Visualising Analytes in Gas Chromatography by Staining and Substance Maps

Chromatographic separations in combination with spectroscopic detectors are a main pillar of today's analytical chemistry. The recorded spectroscopic data is usually not shown in a typical chromatogram, therefore the contained additional information cannot be accessed readily by the analyst and is inspected in tedious additional routines, such as separate database searches. We developed a method to add colors to gas chromatograms with mass spectral detection. The colors make the structural similarity and thus the substance class of the detected analytes visible. The assigned color is stable for a given spectrum and independent of the analytical setup and the sample composition. For "staining" (color coding), the recorded mass spectra are first sorted according to similarity on a self-organizing map that was prepared from a large mass spectral database, and the location of a mass spectrum on this substance map is then converted into a color using the hue, saturation, and lightness color-space. The generated substance maps are retention-time independent summaries of the recorded chromatograms. They represent the sample composition, and they are independent of the analytical setup. They can be used to directly compare separations obtained with different analytical setups. Furthermore, the maps allow straightforward quantification of structurally similar compounds that elute at different retention times, since they are grouped in the same location on the substance map. By calculating the difference between a sample and a reference measurement, the presence of an additional compound, for example an adulterant, or the absence of a typical component becomes readily apparent. Staining (color coding) gas chromatograms and the simple conversion of time-based chromatograms to time-independent substance maps exemplarily demonstrate how chemistry, where medium-sized data sets are commonly inspected visually, can benefit from purposefully designed visualizations and targeted data conversion.