Abstract
Microgreens constitute natural -based foods with health -promoting properties mediated by the accumulation of glucosinolates (GLs) and phenolic compounds (PCs), although their bioaccessibility may limit their nutritional potential. This work subjected eight Brassicaceae microgreens to in vitro gastrointestinal digestion and large intestine fermentation before the metabolomics profiling of PCs and GLs. The application of multivariate statistics effectively discriminated among species and their interaction with in vitro digestion phases. The flavonoids associated with arugula and the aliphatic GLs related to red cabbage and cauliflower were identified as discriminant markers among microgreen species. The multi-omics integration along in vitro digestion and fermentation predicted bioaccessible markers, featuring potential candidates that may eventually be responsible for these functional foods' nutritional properties. This combined analytical and computational framework provided a promising platform to predict the nutritional metabolome-wide outcome of functional food consumption, as in the case of microgreens.
Lingua originale | English |
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pagine (da-a) | N/A-N/A |
Numero di pagine | 13 |
Rivista | Food Chemistry |
Volume | 452 |
DOI | |
Stato di pubblicazione | Pubblicato - 2024 |
Keywords
- Bioactive compounds
- Cruciferous vegetables
- Machine learning
- Multivariate statistics
- Nutraceuticals