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Aiko Barsch, Gabriela Zurek, Wiebke Lohmann,
Bruker Daltonik GmbH, Bremen, Germany |
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In this study, we performed untargeted metabolic profiling of
different black and green tea extracts by maXis ultra high resolution Q-TOF mass spectrometry coupled to a fast
UHPLC separation. All relevant information was extracted
from the raw data by applying the “Find Molecular Features”
(FMF) algorithm. Processed data was submitted to principal
component analysis (PCA) in order to differentiate sample
groups and identify characteristic compounds of various tea
types. Sum formulae were calculated with SmartFormula
for discriminating metabolites taking accurate mass and
isotopic pattern information into account. The number
of possible sum formulae in a certain mass accuracy
window increases exponentially with higher molecular
masses. Therefore, autoMS/MS runs focusing on those
analytes accounting for the largest differences between
teas as precursor ions were performed. Accurate MS/
MS data enabled a reduction of sum formula suggestions
by intelligently combining sum formulae and neutral
loss information using SmartFormula 3D. A query of
sum formulae in public databases enabled a tentative
identification of several compounds characteristic of a green
tea extract, which was clearly distinguishable from all other
tea samples. Several catechins, identified by comparison
to reference standards, differentiated black and green tea
samples.
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