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We address this challenge from a machine-learning viewpoint by developing a high-performance mass spectral pattern recognition algorithm in this study.
Fatty acids were identified by a direct comparison of their mass spectral pattern and retention index with the NIST 05 mass spectral database.
Identification of 2-NA was based on GC retention time and comparison of the mass spectral pattern to the spectra of an authentic standard.
The automatic de-noising prevents noise from entering feature selection and the following classifier training, which will contribute to the robust mass spectral pattern classification.
Focussing on simple MALDI-ToF analysis of neat or diluted urine we propose a mass spectral pattern analysis test that has the potential to achieve a very high Down syndrome detection rate (>99%) with a low false positive rate (<1%) in the first trimester of pregnancy.
Constituents of the C. verum essential oil were identified by comparing their mass spectral pattern and retention indices (RI) relative to a standard n-alkane series (C9 C24) with those given in the literature [ 24] and the Wiley 138 and Nist 98 databases.
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An elution order analysis of several substance classes was performed based on their distinct mass spectral patterns.
These results indicate that gingerol, shogaol, and their related compounds could be annotated by ESI mass spectral patterns together with high-resolution mass data.
We have here applied substructure characterization using automatic analysis of mass spectral patterns using the AMDIS Substructuree classifier' post processing feature (see workflow Figure 1).
Botryococcenes are important hydrocarbons in this alga for which we identified lycopadiene (C40) and botryococcene (C32) in pyGC-MS data based on the molecular ions, mass spectral patterns and retention times (Additional file 3, table S2).
Typically, isotope-enriched metabolites or globally labeled biological samples are mixed with their native analogues prior to LC-HRMS measurements and the resulting labeling-specific mass spectral patterns are systematically used for data analysis.
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