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Due to the lack of mandatory standards it remains difficult to compare individual mass spectrums.
While the different mass detector technologies, namely
[[Quadrupole mass analyzer|quadrupole]], [[ion trap]] and [[Time-of-flight mass spectrometry|time of flight]], can be deemed irrelevant, the chromatography settings such as temperature programming, type of capillary column and choice of column manufacturer heavily affect the empirically determined RI properties. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared library use. The GMD assesses the accuracy of RI transfer between chromatography variants and
Aiming at the classification and identification of un-identified MSTs, the GMD accesses the information on available reference compounds. These compounds serve as training set of data to apply [[decision trees]] (DT) as a supervised [[machine learning]] approach. Structural feature extraction was applied to classify the metabolite space of the GMD prior to DT training. DT-based predictions of the most frequent substructures classify low resolution GC-MS mass spectra of the linked (potentially unknown) metabolite with respect to the presence or absence of the chemical moieties.
The web-based frontend supports conventional mass spectral and RI comparison by ranked hit lists as well as advanced DT supported substructure prediction. Batch processing is enabled via [[Simple Object Access Protocol]] (SOAP)-based web services while web-based data access services expose particular data base entities adapting [[Representational State Transfer]] (ReST) principles and mass spectral standards such as [[NIST]]-MSP and [[JCAMP]]-DX.
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