Liquid chromatography-Orbitrap Tribrid high-resolution mass spectrometry using data dependent-tandem mass spectrometry with triple stage fragmentation as a screening tool to perform identification and risk assessment of unknown substances in food contact epoxy resin.
Pablo MirallesAntonio LópezPablo DualdeClara CoscollàVicent YusàPublished in: Journal of separation science (2021)
A new, fast, and automatic approach has been applied for the tentative identification of unknown substances released by food contact epoxy resin after performing a migration test with food simulant. This approach combines intelligent data acquisition with AcquireX linked to liquid chromatography-Orbitrap Tribrid high-resolution mass spectrometry using data dependent-tandem mass spectrometry with triple stage fragmentation coupled to Compound Discoverer™ software for automated data processing and compound identification. The identification of the observed features was performed using a set of identification criteria, including exact mass, isotope pattern, tandem mass spectrometry spectra match, and retention time. With these criteria, 263 substances were tentatively identified. Most of the identified compounds were additives, such as plasticisers, stabilizers, and antioxidants, used in different plastic applications. However, metabolites, biological constituents with pharmacological activity, and other substances with industrial applications were also detected. In order to perform a risk assessment of the food contact epoxy resin, threshold of toxicological concern approach was applied for the identified compounds. There was not risk associated with the migration of the identified substances.
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- high resolution mass spectrometry
- ultra high performance liquid chromatography
- gas chromatography
- mass spectrometry
- high performance liquid chromatography
- simultaneous determination
- risk assessment
- human health
- solid phase extraction
- electronic health record
- drinking water
- big data
- bioinformatics analysis
- deep learning
- high resolution
- machine learning
- heavy metals
- data analysis
- density functional theory
- wastewater treatment
- neural network