Evaluation of the DotMap algorithm for locating analytes of interest based on mass spectral similarity in data collected using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry.
J Chromatogr A
; 1086(1-2): 185-92, 2005 Sep 09.
Article
em En
| MEDLINE
| ID: mdl-16130672
Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC x GC-TOF-MS) is a highly selective technique ideal for the analysis of complex mixtures. The instrument yields an abundance of data, with complete mass spectral scans at every time point in the GC x GC separation space. The development and application of appropriate tools for data mining is essential in making sense of the wealth of information available. An algorithm for locating analytes of interest based on mass spectral similarity in GC x GC-TOF-MS data, called DotMap, has been previously reported and is rigorously evaluated herein. A thorough investigation into the performance characteristics of DotMap, including the performance near the limit of detection and dynamic range of the algorithm as well as the capacity of the algorithm to deal with peak overlap, is investigated using jet fuel as a complex sample matrix. For instance, the algorithm can successfully identify a spiked compound at the single microg/ml level in a jet fuel sample with an overlapping interferent. The performance of the DotMap algorithm in situations with very limited mass spectral selectivity, specifically in the evaluation of spectra from isomer compounds, as well as the ability to tune DotMap results to provide the location of a specific analyte or of a class of compounds is demonstrated. The DotMap algorithm is demonstrated to be a sensitive tool that is useful in the analysis of complex mixtures and which possesses the capacity to be easily "tuned" to discern the location of analytes of interest.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Cromatografia Gasosa-Espectrometria de Massas
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article