Authors
Chao Yan, Ning Li, Xuteng Wang, Huiwen Guan, Xuchun Zhu, Bingyu Chen, Xinyu Ma, Hongzhi Liu
Published in
Food research international (Ottawa, Ont.). Volume 242. Issue Pt 2. Pages 119942. Oct 31, 2026. Epub Jul 10, 2026.
Abstract
Cereal and oil crops are dietary staples worldwide, with their phenolic compounds predominantly occurring as bound forms covalently linked to cell wall polysaccharides and concentrated in outer layers such as bran and seed coat. The health effects of these compounds depend not only on their total content but critically on release efficiency during food processing and gastrointestinal digestion. This review critically examines the chemical diversity, tissue distribution, and health-related functions of phenolics derived from cereal and oil crops, systematically evaluating their processing fate, including migration, release, degradation, and transformation across physical processing, biotransformation, and oil extraction. It further assesses how phenolic-macromolecule interactions influence both food processing quality and nutritional value, and summarizes existing applications alongside future research directions. Phenolic profiles vary considerably across species and tissue types, and their health benefits, including antioxidant, anti-inflammatory, and metabolic regulatory effects, are structure-dependent and partially mediated by gut microbiota metabolism. Processing exerts dual effects, leading to either phenolic loss or enrichment while releasing bound forms or generating new bioactive derivatives. Through interactions with proteins, starch, and other macromolecules, phenolics modulate protein digestibility, starch digestion rates, and micronutrient bioavailability. Current applications include nutritionally fortified staples, stabilized delivery systems, intelligent packaging, and valorized by-products. Future research should prioritize understanding bound phenolic release kinetics, developing personalized nutrition strategies based on gut microbiota characteristics, and establishing predictive models linking processing parameters to phenolic transformations.
PMID:
42632700
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.
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