The use of gas chromatography mass spectrometry (GC-MS) in metabolomics analyses has been studied by a team of scientists, who have discovered a new algorithm to help improve the identification of metabolites.
Published in BMC Bioinformatics, the study saw a team of five researchers delve into how to hone the systematic analysis of metabolites when looking at biological samples.
The team highlights that while 100 peaks typically can be indentified during this type of testing, there can be up to 500 that remain unidentified.
To combat this issue, the scientists employed the soft independent modelling of class analogy method, teamed with Pearson's product-moment correlation coefficient.
Used together, these systems automatically identified any unknown peaks.
"Our data mining system can offer a wealth of metabolite information quickly and easily and it provides new insights, particularly into
food quality evaluation and prediction," the team concludes.
BMC Bioinformatics publishes research articles following peer review and acts as an open-access resource for scientists.
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