Adulteration of edible
oils by the manufacturers has been found
frequently in modern societies. Due to the complexity of the chemical
contents in edible oils, it is challenging to quantitatively determine
the extent of adulteration and prove the authenticity of edible oils.
In this study, a robust and simple MALDI-TOF-MS platform for rapid
fingerprinting of triacylglycerols (TAGs) in edible oils was developed,
where spectral similarity analysis was performed to quantitatively
reveal correlations among edible oils in the chemical level. Specifically,
we proposed oil networking, a spectral similarity-based illustration,
which enabled reliable classifications of tens of commercial edible
oils from vegetable and animal origins. The strategy was superior
to traditional multivariate statistics due to its high sensitivity
in probing subtle changes in TAG profiles, as further demonstrated
by the success in determination of the adulterated lard in a food
fraud in Taiwan. Finally, we showed that the platform allowed quantitative
assessment of the binary mixture of olive oil and canola oil, which
is a common type of olive oil adulteration in the market. Overall,
these results suggested a novel strategy for chemical fingerprint-based
quality control and authentication of oils in the food industry.