabling analysis of micro samples and unknown components that were not observable before. As the volume of information acquired from mass spectrometry increases, researchers are calling for simple techniques to analyze the numerous components observed, and as a result, there is a rise in demand for comprehensive analytical techniques including multiple classification analysis.

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Advances in mass spectrometry are enabling analysis of micro samples and unknown components that were not observable before. As the volume of information acquired from mass spectrometry increases, researchers are calling for simple techniques to analyze numerous components observed, and as a result, there is a rise in demand for comprehensive analytical techniques including multiple classification analysis.   In this work, we will introduce a new technique for non-target analysis, which combines comprehensive analysis using high resolution GC-TOFMS and unknown component analysis using soft ionization.

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Advances in mass spectrometry are enabling analysis of micro samples and unknown components that were not observable before. As the volume of information acquired from mass spectrometry increases, researchers are calling for simple techniques to analyze numerous components observed, and as a result, there is a rise in demand for comprehensive analytical techniques including multiple classification analysis. In this work, we will introduce a new technique of non-targeted analysis, which combines comprehensive analysis using high resolution GC-MS and unknown component analysis using soft ionization and EI.

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Peak overlapping or partial co-elution is a common problem in any chromatographic separation technique. Where a detector which produces spectral characteristics is used, such as a mass spectrometer, the deconvolution of partially overlapping peaks can be achieved without any assumptions being made regarding the peak shape or underlying spectra of the individual components.

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